WorldWideScience

Sample records for seasonal climate prediction

  1. Climate Prediction Center - Seasonal Outlook

    Science.gov (United States)

    Weather Service NWS logo - Click to go to the NWS home page Climate Prediction Center Site Map News Forecast Discussion PROGNOSTIC DISCUSSION FOR MONTHLY OUTLOOK NWS CLIMATE PREDICTION CENTER COLLEGE PARK MD INFLUENCE ON THE MONTHLY-AVERAGED CLIMATE. OUR MID-MONTH ASSESSMENT OF LOW-FREQUENCY CLIMATE VARIABILITY IS

  2. Seasonal climate prediction for North Eurasia

    International Nuclear Information System (INIS)

    Kryjov, Vladimir N

    2012-01-01

    An overview of the current status of the operational seasonal climate prediction for North Eurasia is presented. It is shown that the performance of existing climate models is rather poor in seasonal prediction for North Eurasia. Multi-model ensemble forecasts are more reliable than single-model ones; however, for North Eurasia they tend to be close to climatological ones. Application of downscaling methods may improve predictions for some locations (or regions). However, general improvement of the reliability of seasonal forecasts for North Eurasia requires improvement of the climate prediction models. (letter)

  3. Climate Prediction Center - Outlooks: CFS Forecast of Seasonal Climate

    Science.gov (United States)

    National Weather Service NWS logo - Click to go to the NWS home page Climate Prediction Center Home Site government Web resources and services. CFS Seasonal Climate Forecasts CFS Forecast of Seasonal Climate discontinued after October 2012. This page displays seasonal climate anomalies from the NCEP coupled forecast

  4. The Impact of Ocean Observations in Seasonal Climate Prediction

    Science.gov (United States)

    Rienecker, Michele; Keppenne, Christian; Kovach, Robin; Marshak, Jelena

    2010-01-01

    The ocean provides the most significant memory for the climate system. Hence, a critical element in climate forecasting with coupled models is the initialization of the ocean with states from an ocean data assimilation system. Remotely-sensed ocean surface fields (e.g., sea surface topography, SST, winds) are now available for extensive periods and have been used to constrain ocean models to provide a record of climate variations. Since the ocean is virtually opaque to electromagnetic radiation, the assimilation of these satellite data is essential to extracting the maximum information content. More recently, the Argo drifters have provided unprecedented sampling of the subsurface temperature and salinity. Although the duration of this observation set has been too short to provide solid statistical evidence of its impact, there are indications that Argo improves the forecast skill of coupled systems. This presentation will address the impact these different observations have had on seasonal climate predictions with the GMAO's coupled model.

  5. Nudging atmosphere and ocean reanalyses for seasonal climate predictions

    Science.gov (United States)

    Piontek, Robert; Baehr, Johanna; Kornblueh, Luis; Müller, Wolfgang Alexander; Haak, Helmuth; Botzet, Michael; Matei, Daniela

    2010-05-01

    Seasonal climate forecasts based on state-of-the-art climate models have been developed recently. Here, we critically discuss the obstacles encountered in the setup of the ECHAM6/MPIOM global coupled climate model to perform climate predictions on seasonal to decadal time scales. We particularly focus on the initialization procedure, especially on the implementation of the nudging scheme, in which different reanalysis products are used in the atmosphere (e.g.ERA40), and the ocean (e.g., GECCO). Nudging in the atmosphere appears to be sensitive to the following choices: limiting the spectral range of nudging, whether or not temperature is nudged, the strength of the nudging coefficient for surface pressure, and the height at which the planetary boundary layer is excluded from nudging. We find that including nudging in both the atmosphere and the ocean gives improved results over nudging only the ocean or the atmosphere. For the implementation of the nudging in the atmosphere, we find the most significant improvements in the solution when either the planetary boundary layer is excluded, or if nudging of temperature is omitted. There are significant improvements in the solution when resolution is increased in both the atmosphere and in the ocean. Our tests form the basis for the prediction system introduced in the abstract of Müller et al., where hindcasts are analysed as well.

  6. Probabilistic empirical prediction of seasonal climate: evaluation and potential applications

    Science.gov (United States)

    Dieppois, B.; Eden, J.; van Oldenborgh, G. J.

    2017-12-01

    Preparing for episodes with risks of anomalous weather a month to a year ahead is an important challenge for governments, non-governmental organisations, and private companies and is dependent on the availability of reliable forecasts. The majority of operational seasonal forecasts are made using process-based dynamical models, which are complex, computationally challenging and prone to biases. Empirical forecast approaches built on statistical models to represent physical processes offer an alternative to dynamical systems and can provide either a benchmark for comparison or independent supplementary forecasts. Here, we present a new evaluation of an established empirical system used to predict seasonal climate across the globe. Forecasts for surface air temperature, precipitation and sea level pressure are produced by the KNMI Probabilistic Empirical Prediction (K-PREP) system every month and disseminated via the KNMI Climate Explorer (climexp.knmi.nl). K-PREP is based on multiple linear regression and built on physical principles to the fullest extent with predictive information taken from the global CO2-equivalent concentration, large-scale modes of variability in the climate system and regional-scale information. K-PREP seasonal forecasts for the period 1981-2016 will be compared with corresponding dynamically generated forecasts produced by operational forecast systems. While there are many regions of the world where empirical forecast skill is extremely limited, several areas are identified where K-PREP offers comparable skill to dynamical systems. We discuss two key points in the future development and application of the K-PREP system: (a) the potential for K-PREP to provide a more useful basis for reference forecasts than those based on persistence or climatology, and (b) the added value of including K-PREP forecast information in multi-model forecast products, at least for known regions of good skill. We also discuss the potential development of

  7. An empirical system for probabilistic seasonal climate prediction

    Science.gov (United States)

    Eden, Jonathan; van Oldenborgh, Geert Jan; Hawkins, Ed; Suckling, Emma

    2016-04-01

    Preparing for episodes with risks of anomalous weather a month to a year ahead is an important challenge for governments, non-governmental organisations, and private companies and is dependent on the availability of reliable forecasts. The majority of operational seasonal forecasts are made using process-based dynamical models, which are complex, computationally challenging and prone to biases. Empirical forecast approaches built on statistical models to represent physical processes offer an alternative to dynamical systems and can provide either a benchmark for comparison or independent supplementary forecasts. Here, we present a simple empirical system based on multiple linear regression for producing probabilistic forecasts of seasonal surface air temperature and precipitation across the globe. The global CO2-equivalent concentration is taken as the primary predictor; subsequent predictors, including large-scale modes of variability in the climate system and local-scale information, are selected on the basis of their physical relationship with the predictand. The focus given to the climate change signal as a source of skill and the probabilistic nature of the forecasts produced constitute a novel approach to global empirical prediction. Hindcasts for the period 1961-2013 are validated against observations using deterministic (correlation of seasonal means) and probabilistic (continuous rank probability skill scores) metrics. Good skill is found in many regions, particularly for surface air temperature and most notably in much of Europe during the spring and summer seasons. For precipitation, skill is generally limited to regions with known El Niño-Southern Oscillation (ENSO) teleconnections. The system is used in a quasi-operational framework to generate empirical seasonal forecasts on a monthly basis.

  8. A global empirical system for probabilistic seasonal climate prediction

    Science.gov (United States)

    Eden, J. M.; van Oldenborgh, G. J.; Hawkins, E.; Suckling, E. B.

    2015-12-01

    Preparing for episodes with risks of anomalous weather a month to a year ahead is an important challenge for governments, non-governmental organisations, and private companies and is dependent on the availability of reliable forecasts. The majority of operational seasonal forecasts are made using process-based dynamical models, which are complex, computationally challenging and prone to biases. Empirical forecast approaches built on statistical models to represent physical processes offer an alternative to dynamical systems and can provide either a benchmark for comparison or independent supplementary forecasts. Here, we present a simple empirical system based on multiple linear regression for producing probabilistic forecasts of seasonal surface air temperature and precipitation across the globe. The global CO2-equivalent concentration is taken as the primary predictor; subsequent predictors, including large-scale modes of variability in the climate system and local-scale information, are selected on the basis of their physical relationship with the predictand. The focus given to the climate change signal as a source of skill and the probabilistic nature of the forecasts produced constitute a novel approach to global empirical prediction. Hindcasts for the period 1961-2013 are validated against observations using deterministic (correlation of seasonal means) and probabilistic (continuous rank probability skill scores) metrics. Good skill is found in many regions, particularly for surface air temperature and most notably in much of Europe during the spring and summer seasons. For precipitation, skill is generally limited to regions with known El Niño-Southern Oscillation (ENSO) teleconnections. The system is used in a quasi-operational framework to generate empirical seasonal forecasts on a monthly basis.

  9. Climate Prediction Center - Monitoring & Data: Seasonal ENSO Impacts on

    Science.gov (United States)

    page National Weather Service NWS logo - Click to go to the NWS home page Climate Prediction Center , state and local government Web resources and services. HOME > Monitoring and Data > U.S. Climate and Climate Prediction Climate Prediction Center 5830 University Research Court College Park, Maryland

  10. Measuring the potential utility of seasonal climate predictions

    Science.gov (United States)

    Tippett, Michael K.; Kleeman, Richard; Tang, Youmin

    2004-11-01

    Variation of sea surface temperature (SST) on seasonal-to-interannual time-scales leads to changes in seasonal weather statistics and seasonal climate anomalies. Relative entropy, an information theory measure of utility, is used to quantify the impact of SST variations on seasonal precipitation compared to natural variability. An ensemble of general circulation model (GCM) simulations is used to estimate this quantity in three regions where tropical SST has a large impact on precipitation: South Florida, the Nordeste of Brazil and Kenya. We find the yearly variation of relative entropy is strongly correlated with shifts in ensemble mean precipitation and weakly correlated with ensemble variance. Relative entropy is also found to be related to measures of the ability of the GCM to reproduce observations.

  11. Evaluation and attribution of vegetation contribution to seasonal climate predictability

    Science.gov (United States)

    Catalano, Franco; Alessandri, Andrea; De Felice, Matteo

    2015-04-01

    The land surface model of EC-Earth has been modified to include dependence of vegetation densities on the Leaf Area Index (LAI), based on the Lambert-Beer formulation. Effective vegetation fractional coverage can now vary at seasonal and interannual time-scales and therefore affect biophysical parameters such as the surface roughness, albedo and soil field capacity. The modified model is used to perform a real predictability seasonal hindcast experiment. LAI is prescribed using a recent observational dataset based on the third generation GIMMS and MODIS satellite data. Hindcast setup is: 7 months forecast length, 2 start dates (1st May and 1st November), 10 members, 28 years (1982-2009). The effect of the realistic LAI prescribed from observation is evaluated with respect to a control experiment where LAI does not vary. Hindcast results demonstrate that a realistic representation of vegetation significantly improves the forecasts of temperature and precipitation. The sensitivity is particularly large for temperature during boreal winter over central North America and Central Asia. This may be attributed in particular to the effect of the high vegetation component on the snow cover. Summer forecasts are improved in particular for precipitation over Europe, Sahel, North America, West Russia and Nordeste. Correlation improvements depends on the links between targets (temperature and precipitation) and drivers (surface heat fluxes, albedo, soil moisture, evapotranspiration, moisture divergence) which varies from region to region.

  12. Visualization of uncertainties and forecast skill in user-tailored seasonal climate predictions for agriculture

    Science.gov (United States)

    Sedlmeier, Katrin; Gubler, Stefanie; Spierig, Christoph; Flubacher, Moritz; Maurer, Felix; Quevedo, Karim; Escajadillo, Yury; Avalos, Griña; Liniger, Mark A.; Schwierz, Cornelia

    2017-04-01

    Seasonal climate forecast products potentially have a high value for users of different sectors. During the first phase (2012-2015) of the project CLIMANDES (a pilot project of the Global Framework for Climate Services led by WMO [http://www.wmo.int/gfcs/climandes]), a demand study conducted with Peruvian farmers indicated a large interest in seasonal climate information for agriculture. The study further showed that the required information should by precise, timely, and understandable. In addition to the actual forecast, two complex measures are essential to understand seasonal climate predictions and their limitations correctly: forecast uncertainty and forecast skill. The former can be sampled by using an ensemble of climate simulations, the latter derived by comparing forecasts of past time periods to observations. Including uncertainty and skill information in an understandable way for end-users (who are often not technically educated) poses a great challenge. However, neglecting this information would lead to a false sense of determinism which could prove fatal to the credibility of climate information. Within the second phase (2016-2018) of the project CLIMANDES, one goal is to develop a prototype of a user-tailored seasonal forecast for the agricultural sector in Peru. In this local context, the basic education level of the rural farming community presents a major challenge for the communication of seasonal climate predictions. This contribution proposes different graphical presentations of climate forecasts along with possible approaches to visualize and communicate the associated skill and uncertainties, considering end users with varying levels of technical knowledge.

  13. Seasonal Climate Extremes : Mechanism, Predictability and Responses to Global Warming

    NARCIS (Netherlands)

    Shongwe, M.E.

    2010-01-01

    Climate extremes are rarely occurring natural phenomena in the climate system. They often pose one of the greatest environmental threats to human and natural systems. Statistical methods are commonly used to investigate characteristics of climate extremes. The fitted statistical properties are often

  14. Land-surface initialisation improves seasonal climate prediction skill for maize yield forecast.

    Science.gov (United States)

    Ceglar, Andrej; Toreti, Andrea; Prodhomme, Chloe; Zampieri, Matteo; Turco, Marco; Doblas-Reyes, Francisco J

    2018-01-22

    Seasonal crop yield forecasting represents an important source of information to maintain market stability, minimise socio-economic impacts of crop losses and guarantee humanitarian food assistance, while it fosters the use of climate information favouring adaptation strategies. As climate variability and extremes have significant influence on agricultural production, the early prediction of severe weather events and unfavourable conditions can contribute to the mitigation of adverse effects. Seasonal climate forecasts provide additional value for agricultural applications in several regions of the world. However, they currently play a very limited role in supporting agricultural decisions in Europe, mainly due to the poor skill of relevant surface variables. Here we show how a combined stress index (CSI), considering both drought and heat stress in summer, can predict maize yield in Europe and how land-surface initialised seasonal climate forecasts can be used to predict it. The CSI explains on average nearly 53% of the inter-annual maize yield variability under observed climate conditions and shows how concurrent heat stress and drought events have influenced recent yield anomalies. Seasonal climate forecast initialised with realistic land-surface achieves better (and marginally useful) skill in predicting the CSI than with climatological land-surface initialisation in south-eastern Europe, part of central Europe, France and Italy.

  15. The prediction of surface temperature in the new seasonal prediction system based on the MPI-ESM coupled climate model

    Science.gov (United States)

    Baehr, J.; Fröhlich, K.; Botzet, M.; Domeisen, D. I. V.; Kornblueh, L.; Notz, D.; Piontek, R.; Pohlmann, H.; Tietsche, S.; Müller, W. A.

    2015-05-01

    A seasonal forecast system is presented, based on the global coupled climate model MPI-ESM as used for CMIP5 simulations. We describe the initialisation of the system and analyse its predictive skill for surface temperature. The presented system is initialised in the atmospheric, oceanic, and sea ice component of the model from reanalysis/observations with full field nudging in all three components. For the initialisation of the ensemble, bred vectors with a vertically varying norm are implemented in the ocean component to generate initial perturbations. In a set of ensemble hindcast simulations, starting each May and November between 1982 and 2010, we analyse the predictive skill. Bias-corrected ensemble forecasts for each start date reproduce the observed surface temperature anomalies at 2-4 months lead time, particularly in the tropics. Niño3.4 sea surface temperature anomalies show a small root-mean-square error and predictive skill up to 6 months. Away from the tropics, predictive skill is mostly limited to the ocean, and to regions which are strongly influenced by ENSO teleconnections. In summary, the presented seasonal prediction system based on a coupled climate model shows predictive skill for surface temperature at seasonal time scales comparable to other seasonal prediction systems using different underlying models and initialisation strategies. As the same model underlying our seasonal prediction system—with a different initialisation—is presently also used for decadal predictions, this is an important step towards seamless seasonal-to-decadal climate predictions.

  16. Development of an integrated method for long-term water quality prediction using seasonal climate forecast

    Directory of Open Access Journals (Sweden)

    J. Cho

    2016-10-01

    Full Text Available The APEC Climate Center (APCC produces climate prediction information utilizing a multi-climate model ensemble (MME technique. In this study, four different downscaling methods, in accordance with the degree of utilizing the seasonal climate prediction information, were developed in order to improve predictability and to refine the spatial scale. These methods include: (1 the Simple Bias Correction (SBC method, which directly uses APCC's dynamic prediction data with a 3 to 6 month lead time; (2 the Moving Window Regression (MWR method, which indirectly utilizes dynamic prediction data; (3 the Climate Index Regression (CIR method, which predominantly uses observation-based climate indices; and (4 the Integrated Time Regression (ITR method, which uses predictors selected from both CIR and MWR. Then, a sampling-based temporal downscaling was conducted using the Mahalanobis distance method in order to create daily weather inputs to the Soil and Water Assessment Tool (SWAT model. Long-term predictability of water quality within the Wecheon watershed of the Nakdong River Basin was evaluated. According to the Korean Ministry of Environment's Provisions of Water Quality Prediction and Response Measures, modeling-based predictability was evaluated by using 3-month lead prediction data issued in February, May, August, and November as model input of SWAT. Finally, an integrated approach, which takes into account various climate information and downscaling methods for water quality prediction, was presented. This integrated approach can be used to prevent potential problems caused by extreme climate in advance.

  17. Seasonal streamflow prediction by a combined climate-hydrologic system for river basins of Taiwan

    Science.gov (United States)

    Kuo, Chun-Chao; Gan, Thian Yew; Yu, Pao-Shan

    2010-06-01

    SummaryA combined, climate-hydrologic system with three components to predict the streamflow of two river basins of Taiwan at one season (3-month) lead time for the NDJ and JFM seasons was developed. The first component consists of the wavelet-based, ANN-GA model (Artificial Neural Network calibrated by Genetic Algorithm) which predicts the seasonal rainfall by using selected sea surface temperature (SST) as predictors, given that SST are generally predictable by climate models up to 6-month lead time. For the second component, three disaggregation models, Valencia and Schaake (VS), Lane, and Canonical Random Cascade Model (CRCM), were tested to compare the accuracy of seasonal rainfall disaggregated by these three models to 3-day time scale rainfall data. The third component consists of the continuous rainfall-runoff model modified from HBV (called the MHBV) and calibrated by a global optimization algorithm against the observed rainfall and streamflow data of the Shihmen and Tsengwen river basins of Taiwan. The proposed system was tested, first by disaggregating the predicted seasonal rainfall of ANN-GA to rainfall of 3-day time step using the Lane model; then the disaggregated rainfall data was used to drive the calibrated MHBV to predict the streamflow for both river basins at 3-day time step up to a season's lead time. Overall, the streamflow predicted by this combined system for the NDJ season, which is better than that of the JFM season, will be useful for the seasonal planning and management of water resources of these two river basins of Taiwan.

  18. A robust empirical seasonal prediction of winter NAO and surface climate.

    Science.gov (United States)

    Wang, L; Ting, M; Kushner, P J

    2017-03-21

    A key determinant of winter weather and climate in Europe and North America is the North Atlantic Oscillation (NAO), the dominant mode of atmospheric variability in the Atlantic domain. Skilful seasonal forecasting of the surface climate in both Europe and North America is reflected largely in how accurately models can predict the NAO. Most dynamical models, however, have limited skill in seasonal forecasts of the winter NAO. A new empirical model is proposed for the seasonal forecast of the winter NAO that exhibits higher skill than current dynamical models. The empirical model provides robust and skilful prediction of the December-January-February (DJF) mean NAO index using a multiple linear regression (MLR) technique with autumn conditions of sea-ice concentration, stratospheric circulation, and sea-surface temperature. The predictability is, for the most part, derived from the relatively long persistence of sea ice in the autumn. The lower stratospheric circulation and sea-surface temperature appear to play more indirect roles through a series of feedbacks among systems driving NAO evolution. This MLR model also provides skilful seasonal outlooks of winter surface temperature and precipitation over many regions of Eurasia and eastern North America.

  19. Prediction of seasonal climate-induced variations in global food production

    Science.gov (United States)

    Iizumi, Toshichika; Sakuma, Hirofumi; Yokozawa, Masayuki; Luo, Jing-Jia; Challinor, Andrew J.; Brown, Molly E.; Sakurai, Gen; Yamagata, Toshio

    2013-10-01

    Consumers, including the poor in many countries, are increasingly dependent on food imports and are thus exposed to variations in yields, production and export prices in the major food-producing regions of the world. National governments and commercial entities are therefore paying increased attention to the cropping forecasts of important food-exporting countries as well as to their own domestic food production. Given the increased volatility of food markets and the rising incidence of climatic extremes affecting food production, food price spikes may increase in prevalence in future years. Here we present a global assessment of the reliability of crop failure hindcasts for major crops at two lead times derived by linking ensemble seasonal climatic forecasts with statistical crop models. We found that moderate-to-marked yield loss over a substantial percentage (26-33%) of the harvested area of these crops is reliably predictable if climatic forecasts are near perfect. However, only rice and wheat production are reliably predictable at three months before the harvest using within-season hindcasts. The reliabilities of estimates varied substantially by crop--rice and wheat yields were the most predictable, followed by soybean and maize. The reasons for variation in the reliability of the estimates included the differences in crop sensitivity to the climate and the technology used by the crop-producing regions. Our findings reveal that the use of seasonal climatic forecasts to predict crop failures will be useful for monitoring global food production and will encourage the adaptation of food systems toclimatic extremes.

  20. Spatial, seasonal and climatic predictive models of Rift Valley fever disease across Africa.

    Science.gov (United States)

    Redding, David W; Tiedt, Sonia; Lo Iacono, Gianni; Bett, Bernard; Jones, Kate E

    2017-07-19

    Understanding the emergence and subsequent spread of human infectious diseases is a critical global challenge, especially for high-impact zoonotic and vector-borne diseases. Global climate and land-use change are likely to alter host and vector distributions, but understanding the impact of these changes on the burden of infectious diseases is difficult. Here, we use a Bayesian spatial model to investigate environmental drivers of one of the most important diseases in Africa, Rift Valley fever (RVF). The model uses a hierarchical approach to determine how environmental drivers vary both spatially and seasonally, and incorporates the effects of key climatic oscillations, to produce a continental risk map of RVF in livestock (as a proxy for human RVF risk). We find RVF risk has a distinct seasonal spatial pattern influenced by climatic variation, with the majority of cases occurring in South Africa and Kenya in the first half of an El Niño year. Irrigation, rainfall and human population density were the main drivers of RVF cases, independent of seasonal, climatic or spatial variation. By accounting more subtly for the patterns in RVF data, we better determine the importance of underlying environmental drivers, and also make space- and time-sensitive predictions to better direct future surveillance resources.This article is part of the themed issue 'One Health for a changing world: zoonoses, ecosystems and human well-being'. © 2017 The Authors.

  1. Climate services for health: predicting the evolution of the 2016 dengue season in Machala, Ecuador.

    Science.gov (United States)

    Lowe, Rachel; Stewart-Ibarra, Anna M; Petrova, Desislava; García-Díez, Markel; Borbor-Cordova, Mercy J; Mejía, Raúl; Regato, Mary; Rodó, Xavier

    2017-07-01

    El Niño and its effect on local meteorological conditions potentially influences interannual variability in dengue transmission in southern coastal Ecuador. El Oro province is a key dengue surveillance site, due to the high burden of dengue, seasonal transmission, co-circulation of all four dengue serotypes, and the recent introduction of chikungunya and Zika. In this study, we used climate forecasts to predict the evolution of the 2016 dengue season in the city of Machala, following one of the strongest El Niño events on record. We incorporated precipitation, minimum temperature, and Niño3·4 index forecasts in a Bayesian hierarchical mixed model to predict dengue incidence. The model was initiated on Jan 1, 2016, producing monthly dengue forecasts until November, 2016. We accounted for misreporting of dengue due to the introduction of chikungunya in 2015, by using active surveillance data to correct reported dengue case data from passive surveillance records. We then evaluated the forecast retrospectively with available epidemiological information. The predictions correctly forecast an early peak in dengue incidence in March, 2016, with a 90% chance of exceeding the mean dengue incidence for the previous 5 years. Accounting for the proportion of chikungunya cases that had been incorrectly recorded as dengue in 2015 improved the prediction of the magnitude of dengue incidence in 2016. This dengue prediction framework, which uses seasonal climate and El Niño forecasts, allows a prediction to be made at the start of the year for the entire dengue season. Combining active surveillance data with routine dengue reports improved not only model fit and performance, but also the accuracy of benchmark estimates based on historical seasonal averages. This study advances the state-of-the-art of climate services for the health sector, by showing the potential value of incorporating climate information in the public health decision-making process in Ecuador. European Union

  2. Evaluation of a new CNRM-CM6 model version for seasonal climate predictions

    Science.gov (United States)

    Volpi, Danila; Ardilouze, Constantin; Batté, Lauriane; Dorel, Laurant; Guérémy, Jean-François; Déqué, Michel

    2017-04-01

    This work presents the quality assessment of a new version of the Météo-France coupled climate prediction system, which has been developed in the EU COPERNICUS Climate Change Services framework to carry out seasonal forecast. The system is based on the CNRM-CM6 model, with Arpege-Surfex 6.2.2 as atmosphere/land component and Nemo 3.2 as ocean component, which has directly embedded the sea-ice component Gelato 6.0. In order to have a robust diagnostic, the experiment is composed by 60 ensemble members generated with stochastic dynamic perturbations. The experiment has been performed over a 37-year re-forecast period from 1979 to 2015, with two start dates per year, respectively in May 1st and November 1st. The evaluation of the predictive skill of the model is shown under two perspectives: on the one hand, the ability of the model to faithfully respond to positive or negative ENSO, NAO and QBO events, independently of the predictability of these events. Such assessment is carried out through a composite analysis, and shows that the model succeeds in reproducing the main patterns for 2-meter temperature, precipitation and geopotential height at 500 hPa during the winter season. On the other hand, the model predictive skill of the same events (positive and negative ENSO, NAO and QBO) is evaluated.

  3. Predicting optimum crop designs using crop models and seasonal climate forecasts.

    Science.gov (United States)

    Rodriguez, D; de Voil, P; Hudson, D; Brown, J N; Hayman, P; Marrou, H; Meinke, H

    2018-02-02

    Expected increases in food demand and the need to limit the incorporation of new lands into agriculture to curtail emissions, highlight the urgency to bridge productivity gaps, increase farmers profits and manage risks in dryland cropping. A way to bridge those gaps is to identify optimum combination of genetics (G), and agronomic managements (M) i.e. crop designs (GxM), for the prevailing and expected growing environment (E). Our understanding of crop stress physiology indicates that in hindsight, those optimum crop designs should be known, while the main problem is to predict relevant attributes of the E, at the time of sowing, so that optimum GxM combinations could be informed. Here we test our capacity to inform that "hindsight", by linking a tested crop model (APSIM) with a skillful seasonal climate forecasting system, to answer "What is the value of the skill in seasonal climate forecasting, to inform crop designs?" Results showed that the GCM POAMA-2 was reliable and skillful, and that when linked with APSIM, optimum crop designs could be informed. We conclude that reliable and skillful GCMs that are easily interfaced with crop simulation models, can be used to inform optimum crop designs, increase farmers profits and reduce risks.

  4. Climate Prediction Center - Monitoring & Data: La Niña Seasonal Maps and

    Science.gov (United States)

    page National Weather Service NWS logo - Click to go to the NWS home page Climate Prediction Center ±a Case Selection Contact Richard Tinker [rtinker@ncep.noaa.gov], Climate Prediction Center significant climate signals: The La Niña episode, and long-term trends in average temperature and total

  5. The value of model averaging and dynamical climate model predictions for improving statistical seasonal streamflow forecasts over Australia

    Science.gov (United States)

    Pokhrel, Prafulla; Wang, Q. J.; Robertson, David E.

    2013-10-01

    Seasonal streamflow forecasts are valuable for planning and allocation of water resources. In Australia, the Bureau of Meteorology employs a statistical method to forecast seasonal streamflows. The method uses predictors that are related to catchment wetness at the start of a forecast period and to climate during the forecast period. For the latter, a predictor is selected among a number of lagged climate indices as candidates to give the "best" model in terms of model performance in cross validation. This study investigates two strategies for further improvement in seasonal streamflow forecasts. The first is to combine, through Bayesian model averaging, multiple candidate models with different lagged climate indices as predictors, to take advantage of different predictive strengths of the multiple models. The second strategy is to introduce additional candidate models, using rainfall and sea surface temperature predictions from a global climate model as predictors. This is to take advantage of the direct simulations of various dynamic processes. The results show that combining forecasts from multiple statistical models generally yields more skillful forecasts than using only the best model and appears to moderate the worst forecast errors. The use of rainfall predictions from the dynamical climate model marginally improves the streamflow forecasts when viewed over all the study catchments and seasons, but the use of sea surface temperature predictions provide little additional benefit.

  6. Attribution of Large-Scale Climate Patterns to Seasonal Peak-Flow and Prospects for Prediction Globally

    Science.gov (United States)

    Lee, Donghoon; Ward, Philip; Block, Paul

    2018-02-01

    Flood-related fatalities and impacts on society surpass those from all other natural disasters globally. While the inclusion of large-scale climate drivers in streamflow (or high-flow) prediction has been widely studied, an explicit link to global-scale long-lead prediction is lacking, which can lead to an improved understanding of potential flood propensity. Here we attribute seasonal peak-flow to large-scale climate patterns, including the El Niño Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), North Atlantic Oscillation (NAO), and Atlantic Multidecadal Oscillation (AMO), using streamflow station observations and simulations from PCR-GLOBWB, a global-scale hydrologic model. Statistically significantly correlated climate patterns and streamflow autocorrelation are subsequently applied as predictors to build a global-scale season-ahead prediction model, with prediction performance evaluated by the mean squared error skill score (MSESS) and the categorical Gerrity skill score (GSS). Globally, fair-to-good prediction skill (20% ≤ MSESS and 0.2 ≤ GSS) is evident for a number of locations (28% of stations and 29% of land area), most notably in data-poor regions (e.g., West and Central Africa). The persistence of such relevant climate patterns can improve understanding of the propensity for floods at the seasonal scale. The prediction approach developed here lays the groundwork for further improving local-scale seasonal peak-flow prediction by identifying relevant global-scale climate patterns. This is especially attractive for regions with limited observations and or little capacity to develop flood early warning systems.

  7. Seasonal prediction and predictability of Eurasian spring snow water equivalent in NCEP Climate Forecast System version 2 reforecasts

    Science.gov (United States)

    He, Qiong; Zuo, Zhiyan; Zhang, Renhe; Zhang, Ruonan

    2018-01-01

    The spring snow water equivalent (SWE) over Eurasia plays an important role in East Asian and Indian monsoon rainfall. This study evaluates the seasonal prediction capability of NCEP Climate Forecast System version 2 (CFSv2) retrospective forecasts (1983-2010) for the Eurasian spring SWE. The results demonstrate that CFSv2 is able to represent the climatological distribution of the observed Eurasian spring SWE with a lead time of 1-3 months, with the maximum SWE occurring over western Siberia and Northeastern Europe. For a longer lead time, the SWE is exaggerated in CFSv2 because the start of snow ablation in CFSv2 lags behind that of the observation, and the simulated snowmelt rate is less than that in the observation. Generally, CFSv2 can simulate the interannual variations of the Eurasian spring SWE 1-5 months ahead of time but with an exaggerated magnitude. Additionally, the downtrend in CFSv2 is also overestimated. Because the initial conditions (ICs) are related to the corresponding simulation results significantly, the robust interannual variability and the downtrend in the ICs are most likely the causes for these biases. Moreover, CFSv2 exhibits a high potential predictability for the Eurasian spring SWE, especially the spring SWE over Siberia, with a lead time of 1-5 months. For forecasts with lead times longer than 5 months, the model predictability gradually decreases mainly due to the rapid decrease in the model signal.

  8. On the use and potential use of seasonal to decadal climate predictions for decision-making in Europe

    Science.gov (United States)

    Soares, Marta Bruno; Dessai, Suraje

    2014-05-01

    The need for climate information to help inform decision-making in sectors susceptible to climate events and impacts is widely recognised. In Europe, developments in the science and models underpinning the study of climate variability and change have led to an increased interest in seasonal to decadal climate predictions (S2DCP). While seasonal climate forecasts are now routinely produced operationally by a number of centres around the world, decadal climate predictions are still in its infancy restricted to the realm of research. Contrary to other regions of the world, where the use of these types of forecasts, particularly at seasonal timescales, has been pursued in recent years due to higher levels of predictability, little is known about the uptake and climate information needs of end-users regarding S2DCP in Europe. To fill this gap we conducted in-depth interviews with experts and decision-makers across a range of European sectors, a workshop with European climate services providers, and a systematic literature review on the use of S2DCP in Europe. This study is part of the EUropean Provision Of Regional Impact Assessment on a Seasonal-to-decadal timescale (EUPORIAS) project which aims to develop semi-operational prototypes of impact prediction systems in Europe on seasonal to decadal timescales. We found that the emerging landscape of users and potential users of S2DCP in Europe is complex and heterogeneous. Differences in S2DCP information needs across and within organisations and sectors are largely underpinned by factors such as the institutional and regulatory context of the organisations, the plethora of activities and decision-making processes involved, the level of expertise and capacity of the users, and the availability of resources within the organisations. In addition, although the use of S2DCP across Europe is still fairly limited, particular sectors such as agriculture, health, energy, water, (re)insurance, and transport are taking the lead on

  9. Regression-based season-ahead drought prediction for southern Peru conditioned on large-scale climate variables

    Science.gov (United States)

    Mortensen, Eric; Wu, Shu; Notaro, Michael; Vavrus, Stephen; Montgomery, Rob; De Piérola, José; Sánchez, Carlos; Block, Paul

    2018-01-01

    Located at a complex topographic, climatic, and hydrologic crossroads, southern Peru is a semiarid region that exhibits high spatiotemporal variability in precipitation. The economic viability of the region hinges on this water, yet southern Peru is prone to water scarcity caused by seasonal meteorological drought. Meteorological droughts in this region are often triggered during El Niño episodes; however, other large-scale climate mechanisms also play a noteworthy role in controlling the region's hydrologic cycle. An extensive season-ahead precipitation prediction model is developed to help bolster the existing capacity of stakeholders to plan for and mitigate deleterious impacts of drought. In addition to existing climate indices, large-scale climatic variables, such as sea surface temperature, are investigated to identify potential drought predictors. A principal component regression framework is applied to 11 potential predictors to produce an ensemble forecast of regional January-March precipitation totals. Model hindcasts of 51 years, compared to climatology and another model conditioned solely on an El Niño-Southern Oscillation index, achieve notable skill and perform better for several metrics, including ranked probability skill score and a hit-miss statistic. The information provided by the developed model and ancillary modeling efforts, such as extending the lead time of and spatially disaggregating precipitation predictions to the local level as well as forecasting the number of wet-dry days per rainy season, may further assist regional stakeholders and policymakers in preparing for drought.

  10. Sub-Seasonal Prediction of the Maritime Continent Rainfall of Wet-Dry Transitional Seasons in the NCEP Climate Forecast Version 2

    Directory of Open Access Journals (Sweden)

    Tuantuan Zhang

    2016-02-01

    Full Text Available This study investigates the characteristics and prediction of the Maritime Continent (MC rainfall for the transitional periods between wet and dry seasons. Several observational data sets and the output from the 45-day hindcast by the U.S. National Centers for Environmental Prediction (NCEP Climate Forecast System version 2 (CFSv2 are used. Results show that the MC experiences a sudden transition from wet season to dry season (WTD around the 27th pentad, and a gradual transition from dry season to wet season (DTW around the 59th pentad. Correspondingly, the westerlies over the equatorial Indian Ocean, the easterlies over the equatorial Pacific Ocean, and the Australia High become weaker, contributing to weakening of the convergence over the MC. The subtropical western Pacific high intensifies and extends northeastward during the WTD. The Mascarene High becomes weaker, an anomalous anticyclonic circulation forms over the northeast of the Philippines, and an anomalous low-level convergence occurs over the western MC during the DTW. The NCEP CFSv2 captures the major features of rainfall and related atmospheric circulation when forecast lead time is less than three weeks for WTD and two weeks for DTW. The model predicts a weaker amplitude of the changes in rainfall and related atmospheric circulation for both WTD and DTW as lead time increases.

  11. Seasonal Climate Predictability in a Coupled OAGCM Using a Different Approach for Ensemble Forecasts.

    Science.gov (United States)

    Luo, Jing-Jia; Masson, Sebastien; Behera, Swadhin; Shingu, Satoru; Yamagata, Toshio

    2005-11-01

    Predictabilities of tropical climate signals are investigated using a relatively high resolution Scale Interaction Experiment Frontier Research Center for Global Change (FRCGC) coupled GCM (SINTEX-F). Five ensemble forecast members are generated by perturbing the model’s coupling physics, which accounts for the uncertainties of both initial conditions and model physics. Because of the model’s good performance in simulating the climatology and ENSO in the tropical Pacific, a simple coupled SST-nudging scheme generates realistic thermocline and surface wind variations in the equatorial Pacific. Several westerly and easterly wind bursts in the western Pacific are also captured.Hindcast results for the period 1982 2001 show a high predictability of ENSO. All past El Niño and La Niña events, including the strongest 1997/98 warm episode, are successfully predicted with the anomaly correlation coefficient (ACC) skill scores above 0.7 at the 12-month lead time. The predicted signals of some particular events, however, become weak with a delay in the phase at mid and long lead times. This is found to be related to the intraseasonal wind bursts that are unpredicted beyond a few months of lead time. The model forecasts also show a “spring prediction barrier” similar to that in observations. Spatial SST anomalies, teleconnection, and global drought/flood during three different phases of ENSO are successfully predicted at 9 12-month lead times.In the tropical North Atlantic and southwestern Indian Ocean, where ENSO has predominant influences, the model shows skillful predictions at the 7 12-month lead times. The distinct signal of the Indian Ocean dipole (IOD) event in 1994 is predicted at the 6-month lead time. SST anomalies near the western coast of Australia are also predicted beyond the 12-month lead time because of pronounced decadal signals there.

  12. Towards a Seamless Framework for Drought Analysis and Prediction from Seasonal to Climate Change Time Scales (Plinius Medal Lecture)

    Science.gov (United States)

    Sheffield, Justin

    2013-04-01

    Droughts arguably cause the most impacts of all natural hazards in terms of the number of people affected and the long-term economic costs and ecosystem stresses. Recent droughts worldwide have caused humanitarian and economic problems such as food insecurity across the Horn of Africa, agricultural economic losses across the central US and loss of livelihoods in rural western India. The prospect of future increases in drought severity and duration driven by projected changes in precipitation patterns and increasing temperatures is worrisome. Some evidence for climate change impacts on drought is already being seen for some regions, such as the Mediterranean and east Africa. Mitigation of the impacts of drought requires advance warning of developing conditions and enactment of drought plans to reduce vulnerability. A key element of this is a drought early warning system that at its heart is the capability to monitor evolving hydrological conditions and water resources storage, and provide reliable and robust predictions out to several months, as well as the capacity to act on this information. At longer time scales, planning and policy-making need to consider the potential impacts of climate change and its impact on drought risk, and do this within the context of natural climate variability, which is likely to dominate any climate change signal over the next few decades. There are several challenges that need to be met to advance our capability to provide both early warning at seasonal time scales and risk assessment under climate change, regionally and globally. Advancing our understanding of drought predictability and risk requires knowledge of drought at all time scales. This includes understanding of past drought occurrence, from the paleoclimate record to the recent past, and understanding of drought mechanisms, from initiation, through persistence to recovery and translation of this understanding to predictive models. Current approaches to monitoring and

  13. Dynamical Downscaling of Seasonal Climate Prediction over Nordeste Brazil with ECHAM3 and NCEP's Regional Spectral Models at IRI.

    Science.gov (United States)

    Nobre, Paulo; Moura, Antonio D.; Sun, Liqiang

    2001-12-01

    This study presents an evaluation of a seasonal climate forecast done with the International Research Institute for Climate Prediction (IRI) dynamical forecast system (regional model nested into a general circulation model) over northern South America for January-April 1999, encompassing the rainy season over Brazil's Nordeste. The one-way nesting is one in two tiers: first the NCEP's Regional Spectral Model (RSM) runs with an 80-km grid mesh forced by the ECHAM3 atmospheric general circulation model (AGCM) outputs; then the RSM runs with a finer grid mesh (20 km) forced by the forecasts generated by the RSM-80. An ensemble of three realizations is done. Lower boundary conditions over the oceans for both ECHAM and RSM model runs are sea surface temperature forecasts over the tropical oceans. Soil moisture is initialized by ECHAM's inputs. The rainfall forecasts generated by the regional model are compared with those of the AGCM and observations. It is shown that the regional model at 80-km resolution improves upon the AGCM rainfall forecast, reducing both seasonal bias and root-mean-square error. On the other hand, the RSM-20 forecasts presented larger errors, with spatial patterns that resemble those of local topography. The better forecast of the position and width of the intertropical convergence zone (ITCZ) over the tropical Atlantic by the RSM-80 model is one of the principal reasons for better-forecast scores of the RSM-80 relative to the AGCM. The regional model improved the spatial as well as the temporal details of rainfall distribution, and also presenting the minimum spread among the ensemble members. The statistics of synoptic-scale weather variability on seasonal timescales were best forecast with the regional 80-km model over the Nordeste. The possibility of forecasting the frequency distribution of dry and wet spells within the rainy season is encouraging.

  14. Climate Prediction Center - Site Index

    Science.gov (United States)

    Weather Service NWS logo - Click to go to the NWS home page Climate Prediction Center Home Site Map News Means Bulletins Annual Winter Stratospheric Ozone Climate Diagnostics Bulletin (Most Recent) Climate (Hazards Outlook) Climate Assessment: Dec. 1999-Feb. 2000 (Seasonal) Climate Assessment: Mar-May 2000

  15. Potential use of a regional climate model in seasonal tropical cyclone activity predictions in the western North Pacific

    Energy Technology Data Exchange (ETDEWEB)

    Au-Yeung, Andie Y.M.; Chan, Johnny C.L. [City University of Hong Kong, Guy Carpenter Asia-Pacific Climate Impact Centre, School of Energy and Environment, Kowloon, Hong Kong (China)

    2012-08-15

    This study investigates the potential use of a regional climate model in forecasting seasonal tropical cyclone (TC) activity. A modified version of Regional Climate Model Version 3 (RegCM3) is used to examine the ability of the model to simulate TC genesis and landfalling TC tracks for the active TC season in the western North Pacific. In the model, a TC is identified as a vortex satisfying several conditions, including local maximum relative vorticity at 850 hPa with a value {>=}450 x 10{sup -6} s{sup -1}, and the temperature at 300 hPa being 1 C higher than the average temperature within 15 latitude radius from the TC center. Tracks are traced by following these found vortices. Six-month ensemble (8 members each) simulations are performed for each year from 1982 to 2001 so that the climatology of the model can be compared to the Joint Typhoon Warning Center (JTWC) observed best-track dataset. The 20-year ensemble experiments show that the RegCM3 can be used to simulate vortices with a wind structure and temperature profile similar to those of real TCs. The model also reproduces tracks very similar to those observed with features like genesis in the tropics, recurvature at higher latitudes and landfall/decay. The similarity of the 500-hPa geopotential height patterns between RegCM3 and the European Centre for Medium-Range Weather Forecasts 40 Year Re-analysis (ERA-40) shows that the model can simulate the subtropical high to a large extent. The simulated climatological monthly spatial distributions as well as the interannual variability of TC occurrence are also similar to the JTWC data. These results imply the possibility of producing seasonal forecasts of tropical cyclones using real-time global climate model predictions as boundary conditions for the RegCM3. (orig.)

  16. Climate Prediction Center (CPC)Ensemble Canonical Correlation Analysis 90-Day Seasonal Forecast of Precipitation

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Ensemble Canonical Correlation Analysis (ECCA) precipitation forecast is a 90-day (seasonal) outlook of US surface precipitation anomalies. The ECCA uses...

  17. Climate Prediction Center (CPC)U.S. Seasonal Drought Outlook (SDO)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Abstract: A CPC forecaster (from a rotating schedule of 5 as of August 2013) creates the Seasonal Drought Outlook map and narratives. The map, produced using GIS,...

  18. In a warming climate, just how predictable are temperature extremes at weather and seasonal time scales?

    CSIR Research Space (South Africa)

    Landman, WA

    2011-10-01

    Full Text Available stream_source_info Landman7_2011.pdf.txt stream_content_type text/plain stream_size 3538 Content-Encoding ISO-8859-1 stream_name Landman7_2011.pdf.txt Content-Type text/plain; charset=ISO-8859-1 In a warming climate... at UK Met Office N9 members SA Japan UKUSA USA Brazil* SA SASA * IBSA-Ocean In use Near future Far future VCM/UTCM ENSEMBLES Strong anthropogenically forced warming trends have been observed over southern Africa and are projected...

  19. Prediction of seasonal climate-induced variations in global food production

    DEFF Research Database (Denmark)

    Iizumi, Toshichika; Sakuma, Hirofumi; Yokozawa, Masayuki

    2013-01-01

    attention to the cropping forecasts of important food-exporting countries as well as to their own domestic food production. Given the increased volatility of food markets and the rising incidence of climatic extremes affecting food production, food price spikes may increase in prevalence in future years(2......Consumers, including the poor in many countries, are increasingly dependent on food imports(1) and are thus exposed to variations in yields, production and export prices in the major food-producing regions of the world. National governments and commercial entities are therefore paying increased...

  20. Potential for western US seasonal snowpack prediction

    Science.gov (United States)

    Kapnick, Sarah B.; Yang, Xiaosong; Vecchi, Gabriel A.; Delworth, Thomas L.; Gudgel, Rich; Malyshev, Sergey; Milly, Paul C. D.; Shevliakova, Elena; Underwood, Seth; Margulis, Steven A.

    2018-01-01

    Western US snowpack—snow that accumulates on the ground in the mountains—plays a critical role in regional hydroclimate and water supply, with 80% of snowmelt runoff being used for agriculture. While climate projections provide estimates of snowpack loss by the end of th ecentury and weather forecasts provide predictions of weather conditions out to 2 weeks, less progress has been made for snow predictions at seasonal timescales (months to 2 years), crucial for regional agricultural decisions (e.g., plant choice and quantity). Seasonal predictions with climate models first took the form of El Niño predictions 3 decades ago, with hydroclimate predictions emerging more recently. While the field has been focused on single-season predictions (3 months or less), we are now poised to advance our predictions beyond this timeframe. Utilizing observations, climate indices, and a suite of global climate models, we demonstrate the feasibility of seasonal snowpack predictions and quantify the limits of predictive skill 8 month sin advance. This physically based dynamic system outperforms observation-based statistical predictions made on July 1 for March snowpack everywhere except the southern Sierra Nevada, a region where prediction skill is nonexistent for every predictor presently tested. Additionally, in the absence of externally forced negative trends in snowpack, narrow maritime mountain ranges with high hydroclimate variability pose a challenge for seasonal prediction in our present system; natural snowpack variability may inherently be unpredictable at this timescale. This work highlights present prediction system successes and gives cause for optimism for developing seasonal predictions for societal needs.

  1. Climate Prediction Center - Outlooks

    Science.gov (United States)

    Weather Service NWS logo - Click to go to the NWS home page Climate Prediction Center Home Site Map News Web resources and services. HOME > Outreach > Publications > Climate Diagnostics Bulletin Climate Diagnostics Bulletin - Tropics Climate Diagnostics Bulletin - Forecast Climate Diagnostics

  2. Climate prediction and predictability

    Science.gov (United States)

    Allen, Myles

    2010-05-01

    Climate prediction is generally accepted to be one of the grand challenges of the Geophysical Sciences. What is less widely acknowledged is that fundamental issues have yet to be resolved concerning the nature of the challenge, even after decades of research in this area. How do we verify or falsify a probabilistic forecast of a singular event such as anthropogenic warming over the 21st century? How do we determine the information content of a climate forecast? What does it mean for a modelling system to be "good enough" to forecast a particular variable? How will we know when models and forecasting systems are "good enough" to provide detailed forecasts of weather at specific locations or, for example, the risks associated with global geo-engineering schemes. This talk will provide an overview of these questions in the light of recent developments in multi-decade climate forecasting, drawing on concepts from information theory, machine learning and statistics. I will draw extensively but not exclusively from the experience of the climateprediction.net project, running multiple versions of climate models on personal computers.

  3. Climate Prediction Center - Atlantic Hurricane Outlook

    Science.gov (United States)

    Weather Service NWS logo - Click to go to the NWS home page Climate Prediction Center Home Site Map News ; Seasonal Climate Summary Archive The 2018 Atlantic hurricane season outlook is an official product of the National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center (CPC). The outlook is

  4. Mid-21st-century climate changes increase predicted fire occurrence and fire season length, Northern Rocky Mountains, United States

    Science.gov (United States)

    Riley, Karin L.; Loehman, Rachel A.

    2016-01-01

    Climate changes are expected to increase fire frequency, fire season length, and cumulative area burned in the western United States. We focus on the potential impact of mid-21st-century climate changes on annual burn probability, fire season length, and large fire characteristics including number and size for a study area in the Northern Rocky Mountains. Although large fires are rare they account for most of the area burned in western North America, burn under extreme weather conditions, and exhibit behaviors that preclude methods of direct control. Allocation of resources, development of management plans, and assessment of fire effects on ecosystems all require an understanding of when and where fires are likely to burn, particularly under altered climate regimes that may increase large fire occurrence. We used the large fire simulation model FSim to model ignition, growth, and containment of wildfires under two climate scenarios: contemporary (based on instrumental weather) and mid-century (based on an ensemble average of global climate models driven by the A1B SRES emissions scenario). Modeled changes in fire patterns include increased annual burn probability, particularly in areas of the study region with relatively short contemporary fire return intervals; increased individual fire size and annual area burned; and fewer years without large fires. High fire danger days, represented by threshold values of Energy Release Component (ERC), are projected to increase in number, especially in spring and fall, lengthening the climatic fire season. For fire managers, ERC is an indicator of fire intensity potential and fire economics, with higher ERC thresholds often associated with larger, more expensive fires. Longer periods of elevated ERC may significantly increase the cost and complexity of fire management activities, requiring new strategies to maintain desired ecological conditions and limit fire risk. Increased fire activity (within the historical range of

  5. Seasonal forecasts: communicating current climate variability in southern Africa

    CSIR Research Space (South Africa)

    Landman, WA

    2011-11-01

    Full Text Available seasonal time scale. Seasonal climate forecasts are defined as probabilistic predictions of how much rain is expected during the season and how warm or cool it will be, based primarily on the principle that the ocean (sea-surface temperatures) influences...

  6. Seasonal predictability of the North Atlantic Oscillation

    Science.gov (United States)

    Vellinga, Michael; Scaife, Adam

    2015-04-01

    Until recently, long-range forecast systems showed only modest levels of skill in predicting surface winter climate around the Atlantic Basin and associated fluctuations in the North Atlantic Oscillation at seasonal lead times. Here we use a new forecast system to assess seasonal predictability of winter North Atlantic climate. We demonstrate that key aspects of European and North American winter climate and the surface North Atlantic Oscillation are highly predictable months ahead. We demonstrate high levels of prediction skill in retrospective forecasts of the surface North Atlantic Oscillation, winter storminess, near-surface temperature, and wind speed, all of which have high value for planning and adaptation to extreme winter conditions. Analysis of forecast ensembles suggests that while useful levels of seasonal forecast skill have now been achieved, key sources of predictability are still only partially represented and there is further untapped predictability. This work is distributed under the Creative Commons Attribution 3.0 Unported License together with an author copyright. This license does not conflict with the regulations of the Crown Copyright.

  7. Ageing, exposure to pollution, and interactions between climate change and local seasons as oxidant conditions predicting incident hematologic malignancy at KINSHASA University clinics, Democratic Republic of CONGO (DRC).

    Science.gov (United States)

    Nkanga, Mireille Solange Nganga; Longo-Mbenza, Benjamin; Adeniyi, Oladele Vincent; Ngwidiwo, Jacques Bikaula; Katawandja, Antoine Lufimbo; Kazadi, Paul Roger Beia; Nzonzila, Alain Nganga

    2017-08-23

    The global burden of hematologic malignancy (HM) is rapidly rising with aging, exposure to polluted environments, and global and local climate variability all being well-established conditions of oxidative stress. However, there is currently no information on the extent and predictors of HM at Kinshasa University Clinics (KUC), DR Congo (DRC). This study evaluated the impact of bio-clinical factors, exposure to polluted environments, and interactions between global climate changes (EL Nino and La Nina) and local climate (dry and rainy seasons) on the incidence of HM. This hospital-based prospective cohort study was conducted at Kinshasa University Clinics in DR Congo. A total of 105 black African adult patients with anaemia between 2009 and 2016 were included. HM was confirmed by morphological typing according to the French-American-British (FAB) Classification System. Gender, age, exposure to traffic pollution and garages/stations, global climate variability (El Nino and La Nina), and local climate (dry and rainy seasons) were potential independent variables to predict incident HM using Cox regression analysis and Kaplan Meier curves. Out of the total 105 patients, 63 experienced incident HM, with an incidence rate of 60%. After adjusting for gender, HIV/AIDS, and other bio-clinical factors, the most significant independent predictors of HM were age ≥ 55 years (HR = 2.4; 95% CI 1.4-4.3; P = 0.003), exposure to pollution and garages or stations (HR = 4.9; 95% CI 2-12.1; P pollution, combined local dry season + La Nina and combined local dry season + El Nino were the most significant predictors of incident hematologic malignancy. These findings highlight the importance of aging, pollution, the dry season, El Nino and La Nina as related to global warming as determinants of hematologic malignancies among African patients from Kinshasa, DR Congo. Cancer registries in DRC and other African countries will provide more robust database for future researches on

  8. Climatic controls of the interannual to decadal variability in Saudi Arabian dust activity: Towards the development of a seasonal prediction tool

    Science.gov (United States)

    Yu, Y.; Notaro, M.; Liu, Z.; Alkolibi, F.; Fadda, E.; Bakhrjy, F.

    2013-12-01

    Atmospheric dust significantly influences the climate system, as well as human life in Saudi Arabia. Skillful seasonal prediction of dust activity with climatic variables will help prevent some negative social impacts of dust storms. Yet, the climatic regulators on Saudi Arabian dust activity remain largely unaddressed. Remote sensing and station observations show consistent seasonal cycles in Saudi Arabian dust activity, which peaks in spring and summer. The climatic controls on springtime and summertime Saudi Arabian dust activity during 1975-2010 are studied using observational and reanalysis data. Empirical Orthogonal Function (EOF) of the observed Saudi Arabian dust storm frequency shows a dominant homogeneous pattern across the country, which has distinct interannual and decadal variations, as revealed by the power spectrum. Regression and correlation analyses reveal that Saudi Arabian dust activity is largely tied to precipitation on the Arabian Peninsula in spring and northwesterly (Shamal) wind in summer. On the seasonal-interannual time scale, warm El Niño-Southern Oscillation (ENSO) phase (El Niño) in winter-to-spring inhibits spring dust activity by increasing the precipitation over the Rub'al Khali Desert, a major dust source region on the southern Arabian Peninsula; warm ENSO and warm Indian Ocean Basin Mode (IOBM) in winter-to-spring favor less summer dust activity by producing anomalously low sea-level pressure over eastern north Africa and Arabian Peninsula, which leads to the reduced Shamal wind speed. The decadal variation in dust activity is likely associated with the Atlantic Multidecadal Oscillation (AMO), which impacts Sahel rainfall and North African dust, and likely dust transport to Saudi Arabia. The Pacific Decadal Oscillation (PDO) and tropical Indian Ocean SST also have influence on the decadal variation in Saudi Arabian dust activity, by altering precipitation over the Arabian Peninsula and summer Shamal wind speed. Using eastern

  9. Towards seasonal Arctic shipping route predictions

    Science.gov (United States)

    Haines, K.; Melia, N.; Hawkins, E.; Day, J. J.

    2017-12-01

    In our previous work [1] we showed how trans-Arctic shipping routes would become more available through the 21st century as sea ice declines, using CMIP5 models with means and stds calibrated to PIOMAS sea ice observations. Sea ice will continue to close shipping routes to open water vessels through the winter months for the foreseeable future so the availability of open sea routes will vary greatly from year to year. Here [2] we look at whether the trans-Arctic shipping season period can be predicted in seasonal forecasts, again using several climate models, and testing both perfect and imperfect knowledge of the initial sea ice conditions. We find skilful predictions of the upcoming summer shipping season can be made from as early as January, although typically forecasts may show lower skill before a May `predictability barrier'. Focussing on the northern sea route (NSR) off Siberia, the date of opening of this sea route is twice as variable as the closing date, and this carries through to reduced predictability at the start of the season. Under climate change the later freeze-up date accounts for 60% of the lengthening season, Fig1 We find that predictive skill is state dependent with predictions for high or low ice years exhibiting greater skill than for average ice years. Forecasting the exact timing of route open periods is harder (more weather dependent) under average ice conditions while in high and low ice years the season is more controlled by the initial ice conditions from spring onwards. This could be very useful information for companies planning vessel routing for the coming season. We tested this dependence on the initial ice conditions by changing the initial ice state towards climatologically average conditions and show directly that early summer sea-ice thickness information is crucial to obtain skilful forecasts of the coming shipping season. Mechanisms for this are discussed. This strongly suggests that good sea ice thickness observations

  10. Climate Prediction Center

    Science.gov (United States)

    Weather Service NWS logo - Click to go to the NWS home page Climate Prediction Center Home Site Map News Organization Enter Search Term(s): Search Search the CPC Go NCEP Quarterly Newsletter Climate Highlights U.S Climate-Weather El Niño/La Niña MJO Blocking AAO, AO, NAO, PNA Climatology Global Monsoons Expert

  11. The impact of different sea-surface temperature prediction scenarios on Southern African seasonal climate forecast skill

    CSIR Research Space (South Africa)

    Landman, WA

    2009-12-01

    Full Text Available Retro-active forecasts produced at a 1-month lead-time by the ECHAM4.5 AGCM are statistically downscaled to South African district rainfall totals for the austral mid-summer season of December to February. The AGCM is forced with SST forecasts...

  12. Seasonal and decadal information towards climate services: EUPORIAS

    Science.gov (United States)

    Buontempo, Carlo; Hewitt, Chris

    2013-04-01

    Societies have always faced challenges and opportunities arising from variations in climate, and have often flourished or collapsed depending on their ability to adapt to such changes. Recent advances in our understanding and ability to forecast climate variability and climate change have meant that skilful predictions are beginning to be routinely made on seasonal to decadal (s2d) timescales. Such forecasts have the potential to be of great value to a wide range of decision-making, where outcomes are strongly influenced by variations in the climate. The European Commission have recently commissioned a major four year long project (EUPORIAS) to develop prototype end-to-end climate impact prediction services operating on a seasonal to decadal timescale, and assess their value in informing decision-making. EUPORIAS commenced on 1 November 2012, coordinated by the UK Met Office leading a consortium of 24 organisations representing world-class European climate research and climate service centres, expertise in impacts assessments and seasonal predictions, two United Nations agencies, specialists in new media, and commercial companies in climate-vulnerable sectors such as energy, water and tourism. The paper describes the setup of the project, its main outcome and some of the very preliminary results.

  13. Climate Prediction Center - Forecasts & Outlook Maps, Graphs and Tables

    Science.gov (United States)

    Weather Service NWS logo - Click to go to the NWS home page Climate Prediction Center Home Site Map News list below The Climate Prediction Center (CPC) is responsible for issuing seasonal climate outlook maps , and National Centers for Environmental Prediction). These weather and climate products comprise the

  14. Prediction of interannual climate variations

    International Nuclear Information System (INIS)

    Shukla, J.

    1993-01-01

    It has been known for some time that the behavior of the short-term fluctuations of the earth's atmosphere resembles that of a chaotic non-linear dynamical system, and that the day-to-day weather cannot be predicted beyond a few weeks. However, it has also been found that the interactions of the atmosphere with the underlying oceans and the land surfaces can produce fluctuations whose time scales are much longer than the limits of deterministic prediction of weather. It is, therefore, natural to ask whether it is possible that the seasonal and longer time averages of climate fluctuations can be predicted with sufficient skill to be beneficial for social and economic applications, even though the details of day-to-day weather cannot be predicted beyond a few weeks. The main objective of the workshop was to address this question by assessing the current state of knowledge on predictability of seasonal and interannual climate variability and to investigate various possibilities for its prediction. (orig./KW)

  15. Dynamical seasonal climate prediction using an ocean-atmosphere coupled climate model developed in partnership between South Africa and the IRI

    CSIR Research Space (South Africa)

    Beraki, AF

    2014-02-01

    Full Text Available dedicated a large amount of resources to utilize Atmospheric General Circulation Models 66 (AGCMs) as operational seasonal forecast tools (Landman et al. 2012). These models 67 have all been developed outside of South Africa, but have been used extensively... Niña seasons (Landman et al. 2012; Landman and Beraki 2012). As noted above, coupled 99 models are largely assumed or hypothesized to represent the state of the art of seasonal 100 forecasting. In fact, it has been conclusively shown through...

  16. An analysis of seasonal predictability in coupled model forecasts

    Energy Technology Data Exchange (ETDEWEB)

    Peng, P.; Wang, W. [NOAA, Climate Prediction Center, Washington, DC (United States); Kumar, A. [NOAA, Climate Prediction Center, Washington, DC (United States); NCEP/NWS/NOAA, Climate Prediction Center, Camp Springs, MD (United States)

    2011-02-15

    In the recent decade, operational seasonal prediction systems based on initialized coupled models have been developed. An analysis of how the predictability of seasonal means in the initialized coupled predictions evolves with lead-time is presented. Because of the short lead-time, such an analysis for the temporal behavior of seasonal predictability involves a mix of both the predictability of the first and the second kind. The analysis focuses on the lead-time dependence of ensemble mean variance, and the forecast spread. Further, the analysis is for a fixed target season of December-January-February, and is for sea surface temperature, rainfall, and 200-mb height. The analysis is based on a large set of hindcasts from an initialized coupled seasonal prediction system. Various aspects of predictability of the first and the second kind are highlighted for variables with long (for example, SST), and fast (for example, atmospheric) adjustment time scale. An additional focus of the analysis is how the predictability in the initialized coupled seasonal predictions compares with estimates based on the AMIP simulations. The results indicate that differences in the set up of AMIP simulations and coupled predictions, for example, representation of air-sea interactions, and evolution of forecast spread from initial conditions do not change fundamental conclusion about the seasonal predictability. A discussion of the analysis presented herein, and its implications for the use of AMIP simulations for climate attribution, and for time-slice experiments to provide regional information, is also included. (orig.)

  17. Seasonal Prediction of Taiwan's Streamflow Using Teleconnection Patterns

    Science.gov (United States)

    Chen, Chia-Jeng; Lee, Tsung-Yu

    2017-04-01

    Seasonal streamflow as an integrated response to complex hydro-climatic processes can be subject to activity of prevailing weather systems potentially modulated by large-scale climate oscillations (e.g., El Niño-Southern Oscillation, ENSO). To develop a seamless seasonal forecasting system in Taiwan, this study assesses how significant Taiwan's precipitation and streamflow in different seasons correlate with selected teleconnection patterns. Long-term precipitation and streamflow data in three major precipitation seasons, namely the spring rains (February to April), Mei-Yu (May and June), and typhoon (July to September) seasons, are derived at 28 upstream and 13 downstream catchments in Taiwan. The three seasons depict a complete wet period of Taiwan as well as many regions bearing similar climatic conditions in East Asia. Lagged correlation analysis is then performed to investigate how the precipitation and streamflow data correlate with predominant teleconnection indices at varied lead times. Teleconnection indices are selected only if they show certain linkage with weather systems and activity in the three seasons based on previous literature. For instance, the ENSO and Quasi-Biennial Oscillation, proven to influence East Asian climate across seasons and summer typhoon activity, respectively, are included in the list of climate indices for correlation analysis. Significant correlations found between Taiwan's precipitation and streamflow and teleconnection indices are further examined by a climate regime shift (CRS) test to identify any abrupt changes in the correlations. The understanding of existing CRS is useful for informing the forecasting system of the changes in the predictor-predictand relationship. To evaluate prediction skill in the three seasons and skill differences between precipitation and streamflow, hindcasting experiments of precipitation and streamflow are conducted using stepwise linear regression models. Discussion and suggestions for coping

  18. On the reliability of seasonal climate forecasts

    Science.gov (United States)

    Weisheimer, A.; Palmer, T. N.

    2014-01-01

    Seasonal climate forecasts are being used increasingly across a range of application sectors. A recent UK governmental report asked: how good are seasonal forecasts on a scale of 1–5 (where 5 is very good), and how good can we expect them to be in 30 years time? Seasonal forecasts are made from ensembles of integrations of numerical models of climate. We argue that ‘goodness’ should be assessed first and foremost in terms of the probabilistic reliability of these ensemble-based forecasts; reliable inputs are essential for any forecast-based decision-making. We propose that a ‘5’ should be reserved for systems that are not only reliable overall, but where, in particular, small ensemble spread is a reliable indicator of low ensemble forecast error. We study the reliability of regional temperature and precipitation forecasts of the current operational seasonal forecast system of the European Centre for Medium-Range Weather Forecasts, universally regarded as one of the world-leading operational institutes producing seasonal climate forecasts. A wide range of ‘goodness’ rankings, depending on region and variable (with summer forecasts of rainfall over Northern Europe performing exceptionally poorly) is found. Finally, we discuss the prospects of reaching ‘5’ across all regions and variables in 30 years time. PMID:24789559

  19. Seasonal climate change patterns due to cumulative CO2 emissions

    Science.gov (United States)

    Partanen, Antti-Ilari; Leduc, Martin; Damon Matthews, H.

    2017-07-01

    Cumulative CO2 emissions are near linearly related to both global and regional changes in annual-mean surface temperature. These relationships are known as the transient climate response to cumulative CO2 emissions (TCRE) and the regional TCRE (RTCRE), and have been shown to remain approximately constant over a wide range of cumulative emissions. Here, we assessed how well this relationship holds for seasonal patterns of temperature change, as well as for annual-mean and seasonal precipitation patterns. We analyzed an idealized scenario with CO2 concentration growing at an annual rate of 1% using data from 12 Earth system models from the Coupled Model Intercomparison Project Phase 5 (CMIP5). Seasonal RTCRE values for temperature varied considerably, with the highest seasonal variation evident in the Arctic, where RTCRE was about 5.5 °C per Tt C for boreal winter and about 2.0 °C per Tt C for boreal summer. Also the precipitation response in the Arctic during boreal winter was stronger than during other seasons. We found that emission-normalized seasonal patterns of temperature change were relatively robust with respect to time, though they were sub-linear with respect to emissions particularly near the Arctic. Moreover, RTCRE patterns for precipitation could not be quantified robustly due to the large internal variability of precipitation. Our results suggest that cumulative CO2 emissions are a useful metric to predict regional and seasonal changes in precipitation and temperature. This extension of the TCRE framework to seasonal and regional climate change is helpful for communicating the link between emissions and climate change to policy-makers and the general public, and is well-suited for impact studies that could make use of estimated regional-scale climate changes that are consistent with the carbon budgets associated with global temperature targets.

  20. Prediction uncertainty in seasonal partial duration series

    DEFF Research Database (Denmark)

    Rasmussen, Peter Funder; Rosbjerg, Dan

    1991-01-01

    In order to obtain a good description of the exceedances in a partial duration series it is often necessary to divide the year into a number (2-4) of seasons. Hereby a stationary exceedance distribution can be maintained within each season. This type of seasonal models may, however, not be suitable...... for prediction purposes due to the large number of parameters required. In the particular case with exponentially distributed exceedances and Poissonian occurrence times the precision of the T year event estimator has been thoroughly examined considering both seasonal and nonseasonal models. The two......-seasonal probability density function of the T year event estimator has been deduced and used in the assessment of the precision of approximate moments. The nonseasonal approach covered both a total omission of seasonality by pooling data from different flood seasons and a discarding of nonsignificant season(s) before...

  1. The changing seasonal climate in the Arctic.

    Science.gov (United States)

    Bintanja, R; van der Linden, E C

    2013-01-01

    Ongoing and projected greenhouse warming clearly manifests itself in the Arctic regions, which warm faster than any other part of the world. One of the key features of amplified Arctic warming concerns Arctic winter warming (AWW), which exceeds summer warming by at least a factor of 4. Here we use observation-driven reanalyses and state-of-the-art climate models in a variety of standardised climate change simulations to show that AWW is strongly linked to winter sea ice retreat through the associated release of surplus ocean heat gained in summer through the ice-albedo feedback (~25%), and to infrared radiation feedbacks (~75%). Arctic summer warming is surprisingly modest, even after summer sea ice has completely disappeared. Quantifying the seasonally varying changes in Arctic temperature and sea ice and the associated feedbacks helps to more accurately quantify the likelihood of Arctic's climate changes, and to assess their impact on local ecosystems and socio-economic activities.

  2. Predictability of Seasonal Rainfall over the Greater Horn of Africa

    Science.gov (United States)

    Ngaina, J. N.

    2016-12-01

    The El Nino-Southern Oscillation (ENSO) is a primary mode of climate variability in the Greater of Africa (GHA). The expected impacts of climate variability and change on water, agriculture, and food resources in GHA underscore the importance of reliable and accurate seasonal climate predictions. The study evaluated different model selection criteria which included the Coefficient of determination (R2), Akaike's Information Criterion (AIC), Bayesian Information Criterion (BIC), and the Fisher information approximation (FIA). A forecast scheme based on the optimal model was developed to predict the October-November-December (OND) and March-April-May (MAM) rainfall. The predictability of GHA rainfall based on ENSO was quantified based on composite analysis, correlations and contingency tables. A test for field-significance considering the properties of finiteness and interdependence of the spatial grid was applied to avoid correlations by chance. The study identified FIA as the optimal model selection criterion. However, complex model selection criteria (FIA followed by BIC) performed better compared to simple approach (R2 and AIC). Notably, operational seasonal rainfall predictions over the GHA makes of simple model selection procedures e.g. R2. Rainfall is modestly predictable based on ENSO during OND and MAM seasons. El Nino typically leads to wetter conditions during OND and drier conditions during MAM. The correlations of ENSO indices with rainfall are statistically significant for OND and MAM seasons. Analysis based on contingency tables shows higher predictability of OND rainfall with the use of ENSO indices derived from the Pacific and Indian Oceans sea surfaces showing significant improvement during OND season. The predictability based on ENSO for OND rainfall is robust on a decadal scale compared to MAM. An ENSO-based scheme based on an optimal model selection criterion can thus provide skillful rainfall predictions over GHA. This study concludes that the

  3. Assessing impact of climate change on season length in Karnataka

    Indian Academy of Sciences (India)

    Changes in seasons and season length are an indicator, as well as an effect, of climate change. Seasonal change profoundly affects the balance of life in ecosystems and impacts essential human activities such as agriculture and irrigation. This study investigates the uncertainty of season length in Karnataka state, India, ...

  4. Linking seasonal climate forecasts with crop models in Iberian Peninsula

    Science.gov (United States)

    Capa, Mirian; Ines, Amor; Baethgen, Walter; Rodriguez-Fonseca, Belen; Han, Eunjin; Ruiz-Ramos, Margarita

    2015-04-01

    Translating seasonal climate forecasts into agricultural production forecasts could help to establish early warning systems and to design crop management adaptation strategies that take advantage of favorable conditions or reduce the effect of adverse conditions. In this study, we use seasonal rainfall forecasts and crop models to improve predictability of wheat yield in the Iberian Peninsula (IP). Additionally, we estimate economic margins and production risks associated with extreme scenarios of seasonal rainfall forecast. This study evaluates two methods for disaggregating seasonal climate forecasts into daily weather data: 1) a stochastic weather generator (CondWG), and 2) a forecast tercile resampler (FResampler). Both methods were used to generate 100 (with FResampler) and 110 (with CondWG) weather series/sequences for three scenarios of seasonal rainfall forecasts. Simulated wheat yield is computed with the crop model CERES-wheat (Ritchie and Otter, 1985), which is included in Decision Support System for Agrotechnology Transfer (DSSAT v.4.5, Hoogenboom et al., 2010). Simulations were run at two locations in northeastern Spain where the crop model was calibrated and validated with independent field data. Once simulated yields were obtained, an assessment of farmer's gross margin for different seasonal climate forecasts was accomplished to estimate production risks under different climate scenarios. This methodology allows farmers to assess the benefits and risks of a seasonal weather forecast in IP prior to the crop growing season. The results of this study may have important implications on both, public (agricultural planning) and private (decision support to farmers, insurance companies) sectors. Acknowledgements Research by M. Capa-Morocho has been partly supported by a PICATA predoctoral fellowship of the Moncloa Campus of International Excellence (UCM-UPM) and MULCLIVAR project (CGL2012-38923-C02-02) References Hoogenboom, G. et al., 2010. The Decision

  5. Seasonal Drought Prediction: Advances, Challenges, and Future Prospects

    Science.gov (United States)

    Hao, Zengchao; Singh, Vijay P.; Xia, Youlong

    2018-03-01

    Drought prediction is of critical importance to early warning for drought managements. This review provides a synthesis of drought prediction based on statistical, dynamical, and hybrid methods. Statistical drought prediction is achieved by modeling the relationship between drought indices of interest and a suite of potential predictors, including large-scale climate indices, local climate variables, and land initial conditions. Dynamical meteorological drought prediction relies on seasonal climate forecast from general circulation models (GCMs), which can be employed to drive hydrological models for agricultural and hydrological drought prediction with the predictability determined by both climate forcings and initial conditions. Challenges still exist in drought prediction at long lead time and under a changing environment resulting from natural and anthropogenic factors. Future research prospects to improve drought prediction include, but are not limited to, high-quality data assimilation, improved model development with key processes related to drought occurrence, optimal ensemble forecast to select or weight ensembles, and hybrid drought prediction to merge statistical and dynamical forecasts.

  6. Climate Prediction Center - monthly Outlook

    Science.gov (United States)

    Weather Service NWS logo - Click to go to the NWS home page Climate Prediction Center Site Map News Outlooks monthly Climate Outlooks Banner OFFICIAL Forecasts June 2018 [UPDATED MONTHLY FORECASTS SERVICE ) Canonical Correlation Analysis ECCA - Ensemble Canonical Correlation Analysis Optimal Climate Normals

  7. The value of seasonal forecasting and crop mix adaptation to climate variability for agriculture under climate change

    Science.gov (United States)

    Choi, H. S.; Schneider, U.; Schmid, E.; Held, H.

    2012-04-01

    Changes to climate variability and frequency of extreme weather events are expected to impose damages to the agricultural sector. Seasonal forecasting and long range prediction skills have received attention as an option to adapt to climate change because seasonal climate and yield predictions could improve farmers' management decisions. The value of seasonal forecasting skill is assessed with a crop mix adaptation option in Spain where drought conditions are prevalent. Yield impacts of climate are simulated for six crops (wheat, barely, cotton, potato, corn and rice) with the EPIC (Environmental Policy Integrated Climate) model. Daily weather data over the period 1961 to 1990 are used and are generated by the regional climate model REMO as reference period for climate projection. Climate information and its consequent yield variability information are given to the stochastic agricultural sector model to calculate the value of climate information in the agricultural market. Expected consumers' market surplus and producers' revenue is compared with and without employing climate forecast information. We find that seasonal forecasting benefits not only consumers but also producers if the latter adopt a strategic crop mix. This mix differs from historical crop mixes by having higher shares of crops which fare relatively well under climate change. The corresponding value of information is highly sensitive to farmers' crop mix choices.

  8. Modeled seasonality of glacial abrupt climate events

    Energy Technology Data Exchange (ETDEWEB)

    Flueckiger, Jacqueline [Institute of Arctic and Alpine Research, University of Colorado, Boulder, CO (United States); Environmental Physics, Institute of Biogeochemistry and Pollutant Dynamics, ETH Zuerich, Zurich (Switzerland); Knutti, Reto [Institute for Atmospheric and Climate Science, ETH Zuerich, Zurich (Switzerland); White, James W.C. [Institute of Arctic and Alpine Research, University of Colorado, Boulder, CO (United States); Renssen, Hans [Vrije Universiteit Amsterdam, Faculty of Earth and Life Sciences, Amsterdam (Netherlands)

    2008-11-15

    Greenland ice cores, as well as many other paleo-archives from the northern hemisphere, recorded a series of 25 warm interstadial events, the so-called Dansgaard-Oeschger (D-O) events, during the last glacial period. We use the three-dimensional coupled global ocean-atmosphere-sea ice model ECBILT-CLIO and force it with freshwater input into the North Atlantic to simulate abrupt glacial climate events, which we use as analogues for D-O events. We focus our analysis on the Northern Hemisphere. The simulated events show large differences in the regional and seasonal distribution of the temperature and precipitation changes. While the temperature changes in high northern latitudes and in the North Atlantic region are dominated by winter changes, the largest temperature increases in most other land regions are seen in spring. Smallest changes over land are found during the summer months. Our model simulations also demonstrate that the temperature and precipitation change patterns for different intensifications of the Atlantic meridional overturning circulation are not linear. The extent of the transitions varies, and local non-linearities influence the amplitude of the annual mean response as well as the response in different seasons. Implications for the interpretation of paleo-records are discussed. (orig.)

  9. Climate Prediction - NOAA's National Weather Service

    Science.gov (United States)

    Statistical Models... MOS Prod GFS-LAMP Prod Climate Past Weather Predictions Weather Safety Weather Radio National Weather Service on FaceBook NWS on Facebook NWS Director Home > Climate > Predictions Climate Prediction Long range forecasts across the U.S. Climate Prediction Web Sites Climate Prediction

  10. SEASONAL PREDICTION OF PRECIPITATION OVER NIGERIA

    African Journals Online (AJOL)

    DEPT OF AGRICULTURAL ENGINEERING

    nificant difference between the means of predicted and observed seasonal rainfall amount for all ... and the Gulf of Guinea in the north, east, west ... from the northern part to the southern part of ... and Savanna regions of Nigeria than the other.

  11. Climate-driven seasonal geocenter motion during the GRACE period

    Science.gov (United States)

    Zhang, Hongyue; Sun, Yu

    2018-03-01

    Annual cycles in the geocenter motion time series are primarily driven by mass changes in the Earth's hydrologic system, which includes land hydrology, atmosphere, and oceans. Seasonal variations of the geocenter motion have been reliably determined according to Sun et al. (J Geophys Res Solid Earth 121(11):8352-8370, 2016) by combining the Gravity Recovery And Climate Experiment (GRACE) data with an ocean model output. In this study, we reconstructed the observed seasonal geocenter motion with geophysical model predictions of mass variations in the polar ice sheets, continental glaciers, terrestrial water storage (TWS), and atmosphere and dynamic ocean (AO). The reconstructed geocenter motion time series is shown to be in close agreement with the solution based on GRACE data supporting with an ocean bottom pressure model. Over 85% of the observed geocenter motion time series, variance can be explained by the reconstructed solution, which allows a further investigation of the driving mechanisms. We then demonstrated that AO component accounts for 54, 62, and 25% of the observed geocenter motion variances in the X, Y, and Z directions, respectively. The TWS component alone explains 42, 32, and 39% of the observed variances. The net mass changes over oceans together with self-attraction and loading effects also contribute significantly (about 30%) to the seasonal geocenter motion in the X and Z directions. Other contributing sources, on the other hand, have marginal (less than 10%) impact on the seasonal variations but introduce a linear trend in the time series.

  12. Sub-Seasonal Climate Forecast Rodeo

    Science.gov (United States)

    Webb, R. S.; Nowak, K.; Cifelli, R.; Brekke, L. D.

    2017-12-01

    The Bureau of Reclamation, as the largest water wholesaler and the second largest producer of hydropower in the United States, benefits from skillful forecasts of future water availability. Researchers, water managers from local, regional, and federal agencies, and groups such as the Western States Water Council agree that improved precipitation and temperature forecast information at the sub-seasonal to seasonal (S2S) timescale is an area with significant potential benefit to water management. In response, and recognizing NOAA's leadership in forecasting, Reclamation has partnered with NOAA to develop and implement a real-time S2S forecasting competition. For a year, solvers are submitting forecasts of temperature and precipitation for weeks 3&4 and 5&6 every two weeks on a 1x1 degree grid for the 17 western state domain where Reclamation operates. The competition began on April 18, 2017 and the final real-time forecast is due April 3, 2018. Forecasts are evaluated once observational data become available using spatial anomaly correlation. Scores are posted on a competition leaderboard hosted by the National Integrated Drought Information System (NIDIS). The leaderboard can be accessed at: https://www.drought.gov/drought/sub-seasonal-climate-forecast-rodeo. To be eligible for cash prizes - which total $800,000 - solvers must outperform two benchmark forecasts during the real-time competition as well as in a required 11-year hind-cast. To receive a prize, competitors must grant a non-exclusive license to practice their forecast technique and make it available as open source software. At approximately one quarter complete, there are teams outperforming the benchmarks in three of the four competition categories. With prestige and monetary incentives on the line, it is hoped that the competition will spur innovation of improved S2S forecasts through novel approaches, enhancements to established models, or otherwise. Additionally, the competition aims to raise

  13. Climate Prediction Center - Outreach: 41st Annual Climate Diagnostics &

    Science.gov (United States)

    home page National Weather Service NWS logo - Click to go to the NWS home page Climate Prediction Annual Climate Diagnostics & Prediction Workshop NOAA's 41st Climate Diagnostics and Prediction Climate Diagnostics Prediction Workshop (CDPW) news, visit the CDPW list server Abstract Submission Has

  14. Managing living marine resources in a dynamic environment: the role of seasonal to decadal climate forecasts

    DEFF Research Database (Denmark)

    Tommasi, Desiree; Stock, Charles A.; Hobday, Alistair J.

    2017-01-01

    and industry operations, as well as new research avenues in fisheries science. LMRs respond to climate variability via changes in physiology and behavior. For species and systems where climate-fisheries links are well established, forecasted LMR responses can lead to anticipatory and more effective decisions......Recent developments in global dynamical climate prediction systems have allowed for skillful predictions of climate variables relevant to living marine resources (LMRs) at a scale useful to understanding and managing LMRs. Such predictions present opportunities for improved LMR management......, benefitting both managers and stakeholders. Here, we provide an overview of climate prediction systems and advances in seasonal to decadal prediction of marine-resource relevant environmental variables. We then describe a range of climate-sensitive LMR decisions that can be taken at lead-times of months...

  15. Predictability of rainy season onset and cessation in east Africa

    Science.gov (United States)

    Joseph, B.-M.

    2012-04-01

    PREDICTABILITY OF RAINY SEASONS ONSET AND CESSATION IN EAST AFRICA Boyard-Micheau Joseph, joseph.boyard-micheau@u-bourgogne.fr Camberlin Pierre, Kenya and northern Tanzania mainly display bimodal rainfall regimes, which are controlled by the annual migration of the Intertropical Convergence Zone on both sides of the equator. In the low-income, semi-arid areas, food security is highly dependent on cereal yields (maize, millet and sorghum). Vulnerability is aggravated by the fact that these crops are mostly rainfed, and rely on the performance of the two, relatively brief rainy seasons. This performance depends on a combination of several rainy season characteristics, or rainfall descriptors, such as the onset and cessation dates of the rains, the frequency of rainy days, their intensity and the occurrence of wet/dry spells. The prediction of these descriptors some time (>15 days) before the real onset of the rainy season can be seen as a useful tool to help in the establishment of agricultural adaptation strategies. The main objective consists to understand linkages between regional variability of these rainfall descriptors and global modes of the climate system, in order to set up efficient predictive tools based on Model Output Statistics (MOS). The rainfall descriptors are computed from daily rainfall data collected for the period 1961-2001 from the Kenya Meteorological Department, the IGAD Climate Prediction and Application Center and the Tanzania Meteorological Agency. An initial spatial coherence analysis assesses the potential predictability of each descriptor, permitting eventually to eliminate those which are not spatially coherent, on the assumption that low spatial coherence denotes low potential predictability. Rainfall in East Africa simulated by a 24-ensemble member of the ECHAM 4.5 atmospheric general circulation model is compared with observations, to test the reproducibility of the rainfall descriptors. Canonical Correlation Analysis is next used to

  16. Role of Ocean Initial Conditions to Diminish Dry Bias in the Seasonal Prediction of Indian Summer Monsoon Rainfall: A Case Study Using Climate Forecast System

    Science.gov (United States)

    Koul, Vimal; Parekh, Anant; Srinivas, G.; Kakatkar, Rashmi; Chowdary, Jasti S.; Gnanaseelan, C.

    2018-03-01

    Coupled models tend to underestimate Indian summer monsoon (ISM) rainfall over most of the Indian subcontinent. Present study demonstrates that a part of dry bias is arising from the discrepancies in Oceanic Initial Conditions (OICs). Two hindcast experiments are carried out using Climate Forecast System (CFSv2) for summer monsoons of 2012-2014 in which two different OICs are utilized. With respect to first experiment (CTRL), second experiment (AcSAL) differs by two aspects: usage of high-resolution atmospheric forcing and assimilation of only ARGO observed temperature and salinity profiles for OICs. Assessment of OICs indicates that the quality of OICs is enhanced due to assimilation of actual salinity profiles. Analysis reveals that AcSAL experiment showed 10% reduction in the dry bias over the Indian land region during the ISM compared to CTRL. This improvement is consistently apparent in each month and is highest for June. The better representation of upper ocean thermal structure of tropical oceans at initial stage supports realistic upper ocean stability and mixing. Which in fact reduced the dominant cold bias over the ocean, feedback to air-sea interactions and land sea thermal contrast resulting better representation of monsoon circulation and moisture transport. This reduced bias of tropospheric moisture and temperature over the Indian land mass and also produced better tropospheric temperature gradient over land as well as ocean. These feedback processes reduced the dry bias in the ISM rainfall. Study concludes that initializing the coupled models with realistic OICs can reduce the underestimation of ISM rainfall prediction.

  17. Decadal climate prediction (project GCEP).

    Science.gov (United States)

    Haines, Keith; Hermanson, Leon; Liu, Chunlei; Putt, Debbie; Sutton, Rowan; Iwi, Alan; Smith, Doug

    2009-03-13

    Decadal prediction uses climate models forced by changing greenhouse gases, as in the International Panel for Climate Change, but unlike longer range predictions they also require initialization with observations of the current climate. In particular, the upper-ocean heat content and circulation have a critical influence. Decadal prediction is still in its infancy and there is an urgent need to understand the important processes that determine predictability on these timescales. We have taken the first Hadley Centre Decadal Prediction System (DePreSys) and implemented it on several NERC institute compute clusters in order to study a wider range of initial condition impacts on decadal forecasting, eventually including the state of the land and cryosphere. The eScience methods are used to manage submission and output from the many ensemble model runs required to assess predictive skill. Early results suggest initial condition skill may extend for several years, even over land areas, but this depends sensitively on the definition used to measure skill, and alternatives are presented. The Grid for Coupled Ensemble Prediction (GCEP) system will allow the UK academic community to contribute to international experiments being planned to explore decadal climate predictability.

  18. World climate patterns in grassland and savanna and their relation to growing seasons

    Directory of Open Access Journals (Sweden)

    R. Kirk Steinhorst

    1977-11-01

    Full Text Available The climate at eleven IBP savanna or grassland study sites from five continents are described and principal components analysis is used to compare them. A multivariate linear discriminant function based on mean monthly precipitation, mean monthly temperature, latitude and altitude, is used to predict the length of the growing season at each site. At most sites, the actual and predicted start and end of the growing season agreed closely. It is concluded that growing season on a world-wide basis may be predicted fairly reliably from a small number of abiotic variables by means of a multivariate discriminant function.

  19. Climate Prediction Center - Monitoring & Data Index

    Science.gov (United States)

    Weather Service NWS logo - Click to go to the NWS home page Climate Prediction Center Site Map News Oceanic & Atmospheric Monitoring and Data Monitoring Weather & Climate in Realtime Climate Diagnostics Bulletin Preliminary Climate Diagnostics Bulletin Figures Monthly Atmospheric & Sea Surface

  20. The Rate of Seasonal Changes in Temperature Alters Acclimation of Performance under Climate Change.

    Science.gov (United States)

    Nilsson-Örtman, Viktor; Johansson, Frank

    2017-12-01

    How the ability to acclimate will impact individual performance and ecological interactions under climate change remains poorly understood. Theory predicts that the benefit an organism can gain from acclimating depends on the rate at which temperatures change relative to the time it takes to induce beneficial acclimation. Here, we present a conceptual model showing how slower seasonal changes under climate change can alter species' relative performance when they differ in acclimation rate and magnitude. To test predictions from theory, we performed a microcosm experiment where we reared a mid- and a high-latitude damselfly species alone or together under the rapid seasonality currently experienced at 62°N and the slower seasonality predicted for this latitude under climate change and measured larval growth and survival. To separate acclimation effects from fixed thermal responses, we simulated growth trajectories based on species' growth rates at constant temperatures and quantified how much and how fast species needed to acclimate to match the observed growth trajectories. Consistent with our predictions, the results showed that the midlatitude species had a greater capacity for acclimation than the high-latitude species. Furthermore, since acclimation occurred at a slower rate than seasonal temperature changes, the midlatitude species had a small growth advantage over the high-latitude species under the current seasonality but a greater growth advantage under the slower seasonality predicted for this latitude under climate change. In addition, the two species did not differ in survival under the current seasonality, but the midlatitude species had higher survival under the predicted climate change scenario, possibly because rates of cannibalism were lower when smaller heterospecifics were present. These findings highlight the need to incorporate acclimation rates in ecological models.

  1. Predicting space climate change

    Science.gov (United States)

    Balcerak, Ernie

    2011-10-01

    Galactic cosmic rays and solar energetic particles can be hazardous to humans in space, damage spacecraft and satellites, pose threats to aircraft electronics, and expose aircrew and passengers to radiation. A new study shows that these threats are likely to increase in coming years as the Sun approaches the end of the period of high solar activity known as “grand solar maximum,” which has persisted through the past several decades. High solar activity can help protect the Earth by repelling incoming galactic cosmic rays. Understanding the past record can help scientists predict future conditions. Barnard et al. analyzed a 9300-year record of galactic cosmic ray and solar activity based on cosmogenic isotopes in ice cores as well as on neutron monitor data. They used this to predict future variations in galactic cosmic ray flux, near-Earth interplanetary magnetic field, sunspot number, and probability of large solar energetic particle events. The researchers found that the risk of space weather radiation events will likely increase noticeably over the next century compared with recent decades and that lower solar activity will lead to increased galactic cosmic ray levels. (Geophysical Research Letters, doi:10.1029/2011GL048489, 2011)

  2. Seasonal prediction of the Leeuwin Current using the POAMA dynamical seasonal forecast model

    Energy Technology Data Exchange (ETDEWEB)

    Hendon, Harry H.; Wang, Guomin [Centre for Australian Weather and Climate Research, Bureau of Meteorology, PO Box 1289, Melbourne (Australia)

    2010-06-15

    The potential for predicting interannual variations of the Leeuwin Current along the west coast of Australia is addressed. The Leeuwin Current flows poleward against the prevailing winds and transports warm-fresh tropical water southward along the coast, which has a great impact on local climate and ecosystems. Variations of the current are tightly tied to El Nino/La Nina (weak during El Nino and strong during La Nina). Skilful seasonal prediction of the Leeuwin Current to 9-month lead time is achieved by empirical downscaling of dynamical coupled model forecasts of El Nino and the associated upper ocean heat content anomalies off the north west coast of Australia from the Australian Bureau of Meteorology Predictive Ocean Atmosphere Model for Australia (POAMA) seasonal forecast system. Prediction of the Leeuwin Current is possible because the heat content fluctuations off the north west coast are the primary driver of interannual annual variations of the current and these heat content variations are tightly tied to the occurrence of El Nino/La Nina. POAMA can skilfully predict both the occurrence of El Nino/La Nina and the subsequent transmission of the heat content anomalies from the Pacific onto the north west coast. (orig.)

  3. Climate Prediction Center - The ENSO Cycle

    Science.gov (United States)

    Weather Service NWS logo - Click to go to the NWS home page Climate Prediction Center Home Site Map News Web resources and services. HOME > El Niño/La Niña > The ENSO Cycle ENSO Cycle Banner Climate for Weather and Climate Prediction Climate Prediction Center 5830 University Research Court College

  4. Predictability of weather and climate

    National Research Council Canada - National Science Library

    Palmer, Tim; Hagedorn, Renate

    2006-01-01

    ... and anthropogenic climate change are among those included. Ensemble systems for forecasting predictability are discussed extensively. Ed Lorenz, father of chaos theory, makes a contribution to theoretical analysis with a previously unpublished paper. This well-balanced volume will be a valuable resource for many years. High-quality chapter autho...

  5. Detecting failure of climate predictions

    Science.gov (United States)

    Runge, Michael C.; Stroeve, Julienne C.; Barrett, Andrew P.; McDonald-Madden, Eve

    2016-01-01

    The practical consequences of climate change challenge society to formulate responses that are more suited to achieving long-term objectives, even if those responses have to be made in the face of uncertainty1, 2. Such a decision-analytic focus uses the products of climate science as probabilistic predictions about the effects of management policies3. Here we present methods to detect when climate predictions are failing to capture the system dynamics. For a single model, we measure goodness of fit based on the empirical distribution function, and define failure when the distribution of observed values significantly diverges from the modelled distribution. For a set of models, the same statistic can be used to provide relative weights for the individual models, and we define failure when there is no linear weighting of the ensemble models that produces a satisfactory match to the observations. Early detection of failure of a set of predictions is important for improving model predictions and the decisions based on them. We show that these methods would have detected a range shift in northern pintail 20 years before it was actually discovered, and are increasingly giving more weight to those climate models that forecast a September ice-free Arctic by 2055.

  6. Are abrupt climate changes predictable?

    Science.gov (United States)

    Ditlevsen, Peter

    2013-04-01

    It is taken for granted that the limited predictability in the initial value problem, the weather prediction, and the predictability of the statistics are two distinct problems. Lorenz (1975) dubbed this predictability of the first and the second kind respectively. Predictability of the first kind in a chaotic dynamical system is limited due to the well-known critical dependence on initial conditions. Predictability of the second kind is possible in an ergodic system, where either the dynamics is known and the phase space attractor can be characterized by simulation or the system can be observed for such long times that the statistics can be obtained from temporal averaging, assuming that the attractor does not change in time. For the climate system the distinction between predictability of the first and the second kind is fuzzy. This difficulty in distinction between predictability of the first and of the second kind is related to the lack of scale separation between fast and slow components of the climate system. The non-linear nature of the problem furthermore opens the possibility of multiple attractors, or multiple quasi-steady states. As the ice-core records show, the climate has been jumping between different quasi-stationary climates, stadials and interstadials through the Dansgaard-Oechger events. Such a jump happens very fast when a critical tipping point has been reached. The question is: Can such a tipping point be predicted? This is a new kind of predictability: the third kind. If the tipping point is reached through a bifurcation, where the stability of the system is governed by some control parameter, changing in a predictable way to a critical value, the tipping is predictable. If the sudden jump occurs because internal chaotic fluctuations, noise, push the system across a barrier, the tipping is as unpredictable as the triggering noise. In order to hint at an answer to this question, a careful analysis of the high temporal resolution NGRIP isotope

  7. Modeling seasonal water balance based on catchments' hedging strategy on evapotranspiration for climate seasonality

    Science.gov (United States)

    Wu, S.; Zhao, J.; Wang, H.

    2017-12-01

    This paper develops a seasonal water balance model based on the hypothesis that natural catchments utilize hedging strategy on evapotranspiration for climate seasonality. According to the monthly aridity index, one year is split into wet season and dry season. A seasonal water balance model is developed by analogy to a two-stage reservoir operation model, in which seasonal rainfall infiltration, evapotranspiration and saturation-excess runoff is corresponding to the inflow, release and surplus of the catchment system. Then the optimal hedging between wet season and dry season evapotranspiration is analytically derived with marginal benefit principle. Water budget data sets of 320 catchments in the United States covering the period from 1980 to 2010 are used to evaluate the performance of this model. The Nash-Sutcliffe Efficiency coefficient for evapotranspiration is higher than 0.5 in 84% of the study catchments; while the runoff is 87%. This paper validates catchments' hedging strategy on evapotranspiration for climate seasonality and shows its potential application for seasonal water balance, which is valuable for water resources planning and management.

  8. Predicting phenology by integrating ecology, evolution and climate science

    Science.gov (United States)

    Pau, Stephanie; Wolkovich, Elizabeth M.; Cook, Benjamin I.; Davies, T. Jonathan; Kraft, Nathan J.B.; Bolmgren, Kjell; Betancourt, Julio L.; Cleland, Elsa E.

    2011-01-01

    Forecasting how species and ecosystems will respond to climate change has been a major aim of ecology in recent years. Much of this research has focused on phenology — the timing of life-history events. Phenology has well-demonstrated links to climate, from genetic to landscape scales; yet our ability to explain and predict variation in phenology across species, habitats and time remains poor. Here, we outline how merging approaches from ecology, climate science and evolutionary biology can advance research on phenological responses to climate variability. Using insight into seasonal and interannual climate variability combined with niche theory and community phylogenetics, we develop a predictive approach for species' reponses to changing climate. Our approach predicts that species occupying higher latitudes or the early growing season should be most sensitive to climate and have the most phylogenetically conserved phenologies. We further predict that temperate species will respond to climate change by shifting in time, while tropical species will respond by shifting space, or by evolving. Although we focus here on plant phenology, our approach is broadly applicable to ecological research of plant responses to climate variability.

  9. Seasonal prediction skill of winter temperature over North India

    Science.gov (United States)

    Tiwari, P. R.; Kar, S. C.; Mohanty, U. C.; Dey, S.; Kumari, S.; Sinha, P.

    2016-04-01

    The climatology, amplitude error, phase error, and mean square skill score (MSSS) of temperature predictions from five different state-of-the-art general circulation models (GCMs) have been examined for the winter (December-January-February) seasons over North India. In this region, temperature variability affects the phenological development processes of wheat crops and the grain yield. The GCM forecasts of temperature for a whole season issued in November from various organizations are compared with observed gridded temperature data obtained from the India Meteorological Department (IMD) for the period 1982-2009. The MSSS indicates that the models have skills of varying degrees. Predictions of maximum and minimum temperature obtained from the National Centers for Environmental Prediction (NCEP) climate forecast system model (NCEP_CFSv2) are compared with station level observations from the Snow and Avalanche Study Establishment (SASE). It has been found that when the model temperatures are corrected to account the bias in the model and actual orography, the predictions are able to delineate the observed trend compared to the trend without orography correction.

  10. The Effect of Soil Temperature Seasonality on Climate Reconstructions from Paleosols

    Science.gov (United States)

    Gallagher, T. M.; Hren, M. T.; Sheldon, N. D.

    2017-12-01

    Accurate continental temperature reconstructions provide important constraints on climate sensitivity to changes in atmospheric pCO2, the timing and rates of tectonic uplift, and the driving mechanisms and feedbacks associated with major climate events. Temperature seasonality is an important variable to consider, because not only does it exert a strong control on the biosphere, but it can obfuscate changes in mean annual air temperature (MAAT) in the geologic record. In order to better understand the effect temperature seasonality has on paleosol temperature proxies, soil temperature data was compiled from over 200 stations that comprise the NCDC Soil Climate Analysis Network. Observed soil temperature variations were then compared to predicted soil temperature values based on normal seasonal air temperature trends. Approximately one quarter of sites record less temperature variation than predicted. This reduction in soil temperature seasonality is a result of warmer than predicted cold-season temperatures, driven by cold-season processes such as snow cover insulation. The reduction in soil temperature seasonality explains why pedo-transfer functions to break down below MAAT values of 6-8 °C. Greater than predicted soil temperature seasonality is observed at nearly half of the sites, driven primarily by direct heating of the soil surface by solar radiation. Deviations larger than 2 °C are not common until mean annual precipitation falls below 300 mm, suggesting that complications introduced by ground heating are primarily restricted to paleosols that formed in more arid environments. Clumped isotope measurements of pedogenic carbonate and bulk paleosol elemental data from a stacked series of paleosols spanning the Eocene-Oligocene in Northeastern Spain are also examined to demonstrate how the documented seasonal trends in modern soils can help inform paleo-applications.

  11. Ocean eddies and climate predictability.

    Science.gov (United States)

    Kirtman, Ben P; Perlin, Natalie; Siqueira, Leo

    2017-12-01

    A suite of coupled climate model simulations and experiments are used to examine how resolved mesoscale ocean features affect aspects of climate variability, air-sea interactions, and predictability. In combination with control simulations, experiments with the interactive ensemble coupling strategy are used to further amplify the role of the oceanic mesoscale field and the associated air-sea feedbacks and predictability. The basic intent of the interactive ensemble coupling strategy is to reduce the atmospheric noise at the air-sea interface, allowing an assessment of how noise affects the variability, and in this case, it is also used to diagnose predictability from the perspective of signal-to-noise ratios. The climate variability is assessed from the perspective of sea surface temperature (SST) variance ratios, and it is shown that, unsurprisingly, mesoscale variability significantly increases SST variance. Perhaps surprising is the fact that the presence of mesoscale ocean features even further enhances the SST variance in the interactive ensemble simulation beyond what would be expected from simple linear arguments. Changes in the air-sea coupling between simulations are assessed using pointwise convective rainfall-SST and convective rainfall-SST tendency correlations and again emphasize how the oceanic mesoscale alters the local association between convective rainfall and SST. Understanding the possible relationships between the SST-forced signal and the weather noise is critically important in climate predictability. We use the interactive ensemble simulations to diagnose this relationship, and we find that the presence of mesoscale ocean features significantly enhances this link particularly in ocean eddy rich regions. Finally, we use signal-to-noise ratios to show that the ocean mesoscale activity increases model estimated predictability in terms of convective precipitation and atmospheric upper tropospheric circulation.

  12. Decadal climate predictions improved by ocean ensemble dispersion filtering

    Science.gov (United States)

    Kadow, C.; Illing, S.; Kröner, I.; Ulbrich, U.; Cubasch, U.

    2017-06-01

    Decadal predictions by Earth system models aim to capture the state and phase of the climate several years in advance. Atmosphere-ocean interaction plays an important role for such climate forecasts. While short-term weather forecasts represent an initial value problem and long-term climate projections represent a boundary condition problem, the decadal climate prediction falls in-between these two time scales. In recent years, more precise initialization techniques of coupled Earth system models and increased ensemble sizes have improved decadal predictions. However, climate models in general start losing the initialized signal and its predictive skill from one forecast year to the next. Here we show that the climate prediction skill of an Earth system model can be improved by a shift of the ocean state toward the ensemble mean of its individual members at seasonal intervals. We found that this procedure, called ensemble dispersion filter, results in more accurate results than the standard decadal prediction. Global mean and regional temperature, precipitation, and winter cyclone predictions show an increased skill up to 5 years ahead. Furthermore, the novel technique outperforms predictions with larger ensembles and higher resolution. Our results demonstrate how decadal climate predictions benefit from ocean ensemble dispersion filtering toward the ensemble mean.Plain Language SummaryDecadal predictions aim to predict the climate several years in advance. Atmosphere-ocean interaction plays an important role for such climate forecasts. The ocean memory due to its heat capacity holds big potential skill. In recent years, more precise initialization techniques of coupled Earth system models (incl. atmosphere and ocean) have improved decadal predictions. Ensembles are another important aspect. Applying slightly perturbed predictions to trigger the famous butterfly effect results in an ensemble. Instead of evaluating one prediction, but the whole ensemble with its

  13. Seasonal temperature prediction skill over Southern Africa and human health

    CSIR Research Space (South Africa)

    Lazenby, MJ

    2014-10-01

    Full Text Available An assessment of probabilistic prediction skill of seasonal temperature extremes over Southern Africa is presented. Verification results are presented for six run-on seasons; September to November, October to December, November to January, December...

  14. Impact of climate change on mid-twenty-first century growing seasons in Africa

    Energy Technology Data Exchange (ETDEWEB)

    Cook, Kerry H.; Vizy, Edward K. [The University of Texas at Austin, Department of Geological Sciences, Jackson School of Geosciences, Austin, TX (United States)

    2012-12-15

    Changes in growing seasons for 2041-2060 across Africa are projected using a regional climate model at 90-km resolution, and confidence in the predictions is evaluated. The response is highly regional over West Africa, with decreases in growing season days up to 20% in the western Guinean coast and some regions to the east experiencing 5-10% increases. A longer growing season up to 30% in the central and eastern Sahel is predicted, with shorter seasons in parts of the western Sahel. In East Africa, the short rains (boreal fall) growing season is extended as the Indian Ocean warms, but anomalous mid-tropospheric moisture divergence and a northward shift of Sahel rainfall severely curtails the long rains (boreal spring) season. Enhanced rainfall in January and February increases the growing season in the Congo basin by 5-15% in association with enhanced southwesterly moisture transport from the tropical Atlantic. In Angola and the southern Congo basin, 40-80% reductions in austral spring growing season days are associated with reduced precipitation and increased evapotranspiration. Large simulated reductions in growing season over southeastern Africa are judged to be inaccurate because they occur due to a reduction in rainfall in winter which is over-produced in the model. Only small decreases in the actual growing season are simulated when evapotranspiration increases in the warmer climate. The continent-wide changes in growing season are primarily the result of increased evapotranspiration over the warmed land, changes in the intensity and seasonal cycle of the thermal low, and warming of the Indian Ocean. (orig.)

  15. Seasonal changes in climatic parameters and their relationship with the incidence of pneumococcal bacteraemia in Denmark

    DEFF Research Database (Denmark)

    Tvedebrink, Torben; Lundbye-Christensen, Søren; Thomsen, R.W.

    2008-01-01

    The seasonal variation in the incidence of invasive pneumococcal disease is well recognized, but little is known about its relationship with actual changes in climatic parameters. In this 8-year longitudinal population-based study in Denmark, a harmonic sinusoidal regression model was used...... to examine whether preceding changes in climatic parameters corresponded with subsequent variations in the incidence of pneumococcal bacteraemia, independently of seasonal variation. The study shows that changes in temperature can be used to closely predict peaks in the incidence of pneumococcal bacteraemia...

  16. Benchmark analysis of forecasted seasonal temperature over different climatic areas

    Science.gov (United States)

    Giunta, G.; Salerno, R.; Ceppi, A.; Ercolani, G.; Mancini, M.

    2015-12-01

    From a long-term perspective, an improvement of seasonal forecasting, which is often exclusively based on climatology, could provide a new capability for the management of energy resources in a time scale of just a few months. This paper regards a benchmark analysis in relation to long-term temperature forecasts over Italy in the year 2010, comparing the eni-kassandra meteo forecast (e-kmf®) model, the Climate Forecast System-National Centers for Environmental Prediction (CFS-NCEP) model, and the climatological reference (based on 25-year data) with observations. Statistical indexes are used to understand the reliability of the prediction of 2-m monthly air temperatures with a perspective of 12 weeks ahead. The results show how the best performance is achieved by the e-kmf® system which improves the reliability for long-term forecasts compared to climatology and the CFS-NCEP model. By using the reliable high-performance forecast system, it is possible to optimize the natural gas portfolio and management operations, thereby obtaining a competitive advantage in the European energy market.

  17. Global Analysis of Empirical Relationships Between Annual Climate and Seasonality of NDVI

    Science.gov (United States)

    Potter, C. S.

    1997-01-01

    This study describes the use of satellite data to calibrate a new climate-vegetation greenness function for global change studies. We examined statistical relationships between annual climate indexes (temperature, precipitation, and surface radiation) and seasonal attributes of the AVHRR Normalized Difference Vegetation Index (NDVI) time series for the mid-1980s in order to refine our empirical understanding of intraannual patterns and global abiotic controls on natural vegetation dynamics. Multiple linear regression results using global l(sup o) gridded data sets suggest that three climate indexes: growing degree days, annual precipitation total, and an annual moisture index together can account to 70-80 percent of the variation in the NDVI seasonal extremes (maximum and minimum values) for the calibration year 1984. Inclusion of the same climate index values from the previous year explained no significant additional portion of the global scale variation in NDVI seasonal extremes. The monthly timing of NDVI extremes was closely associated with seasonal patterns in maximum and minimum temperature and rainfall, with lag times of 1 to 2 months. We separated well-drained areas from l(sup o) grid cells mapped as greater than 25 percent inundated coverage for estimation of both the magnitude and timing of seasonal NDVI maximum values. Predicted monthly NDVI, derived from our climate-based regression equations and Fourier smoothing algorithms, shows good agreement with observed NDVI at a series of ecosystem test locations from around the globe. Regions in which NDVI seasonal extremes were not accurately predicted are mainly high latitude ecosystems and other remote locations where climate station data are sparse.

  18. Pan-Tropical Analysis of Climate Effects on Seasonal Tree Growth

    Science.gov (United States)

    Wagner, Fabien; Rossi, Vivien; Aubry-Kientz, Mélaine; Bonal, Damien; Dalitz, Helmut; Gliniars, Robert; Stahl, Clément; Trabucco, Antonio; Hérault, Bruno

    2014-01-01

    Climate models predict a range of changes in tropical forest regions, including increased average temperatures, decreased total precipitation, reduced soil moisture and alterations in seasonal climate variations. These changes are directly related to the increase in anthropogenic greenhouse gas concentrations, primarily CO2. Assessing seasonal forest growth responses to climate is of utmost importance because woody tissues, produced by photosynthesis from atmospheric CO2, water and light, constitute the main component of carbon sequestration in the forest ecosystem. In this paper, we combine intra-annual tree growth measurements from published tree growth data and the corresponding monthly climate data for 25 pan-tropical forest sites. This meta-analysis is designed to find the shared climate drivers of tree growth and their relative importance across pan-tropical forests in order to improve carbon uptake models in a global change context. Tree growth reveals significant intra-annual seasonality at seasonally dry sites or in wet tropical forests. Of the overall variation in tree growth, 28.7% was explained by the site effect, i.e. the tree growth average per site. The best predictive model included four climate variables: precipitation, solar radiation (estimated with extrasolar radiation reaching the atmosphere), temperature amplitude and relative soil water content. This model explained more than 50% of the tree growth variations across tropical forests. Precipitation and solar radiation are the main seasonal drivers of tree growth, causing 19.8% and 16.3% of the tree growth variations. Both have a significant positive association with tree growth. These findings suggest that forest productivity due to tropical tree growth will be reduced in the future if climate extremes, such as droughts, become more frequent. PMID:24670981

  19. Managing living marine resources in a dynamic environment: The role of seasonal to decadal climate forecasts

    Science.gov (United States)

    Tommasi, Desiree; Stock, Charles A.; Hobday, Alistair J.; Methot, Rick; Kaplan, Isaac C.; Eveson, J. Paige; Holsman, Kirstin; Miller, Timothy J.; Gaichas, Sarah; Gehlen, Marion; Pershing, Andrew; Vecchi, Gabriel A.; Msadek, Rym; Delworth, Tom; Eakin, C. Mark; Haltuch, Melissa A.; Séférian, Roland; Spillman, Claire M.; Hartog, Jason R.; Siedlecki, Samantha; Samhouri, Jameal F.; Muhling, Barbara; Asch, Rebecca G.; Pinsky, Malin L.; Saba, Vincent S.; Kapnick, Sarah B.; Gaitan, Carlos F.; Rykaczewski, Ryan R.; Alexander, Michael A.; Xue, Yan; Pegion, Kathleen V.; Lynch, Patrick; Payne, Mark R.; Kristiansen, Trond; Lehodey, Patrick; Werner, Francisco E.

    2017-03-01

    Recent developments in global dynamical climate prediction systems have allowed for skillful predictions of climate variables relevant to living marine resources (LMRs) at a scale useful to understanding and managing LMRs. Such predictions present opportunities for improved LMR management and industry operations, as well as new research avenues in fisheries science. LMRs respond to climate variability via changes in physiology and behavior. For species and systems where climate-fisheries links are well established, forecasted LMR responses can lead to anticipatory and more effective decisions, benefitting both managers and stakeholders. Here, we provide an overview of climate prediction systems and advances in seasonal to decadal prediction of marine-resource relevant environmental variables. We then describe a range of climate-sensitive LMR decisions that can be taken at lead-times of months to decades, before highlighting a range of pioneering case studies using climate predictions to inform LMR decisions. The success of these case studies suggests that many additional applications are possible. Progress, however, is limited by observational and modeling challenges. Priority developments include strengthening of the mechanistic linkages between climate and marine resource responses, development of LMR models able to explicitly represent such responses, integration of climate driven LMR dynamics in the multi-driver context within which marine resources exist, and improved prediction of ecosystem-relevant variables at the fine regional scales at which most marine resource decisions are made. While there are fundamental limits to predictability, continued advances in these areas have considerable potential to make LMR managers and industry decision more resilient to climate variability and help sustain valuable resources. Concerted dialog between scientists, LMR managers and industry is essential to realizing this potential.

  20. Mean Bias in Seasonal Forecast Model and ENSO Prediction Error.

    Science.gov (United States)

    Kim, Seon Tae; Jeong, Hye-In; Jin, Fei-Fei

    2017-07-20

    This study uses retrospective forecasts made using an APEC Climate Center seasonal forecast model to investigate the cause of errors in predicting the amplitude of El Niño Southern Oscillation (ENSO)-driven sea surface temperature variability. When utilizing Bjerknes coupled stability (BJ) index analysis, enhanced errors in ENSO amplitude with forecast lead times are found to be well represented by those in the growth rate estimated by the BJ index. ENSO amplitude forecast errors are most strongly associated with the errors in both the thermocline slope response and surface wind response to forcing over the tropical Pacific, leading to errors in thermocline feedback. This study concludes that upper ocean temperature bias in the equatorial Pacific, which becomes more intense with increasing lead times, is a possible cause of forecast errors in the thermocline feedback and thus in ENSO amplitude.

  1. Climate Prediction Center - Expert Assessments Index

    Science.gov (United States)

    Weather Service NWS logo - Click to go to the NWS home page Climate Prediction Center Home Site Map News Web resources and services. HOME > Monitoring and Data > Global Climate Data & Maps > ; Global Regional Climate Maps Regional Climate Maps Banner The Monthly regional analyses products are

  2. Climate Prediction Center - Global Tropical Hazards Assessment

    Science.gov (United States)

    Weather Service NWS logo - Click to go to the NWS home page Climate Prediction Center Home Site Map News Organization Search Go Search the CPC Go Climate Outlooks Climate & Weather Link El Niño/La Niña MJO Teleconnections AO NAO PNA AAO Blocking Storm Tracks Climate Glossary Outreach About Us Our Mission Who We Are

  3. Climate Prediction Center - Monitoring and Data Index

    Science.gov (United States)

    Weather Service NWS logo - Click to go to the NWS home page Climate Prediction Center Home Site Map News ; Atmospheric Monitoring and Data Monitoring Weather & Climate in Realtime Climate Diagnostics Bulletin Preliminary Climate Diagnostics Bulletin Figures Monthly Atmospheric & Sea Surface Temperature Indices

  4. Seasonal predictions of Fire Weather Index: Paving the way for their operational applicability in Mediterranean Europe

    OpenAIRE

    Joaquín Bedia; Nicola Golding; Ana Casanueva; Maialen Iturbide; Carlo Buontempo; Jose Manuel Gutiérrez

    2018-01-01

    Managers of wildfire-prone landscapes in the Euro-Mediterranean region would greatly benefit from fire weather predictions a few months in advance, and particularly from the reliable prediction of extreme fire seasons. However, in some cases model biases prevent from a direct application of these predictions in an operational context. Fire risk management requires precise knowledge of the likely consequences of climate on fire risk, and the interest for decision-makers is focused on multi-var...

  5. Northern Great Basin Seasonal Lakes: Vulnerability to Climate Change.

    Science.gov (United States)

    Russell, M.; Eitel, J.

    2017-12-01

    Seasonal alkaline lakes in southeast Oregon, northeast California, and northwest Nevada serve as important habitat for migrating birds utilizing the Pacific Flyway, as well as local plant and animal communities. Despite their ecological importance, and anecdotal suggestions that these lakes are becoming less reliable, little is known about the vulnerability of these lakes to climate change. Our research seeks to understand the vulnerability of Northern Great Basin seasonal lakes to climate change. For this, we will be using historical information from the European Space Agency's Global Surface Water Explorer and the University of Idaho's gridMET climate product, to build a model that allows estimating surface water extent and timing based on climate variables. We will then utilize downscaled future climate projections to model surface water extent and timing in the coming decades. In addition, an unmanned aerial system (UAS) will be utilized at a subset of dried basins to obtain precise 3D bathymetry and calculate water volume hypsographs, a critical factor in understanding the likelihood of water persistence and biogeochemical habitat suitability. These results will be incorporated into decision support tools that land managers can utilize in water conservation, wildlife management, and climate mitigation actions. Future research may pair these forecasts with animal movement data to examine fragmentation of migratory corridors and species-specific impacts.

  6. Predicting Climate Change Impacts to the Canadian Boreal Forest

    Directory of Open Access Journals (Sweden)

    Trisalyn A. Nelson

    2014-03-01

    Full Text Available Climate change is expected to alter temperature, precipitation, and seasonality with potentially acute impacts on Canada’s boreal. In this research we predicted future spatial distributions of biodiversity in Canada’s boreal for 2020, 2050, and 2080 using indirect indicators derived from remote sensing and based on vegetation productivity. Vegetation productivity indices, representing annual amounts and variability of greenness, have been shown to relate to tree and wildlife richness in Canada’s boreal. Relationships between historical satellite-derived productivity and climate data were applied to modelled scenarios of future climate to predict and map potential future vegetation productivity for 592 regions across Canada. Results indicated that the pattern of vegetation productivity will become more homogenous, particularly west of Hudson Bay. We expect climate change to impact biodiversity along north/south gradients and by 2080 vegetation distributions will be dominated by processes of seasonality in the north and a combination of cumulative greenness and minimum cover in the south. The Hudson Plains, which host the world’s largest and most contiguous wetland, are predicted to experience less seasonality and more greenness. The spatial distribution of predicted trends in vegetation productivity was emphasized over absolute values, in order to support regional biodiversity assessments and conservation planning.

  7. Climate Prediction Center - Monitoring and Data - Regional Climate Maps:

    Science.gov (United States)

    National Weather Service NWS logo - Click to go to the NWS home page Climate Prediction Center Home Site government Web resources and services. HOME > Monitoring and Data > U.S. Climate Data > ; Precipitation & Temperature > Regional Climate Maps: USA Menu Weekly 1-Month 3-Month 12-Month Weekly

  8. Do planetary seasons play a role in attaining stable climates?

    Science.gov (United States)

    Olsen, Kasper Wibeck; Bohr, Jakob

    2018-05-01

    A simple phenomenological account for planetary climate instabilities is presented. The description is based on the standard model where the balance of incoming stellar radiation and outward thermal radiation is described by the effective planet temperature. Often, it is found to have three different points, or temperatures, where the influx of radiation is balanced with the out-flux, even with conserved boundary conditions. Two of these points are relatively long-term stable, namely the point corresponding to a cold climate and the point corresponding to a hot climate. In a classical sense these points are equilibrium balance points. The hypothesis promoted in this paper is the possibility that the intermediate third point can become long-term stable by being driven dynamically. The initially unstable point is made relatively stable over a long period by the presence of seasonal climate variations.

  9. Decadel climate prediction: challenges and opportunities

    International Nuclear Information System (INIS)

    Hurrell, J W

    2008-01-01

    The scientific understanding of climate change is now sufficiently clear to show that climate change from global warming is already upon us, and the rate of change as projected exceeds anything seen in nature in the past 10,000 years. Uncertainties remain, however, especially regarding how climate will change at regional and local scales where the signal of natural variability is large. Addressing many of these uncertainties will require a movement toward high resolution climate system predictions, with a blurring of the distinction between shorter-term predictions and longer-term climate projections. The key is the realization that climate system predictions, regardless of timescale, will require initialization of coupled general circulation models with best estimates of the current observed state of the atmosphere, oceans, cryosphere, and land surface. Formidable challenges exist: for instance, what is the best method of initialization given imperfect observations and systematic errors in models? What effect does initialization have on climate predictions? What predictions should be attempted, and how would they be verified? Despite such challenges, the unrealized predictability that resides in slowly evolving phenomena, such as ocean current systems, is of paramount importance for society to plan and adapt for the next few decades. Moreover, initialized climate predictions will require stronger collaboration with shared knowledge, infrastructure and technical capabilities among those in the weather and climate prediction communities. The potential benefits include improved understanding and predictions on all time scales

  10. Where the wild things are: Seasonal variation in caribou distribution in relation to climate change

    Directory of Open Access Journals (Sweden)

    Philippa McNeil

    2005-05-01

    Full Text Available In this study, we develop a method to analyse the relationships between seasonal caribou distribution and climate, to estimate how climatic conditions affect interactions between humans and caribou, and ultimately to predict patterns of distribution relative to climate change. Satellite locations for the Porcupine (Rangifer tarandus granti and Bathurst (R. t. groenlandicus caribou herds were analysed for eight ecologically-defined seasons. For each season, two levels of a key environmental factor influencing caribou distribution were identified, as well as the best climate data available to indicate the factor's annual state. Satellite locations were grouped according to the relevant combination of season and environmental factor. Caribou distributions were compared for opposing environmental factors; this comparison was undertaken relative to hunting access for the Porcupine Herd and relative to exposure to mining activity for the Bathurst Herd. Expected climate trends suggest an overall increase in access to Porcupine caribou for Aklavik (NWT hunters during the winter and rut seasons, for Venetie (Alaska hunters during midsummer and fall migration and for Arctic Village (Alaska during midsummer. Arctic Village may experience reduced availability with early snowfalls in the fall, but we expect there to be little directional shift in the spring migration patterns. For the Bathurst Herd, we expect that fewer caribou would be exposed to the mines during the winter, while more caribou would be exposed to the combined Ekati and Diavik mining zone in the early summer and to the Lupin-Jericho mining zone during the fall migration. If changes in climate cause an increased presence of caribou in the mining sites, monitoring and mitigation measures may need to be intensified.

  11. Robustness of Ensemble Climate Projections Analyzed with Climate Signal Maps: Seasonal and Extreme Precipitation for Germany

    Directory of Open Access Journals (Sweden)

    Susanne Pfeifer

    2015-05-01

    Full Text Available Climate signal maps can be used to identify regions where robust climate changes can be derived from an ensemble of climate change simulations. Here, robustness is defined as a combination of model agreement and the significance of the individual model projections. Climate signal maps do not show all information available from the model ensemble, but give a condensed view in order to be useful for non-climate scientists who have to assess climate change impact during the course of their work. Three different ensembles of regional climate projections have been analyzed regarding changes of seasonal mean and extreme precipitation (defined as the number of days exceeding the 95th percentile threshold of daily precipitation for Germany, using climate signal maps. Although the models used and the scenario assumptions differ for the three ensembles (representative concentration pathway (RCP 4.5 vs. RCP8.5 vs. A1B, some similarities in the projections of future seasonal and extreme precipitation can be seen. For the winter season, both mean and extreme precipitation are projected to increase. The strength, robustness and regional pattern of this increase, however, depends on the ensemble. For summer, a robust decrease of mean precipitation can be detected only for small regions in southwestern Germany and only from two of the three ensembles, whereas none of them projects a robust increase of summer extreme precipitation.

  12. Climate Prediction Center - Monitoring and Data

    Science.gov (United States)

    Weather Service NWS logo - Click to go to the NWS home page Climate Prediction Center Home Site Map News monthly data, time series, and maps for various climate parameters, such as precipitation, temperature Oscillations (ENSO) and other climate patterns such as the North Atlantic and Pacific Decadal Oscillations, and

  13. Seasonal predictability of Kiremt rainfall in coupled general circulation models

    Science.gov (United States)

    Gleixner, Stephanie; Keenlyside, Noel S.; Demissie, Teferi D.; Counillon, François; Wang, Yiguo; Viste, Ellen

    2017-11-01

    The Ethiopian economy and population is strongly dependent on rainfall. Operational seasonal predictions for the main rainy season (Kiremt, June-September) are based on statistical approaches with Pacific sea surface temperatures (SST) as the main predictor. Here we analyse dynamical predictions from 11 coupled general circulation models for the Kiremt seasons from 1985-2005 with the forecasts starting from the beginning of May. We find skillful predictions from three of the 11 models, but no model beats a simple linear prediction model based on the predicted Niño3.4 indices. The skill of the individual models for dynamically predicting Kiremt rainfall depends on the strength of the teleconnection between Kiremt rainfall and concurrent Pacific SST in the models. Models that do not simulate this teleconnection fail to capture the observed relationship between Kiremt rainfall and the large-scale Walker circulation.

  14. The seasonal influence of climate and environment on yellow fever transmission across Africa.

    Science.gov (United States)

    Hamlet, Arran; Jean, Kévin; Perea, William; Yactayo, Sergio; Biey, Joseph; Van Kerkhove, Maria; Ferguson, Neil; Garske, Tini

    2018-03-01

    Yellow fever virus (YFV) is a vector-borne flavivirus endemic to Africa and Latin America. Ninety per cent of the global burden occurs in Africa where it is primarily transmitted by Aedes spp, with Aedes aegypti the main vector for urban yellow fever (YF). Mosquito life cycle and viral replication in the mosquito are heavily dependent on climate, particularly temperature and rainfall. We aimed to assess whether seasonal variations in climatic factors are associated with the seasonality of YF reports. We constructed a temperature suitability index for YFV transmission, capturing the temperature dependence of mosquito behaviour and viral replication within the mosquito. We then fitted a series of multilevel logistic regression models to a dataset of YF reports across Africa, considering location and seasonality of occurrence for seasonal models, against the temperature suitability index, rainfall and the Enhanced Vegetation Index (EVI) as covariates alongside further demographic indicators. Model fit was assessed by the Area Under the Curve (AUC), and models were ranked by Akaike's Information Criterion which was used to weight model outputs to create combined model predictions. The seasonal model accurately captured both the geographic and temporal heterogeneities in YF transmission (AUC = 0.81), and did not perform significantly worse than the annual model which only captured the geographic distribution. The interaction between temperature suitability and rainfall accounted for much of the occurrence of YF, which offers a statistical explanation for the spatio-temporal variability in transmission. The description of seasonality offers an explanation for heterogeneities in the West-East YF burden across Africa. Annual climatic variables may indicate a transmission suitability not always reflected in seasonal interactions. This finding, in conjunction with forecasted data, could highlight areas of increased transmission and provide insights into the occurrence of

  15. The Seasonal cycle of the Tropical Lower Stratospheric Water Vapor in Chemistry-Climate Models in Comparison with Observations

    Science.gov (United States)

    Wang, X.; Dessler, A. E.

    2017-12-01

    The seasonal cycle is one of the key features of the tropical lower stratospheric water vapor, so it is important that the climate models reproduce it. In this analysis, we evaluate how well the Goddard Earth Observing System Chemistry Climate Model (GEOSCCM) and the Whole Atmosphere Community Climate Model (WACCM) reproduce the seasonal cycle of tropical lower stratospheric water vapor. We do this by comparing the models to observations from the Microwave Limb Sounder (MLS) and the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim (ERAi). We also evaluate if the chemistry-climate models (CCMs) reproduce the key transport and dehydration processes that regulate the seasonal cycle using a forward, domain filling, diabatic trajectory model. Finally, we explore the changes of the seasonal cycle during the 21st century in the two CCMs. Our results show general agreement in the seasonal cycles from the MLS, the ERAi, and the CCMs. Despite this agreement, there are some clear disagreements between the models and the observations on the details of transport and dehydration in the TTL. Finally, both the CCMs predict a moister seasonal cycle by the end of the 21st century. But they disagree on the changes of the seasonal amplitude, which is predicted to increase in the GEOSCCM and decrease in the WACCM.

  16. Climate Modeling and Causal Identification for Sea Ice Predictability

    Energy Technology Data Exchange (ETDEWEB)

    Hunke, Elizabeth Clare [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Urrego Blanco, Jorge Rolando [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Urban, Nathan Mark [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2018-02-12

    This project aims to better understand causes of ongoing changes in the Arctic climate system, particularly as decreasing sea ice trends have been observed in recent decades and are expected to continue in the future. As part of the Sea Ice Prediction Network, a multi-agency effort to improve sea ice prediction products on seasonal-to-interannual time scales, our team is studying sensitivity of sea ice to a collection of physical process and feedback mechanism in the coupled climate system. During 2017 we completed a set of climate model simulations using the fully coupled ACME-HiLAT model. The simulations consisted of experiments in which cloud, sea ice, and air-ocean turbulent exchange parameters previously identified as important for driving output uncertainty in climate models were perturbed to account for parameter uncertainty in simulated climate variables. We conducted a sensitivity study to these parameters, which built upon a previous study we made for standalone simulations (Urrego-Blanco et al., 2016, 2017). Using the results from the ensemble of coupled simulations, we are examining robust relationships between climate variables that emerge across the experiments. We are also using causal discovery techniques to identify interaction pathways among climate variables which can help identify physical mechanisms and provide guidance in predictability studies. This work further builds on and leverages the large ensemble of standalone sea ice simulations produced in our previous w14_seaice project.

  17. Seasonal rainfall predictability over the Lake Kariba catchment area

    CSIR Research Space (South Africa)

    Muchuru, S

    2014-07-01

    Full Text Available The Lake Kariba catchment area in southern Africa has one of the most variable climates of any major river basin, with an extreme range of conditions across the catchment and through time. Marked seasonal and interannual fluctuations in rainfall...

  18. Winter Season Mortality: Will Climate Warming Bring Benefits?

    Science.gov (United States)

    Kinney, Patrick L; Schwartz, Joel; Pascal, Mathilde; Petkova, Elisaveta; Tertre, Alain Le; Medina, Sylvia; Vautard, Robert

    2015-06-01

    Extreme heat events are associated with spikes in mortality, yet death rates are on average highest during the coldest months of the year. Under the assumption that most winter excess mortality is due to cold temperature, many previous studies have concluded that winter mortality will substantially decline in a warming climate. We analyzed whether and to what extent cold temperatures are associated with excess winter mortality across multiple cities and over multiple years within individual cities, using daily temperature and mortality data from 36 US cities (1985-2006) and 3 French cities (1971-2007). Comparing across cities, we found that excess winter mortality did not depend on seasonal temperature range, and was no lower in warmer vs. colder cities, suggesting that temperature is not a key driver of winter excess mortality. Using regression models within monthly strata, we found that variability in daily mortality within cities was not strongly influenced by winter temperature. Finally we found that inadequate control for seasonality in analyses of the effects of cold temperatures led to spuriously large assumed cold effects, and erroneous attribution of winter mortality to cold temperatures. Our findings suggest that reductions in cold-related mortality under warming climate may be much smaller than some have assumed. This should be of interest to researchers and policy makers concerned with projecting future health effects of climate change and developing relevant adaptation strategies.

  19. The Value of Seasonal Climate Forecasts in Managing Energy Resources.

    Science.gov (United States)

    Brown Weiss, Edith

    1982-04-01

    Research and interviews with officials of the United States energy industry and a systems analysis of decision making in a natural gas utility lead to the conclusion that seasonal climate forecasts would only have limited value in fine tuning the management of energy supply, even if the forecasts were more reliable and detailed than at present.On the other hand, reliable forecasts could be useful to state and local governments both as a signal to adopt long-term measures to increase the efficiency of energy use and to initiate short-term measures to reduce energy demand in anticipation of a weather-induced energy crisis.To be useful for these purposes, state governments would need better data on energy demand patterns and available energy supplies, staff competent to interpret climate forecasts, and greater incentive to conserve. The use of seasonal climate forecasts is not likely to be constrained by fear of legal action by those claiming to be injured by a possible incorrect forecast.

  20. National Assessment of Climate Resources for Tourism Seasonality in China Using the Tourism Climate Index

    Directory of Open Access Journals (Sweden)

    Yan Fang

    2015-01-01

    Full Text Available Tourism is a very important industry, and it is deeply affected by climate. This article focuses on the role of climate in tourism seasonality and attempts to assess the impacts of climate resources on China’s tourism seasonality by using the Tourism Climate Index (TCI. Seasonal distribution maps of TCI scores indicate that the climates of most regions in China are comfortable for tourists during spring and autumn, while the climate conditions differ greatly in summer and winter, with “excellent”, “good”, “acceptable” and “unfavorable” existing almost by a latitudinal gradation. The number of good months throughout China varies from zero (the Tibetan Plateau area to 10 (Yunnan Province, and most localities have five to eight good months. Moreover, all locations in China can be classified as winter peak, summer peak and bi-modal shoulder peak. The results will provide some useful information for tourist destinations, travel agencies, tourism authorities and tourists.

  1. Climate change affects rainmakers' predictions | IDRC - International ...

    International Development Research Centre (IDRC) Digital Library (Canada)

    2010-10-08

    Oct 8, 2010 ... English · Français ... animals, associated with seasonal changes,” Mary O'Neill of Climate Change Adaptation in Africa ( CCAA ) told MediaGlobal. ... and the meteorologists forecast apply on the national and regional level.

  2. The North American Multi-Model Ensemble (NMME): Phase-1 Seasonal to Interannual Prediction, Phase-2 Toward Developing Intra-Seasonal Prediction

    Science.gov (United States)

    Kirtman, Ben P.; Min, Dughong; Infanti, Johnna M.; Kinter, James L., III; Paolino, Daniel A.; Zhang, Qin; vandenDool, Huug; Saha, Suranjana; Mendez, Malaquias Pena; Becker, Emily; hide

    2013-01-01

    The recent US National Academies report "Assessment of Intraseasonal to Interannual Climate Prediction and Predictability" was unequivocal in recommending the need for the development of a North American Multi-Model Ensemble (NMME) operational predictive capability. Indeed, this effort is required to meet the specific tailored regional prediction and decision support needs of a large community of climate information users. The multi-model ensemble approach has proven extremely effective at quantifying prediction uncertainty due to uncertainty in model formulation, and has proven to produce better prediction quality (on average) then any single model ensemble. This multi-model approach is the basis for several international collaborative prediction research efforts, an operational European system and there are numerous examples of how this multi-model ensemble approach yields superior forecasts compared to any single model. Based on two NOAA Climate Test Bed (CTB) NMME workshops (February 18, and April 8, 2011) a collaborative and coordinated implementation strategy for a NMME prediction system has been developed and is currently delivering real-time seasonal-to-interannual predictions on the NOAA Climate Prediction Center (CPC) operational schedule. The hindcast and real-time prediction data is readily available (e.g., http://iridl.ldeo.columbia.edu/SOURCES/.Models/.NMME/) and in graphical format from CPC (http://origin.cpc.ncep.noaa.gov/products/people/wd51yf/NMME/index.html). Moreover, the NMME forecast are already currently being used as guidance for operational forecasters. This paper describes the new NMME effort, presents an overview of the multi-model forecast quality, and the complementary skill associated with individual models.

  3. Evaluating Downscaling Methods for Seasonal Climate Forecasts over East Africa

    Science.gov (United States)

    Roberts, J. Brent; Robertson, Franklin R.; Bosilovich, Michael; Lyon, Bradfield; Funk, Chris

    2013-01-01

    The U.S. National Multi-Model Ensemble seasonal forecasting system is providing hindcast and real-time data streams to be used in assessing and improving seasonal predictive capacity. The NASA / USAID SERVIR project, which leverages satellite and modeling-based resources for environmental decision making in developing nations, is focusing on the evaluation of NMME forecasts specifically for use in impact modeling within hub regions including East Africa, the Hindu Kush-Himalayan (HKH) region and Mesoamerica. One of the participating models in NMME is the NASA Goddard Earth Observing System (GEOS5). This work will present an intercomparison of downscaling methods using the GEOS5 seasonal forecasts of temperature and precipitation over East Africa. The current seasonal forecasting system provides monthly averaged forecast anomalies. These anomalies must be spatially downscaled and temporally disaggregated for use in application modeling (e.g. hydrology, agriculture). There are several available downscaling methodologies that can be implemented to accomplish this goal. Selected methods include both a non-homogenous hidden Markov model and an analogue based approach. A particular emphasis will be placed on quantifying the ability of different methods to capture the intermittency of precipitation within both the short and long rain seasons. Further, the ability to capture spatial covariances will be assessed. Both probabilistic and deterministic skill measures will be evaluated over the hindcast period

  4. Development of a Climate Prediction Market

    Science.gov (United States)

    Roulston, M. S.

    2017-12-01

    Winton, a global investment firm, is planning to establish a prediction market for climate. This prediction market will allow participants to place bets on global climate up to several decades in the future. Winton is pursuing this endeavour as part of its philanthropy that funds scientific research and the communication of scientific ideas. The Winton Climate Prediction Market will be based in the U.K. It will be structured as an online gambling site subject to the regulation of the Gambling Commission. Unlike existing betting sites, the Climate Prediction Market will be subsidized: a central market maker will inject money into the market. This is in contrast to traditional bookmakers or betting exchanges who set odds in their favour or charge commissions to make a profit. The philosophy of a subsidized prediction market is that the party seeking information should fund the market, rather than the participants who provide the information. The initial market will allow bets to be placed on the atmospheric concentration of carbon dioxide and the global mean temperature anomaly. It will thus produce implied forecasts of carbon dioxide concentration as well as global temperatures. If the initial market is successful, additional markets could be added which target other climate variables, such as regional temperatures or sea-level rise. These markets could be sponsored by organizations that are interested in predictions of the specific climate variables. An online platform for the Climate Prediction Market has been developed and has been tested internally at Winton.

  5. A model to predict the beginning of the pollen season

    DEFF Research Database (Denmark)

    Toldam-Andersen, Torben Bo

    1991-01-01

    for fruit trees are generally applicable, and give a reasonable description of the growth processes of other trees. This type of model can therefore be of value in predicting the start of the pollen season. The predicted dates were generally within 3-5 days of the observed. Finally the possibility of frost...

  6. Species biogeography predicts drought responses in a seasonally dry tropical forest

    Science.gov (United States)

    Schwartz, N.; Powers, J. S.; Vargas, G.; Xu, X.; Smith, C. M.; Brodribb, T.; Werden, L. K.; Becknell, J.; Medvigy, D.

    2017-12-01

    The timing, distribution, and amount of rainfall in the seasonal tropics have shifted in recent years, with consequences for seasonally dry tropical forests (SDTF). SDTF are sensitive to changing rainfall regimes and drought conditions, but sensitivity to drought varies substantially across species. One potential explanation of species differences is that species that experience dry conditions more frequently throughout their range will be better able to cope with drought than species from wetter climates, because species from drier climates will be better adapted to drought. An El-Niño induced drought in 2015 presented an opportunity to assess species-level differences in mortality in SDTF, and to ask whether the ranges of rainfall conditions species experience and the average rainfall regimes in species' ranges predict differences in mortality rates in Costa Rican SDTF. We used field plot data from northwest Costa Rica to determine species' level mortality rates. Mortality rates ranged substantially across species, with some species having no dead individuals to as high as 50% mortality. To quantify rainfall conditions across species' ranges, we used species occurrence data from the Global Biodiversity Information Facility, and rainfall data from the Chelsa climate dataset. We found that while the average and range of mean annual rainfall across species ranges did not predict drought-induced mortality in the field plots, across-range averages of the seasonality index, a measure of rainfall seasonality, was strongly correlated with species-level drought mortality (r = -0.62, p < 0.05), with species from more strongly seasonal climates experiencing less severe drought mortality. Furthermore, we found that the seasonality index was a stronger predictor of mortality than any individual functional trait we considered. This result shows that species' biogeography may be an important factor for how species will respond to future drought, and may be a more integrative

  7. Seasonal streamflow forecast with machine learning and teleconnection indices in the context non-stationary climate

    Science.gov (United States)

    Haguma, D.; Leconte, R.

    2017-12-01

    Spatial and temporal water resources variability are associated with large-scale pressure and circulation anomalies known as teleconnections that influence the pattern of the atmospheric circulation. Teleconnection indices have been used successfully to forecast streamflow in short term. However, in some watersheds, classical methods cannot establish relationships between seasonal streamflow and teleconnection indices because of weak correlation. In this study, machine learning algorithms have been applied for seasonal streamflow forecast using teleconnection indices. Machine learning offers an alternative to classical methods to address the non-linear relationship between streamflow and teleconnection indices the context non-stationary climate. Two machine learning algorithms, random forest (RF) and support vector machine (SVM), with teleconnection indices associated with North American climatology, have been used to forecast inflows for one and two leading seasons for the Romaine River and Manicouagan River watersheds, located in Quebec, Canada. The indices are Pacific-North America (PNA), North Atlantic Oscillation (NAO), El Niño-Southern Oscillation (ENSO), Arctic Oscillation (AO) and Pacific Decadal Oscillation (PDO). The results showed that the machine learning algorithms have an important predictive power for seasonal streamflow for one and two leading seasons. The RF performed better for training and SVM generally have better results with high predictive capability for testing. The RF which is an ensemble method, allowed to assess the uncertainty of the forecast. The integration of teleconnection indices responds to the seasonal forecast of streamflow in the conditions of the non-stationarity the climate, although the teleconnection indices have a weak correlation with streamflow.

  8. Predicting impacts of climate change on Fasciola hepatica risk.

    Science.gov (United States)

    Fox, Naomi J; White, Piran C L; McClean, Colin J; Marion, Glenn; Evans, Andy; Hutchings, Michael R

    2011-01-10

    Fasciola hepatica (liver fluke) is a physically and economically devastating parasitic trematode whose rise in recent years has been attributed to climate change. Climate has an impact on the free-living stages of the parasite and its intermediate host Lymnaea truncatula, with the interactions between rainfall and temperature having the greatest influence on transmission efficacy. There have been a number of short term climate driven forecasts developed to predict the following season's infection risk, with the Ollerenshaw index being the most widely used. Through the synthesis of a modified Ollerenshaw index with the UKCP09 fine scale climate projection data we have developed long term seasonal risk forecasts up to 2070 at a 25 km square resolution. Additionally UKCIP gridded datasets at 5 km square resolution from 1970-2006 were used to highlight the climate-driven increase to date. The maps show unprecedented levels of future fasciolosis risk in parts of the UK, with risk of serious epidemics in Wales by 2050. The seasonal risk maps demonstrate the possible change in the timing of disease outbreaks due to increased risk from overwintering larvae. Despite an overall long term increase in all regions of the UK, spatio-temporal variation in risk levels is expected. Infection risk will reduce in some areas and fluctuate greatly in others with a predicted decrease in summer infection for parts of the UK due to restricted water availability. This forecast is the first approximation of the potential impacts of climate change on fasciolosis risk in the UK. It can be used as a basis for indicating where active disease surveillance should be targeted and where the development of improved mitigation or adaptation measures is likely to bring the greatest benefits.

  9. Predicting impacts of climate change on Fasciola hepatica risk.

    Directory of Open Access Journals (Sweden)

    Naomi J Fox

    2011-01-01

    Full Text Available Fasciola hepatica (liver fluke is a physically and economically devastating parasitic trematode whose rise in recent years has been attributed to climate change. Climate has an impact on the free-living stages of the parasite and its intermediate host Lymnaea truncatula, with the interactions between rainfall and temperature having the greatest influence on transmission efficacy. There have been a number of short term climate driven forecasts developed to predict the following season's infection risk, with the Ollerenshaw index being the most widely used. Through the synthesis of a modified Ollerenshaw index with the UKCP09 fine scale climate projection data we have developed long term seasonal risk forecasts up to 2070 at a 25 km square resolution. Additionally UKCIP gridded datasets at 5 km square resolution from 1970-2006 were used to highlight the climate-driven increase to date. The maps show unprecedented levels of future fasciolosis risk in parts of the UK, with risk of serious epidemics in Wales by 2050. The seasonal risk maps demonstrate the possible change in the timing of disease outbreaks due to increased risk from overwintering larvae. Despite an overall long term increase in all regions of the UK, spatio-temporal variation in risk levels is expected. Infection risk will reduce in some areas and fluctuate greatly in others with a predicted decrease in summer infection for parts of the UK due to restricted water availability. This forecast is the first approximation of the potential impacts of climate change on fasciolosis risk in the UK. It can be used as a basis for indicating where active disease surveillance should be targeted and where the development of improved mitigation or adaptation measures is likely to bring the greatest benefits.

  10. Monthly to seasonal low flow prediction: statistical versus dynamical models

    Science.gov (United States)

    Ionita-Scholz, Monica; Klein, Bastian; Meissner, Dennis; Rademacher, Silke

    2016-04-01

    the Alfred Wegener Institute a purely statistical scheme to generate streamflow forecasts for several months ahead. Instead of directly using teleconnection indices (e.g. NAO, AO) the idea is to identify regions with stable teleconnections between different global climate information (e.g. sea surface temperature, geopotential height etc.) and streamflow at different gauges relevant for inland waterway transport. So-called stability (correlation) maps are generated showing regions where streamflow and climate variable from previous months are significantly correlated in a 21 (31) years moving window. Finally, the optimal forecast model is established based on a multiple regression analysis of the stable predictors. We will present current results of the aforementioned approaches with focus on the River Rhine (being one of the world's most frequented waterways and the backbone of the European inland waterway network) and the Elbe River. Overall, our analysis reveals the existence of a valuable predictability of the low flows at monthly and seasonal time scales, a result that may be useful to water resources management. Given that all predictors used in the models are available at the end of each month, the forecast scheme can be used operationally to predict extreme events and to provide early warnings for upcoming low flows.

  11. Droughts in Amazonia: Spatiotemporal Variability, Teleconnections, and Seasonal Predictions

    Science.gov (United States)

    Lima, Carlos H. R.; AghaKouchak, Amir

    2017-12-01

    Most Amazonia drought studies have focused on rainfall deficits and their impact on river discharges, while the analysis of other important driver variables, such as temperature and soil moisture, has attracted less attention. Here we try to better understand the spatiotemporal dynamics of Amazonia droughts and associated climate teleconnections as characterized by the Palmer Drought Severity Index (PDSI), which integrates information from rainfall deficit, temperature anomalies, and soil moisture capacity. The results reveal that Amazonia droughts are most related to one dominant pattern across the entire region, followed by two seesaw kind of patterns: north-south and east-west. The main two modes are correlated with sea surface temperature (SST) anomalies in the tropical Pacific and Atlantic oceans. The teleconnections associated with global SST are then used to build a seasonal forecast model for PDSI over Amazonia based on predictors obtained from a sparse canonical correlation analysis approach. A unique feature of the presented drought prediction method is using only a few number of predictors to avoid excessive noise in the predictor space. Cross-validated results show correlations between observed and predicted spatial average PDSI up to 0.60 and 0.45 for lead times of 5 and 9 months, respectively. To the best of our knowledge, this is the first study in the region that, based on cross-validation results, leads to appreciable forecast skills for lead times beyond 4 months. This is a step forward in better understanding the dynamics of Amazonia droughts and improving risk assessment and management, through improved drought forecasting.

  12. Seasonal changes in the human alteration of fire regimes beyond the climate forcing

    Science.gov (United States)

    Fréjaville, Thibaut; Curt, Thomas

    2017-03-01

    Human activities have altered fire regimes for millennia by suppressing or enhancing natural fire activity. However, whether these anthropogenic pressures on fire activity have exceeded and will surpass climate forcing still remains uncertain. We tested if, how and the extent to which seasonal fire activity in southern France has recently (1976-2009) deviated from climate-expected trends. The latter were simulated using an ensemble of detrended fire-climate models. We found both seasonal and regional contrasts in climatic effects through a mixture of drought-driven and fuel-limited fire regimes. Dry contemporary conditions chiefly drove fire frequency and burned area, although higher fire activity was related to wetter conditions in the last three years. Surprisingly, the relative importance of preceding wet conditions was higher in winter than in summer, illustrating the strong potential dependency of regional fire-climate relationships on the human use and control of fires. In the Mediterranean mountains, warm winters and springs favour extensive fires in the following dry summer. These results highlight that increasing dryness with climate change could have antagonistic effects on fire regime by leading to larger fires in summer (moisture-limited), but lower fire activity in winter (fuel-limited fire regime). Furthermore, fire trends have significantly diverged from climatic expectations, with a strong negative alteration in fire activity in the Mediterranean lowlands and the summer burned area in the mountains. In contrast, alteration of winter fire frequency in the Mediterranean and Temperate mountains has shifted from positive to negative (or null) trends during the mid-1990s, a period when fire suppression policy underwent major revisions. Our findings demonstrate that changes in land-use and fire suppression policy have probably exceeded the strength of climate change effects on changing fire regime in southern Europe, making regional predictions of future

  13. Sub-seasonal prediction over East Asia during boreal summer using the ECCC monthly forecasting system

    Science.gov (United States)

    Liang, Ping; Lin, Hai

    2018-02-01

    A useful sub-seasonal forecast is of great societal and economical value in the highly populated East Asian region, especially during boreal summer when frequent extreme events such as heat waves and persistent heavy rainfalls occur. Despite recent interest and development in sub-seasonal prediction, it is still unclear how skillful dynamical forecasting systems are in East Asia beyond 2 weeks. In this study we evaluate the sub-seasonal prediction over East Asia during boreal summer in the operational monthly forecasting system of Environment and Climate Change Canada (ECCC).Results show that the climatological intra-seasonal oscillation (CISO) of East Asian summer monsoonis reasonably well captured. Statistically significant forecast skill of 2-meter air temperature (T2m) is achieved for all lead times up to week 4 (days 26-32) over East China and Northeast Asia, which is consistent with the skill in 500 hPa geopotential height (Z500). Significant forecast skill of precipitation, however, is limited to the week of days 5-11. Possible sources of predictability on the sub-seasonal time scale are analyzed. The weekly mean T2m anomaly over East China is found to be linked to an eastward propagating extratropical Rossby wave from the North Atlantic across Europe to East Asia. The Madden-Julian Oscillation (MJO) and El Nino-Southern Oscillation (ENSO) are also likely to influence the forecast skill of T2m at the sub-seasonal timescale over East Asia.

  14. Empirically derived climate predictability over the extratropical northern hemisphere

    Directory of Open Access Journals (Sweden)

    J. B. Elsner

    1994-01-01

    Full Text Available A novel application of a technique developed from chaos theory is used in describing seasonal to interannual climate predictability over the Northern Hemisphere (NH. The technique is based on an empirical forecast scheme - local approximation in a reconstructed phase space - for time-series data. Data are monthly 500 hPa heights on a latitude-longitude grid covering the NH from 20° N to the equator. Predictability is estimated based on the linear correlation between actual and predicted heights averaged over a forecast range of one- to twelve.month lead. The method is capable of extracting the major climate signals on this time scale including ENSO and the North Atlantic Oscillation.

  15. Parameterization of a bucket model for soil-vegetation-atmosphere modeling under seasonal climatic regimes

    Directory of Open Access Journals (Sweden)

    N. Romano

    2011-12-01

    Full Text Available We investigate the potential impact of accounting for seasonal variations in the climatic forcing and using different methods to parameterize the soil water content at field capacity on the water balance components computed by a bucket model (BM. The single-layer BM of Guswa et al. (2002 is employed, whereas the Richards equation (RE based Soil Water Atmosphere Plant (SWAP model is used as a benchmark model. The results are analyzed for two differently-textured soils and for some synthetic runs under real-like seasonal weather conditions, using stochastically-generated daily rainfall data for a period of 100 years. Since transient soil-moisture dynamics and climatic seasonality play a key role in certain zones of the World, such as in Mediterranean land areas, a specific feature of this study is to test the prediction capability of the bucket model under a condition where seasonal variations in rainfall are not in phase with the variations in plant transpiration. Reference is made to a hydrologic year in which we have a rainy period (starting 1 November and lasting 151 days where vegetation is basically assumed in a dormant stage, followed by a drier and rainless period with a vegetation regrowth phase. Better agreement between BM and RE-SWAP intercomparison results are obtained when BM is parameterized by a field capacity value determined through the drainage method proposed by Romano and Santini (2002. Depending on the vegetation regrowth or dormant seasons, rainfall variability within a season results in transpiration regimes and soil moisture fluctuations with distinctive features. During the vegetation regrowth season, transpiration exerts a key control on soil water budget with respect to rainfall. During the dormant season of vegetation, the precipitation regime becomes an important climate forcing. Simulations also highlight the occurrence of bimodality in the probability distribution of soil moisture during the season when plants are

  16. Seasonal predictions of Fire Weather Index: Paving the way for their operational applicability in Mediterranean Europe

    Directory of Open Access Journals (Sweden)

    Joaquín Bedia

    2018-01-01

    Full Text Available Managers of wildfire-prone landscapes in the Euro-Mediterranean region would greatly benefit from fire weather predictions a few months in advance, and particularly from the reliable prediction of extreme fire seasons. However, in some cases model biases prevent from a direct application of these predictions in an operational context. Fire risk management requires precise knowledge of the likely consequences of climate on fire risk, and the interest for decision-makers is focused on multi-variable fire danger indices, calculated through the combination of different model output variables. In this paper we consider whether the skill in dynamical seasonal predictions of one of the most widely applied of such indices (the Canadian Fire Weather Index, FWI is sufficient to inform management decisions, and we examine various methodological aspects regarding the calibration of model outputs prior to its verification and operational applicability. We find that there is significant skill in predicting above average summer FWI in parts of SE Europe at 1 month lead time, but poor skill elsewhere. These results are largely linked to the predictability of relative humidity. Moreover, practical recommendations are given for the use of empirical quantile mapping in probabilistic seasonal FWI forecasts. Furthermore, we show how researchers, fire managers and other stakeholders can take advantage of a new open-source climate service in order to undertake all the necessary steps for data download, post-processing, analysis and verification in a straightforward and fully reproducible manner. Keywords: Climate impact indicators, Quantile mapping, Bias correction, System 4, Fire danger, Seasonal forecasting

  17. Climate Prediction Center - ENSO FAQ

    Science.gov (United States)

    data buoys used to monitor ocean temperatures? What is climate variability? A prominent aspect of our Niño or La Niña? During an El Niño or La Niña, the changes in Pacific Ocean temperatures affect Pacific. Changes in the ocean surface temperatures affect tropical rainfall patterns and atmospheric winds

  18. Climate Research and Seasonal Forecasting for West Africans: Perceptions, Dissemination, and Use?.

    Science.gov (United States)

    Tarhule, Aondover; Lamb, Peter J.

    2003-12-01

    Beginning in response to the disastrous drought of 1968 73, considerable research and monitoring have focused on the characteristics, causes, predictability, and impacts of West African Soudano Sahel (10° 18°N) rainfall variability and drought. While these efforts have generated substantial information on a range of these topics, very little is known of the extent to which communities, activities at risk, and policy makers are aware of, have access to, or use such information. This situation has prevailed despite Glantz&;s provocative BAMS paper on the use and value of seasonal forecasts for the Sahel more than a quarter century ago. We now provide a systematic reevaluation of these issues based on questionnaire responses of 566 participants (in 13 communities) and 26 organizations in Burkina Faso, Mali, Niger, and Nigeria. The results reveal that rural inhabitants have limited access to climate information, with nongovernmental organizations (NGOs) being the most important source. Moreover, the pathways for information flow are generally weakly connected and informal. As a result, utilization of the results of climate research is very low to nonexistent, even by organizations responsible for managing the effects of climate variability. Similarly, few people have access to seasonal climate forecasts, although the vast majority expressed a willingness to use such information when it becomes available. Those respondents with access expressed great enthusiasm and satisfaction with seasonal forecasts. The results suggest that inhabitants of the Soudano Sahel savanna are keen for changes that improve their ability to cope with climate variability, but the lack of information on alternative courses of action is a major constraint. Our study, thus, essentially leaves unchanged both Glantz&;s negative “tentative conclusion” and more positive “preliminary assessment” of 25 years ago. Specifically, while many of the infrastructural deficiencies and socioeconomic

  19. An analysis of prediction skill of monthly mean climate variability

    Energy Technology Data Exchange (ETDEWEB)

    Kumar, Arun; Chen, Mingyue; Wang, Wanqiu [Climate Prediction Center, National Centers for Environmental Prediction (CPC/NCEP), Camp Springs, MD (United States)

    2011-09-15

    In this paper, lead-time and spatial dependence in skill for prediction of monthly mean climate variability is analyzed. The analysis is based on a set of extensive hindcasts from the Climate Forecast System at the National Centers for Environmental Prediction. The skill characteristics of initialized predictions is also compared with the AMIP simulations forced with the observed sea surface temperature (SST) to quantify the role of initial versus boundary conditions in the prediction of monthly means. The analysis is for prediction of monthly mean SST, precipitation, and 200-hPa height. The results show a rapid decay in skill with lead time for the atmospheric variables in the extratropical latitudes. Further, after a lead-time of approximately 30-40 days, the skill of monthly mean prediction is essentially a boundary forced problem, with SST anomalies in the tropical central/eastern Pacific playing a dominant role. Because of the larger contribution from the atmospheric internal variability to monthly time-averages (compared to seasonal averages), skill for monthly mean prediction associated with boundary forcing is also lower. The analysis indicates that the prospects of skillful prediction of monthly means may remain a challenging problem, and may be limited by inherent limits in predictability. (orig.)

  20. Predictability of soil moisture and river flows over France for the spring season

    Science.gov (United States)

    Singla, S.; Céron, J.-P.; Martin, E.; Regimbeau, F.; Déqué, M.; Habets, F.; Vidal, J.-P.

    2012-01-01

    Sources of spring predictability of the hydrological system over France were studied on a seasonal time scale over the 1960-2005 period. Two random sampling experiments were set up in order to test the relative importance of the land surface initial state and the atmospheric forcing. The experiments were based on the SAFRAN-ISBA-MODCOU hydrometeorological suite which computed soil moisture and river flow forecasts over a 8-km grid and more than 880 river-gauging stations. Results showed that the predictability of hydrological variables primarily depended on the seasonal atmospheric forcing (mostly temperature and total precipitation) over most plains, whereas it mainly depended on snow cover over high mountains. However, the Seine catchment area was an exception as the skill mainly came from the initial state of its large and complex aquifers. Seasonal meteorological hindcasts with the Météo-France ARPEGE climate model were then used to force the ISBA-MODCOU hydrological model and obtain seasonal hydrological forecasts from 1960 to 2005 for the entire March-April-May period. Scores from this seasonal hydrological forecasting suite could thus be compared with the random atmospheric experiment. Soil moisture and river flow skill scores clearly showed the added value in seasonal meteorological forecasts in the north of France, contrary to the Mediterranean area where values worsened.

  1. How well do growing season dynamics of photosynthetic capacity correlate with leaf biochemistry and climate fluctuations?

    Science.gov (United States)

    Way, Danielle A; Stinziano, Joseph R; Berghoff, Henry; Oren, Ram

    2017-07-01

    Accurate values of photosynthetic capacity are needed in Earth System Models to predict gross primary productivity. Seasonal changes in photosynthetic capacity in these models are primarily driven by temperature, but recent work has suggested that photoperiod may be a better predictor of seasonal photosynthetic capacity. Using field-grown kudzu (Pueraria lobata (Willd.) Ohwi), a nitrogen-fixing vine species, we took weekly measurements of photosynthetic capacity, leaf nitrogen, and pigment and photosynthetic protein concentrations and correlated these with temperature, irradiance and photoperiod over the growing season. Photosynthetic capacity was more strongly correlated with photoperiod than with temperature or daily irradiance, while the growing season pattern in photosynthetic capacity was uncoupled from changes in leaf nitrogen, chlorophyll and Rubisco. Daily estimates of the maximum carboxylation rate of Rubisco (Vcmax) based on either photoperiod or temperature were correlated in a non-linear manner, but Vcmax estimates from both approaches that also accounted for diurnal temperature fluctuations were similar, indicating that differences between these models depend on the relevant time step. We advocate for considering photoperiod, and not just temperature, when estimating photosynthetic capacity across the year, particularly as climate change alters temperatures but not photoperiod. We also caution that the use of leaf biochemical traits as proxies for estimating photosynthetic capacity may be unreliable when the underlying relationships between proxy leaf traits and photosynthetic capacity are established outside of a seasonal framework. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  2. Simulated Vegetation Response to Climate Change in California: The Importance of Seasonal Production Patterns

    Science.gov (United States)

    Kim, J. B.; Pitts, B.

    2013-12-01

    MC1 dynamic global vegetation model simulates vegetation response to climate change by simulating vegetation production, soil biogeochemistry, plant biogeography and fire. It has been applied at a wide range of spatial scales, yet the spatio-temporal patterns of simulated vegetation production, which drives the model's response to climate change, has not been examined in detail. We ran MC1 for California at a relatively fine scale, 30 arc-seconds, for the historical period (1895-2006) and for the future (2007-2100), using downscaled data from four CMIP3-based climate projections: A2 and B1 GHG emissions scenarios simulated by PCM and GFDL GCMs. The use of these four climate projections aligns our work with a body of climate change research work commissioned by the California Public Interest Energy Research (PIER) Program. The four climate projections vary not only in terms of changes in their annual means, but in the seasonality of projected climate change. We calibrated MC1 using MODIS NPP data for 2000-2011 as a guide, and adapting a published technique for adjusting simulated vegetation production by increasing the simulated plant rooting depths. We evaluated the simulation results by comparing the model output for the historical period with several benchmark datasets, summarizing by EPA Level 3 Ecoregions. Multi-year summary statistics of model predictions compare moderately well with Kuchler's potential natural vegetation map, National Biomass and Carbon Dataset, Leenhouts' compilation of fire return intervals, and, of course, the MODIS NPP data for 2000-2011. When we compared MC1's monthly NPP values with MODIS monthly GPP data (2000-2011), however, the seasonal patterns compared very poorly, with NPP/GPP ratio for spring (Mar-Apr-May) often exceeding 1, and the NPP/GPP ratio for summer (Jun-Jul-Aug) often flattening to zero. This suggests MC1's vegetation production algorithms are overly biased for spring production at the cost of summer production. We

  3. Subseasonal to Seasonal Predictions of U.S. West Coast High Water Levels

    Science.gov (United States)

    Khouakhi, A.; Villarini, G.; Zhang, W.; Slater, L. J.

    2017-12-01

    Extreme sea levels pose a significant threat to coastal communities, ecosystems, and assets, as they are conducive to coastal flooding, coastal erosion and inland salt-water intrusion. As sea levels continue to rise, these sea level extremes - including occasional minor coastal flooding experienced during high tide (nuisance floods) - are of concern. Extreme sea levels are increasing at many locations around the globe and have been attributed largely to rising mean sea levels associated with intra-seasonal to interannual climate processes such as the El Niño-Southern Oscillation (ENSO). Here, intra-seasonal to seasonal probabilistic forecasts of high water levels are computed at the Toke Point tide gage station on the US west coast. We first identify the main climate drivers that are responsible for high water levels and examine their predictability using General Circulation Models (GCMs) from the North American Multi-Model Ensemble (NMME). These drivers are then used to develop a probabilistic framework for the seasonal forecasting of high water levels. We focus on the climate controls on the frequency of high water levels using the number of exceedances above the 99.5th percentile and above the nuisance flood level established by the National Weather Service. Our findings indicate good forecast skill at the shortest lead time, with the skill that decreases as we increase the lead time. In general, these models aptly capture the year-to-year variability in the observational records.

  4. A framework for examining climate-driven changes to the seasonality and geographical range of coastal pathogens and harmful algae

    Directory of Open Access Journals (Sweden)

    John Jacobs

    2015-01-01

    Full Text Available Climate change is expected to alter coastal ecosystems in ways which may have predictable consequences for the seasonality and geographical distribution of human pathogens and harmful algae. Here we demonstrate relatively simple approaches for evaluating the risk of occurrence of pathogenic bacteria in the genus Vibrio and outbreaks of toxin-producing harmful algae in the genus Alexandrium, with estimates of uncertainty, in U.S. coastal waters under future climate change scenarios through the end of the 21st century. One approach forces empirical models of growth, abundance and the probability of occurrence of the pathogens and algae at specific locations in the Chesapeake Bay and Puget Sound with ensembles of statistically downscaled climate model projections to produce first order assessments of changes in seasonality. In all of the case studies examined, the seasonal window of occurrence for Vibrio and Alexandrium broadened, indicating longer annual periods of time when there is increased risk for outbreaks. A second approach uses climate model projections coupled with GIS to identify the potential for geographic range shifts for Vibrio spp. in the coastal waters of Alaska. These two approaches could be applied to other coastal pathogens that have climate sensitive drivers to investigate potential changes to the risk of outbreaks in both time (seasonality and space (geographical distribution under future climate change scenarios.

  5. A multimodel approach to interannual and seasonal prediction of Danube discharge anomalies

    Science.gov (United States)

    Rimbu, Norel; Ionita, Monica; Patrut, Simona; Dima, Mihai

    2010-05-01

    Interannual and seasonal predictability of Danube river discharge is investigated using three model types: 1) time series models 2) linear regression models of discharge with large-scale climate mode indices and 3) models based on stable teleconnections. All models are calibrated using discharge and climatic data for the period 1901-1977 and validated for the period 1978-2008 . Various time series models, like autoregressive (AR), moving average (MA), autoregressive and moving average (ARMA) or singular spectrum analysis and autoregressive moving average (SSA+ARMA) models have been calibrated and their skills evaluated. The best results were obtained using SSA+ARMA models. SSA+ARMA models proved to have the highest forecast skill also for other European rivers (Gamiz-Fortis et al. 2008). Multiple linear regression models using large-scale climatic mode indices as predictors have a higher forecast skill than the time series models. The best predictors for Danube discharge are the North Atlantic Oscillation (NAO) and the East Atlantic/Western Russia patterns during winter and spring. Other patterns, like Polar/Eurasian or Tropical Northern Hemisphere (TNH) are good predictors for summer and autumn discharge. Based on stable teleconnection approach (Ionita et al. 2008) we construct prediction models through a combination of sea surface temperature (SST), temperature (T) and precipitation (PP) from the regions where discharge and SST, T and PP variations are stable correlated. Forecast skills of these models are higher than forecast skills of the time series and multiple regression models. The models calibrated and validated in our study can be used for operational prediction of interannual and seasonal Danube discharge anomalies. References Gamiz-Fortis, S., D. Pozo-Vazquez, R.M. Trigo, and Y. Castro-Diez, Quantifying the predictability of winter river flow in Iberia. Part I: intearannual predictability. J. Climate, 2484-2501, 2008. Gamiz-Fortis, S., D. Pozo

  6. Factors affecting seasonal habitat use, and predicted range of two tropical deer in Indonesian rainforest

    Science.gov (United States)

    Rahman, Dede Aulia; Gonzalez, Georges; Haryono, Mohammad; Muhtarom, Aom; Firdaus, Asep Yayus; Aulagnier, Stéphane

    2017-07-01

    There is an urgent recognized need for conservation of tropical forest deer. In order to identify some environmental factors affecting conservation, we analyzed the seasonal habitat use of two Indonesian deer species, Axis kuhlii in Bawean Island and Muntiacus muntjak in south-western Java Island, in response to several physical, climatic, biological, and anthropogenic variables. Camera trapping was performed in different habitat types during both wet and dry season to record these elusive species. The highest number of photographs was recorded in secondary forest and during the dry season for both Bawean deer and red muntjac. In models, anthropogenic and climatic variables were the main predictors of habitat use. Distances to cultivated area and to settlement were the most important for A. kuhlii in the dry season. Distances to cultivated area and annual rainfall were significant for M. muntjak in both seasons. Then we modelled their predictive range using Maximum entropy modelling (Maxent). We concluded that forest landscape is the fundamental scale for deer management, and that secondary forests are potentially important landscape elements for deer conservation. Important areas for conservation were identified accounting of habitat transformation in both study areas.

  7. Climate Prediction Center - Outlooks: Current UV Index Forecast Map

    Science.gov (United States)

    Weather Service NWS logo - Click to go to the NWS home page Climate Prediction Center Home Site Map News Service NOAA Center for Weather and Climate Prediction Climate Prediction Center 5830 University Research Court College Park, Maryland 20740 Page Author: Climate Prediction Center Internet Team Disclaimer

  8. State-of-the-Art Climate Predictions for Energy Climate Services

    Science.gov (United States)

    Torralba-Fernandez, Veronica; Davis, Melanie; Doblas-Reyes, Francisco J.; Gonzalez-Reviriego, Nube

    2015-04-01

    Climate predictions tailored to the energy sector represent the cutting edge in climate sciences to forecast wind power generation. At seasonal time scales, current energy practices use a deterministic approach based on retrospective climatology, but climate predictions have recently been shown to provide additional value. For this reason, probabilistic climate predictions of near surface winds can allow end users to take calculated, precautionary action with a potential cost savings to their operations. As every variable predicted in a coupled model forecast system, the prediction of wind speed is affected by biases. To overcome this, two different techniques for the post-processing of ensemble forecasts are considered: a simple bias correction and a calibration method. The former is based on the assumption that the reference and predicted distributions are well approximated by a normal distribution. The latter is a calibration technique which inflates the model variance, and the inflation of the ensemble is required in order to obtain a reliable outcome. Both methods use the "one-year out" cross-validated mode, and they provide corrected forecasts with improved statistical properties. The impact of these bias corrections on the quality of the ECMWF S4 predictions of near surface wind speed during winter is explored. To offer a comprehensive picture of the post-processing effect on the forecast quality of the system, it is necessary to use several scoring measures: rank histograms, reliability diagrams and skill maps. These tools are essential to assess different aspects of the forecasts, and to observe changes in their properties when the two methods are applied. This study reveals that the different techniques to correct the predictions produce a statistically consistent ensemble. However, the operations performed on the forecasts decrease their skill which correspond to an increase in the uncertainty. Therefore, even though the bias correction is fundamental

  9. Magnitudes and timing of seasonal peak snowpack water equivalents in Arizona: A preliminary study of the possible effects of recent climatic change

    Science.gov (United States)

    Peter F. Ffolliott; Gerald J. Gottfried

    2010-01-01

    Field measurements and computer-based predictions suggest that the magnitudes of seasonal peak snowpack water equivalents are becoming less and the timing of these peaks is occurring earlier in the snowmelt-runoff season of the western United States. These changes in peak snowpack conditions have often been attributed to a warming of the regional climate. To determine...

  10. Developing a Framework for Seamless Prediction of Sub-Seasonal to Seasonal Extreme Precipitation Events in the United States.

    Science.gov (United States)

    Rosendahl, D. H.; Ćwik, P.; Martin, E. R.; Basara, J. B.; Brooks, H. E.; Furtado, J. C.; Homeyer, C. R.; Lazrus, H.; Mcpherson, R. A.; Mullens, E.; Richman, M. B.; Robinson-Cook, A.

    2017-12-01

    Extreme precipitation events cause significant damage to homes, businesses, infrastructure, and agriculture, as well as many injures and fatalities as a result of fast-moving water or waterborne diseases. In the USA, these natural hazard events claimed the lives of more than 300 people during 2015 - 2016 alone, with total damage reaching $24.4 billion. Prior studies of extreme precipitation events have focused on the sub-daily to sub-weekly timeframes. However, many decisions for planning, preparing and resilience-building require sub-seasonal to seasonal timeframes (S2S; 14 to 90 days), but adequate forecasting tools for prediction do not exist. Therefore, the goal of this newly funded project is an enhancement in understanding of the large-scale forcing and dynamics of S2S extreme precipitation events in the United States, and improved capability for modeling and predicting such events. Here, we describe the project goals, objectives, and research activities that will take place over the next 5 years. In this project, a unique team of scientists and stakeholders will identify and understand weather and climate processes connected with the prediction of S2S extreme precipitation events by answering these research questions: 1) What are the synoptic patterns associated with, and characteristic of, S2S extreme precipitation evens in the contiguous U.S.? 2) What role, if any, do large-scale modes of climate variability play in modulating these events? 3) How predictable are S2S extreme precipitation events across temporal scales? 4) How do we create an informative prediction of S2S extreme precipitation events for policymaking and planing? This project will use observational data, high-resolution radar composites, dynamical climate models and workshops that engage stakeholders (water resource managers, emergency managers and tribal environmental professionals) in co-production of knowledge. The overarching result of this project will be predictive models to reduce of

  11. The predictive validity of safety climate.

    Science.gov (United States)

    Johnson, Stephen E

    2007-01-01

    Safety professionals have increasingly turned their attention to social science for insight into the causation of industrial accidents. One social construct, safety climate, has been examined by several researchers [Cooper, M. D., & Phillips, R. A. (2004). Exploratory analysis of the safety climate and safety behavior relationship. Journal of Safety Research, 35(5), 497-512; Gillen, M., Baltz, D., Gassel, M., Kirsch, L., & Vacarro, D. (2002). Perceived safety climate, job Demands, and coworker support among union and nonunion injured construction workers. Journal of Safety Research, 33(1), 33-51; Neal, A., & Griffin, M. A. (2002). Safety climate and safety behaviour. Australian Journal of Management, 27, 66-76; Zohar, D. (2000). A group-level model of safety climate: Testing the effect of group climate on microaccidents in manufacturing jobs. Journal of Applied Psychology, 85(4), 587-596; Zohar, D., & Luria, G. (2005). A multilevel model of safety climate: Cross-level relationships between organization and group-level climates. Journal of Applied Psychology, 90(4), 616-628] who have documented its importance as a factor explaining the variation of safety-related outcomes (e.g., behavior, accidents). Researchers have developed instruments for measuring safety climate and have established some degree of psychometric reliability and validity. The problem, however, is that predictive validity has not been firmly established, which reduces the credibility of safety climate as a meaningful social construct. The research described in this article addresses this problem and provides additional support for safety climate as a viable construct and as a predictive indicator of safety-related outcomes. This study used 292 employees at three locations of a heavy manufacturing organization to complete the 16 item Zohar Safety Climate Questionnaire (ZSCQ) [Zohar, D., & Luria, G. (2005). A multilevel model of safety climate: Cross-level relationships between organization and group

  12. Improving seasonal forecasts of hydroclimatic variables through the state of multiple large-scale climate signals

    Science.gov (United States)

    Castelletti, A.; Giuliani, M.; Block, P. J.

    2017-12-01

    Increasingly uncertain hydrologic regimes combined with more frequent and intense extreme events are challenging water systems management worldwide, emphasizing the need of accurate medium- to long-term predictions to timely prompt anticipatory operations. Despite modern forecasts are skillful over short lead time (from hours to days), predictability generally tends to decrease on longer lead times. Global climate teleconnection, such as El Niño Southern Oscillation (ENSO), may contribute in extending forecast lead times. However, ENSO teleconnection is well defined in some locations, such as Western USA and Australia, while there is no consensus on how it can be detected and used in other regions, particularly in Europe, Africa, and Asia. In this work, we generalize the Niño Index Phase Analysis (NIPA) framework by contributing the Multi Variate Niño Index Phase Analysis (MV-NIPA), which allows capturing the state of multiple large-scale climate signals (i.e. ENSO, North Atlantic Oscillation, Pacific Decadal Oscillation, Atlantic Multi-decadal Oscillation, Indian Ocean Dipole) to forecast hydroclimatic variables on a seasonal time scale. Specifically, our approach distinguishes the different phases of the considered climate signals and, for each phase, identifies relevant anomalies in Sea Surface Temperature (SST) that influence the local hydrologic conditions. The potential of the MV-NIPA framework is demonstrated through an application to the Lake Como system, a regulated lake in northern Italy which is mainly operated for flood control and irrigation supply. Numerical results show high correlations between seasonal SST values and one season-ahead precipitation in the Lake Como basin. The skill of the resulting MV-NIPA forecast outperforms the one of ECMWF products. This information represents a valuable contribution to partially anticipate the summer water availability, especially during drought events, ultimately supporting the improvement of the Lake Como

  13. Climate Change as a Predictable Surprise

    International Nuclear Information System (INIS)

    Bazerman, M.H.

    2006-01-01

    In this article, I analyze climate change as a 'predictable surprise', an event that leads an organization or nation to react with surprise, despite the fact that the information necessary to anticipate the event and its consequences was available (Bazerman and Watkins, 2004). I then assess the cognitive, organizational, and political reasons why society fails to implement wise strategies to prevent predictable surprises generally and climate change specifically. Finally, I conclude with an outline of a set of response strategies to overcome barriers to change

  14. Seasonal prediction of Indian summer monsoon rainfall in NCEP CFSv2: forecast and predictability error

    Science.gov (United States)

    Pokhrel, Samir; Saha, Subodh Kumar; Dhakate, Ashish; Rahman, Hasibur; Chaudhari, Hemantkumar S.; Salunke, Kiran; Hazra, Anupam; Sujith, K.; Sikka, D. R.

    2016-04-01

    A detailed analysis of sensitivity to the initial condition for the simulation of the Indian summer monsoon using retrospective forecast by the latest version of the Climate Forecast System version-2 (CFSv2) is carried out. This study primarily focuses on the tropical region of Indian and Pacific Ocean basin, with special emphasis on the Indian land region. The simulated seasonal mean and the inter-annual standard deviations of rainfall, upper and lower level atmospheric circulations and Sea Surface Temperature (SST) tend to be more skillful as the lead forecast time decreases (5 month lead to 0 month lead time i.e. L5-L0). In general spatial correlation (bias) increases (decreases) as forecast lead time decreases. This is further substantiated by their averaged value over the selected study regions over the Indian and Pacific Ocean basins. The tendency of increase (decrease) of model bias with increasing (decreasing) forecast lead time also indicates the dynamical drift of the model. Large scale lower level circulation (850 hPa) shows enhancement of anomalous westerlies (easterlies) over the tropical region of the Indian Ocean (Western Pacific Ocean), which indicates the enhancement of model error with the decrease in lead time. At the upper level circulation (200 hPa) biases in both tropical easterly jet and subtropical westerlies jet tend to decrease as the lead time decreases. Despite enhancement of the prediction skill, mean SST bias seems to be insensitive to the initialization. All these biases are significant and together they make CFSv2 vulnerable to seasonal uncertainties in all the lead times. Overall the zeroth lead (L0) seems to have the best skill, however, in case of Indian summer monsoon rainfall (ISMR), the 3 month lead forecast time (L3) has the maximum ISMR prediction skill. This is valid using different independent datasets, wherein these maximum skill scores are 0.64, 0.42 and 0.57 with respect to the Global Precipitation Climatology Project

  15. Singular vectors, predictability and ensemble forecasting for weather and climate

    International Nuclear Information System (INIS)

    Palmer, T N; Zanna, Laure

    2013-01-01

    The local instabilities of a nonlinear dynamical system can be characterized by the leading singular vectors of its linearized operator. The leading singular vectors are perturbations with the greatest linear growth and are therefore key in assessing the system’s predictability. In this paper, the analysis of singular vectors for the predictability of weather and climate and ensemble forecasting is discussed. An overview of the role of singular vectors in informing about the error growth rate in numerical models of the atmosphere is given. This is followed by their use in the initialization of ensemble weather forecasts. Singular vectors for the ocean and coupled ocean–atmosphere system in order to understand the predictability of climate phenomena such as ENSO and meridional overturning circulation are reviewed and their potential use to initialize seasonal and decadal forecasts is considered. As stochastic parameterizations are being implemented, some speculations are made about the future of singular vectors for the predictability of weather and climate for theoretical applications and at the operational level. This article is part of a special issue of Journal of Physics A: Mathematical and Theoretical devoted to ‘Lyapunov analysis: from dynamical systems theory to applications’. (review)

  16. A simple next-best alternative to seasonal predictions in Europe

    Science.gov (United States)

    Buontempo, Carlo; De Felice, Matteo

    2016-04-01

    In order to build a climate proof society, we need to learn how to best use the climate information we have. Having spent time and resources in developing complex numerical models has often blinded us on the value some of this information really has in the eyes of a decision maker. An effective way to assess this is to check the quality of the forecast (and its cost) to the quality of the forecast from a prediction system based on simpler assumption (and thus cheaper to run). Such a practice is common in marketing analysis where it is often referred to as the next-best alternative. As a way to facilitate such an analysis, climate service providers should always provide alongside the predictions a set of skill scores. These are usually based on climatological means, anomaly persistence or more recently multiple linear regressions. We here present an equally simple benchmark based on a Markov chain process locally trained at a monthly or seasonal time-scale. We demonstrate that in spite of its simplicity the model easily outperforms not only the standard benchmark but also most of the seasonal predictions system at least in EUROPE. We suggest that a benchmark of this kind could represent a useful next-best alternative for a number of users.

  17. An analytical model for climatic predictions

    International Nuclear Information System (INIS)

    Njau, E.C.

    1990-12-01

    A climatic model based upon analytical expressions is presented. This model is capable of making long-range predictions of heat energy variations on regional or global scales. These variations can then be transformed into corresponding variations of some other key climatic parameters since weather and climatic changes are basically driven by differential heating and cooling around the earth. On the basis of the mathematical expressions upon which the model is based, it is shown that the global heat energy structure (and hence the associated climatic system) are characterized by zonally as well as latitudinally propagating fluctuations at frequencies downward of 0.5 day -1 . We have calculated the propagation speeds for those particular frequencies that are well documented in the literature. The calculated speeds are in excellent agreement with the measured speeds. (author). 13 refs

  18. US Climate Variability and Predictability Project

    Energy Technology Data Exchange (ETDEWEB)

    Patterson, Mike [University Corporation for Atmospheric Research (UCAR), Boulder, CO (United States)

    2017-11-14

    The US CLIVAR Project Office administers the US CLIVAR Program with its mission to advance understanding and prediction of climate variability and change across timescales with an emphasis on the role of the ocean and its interaction with other elements of the Earth system. The Project Office promotes and facilitates scientific collaboration within the US and international climate and Earth science communities, addressing priority topics from subseasonal to centennial climate variability and change; the global energy imbalance; the ocean’s role in climate, water, and carbon cycles; climate and weather extremes; and polar climate changes. This project provides essential one-year support of the Project Office, enabling the participation of US scientists in the meetings of the US CLIVAR bodies that guide scientific planning and implementation, including the scientific steering committee that establishes program goals and evaluates progress of activities to address them, the science team of funded investigators studying the ocean overturning circulation in the Atlantic, and two working groups tackling the priority research topics of Arctic change influence on midlatitude climate and weather extremes and the decadal-scale widening of the tropical belt.

  19. CMIP5 model simulations of Ethiopian Kiremt-season precipitation: current climate and future changes

    Science.gov (United States)

    Li, Laifang; Li, Wenhong; Ballard, Tristan; Sun, Ge; Jeuland, Marc

    2016-05-01

    Kiremt-season (June-September) precipitation provides a significant water supply for Ethiopia, particularly in the central and northern regions. The response of Kiremt-season precipitation to climate change is thus of great concern to water resource managers. However, the complex processes that control Kiremt-season precipitation challenge the capability of general circulation models (GCMs) to accurately simulate precipitation amount and variability. This in turn raises questions about their utility for predicting future changes. This study assesses the impact of climate change on Kiremt-season precipitation using state-of-the-art GCMs participating in the Coupled Model Intercomparison Project Phase 5. Compared to models with a coarse resolution, high-resolution models (horizontal resolution <2°) can more accurately simulate precipitation, most likely due to their ability to capture precipitation induced by topography. Under the Representative Concentration Pathway (RCP) 4.5 scenario, these high-resolution models project an increase in precipitation over central Highlands and northern Great Rift Valley in Ethiopia, but a decrease in precipitation over the southern part of the country. Such a dipole pattern is attributable to the intensification of the North Atlantic subtropical high (NASH) in a warmer climate, which influences Ethiopian Kiremt-season precipitation mainly by modulating atmospheric vertical motion. Diagnosis of the omega equation demonstrates that an intensified NASH increases (decreases) the advection of warm air and positive vorticity into the central Highlands and northern Great Rift Valley (southern part of the country), enhancing upward motion over the northern Rift Valley but decreasing elsewhere. Under the RCP 4.5 scenario, the high-resolution models project an intensification of the NASH by 15 (3 × 105 m2 s-2) geopotential meters (stream function) at the 850-hPa level, contributing to the projected precipitation change over Ethiopia. The

  20. Impact of Soil Moisture Initialization on Seasonal Weather Prediction

    Science.gov (United States)

    Koster, Randal D.; Suarez, Max J.; Houser, Paul (Technical Monitor)

    2002-01-01

    The potential role of soil moisture initialization in seasonal forecasting is illustrated through ensembles of simulations with the NASA Seasonal-to-Interannual Prediction Project (NSIPP) model. For each boreal summer during 1997-2001, we generated two 16-member ensembles of 3-month simulations. The first, "AMIP-style" ensemble establishes the degree to which a perfect prediction of SSTs would contribute to the seasonal prediction of precipitation and temperature over continents. The second ensemble is identical to the first, except that the land surface is also initialized with "realistic" soil moisture contents through the continuous prior application (within GCM simulations leading up to the start of the forecast period) of a daily observational precipitation data set and the associated avoidance of model drift through the scaling of all surface prognostic variables. A comparison of the two ensembles shows that soil moisture initialization has a statistically significant impact on summertime precipitation and temperature over only a handful of continental regions. These regions agree, to first order, with regions that satisfy three conditions: (1) a tendency toward large initial soil moisture anomalies, (2) a strong sensitivity of evaporation to soil moisture, and (3) a strong sensitivity of precipitation to evaporation. The degree to which the initialization improves forecasts relative to observations is mixed, reflecting a critical need for the continued development of model parameterizations and data analysis strategies.

  1. Impact of lower stratospheric ozone on seasonal prediction systems

    CSIR Research Space (South Africa)

    Mathole, K

    2014-01-01

    Full Text Available Circulation Model (called the ECHAM 4.5-MOM3-SA OAGCM)31 integrations for the first lead time (i.e. forecasts are made in early November for December- January-February).This model currently is used for operational forecast production at the South African... through modelling and predictability studies should include the knowledge of stratospheric as well as chemical processes (e.g. CO2 and ozone) which contribute to the so-called ‘complete climate system’. This notion was endorsed by the World Climate...

  2. Climate-Induced Boreal Forest Change: Predictions versus Current Observations

    Science.gov (United States)

    Soja, Amber J.; Tchebakova, Nadezda M.; French, Nancy H. F.; Flannigan, Michael D.; Shugart, Herman H.; Stocks, Brian J.; Sukhinin, Anatoly I.; Parfenova, E. I.; Chapin, F. Stuart, III; Stackhouse, Paul W., Jr.

    2007-01-01

    For about three decades, there have been many predictions of the potential ecological response in boreal regions to the currently warmer conditions. In essence, a widespread, naturally occurring experiment has been conducted over time. In this paper, we describe previously modeled predictions of ecological change in boreal Alaska, Canada and Russia, and then we investigate potential evidence of current climate-induced change. For instance, ecological models have suggested that warming will induce the northern and upslope migration of the treeline and an alteration in the current mosaic structure of boreal forests. We present evidence of the migration of keystone ecosystems in the upland and lowland treeline of mountainous regions across southern Siberia. Ecological models have also predicted a moisture-stress-related dieback in white spruce trees in Alaska, and current investigations show that as temperatures increase, white spruce tree growth is declining. Additionally, it was suggested that increases in infestation and wildfire disturbance would be catalysts that precipitate the alteration of the current mosaic forest composition. In Siberia, five of the last seven years have resulted in extreme fire seasons, and extreme fire years have also been more frequent in both Alaska and Canada. In addition, Alaska has experienced extreme and geographically expansive multi-year outbreaks of the spruce beetle, which had been previously limited by the cold, moist environment. We suggest that there is substantial evidence throughout the circumboreal region to conclude that the biosphere within the boreal terrestrial environment has already responded to the transient effects of climate change. Additionally, temperature increases and warming-induced change are progressing faster than had been predicted in some regions, suggesting a potential non-linear rapid response to changes in climate, as opposed to the predicted slow linear response to climate change.

  3. Incorporating probabilistic seasonal climate forecasts into river management using a risk-based framework

    Science.gov (United States)

    Sojda, Richard S.; Towler, Erin; Roberts, Mike; Rajagopalan, Balaji

    2013-01-01

    [1] Despite the influence of hydroclimate on river ecosystems, most efforts to date have focused on using climate information to predict streamflow for water supply. However, as water demands intensify and river systems are increasingly stressed, research is needed to explicitly integrate climate into streamflow forecasts that are relevant to river ecosystem management. To this end, we present a five step risk-based framework: (1) define risk tolerance, (2) develop a streamflow forecast model, (3) generate climate forecast ensembles, (4) estimate streamflow ensembles and associated risk, and (5) manage for climate risk. The framework is successfully demonstrated for an unregulated watershed in southwest Montana, where the combination of recent drought and water withdrawals has made it challenging to maintain flows needed for healthy fisheries. We put forth a generalized linear modeling (GLM) approach to develop a suite of tools that skillfully model decision-relevant low flow characteristics in terms of climate predictors. Probabilistic precipitation forecasts are used in conjunction with the GLMs, resulting in season-ahead prediction ensembles that provide the full risk profile. These tools are embedded in an end-to-end risk management framework that directly supports proactive fish conservation efforts. Results show that the use of forecasts can be beneficial to planning, especially in wet years, but historical precipitation forecasts are quite conservative (i.e., not very “sharp”). Synthetic forecasts show that a modest “sharpening” can strongly impact risk and improve skill. We emphasize that use in management depends on defining relevant environmental flows and risk tolerance, requiring local stakeholder involvement.

  4. Impacts of climate change on the seasonality of low flows in 134 catchments in the River Rhine basin using an ensemble of bias-corrected regional climate simulations

    Directory of Open Access Journals (Sweden)

    M. C. Demirel

    2013-10-01

    -basins. The WP values are slightly larger, showing that the predictability of low flow events increases as the variability in timing decreases for the future climate. From comparison of the error sources evaluated in this study, it is obvious that different RCMs/GCMs have a larger influence on the timing of low flows than different emission scenarios. Finally, this study complements recent analyses of an international project (Rhineblick by analysing the seasonality aspects of low flows and extends the scope further to understand the effects of hydrological model errors and climate change on three important low flow seasonality properties: regime, timing and persistence.

  5. Towards Experimental Operational Fire Weather Prediction at Subseasonal to Seasonal Scales for Alaska Using the NMME Hindcasts and Realtime Forecasts.

    Science.gov (United States)

    Sampath, A.; Bhatt, U. S.; Bieniek, P.; York, A.; Peng, P.; Brettschneider, B.; Thoman, R.; Jandt, R.; Ziel, R.; Branson, G.; Strader, M. H.; Alden, M. S.

    2017-12-01

    The summer 2004 and 2015 wildfires in Alaska were the two largest fire seasons on record since 1950 where approximately the land area of Massachusetts burned. The record fire year of 2004 resulted in 6.5 million acres burned while the 2015 wildfire season resulted in 5.2 million acres burned. In addition to the logistical cost of fighting fires and the loss of infrastructure, wildfires also lead to dangerous air quality in Alaska. Fires in Alaska result from lightning strikes coupled with persistent (extreme) dry warm conditions in remote areas with limited fire management and the seasonal climate/weather determine the extent of the fire season in Alaska. Advanced weather/climate outlooks for allocating staff and resources from days to a season are particularly needed by fire managers. However, there are no operational seasonal products currently for the Alaska region. Probabilistic forecasts of the expected seasonal climate/weather would aid tremendously in the planning process. Earlier insight of both lightening and fuel conditions would assist fire managers in planning resource allocation for the upcoming season. For fuel conditions, the state-of-the-art NMME (1982-2017) climate predictions were used to compute the Canadian Forest Fire Weather Index System (CFFWIS). The CFFWIS is used by fire managers to forecast forest fires in Alaska. NMME forecast (March and May) based Buildup Index (BUI) values were underestimated compared to BUI based on reanalysis and station data, demonstrating the necessity for bias correction. Post processing of NMME data will include bias correction using the quantile mapping technique. This study will provide guidance as to the what are the best available products for anticipating the fire season.

  6. Engaging farmers on climate risk through targeted integration of bio-economic modelling and seasonal climate forecasts

    NARCIS (Netherlands)

    Nidumolu, U.B.; Lubbers, M.; Kanellopoulos, A.; Ittersum, van M.K.; Kadiyala, D.M.; Sreenivas, G.

    2016-01-01

    Seasonal climate forecasts (SCFs) can be used to identify appropriate risk management strategies and to reduce the sensitivity of rural industries and communities to climate risk. However, these forecasts have low utility among farmers in agricultural decision making, unless translated into a

  7. Changing seasonality patterns in Central Europe from Miocene Climate Optimum to Miocene Climate Transition deduced from the Crassostrea isotope archive

    Science.gov (United States)

    Harzhauser, Mathias; Piller, Werner E.; Müllegger, Stefan; Grunert, Patrick; Micheels, Arne

    2011-03-01

    The Western Tethyan estuarine oyster Crassostrea gryphoides is an excellent climate archive due to its large size and rapid growth. It is geologically long lived and allows a stable isotope-based insight into climatic trends during the Miocene. Herein we utilised the climate archive of 5 oyster shells from the Miocene Climate Optimum (MCO) and the subsequent Miocene Climate Transition (MCT) to evaluate changes of seasonality patterns. MCO shells exhibit highly regular seasonal rhythms of warm-wet and dry-cool seasons. Optimal conditions resulted in extraordinary growth rates of the oysters. δ 13C profiles are in phase with δ 18O although phytoplankton blooms may cause a slight offset. Estuarine waters during the MCO in Central Europe display a seasonal temperature range of c. 9-10 °C. Absolute water temperatures have ranged from 17 to 19 °C during cool seasons and up to 28 °C in warm seasons. Already during the early phase of the MCO, the growth rates are distinctly declining, although gigantic and extremely old shells have been formed at that time. Still, a very regular and well expressed seasonality is dominating the isotope profiles, but episodically occurring extreme climate events influence the environments. The seasonal temperature range is still c. 9 °C but the cool season temperature seems to be slightly lower (16 °C) and the warm season water temperature does not exceed c. 25 °C. In the later MCT at c. 12.5-12.0 Ma the seasonality pattern is breaking down and is replaced by successions of dry years with irregular precipitation events. No correlation between δ 18O and δ 13C is documented maybe due to a suboptimal nutrition level which would explain the low growth rates and small sizes. The amplitude of seasonal temperature range is decreasing to 5-8 °C. No clear cooling trend can be postulated for that time as the winter season water temperatures range from 15 to 20 °C. This may point to unstable precipitation rhythms on a multi-annual to

  8. Visualizing the uncertainty in the relationship between seasonal average climate and malaria risk.

    Science.gov (United States)

    MacLeod, D A; Morse, A P

    2014-12-02

    Around $1.6 billion per year is spent financing anti-malaria initiatives, and though malaria morbidity is falling, the impact of annual epidemics remains significant. Whilst malaria risk may increase with climate change, projections are highly uncertain and to sidestep this intractable uncertainty, adaptation efforts should improve societal ability to anticipate and mitigate individual events. Anticipation of climate-related events is made possible by seasonal climate forecasting, from which warnings of anomalous seasonal average temperature and rainfall, months in advance are possible. Seasonal climate hindcasts have been used to drive climate-based models for malaria, showing significant skill for observed malaria incidence. However, the relationship between seasonal average climate and malaria risk remains unquantified. Here we explore this relationship, using a dynamic weather-driven malaria model. We also quantify key uncertainty in the malaria model, by introducing variability in one of the first order uncertainties in model formulation. Results are visualized as location-specific impact surfaces: easily integrated with ensemble seasonal climate forecasts, and intuitively communicating quantified uncertainty. Methods are demonstrated for two epidemic regions, and are not limited to malaria modeling; the visualization method could be applied to any climate impact.

  9. The seasonal predictability of blocking frequency in two seasonal prediction systems (CMCC, Met-Office) and the associated representation of low-frequency variability.

    Science.gov (United States)

    Athanasiadis, Panos; Gualdi, Silvio; Scaife, Adam A.; Bellucci, Alessio; Hermanson, Leon; MacLachlan, Craig; Arribas, Alberto; Materia, Stefano; Borelli, Andrea

    2014-05-01

    Low-frequency variability is a fundamental component of the atmospheric circulation. Extratropical teleconnections, the occurrence of blocking and the slow modulation of the jet streams and storm tracks are all different aspects of low-frequency variability. Part of the latter is attributed to the chaotic nature of the atmosphere and is inherently unpredictable. On the other hand, primarily as a response to boundary forcings, tropospheric low-frequency variability includes components that are potentially predictable. Seasonal forecasting faces the difficult task of predicting these components. Particularly referring to the extratropics, the current generation of seasonal forecasting systems seem to be approaching this target by realistically initializing most components of the climate system, using higher resolution and utilizing large ensemble sizes. Two seasonal prediction systems (Met-Office GloSea and CMCC-SPS-v1.5) are analyzed in terms of their representation of different aspects of extratropical low-frequency variability. The current operational Met-Office system achieves unprecedented high scores in predicting the winter-mean phase of the North Atlantic Oscillation (NAO, corr. 0.74 at 500 hPa) and the Pacific-N. American pattern (PNA, corr. 0.82). The CMCC system, considering its small ensemble size and course resolution, also achieves good scores (0.42 for NAO, 0.51 for PNA). Despite these positive features, both models suffer from biases in low-frequency variance, particularly in the N. Atlantic. Consequently, it is found that their intrinsic variability patterns (sectoral EOFs) differ significantly from the observed, and the known teleconnections are underrepresented. Regarding the representation of N. hemisphere blocking, after bias correction both systems exhibit a realistic climatology of blocking frequency. In this assessment, instantaneous blocking and large-scale persistent blocking events are identified using daily geopotential height fields at

  10. Extending to seasonal scales the current usage of short range weather forecasts and climate projections for water management in Spain

    Science.gov (United States)

    Rodriguez-Camino, Ernesto; Voces, José; Sánchez, Eroteida; Navascues, Beatriz; Pouget, Laurent; Roldan, Tamara; Gómez, Manuel; Cabello, Angels; Comas, Pau; Pastor, Fernando; Concepción García-Gómez, M.°; José Gil, Juan; Gil, Delfina; Galván, Rogelio; Solera, Abel

    2016-04-01

    This presentation, first, briefly describes the current use of weather forecasts and climate projections delivered by AEMET for water management in Spain. The potential use of seasonal climate predictions for water -in particular dams- management is then discussed more in-depth, using a pilot experience carried out by a multidisciplinary group coordinated by AEMET and DG for Water of Spain. This initiative is being developed in the framework of the national implementation of the GFCS and the European project, EUPORIAS. Among the main components of this experience there are meteorological and hydrological observations, and an empirical seasonal forecasting technique that provides an ensemble of water reservoir inflows. These forecasted inflows feed a prediction model for the dam state that has been adapted for this purpose. The full system is being tested retrospectively, over several decades, for selected water reservoirs located in different Spanish river basins. The assessment includes an objective verification of the probabilistic seasonal forecasts using standard metrics, and the evaluation of the potential social and economic benefits, with special attention to drought and flooding conditions. The methodology of implementation of these seasonal predictions in the decision making process is being developed in close collaboration with final users participating in this pilot experience.

  11. Is Information Enough? User Responses to Seasonal Climate Forecasts in Southern Africa. Report to the World Bank, AFTE1-ENVGC. Adaptation to Climate Change and Variability in Sub{sub S}aharan Africa, Phase II

    Energy Technology Data Exchange (ETDEWEB)

    O' Brien, Karen; Sygna, Linda; Naess, Lars Otto; Kingamkono, Robert; Hochobeb, Ben

    2000-05-01

    Since the mid-1980s, long-lead climate forecasts have been developed and used to predict the onset of El Nino events and their impact on climate variability. This report discusses user responses to seasonal climate forecasts in southern Africa, with an emphasis on small-scale farmers in Namibia and Tanzania. The study examines how farmers received and used the forecasts in the agricultural season of 1997/1998. It also summarises a workshop on user responses to seasonal forecasts in southern Africa. Comparison of case studies across south Africa revealed differences in forecast dissemination strategies and in the capacity to respond to extreme events. However, improving these strategies and the capacity to respond to the forecasts would yield net profit to agriculture in southern Africa. In anticipation of potential changes in the frequency or magnitude of extreme events associated with global climate change, there clearly is a need for improved seasonal forecasts and improved information dissemination.

  12. Seasonality of change: Summer warming rates do not fully represent effects of climate change on lake temperatures

    Science.gov (United States)

    Winslow, Luke; Read, Jordan S.; Hansen, Gretchen J. A.; Rose, Kevin C.; Robertson, Dale M.

    2017-01-01

    Responses in lake temperatures to climate warming have primarily been characterized using seasonal metrics of surface-water temperatures such as summertime or stratified period average temperatures. However, climate warming may not affect water temperatures equally across seasons or depths. We analyzed a long-term dataset (1981–2015) of biweekly water temperature data in six temperate lakes in Wisconsin, U.S.A. to understand (1) variability in monthly rates of surface- and deep-water warming, (2) how those rates compared to summertime average trends, and (3) if monthly heterogeneity in water temperature trends can be predicted by heterogeneity in air temperature trends. Monthly surface-water temperature warming rates varied across the open-water season, ranging from 0.013 in August to 0.073°C yr−1 in September (standard deviation [SD]: 0.025°C yr−1). Deep-water trends during summer varied less among months (SD: 0.006°C yr−1), but varied broadly among lakes (–0.056°C yr−1 to 0.035°C yr−1, SD: 0.034°C yr−1). Trends in monthly surface-water temperatures were well correlated with air temperature trends, suggesting monthly air temperature trends, for which data exist at broad scales, may be a proxy for seasonal patterns in surface-water temperature trends during the open water season in lakes similar to those studied here. Seasonally variable warming has broad implications for how ecological processes respond to climate change, because phenological events such as fish spawning and phytoplankton succession respond to specific, seasonal temperature cues.

  13. Climate seasonality limits leaf carbon assimilation and wood productivity in tropical forests

    Science.gov (United States)

    Fabien H. Wagner; Bruno Herault; Damien Bonal; Clement Stahl; Liana O. Anderson; Timothy R. Baker; Gabriel Sebastian Becker; Hans Beeckman; Danilo Boanerges Souza; Paulo Cesar Botosso; David M. J. S. Bowman; Achim Brauning; Benjamin Brede; Foster Irving Brown; Jesus Julio Camarero; Plinio Barbosa Camargo; Fernanda C. G. Cardoso; Fabricio Alvim Carvalho; Wendeson Castro; Rubens Koloski Chagas; Jerome Chave; Emmanuel N. Chidumayo; Deborah A. Clark; Flavia Regina Capellotto Costa; Camille Couralet; Paulo Henrique da Silva Mauricio; Helmut Dalitz; Vinicius Resende de Castro; Jacanan Eloisa de Freitas Milani; Edilson Consuelo de Oliveira; Luciano de Souza Arruda; Jean-Louis Devineau; David M. Drew; Oliver Dunisch; Giselda Durigan; Elisha Elifuraha; Marcio Fedele; Ligia Ferreira Fedele; Afonso Figueiredo Filho; Cesar Augusto Guimaraes Finger; Augusto Cesar Franco; Joao Lima Freitas Junior; Franklin Galvao; Aster Gebrekirstos; Robert Gliniars; Paulo Mauricio Lima de Alencastro Graca; Anthony D. Griffiths; James Grogan; Kaiyu Guan; Jurgen Homeier; Maria Raquel Kanieski; Lip Khoon Kho; Jennifer Koenig; Sintia Valerio Kohler; Julia Krepkowski; Jose Pires Lemos-Filho; Diana Lieberman; Milton Eugene Lieberman; Claudio Sergio Lisi; Tomaz Longhi Santos; Jose Luis Lopez Ayala; Eduardo Eijji Maeda; Yadvinder Malhi; Vivian R. B. Maria; Marcia C. M. Marques; Renato Marques; Hector Maza Chamba; Lawrence Mbwambo; Karina Liana Lisboa Melgaco; Hooz Angela Mendivelso; Brett P. Murphy; Joseph O' Brien; Steven F. Oberbauer; Naoki Okada; Raphael Pelissier; Lynda D. Prior; Fidel Alejandro Roig; Michael Ross; Davi Rodrigo Rossatto; Vivien Rossi; Lucy Rowland; Ervan Rutishauser; Hellen Santana; Mark Schulze; Diogo Selhorst; Williamar Rodrigues Silva; Marcos Silveira; Susanne Spannl; Michael D. Swaine; Jose Julio Toledo; Marcos Miranda Toledo; Marisol Toledo; Takeshi Toma; Mario Tomazello Filho; Juan Ignacio Valdez Hernandez; Jan Verbesselt; Simone Aparecida Vieira; Gregoire Vincent; Carolina Volkmer de Castilho; Franziska Volland; Martin Worbes; Magda Lea Bolzan Zanon; Luiz E. O. C. Aragao

    2016-01-01

    The seasonal climate drivers of the carbon cycle in tropical forests remain poorly known, although these forests account for more carbon assimilation and storage than any other terrestrial ecosystem. Based on a unique combination of seasonal pan-tropical data sets from 89 experimental sites (68 include aboveground wood productivity measurements and 35 litter...

  14. Impacts of Seasonal Patterns of Climate on Recurrent Fluctuations in Tourism Demand: Evidence from Aruba

    NARCIS (Netherlands)

    Ridderstaat, J.R.; Oderber, M.; Croes, R.; Nijkamp, P.; Martens, P.

    2014-01-01

    This study estimates the effect of seasonal patterns of pull and push climate elements (rainfall, temperature, wind, and cloud coverage) on recurrent fluctuations in tourism demand from the United States (USA) and Venezuela to Aruba. The seasonal patterns were first isolated from the series using

  15. Climate seasonality limits leaf carbon assimilation and wood productivity in tropical forests

    NARCIS (Netherlands)

    Wagner, Fabien H.; Hérault, Bruno; Bonal, Damien; Stahl, Clément; Anderson, Liana O.; Baker, Timothy R.; Becker, Gabriel Sebastian; Beeckman, Hans; Boanerges Souza, Danilo; Botosso, Paulo Cesar; Bowman, David M.J.S.; Bräuning, Achim; Brede, Benjamin; Brown, Foster Irving; Camarero, Jesus Julio; Camargo, Plínio Barbosa; Cardoso, Fernanda C.G.; Carvalho, Fabrício Alvim; Castro, Wendeson; Chagas, Rubens Koloski; Chave, Jérome; Chidumayo, Emmanuel N.; Clark, Deborah A.; Costa, Flavia Regina Capellotto; Couralet, Camille; Silva Mauricio, Da Paulo Henrique; Dalitz, Helmut; Castro, De Vinicius Resende; Freitas Milani, De Jaçanan Eloisa; Oliveira, De Edilson Consuelo; Souza Arruda, De Luciano; Devineau, Jean-Louis; Drew, David M.; Dünisch, Oliver; Durigan, Giselda; Elifuraha, Elisha; Fedele, Marcio; Ferreira Fedele, Ligia; Figueiredo Filho, Afonso; Finger, César Augusto Guimarães; Franco, Augusto César; Freitas Júnior, João Lima; Galvão, Franklin; Gebrekirstos, Aster; Gliniars, Robert; Lima De Alencastro Graça, Paulo Maurício; Griffiths, Anthony D.; Grogan, James; Guan, Kaiyu; Homeier, Jürgen; Kanieski, Maria Raquel; Kho, Lip Khoon; Koenig, Jennifer; Kohler, Sintia Valerio; Krepkowski, Julia; Lemos-filho, José Pires; Lieberman, Diana; Lieberman, Milton Eugene; Lisi, Claudio Sergio; Longhi Santos, Tomaz; López Ayala, José Luis; Maeda, Eduardo Eijji; Malhi, Yadvinder; Maria, Vivian R.B.; Marques, Marcia C.M.; Marques, Renato; Maza Chamba, Hector; Mbwambo, Lawrence; Melgaço, Karina Liana Lisboa; Mendivelso, Hooz Angela; Murphy, Brett P.; O'Brien, Joseph J.; Oberbauer, Steven F.; Okada, Naoki; Pélissier, Raphaël; Prior, Lynda D.; Roig, Fidel Alejandro; Ross, Michael; Rossatto, Davi Rodrigo; Rossi, Vivien; Rowland, Lucy; Rutishauser, Ervan; Santana, Hellen; Schulze, Mark; Selhorst, Diogo; Silva, Williamar Rodrigues; Silveira, Marcos; Spannl, Susanne; Swaine, Michael D.; Toledo, José Julio; Toledo, Marcos Miranda; Toledo, Marisol; Toma, Takeshi; Tomazello Filho, Mario; Valdez Hernández, Juan Ignacio; Verbesselt, Jan; Vieira, Simone Aparecida; Vincent, Grégoire; Volkmer De Castilho, Carolina; Volland, Franziska; Worbes, Martin; Zanon, Magda Lea Bolzan; Aragão, Luiz E.O.C.

    2016-01-01

    The seasonal climate drivers of the carbon cycle in tropical forests remain poorly known, although these forests account for more carbon assimilation and storage than any other terrestrial ecosystem. Based on a unique combination of seasonal pan-tropical data sets from 89 experimental sites (68

  16. Role of the seasonal cycle in coupling climate and carbon cycling in subanartic zone

    CSIR Research Space (South Africa)

    Monteiro, PMS

    2010-08-01

    Full Text Available There is increasing evidence in the Southern Ocean that mesoscales and seasonal scales play an important role in the coupling of ocean carbon cycling and climate. The seasonal cycle is one of the strongest modes of variability in different...

  17. Remotely Sensed Northern Vegetation Response to Changing Climate: Growing Season and Productivity Perspective

    Science.gov (United States)

    Ganguly, S.; Park, Taejin; Choi, Sungho; Bi, Jian; Knyazikhin, Yuri; Myneni, Ranga

    2016-01-01

    Vegetation growing season and maximum photosynthetic state determine spatiotemporal variability of seasonal total gross primary productivity of vegetation. Recent warming induced impacts accelerate shifts on growing season and physiological status over Northern vegetated land. Thus, understanding and quantifying these changes are very important. Here, we first investigate how vegetation growing season and maximum photosynthesis state are evolved and how such components contribute on inter-annual variation of seasonal total gross primary productivity. Furthermore, seasonally different response of northern vegetation to changing temperature and water availability is also investigated. We utilized both long-term remotely sensed data to extract larger scale growing season metrics (growing season start, end and duration) and productivity (i.e., growing season summed vegetation index, GSSVI) for answering these questions. We find that regionally diverged growing season shift and maximum photosynthetic state contribute differently characterized productivity inter-annual variability and trend. Also seasonally different response of vegetation gives different view of spatially varying interaction between vegetation and climate. These results highlight spatially and temporally varying vegetation dynamics and are reflective of biome-specific responses of northern vegetation to changing climate.

  18. 4K Video Traffic Prediction using Seasonal Autoregressive Modeling

    Directory of Open Access Journals (Sweden)

    D. R. Marković

    2017-06-01

    Full Text Available From the perspective of average viewer, high definition video streams such as HD (High Definition and UHD (Ultra HD are increasing their internet presence year over year. This is not surprising, having in mind expansion of HD streaming services, such as YouTube, Netflix etc. Therefore, high definition video streams are starting to challenge network resource allocation with their bandwidth requirements and statistical characteristics. Need for analysis and modeling of this demanding video traffic has essential importance for better quality of service and experience support. In this paper we use an easy-to-apply statistical model for prediction of 4K video traffic. Namely, seasonal autoregressive modeling is applied in prediction of 4K video traffic, encoded with HEVC (High Efficiency Video Coding. Analysis and modeling were performed within R programming environment using over 17.000 high definition video frames. It is shown that the proposed methodology provides good accuracy in high definition video traffic modeling.

  19. Advance and prospectus of seasonal prediction: assessment of the APCC/CliPAS 14-model ensemble retrospective seasonal prediction (1980-2004)

    Science.gov (United States)

    Wang, Bin; Lee, June-Yi; Kang, In-Sik; Shukla, J.; Park, C.-K.; Kumar, A.; Schemm, J.; Cocke, S.; Kug, J.-S.; Luo, J.-J.; Zhou, T.; Wang, B.; Fu, X.; Yun, W.-T.; Alves, O.; Jin, E. K.; Kinter, J.; Kirtman, B.; Krishnamurti, T.; Lau, N. C.; Lau, W.; Liu, P.; Pegion, P.; Rosati, T.; Schubert, S.; Stern, W.; Suarez, M.; Yamagata, T.

    2009-07-01

    We assessed current status of multi-model ensemble (MME) deterministic and probabilistic seasonal prediction based on 25-year (1980-2004) retrospective forecasts performed by 14 climate model systems (7 one-tier and 7 two-tier systems) that participate in the Climate Prediction and its Application to Society (CliPAS) project sponsored by the Asian-Pacific Economic Cooperation Climate Center (APCC). We also evaluated seven DEMETER models’ MME for the period of 1981-2001 for comparison. Based on the assessment, future direction for improvement of seasonal prediction is discussed. We found that two measures of probabilistic forecast skill, the Brier Skill Score (BSS) and Area under the Relative Operating Characteristic curve (AROC), display similar spatial patterns as those represented by temporal correlation coefficient (TCC) score of deterministic MME forecast. A TCC score of 0.6 corresponds approximately to a BSS of 0.1 and an AROC of 0.7 and beyond these critical threshold values, they are almost linearly correlated. The MME method is demonstrated to be a valuable approach for reducing errors and quantifying forecast uncertainty due to model formulation. The MME prediction skill is substantially better than the averaged skill of all individual models. For instance, the TCC score of CliPAS one-tier MME forecast of Niño 3.4 index at a 6-month lead initiated from 1 May is 0.77, which is significantly higher than the corresponding averaged skill of seven individual coupled models (0.63). The MME made by using 14 coupled models from both DEMETER and CliPAS shows an even higher TCC score of 0.87. Effectiveness of MME depends on the averaged skill of individual models and their mutual independency. For probabilistic forecast the CliPAS MME gains considerable skill from increased forecast reliability as the number of model being used increases; the forecast resolution also increases for 2 m temperature but slightly decreases for precipitation. Equatorial Sea Surface

  20. Advance and prospectus of seasonal prediction: assessment of the APCC/CliPAS 14-model ensemble retrospective seasonal prediction (1980-2004)

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Bin; Lee, June-Yi; Fu, X.; Liu, P. [University of Hawaii, Department of Meteorology and International Pacific Research Center, IPRC, School of Ocean and Earth Science and Technology, Honolulu, HI (United States); Kang, In-Sik; Kug, J.S. [Seoul National University, School of Earth and Environmental Sciences, Seoul (Korea); Shukla, J.; Jin, E.K.; Kinter, J.; Kirtman, B. [George Mason University and COLA, Climate Dynamics Program, Calverton, MD (United States); Park, C.K. [APEC Climate Center, Busan (Korea); Kumar, A.; Schemm, J. [Climate Prediction Center/NCEP, Camp Springs, MD (United States); Cocke, S.; Krishnamurti, T. [Florida State University, Tallahassee, FL (United States); Luo, J.J. [Frontier Research Center for Global Chnage, Yokohama (Japan); Zhou, T.; Wang, B. [Chinese Academy of Sciences, LASG/Institute of Atmospheric Physics, Beijing (China); Yun, W.T. [Korean Meteorological Administration, Seoul (Korea); Alves, O. [Bureau of Meteorology Research Center, Melburne (Australia); Lau, N.C.; Rosati, T.; Stern, W. [Princeton University, Geophysical Fluid Dynamics Laboratory/NOAA, Princeton, NJ (United States); Lau, W.; Pegion, P.; Schubert, S.; Suarez, M. [Godard Space Flight Center/NASA, Greenbelt, MD (United States)

    2009-07-15

    We assessed current status of multi-model ensemble (MME) deterministic and probabilistic seasonal prediction based on 25-year (1980-2004) retrospective forecasts performed by 14 climate model systems (7 one-tier and 7 two-tier systems) that participate in the Climate Prediction and its Application to Society (CliPAS) project sponsored by the Asian-Pacific Economic Cooperation Climate Center (APCC). We also evaluated seven DEMETER models' MME for the period of 1981-2001 for comparison. Based on the assessment, future direction for improvement of seasonal prediction is discussed. We found that two measures of probabilistic forecast skill, the Brier Skill Score (BSS) and Area under the Relative Operating Characteristic curve (AROC), display similar spatial patterns as those represented by temporal correlation coefficient (TCC) score of deterministic MME forecast. A TCC score of 0.6 corresponds approximately to a BSS of 0.1 and an AROC of 0.7 and beyond these critical threshold values, they are almost linearly correlated. The MME method is demonstrated to be a valuable approach for reducing errors and quantifying forecast uncertainty due to model formulation. The MME prediction skill is substantially better than the averaged skill of all individual models. For instance, the TCC score of CliPAS one-tier MME forecast of Nino 3.4 index at a 6-month lead initiated from 1 May is 0.77, which is significantly higher than the corresponding averaged skill of seven individual coupled models (0.63). The MME made by using 14 coupled models from both DEMETER and CliPAS shows an even higher TCC score of 0.87. Effectiveness of MME depends on the averaged skill of individual models and their mutual independency. For probabilistic forecast the CliPAS MME gains considerable skill from increased forecast reliability as the number of model being used increases; the forecast resolution also increases for 2 m temperature but slightly decreases for precipitation. Equatorial Sea Surface

  1. Causes of skill in seasonal predictions of the Arctic Oscillation

    Science.gov (United States)

    Kumar, Arun; Chen, Mingyue

    2017-11-01

    Based on an analysis of hindcasts from a seasonal forecast system, complemented by the analysis of a large ensemble of AMIP simulations, possible causes for skillful prediction of the winter Arctic Oscillation (AO) on a seasonal time-scale are analyzed. The possibility that the recent increase in AO skill could be due to model improvements, or due to changes in the persistence characteristics of the AO, is first discounted. The analysis then focuses on exploring the possibility that the recent increase in prediction skill in AO may be due to sampling variations or could have physical causes. Temporal variations in AO skill due entirely to sampling alone cannot be discounted as this is a fundamental constraint on verifications over a short time-series. This notion is supported from theoretical considerations, and from the analysis of the temporal variations in the perfect model skill where substantial variations in skill due to sampling alone are documented. As for the physical causes, the analysis indicates possible links in the prediction skill of AO with the SST forcing from the tropics, particularly related to the SST variations associated with the Trans-Niño Index (TNI). Interannual and low frequency variations in the TNI could have contributed to similar temporal variations in AO skill. For example, a dominance of central Pacific El Niño events after 2000 (a reflection of low-frequency variations in TNI) coincided with an increase in the prediction skill of AO. The analysis approach and results provide an avenue for further investigations; for example, model simulations forced with the SST pattern associated with the TNI, to establish or reaffirm causes for AO skill.

  2. Climate variables as predictors for seasonal forecast of dengue occurrence in Chennai, Tamil Nadu

    Science.gov (United States)

    Subash Kumar, D. D.; Andimuthu, R.

    2013-12-01

    Background Dengue is a recently emerging vector borne diseases in Chennai. As per the WHO report in 2011 dengue is one of eight climate sensitive disease of this century. Objective Therefore an attempt has been made to explore the influence of climate parameters on dengue occurrence and use for forecasting. Methodology Time series analysis has been applied to predict the number of dengue cases in Chennai, a metropolitan city which is the capital of Tamil Nadu, India. Cross correlation of the climate variables with dengue cases revealed that the most influential parameters were monthly relative humidity, minimum temperature at 4 months lag and rainfall at one month lag (Table 1). However due to intercorrelation of relative humidity and rainfall was high and therefore for predictive purpose the rainfall at one month lag was used for the model development. Autoregressive Integrated Moving Average (ARIMA) models have been applied to forecast the occurrence of dengue. Results and Discussion The best fit model was ARIMA (1,0,1). It was seen that the monthly minimum temperature at four months lag (β= 3.612, p = 0.02) and rainfall at one month lag (β= 0.032, p = 0.017) were associated with dengue occurrence and they had a very significant effect. Mean Relative Humidity had a directly significant positive correlation at 99% confidence level, but the lagged effect was not prominent. The model predicted dengue cases showed significantly high correlation of 0.814(Figure 1) with the observed cases. The RMSE of the model was 18.564 and MAE was 12.114. The model is limited by the scarcity of the dataset. Inclusion of socioeconomic conditions and population offset are further needed to be incorporated for effective results. Conclusion Thus it could be claimed that the change in climatic parameters is definitely influential in increasing the number of dengue occurrence in Chennai. The climate variables therefore can be used for seasonal forecasting of dengue with rise in minimum

  3. Regional and climatic controls on seasonal dust deposition in the southwestern U.S.

    Science.gov (United States)

    Reheis, M.C.; Urban, F.E.

    2011-01-01

    Vertical dust deposition rates (dust flux) are a complex response to the interaction of seasonal precipitation, wind, changes in plant cover and land use, dust source type, and local vs. distant dust emission in the southwestern U.S. Seasonal dust flux in the Mojave-southern Great Basin (MSGB) deserts, measured from 1999 to 2008, is similar in summer-fall and winter-spring, and antecedent precipitation tends to suppress dust flux in winter-spring. In contrast, dust flux in the eastern Colorado Plateau (ECP) region is much larger in summer-fall than in winter-spring, and twice as large as in the MSGB. ECP dust is related to wind speed, and in the winter-spring to antecedent moisture. Higher summer dust flux in the ECP is likely due to gustier winds and runoff during monsoonal storms when temperature is also higher. Source types in the MSGB and land use in the ECP have important effects on seasonal dust flux. In the MSGB, wet playas produce salt-rich dust during wetter seasons, whereas antecedent and current moisture suppress dust emission from alluvial and dry-playa sources during winter-spring. In the ECP under drought conditions, dust flux at a grazed-and-plowed site increased greatly, and also increased at three annualized, previously grazed sites. Dust fluxes remained relatively consistent at ungrazed and currently grazed sites that have maintained perennial vegetation cover. Under predicted scenarios of future climate change, these results suggest that an increase in summer storms may increase dust flux in both areas, but resultant effects will depend on source type, land use, and vegetation cover. ?? 2011.

  4. Perceived and Actual Motivational Climate of a Mastery-Involving Sport Education Season

    Science.gov (United States)

    Hastie, Peter; Sinelnikov, Oleg; Wallhead, Tristan; Layne, Todd

    2014-01-01

    The purpose of the study was to implement a Sport Education season designed to be mastery-involving and examine the degree of congruence between the objective measure of the presented climate with the students' perceptions of the saliency of this motivational climate. Twenty-one male high school students (mean age of 15.9 years) and one expert…

  5. The role of climate and environmental variables in structuring bird assemblages in the Seasonally Dry Tropical Forests (SDTFs.

    Directory of Open Access Journals (Sweden)

    Gabriela Silva Ribeiro Gonçalves

    Full Text Available Understanding the processes that influence species diversity is still a challenge in ecological studies. However, there are two main theories to discuss this topic, the niche theory and the neutral theory. Our objective was to understand the importance of environmental and spatial processes in structuring bird communities within the hydrological seasons in dry forest areas in northeastern Brazil. The study was conducted in two National Parks, the Serra da Capivara and Serra das Confusões National Parks, where 36 areas were sampled in different seasons (dry, dry/rainy transition, rainy, rainy/dry transition, in 2012 and 2013. We found with our results that bird species richness is higher in the rainy season and lower during the dry season, indicating a strong influence of seasonality, a pattern also found for environmental heterogeneity. Richness was explained by local environmental factors, while species composition was explained by environmental and spatial factors. The environmental factors were more important in explaining variations in composition. Climate change predictions have currently pointed out frequent drought events and a rise in global temperature by 2050, which would lead to changes in species behavior and to increasing desertification in some regions, including the Caatinga. In addition, the high deforestation rates and the low level of representativeness of the Caatinga in the conservation units negatively affects bird communities. This scenario has demonstrated how climatic factors affect individuals, and, therefore, should be the starting point for conservation initiatives to be developed in xeric environments.

  6. The role of climate and environmental variables in structuring bird assemblages in the Seasonally Dry Tropical Forests (SDTFs).

    Science.gov (United States)

    Gonçalves, Gabriela Silva Ribeiro; Cerqueira, Pablo Vieira; Brasil, Leandro Schlemmer; Santos, Marcos Pérsio Dantas

    2017-01-01

    Understanding the processes that influence species diversity is still a challenge in ecological studies. However, there are two main theories to discuss this topic, the niche theory and the neutral theory. Our objective was to understand the importance of environmental and spatial processes in structuring bird communities within the hydrological seasons in dry forest areas in northeastern Brazil. The study was conducted in two National Parks, the Serra da Capivara and Serra das Confusões National Parks, where 36 areas were sampled in different seasons (dry, dry/rainy transition, rainy, rainy/dry transition), in 2012 and 2013. We found with our results that bird species richness is higher in the rainy season and lower during the dry season, indicating a strong influence of seasonality, a pattern also found for environmental heterogeneity. Richness was explained by local environmental factors, while species composition was explained by environmental and spatial factors. The environmental factors were more important in explaining variations in composition. Climate change predictions have currently pointed out frequent drought events and a rise in global temperature by 2050, which would lead to changes in species behavior and to increasing desertification in some regions, including the Caatinga. In addition, the high deforestation rates and the low level of representativeness of the Caatinga in the conservation units negatively affects bird communities. This scenario has demonstrated how climatic factors affect individuals, and, therefore, should be the starting point for conservation initiatives to be developed in xeric environments.

  7. Thermal and hydrologic responses to climate change predict marked alterations in boreal stream invertebrate assemblages.

    Science.gov (United States)

    Mustonen, Kaisa-Riikka; Mykrä, Heikki; Marttila, Hannu; Sarremejane, Romain; Veijalainen, Noora; Sippel, Kalle; Muotka, Timo; Hawkins, Charles P

    2018-06-01

    Air temperature at the northernmost latitudes is predicted to increase steeply and precipitation to become more variable by the end of the 21st century, resulting in altered thermal and hydrological regimes. We applied five climate scenarios to predict the future (2070-2100) benthic macroinvertebrate assemblages at 239 near-pristine sites across Finland (ca. 1200 km latitudinal span). We used a multitaxon distribution model with air temperature and modeled daily flow as predictors. As expected, projected air temperature increased the most in northernmost Finland. Predicted taxonomic richness also increased the most in northern Finland, congruent with the predicted northwards shift of many species' distributions. Compositional changes were predicted to be high even without changes in richness, suggesting that species replacement may be the main mechanism causing climate-induced changes in macroinvertebrate assemblages. Northern streams were predicted to lose much of the seasonality of their flow regimes, causing potentially marked changes in stream benthic assemblages. Sites with the highest loss of seasonality were predicted to support future assemblages that deviate most in compositional similarity from the present-day assemblages. Macroinvertebrate assemblages were also predicted to change more in headwaters than in larger streams, as headwaters were particularly sensitive to changes in flow patterns. Our results emphasize the importance of focusing protection and mitigation on headwater streams with high-flow seasonality because of their vulnerability to climate change. © 2018 John Wiley & Sons Ltd.

  8. Can decadal climate predictions be improved by ocean ensemble dispersion filtering?

    Science.gov (United States)

    Kadow, C.; Illing, S.; Kröner, I.; Ulbrich, U.; Cubasch, U.

    2017-12-01

    Decadal predictions by Earth system models aim to capture the state and phase of the climate several years inadvance. Atmosphere-ocean interaction plays an important role for such climate forecasts. While short-termweather forecasts represent an initial value problem and long-term climate projections represent a boundarycondition problem, the decadal climate prediction falls in-between these two time scales. The ocean memorydue to its heat capacity holds big potential skill on the decadal scale. In recent years, more precise initializationtechniques of coupled Earth system models (incl. atmosphere and ocean) have improved decadal predictions.Ensembles are another important aspect. Applying slightly perturbed predictions results in an ensemble. Insteadof using and evaluating one prediction, but the whole ensemble or its ensemble average, improves a predictionsystem. However, climate models in general start losing the initialized signal and its predictive skill from oneforecast year to the next. Here we show that the climate prediction skill of an Earth system model can be improvedby a shift of the ocean state toward the ensemble mean of its individual members at seasonal intervals. Wefound that this procedure, called ensemble dispersion filter, results in more accurate results than the standarddecadal prediction. Global mean and regional temperature, precipitation, and winter cyclone predictions showan increased skill up to 5 years ahead. Furthermore, the novel technique outperforms predictions with largerensembles and higher resolution. Our results demonstrate how decadal climate predictions benefit from oceanensemble dispersion filtering toward the ensemble mean. This study is part of MiKlip (fona-miklip.de) - a major project on decadal climate prediction in Germany.We focus on the Max-Planck-Institute Earth System Model using the low-resolution version (MPI-ESM-LR) andMiKlip's basic initialization strategy as in 2017 published decadal climate forecast: http

  9. MJO prediction skill of the subseasonal-to-seasonal (S2S) prediction models

    Science.gov (United States)

    Son, S. W.; Lim, Y.; Kim, D.

    2017-12-01

    The Madden-Julian Oscillation (MJO), the dominant mode of tropical intraseasonal variability, provides the primary source of tropical and extratropical predictability on subseasonal to seasonal timescales. To better understand its predictability, this study conducts quantitative evaluation of MJO prediction skill in the state-of-the-art operational models participating in the subseasonal-to-seasonal (S2S) prediction project. Based on bivariate correlation coefficient of 0.5, the S2S models exhibit MJO prediction skill ranging from 12 to 36 days. These prediction skills are affected by both the MJO amplitude and phase errors, the latter becoming more important with forecast lead times. Consistent with previous studies, the MJO events with stronger initial amplitude are typically better predicted. However, essentially no sensitivity to the initial MJO phase is observed. Overall MJO prediction skill and its inter-model spread are further related with the model mean biases in moisture fields and longwave cloud-radiation feedbacks. In most models, a dry bias quickly builds up in the deep tropics, especially across the Maritime Continent, weakening horizontal moisture gradient. This likely dampens the organization and propagation of MJO. Most S2S models also underestimate the longwave cloud-radiation feedbacks in the tropics, which may affect the maintenance of the MJO convective envelop. In general, the models with a smaller bias in horizontal moisture gradient and longwave cloud-radiation feedbacks show a higher MJO prediction skill, suggesting that improving those processes would enhance MJO prediction skill.

  10. Seasonal difference in brain serotonin transporter binding predicts symptom severity in patients with seasonal affective disorder

    DEFF Research Database (Denmark)

    Mc Mahon, Brenda; Andersen, Sofie B.; Madsen, Martin K.

    2016-01-01

    controls with low seasonality scores and 17 patients diagnosed with seasonal affective disorder were scanned in both summer and winter to investigate differences in cerebral serotonin transporter binding across groups and across seasons. The two groups had similar cerebral serotonin transporter binding...... between summer and winter (Psex-(P = 0.02) and genotype-(P = 0.04) dependent. In the patients with seasonal affective disorder, the seasonal change in serotonin transporter binding was positively associated with change in depressive symptom...

  11. Density and climate influence seasonal population dynamics in an Arctic ungulate

    DEFF Research Database (Denmark)

    Mortensen, Lars O.; Moshøj, Charlotte; Forchhammer, Mads C.

    2016-01-01

    The locally migratory behavior of the high arctic muskox (Ovibos muschatus) is a central component of the breeding and winter survival strategies applied to cope with the highly seasonal arctic climate. However, altered climate regimes affecting plant growth are likely to affect local migration...... cover), forage availability (length of growth season), and the number of adult females available per male (operational sex ratio) influence changes in the seasonal density dependence, abundance, and immigration rate of muskoxen into the valley. The results suggested summer temperature as the major...... controlling factor in the seasonal, local-scale migration of muskoxen at Zackenberg. Specifically, higher summer temperatures, defined as the cumulative average daily positive degrees in June, July, and August, resulted in decreased density dependence and, consequently, increase in the seasonal abundance...

  12. How seasonal forecast could help a decision maker: an example of climate service for water resource management

    Science.gov (United States)

    Viel, Christian; Beaulant, Anne-Lise; Soubeyroux, Jean-Michel; Céron, Jean-Pierre

    2016-04-01

    The FP7 project EUPORIAS was a great opportunity for the climate community to co-design with stakeholders some original and innovative climate services at seasonal time scales. In this framework, Météo-France proposed a prototype that aimed to provide to water resource managers some tailored information to better anticipate the coming season. It is based on a forecasting system, built on a refined hydrological suite, forced by a coupled seasonal forecast model. It particularly delivers probabilistic river flow prediction on river basins all over the French territory. This paper presents the work we have done with "EPTB Seine Grands Lacs" (EPTB SGL), an institutional stakeholder in charge of the management of 4 great reservoirs on the upper Seine Basin. First, we present the co-design phase, which means the translation of classical climate outputs into several indices, relevant to influence the stakeholder's decision making process (DMP). And second, we detail the evaluation of the impact of the forecast on the DMP. This evaluation is based on an experiment realised in collaboration with the stakeholder. Concretely EPTB SGL has replayed some past decisions, in three different contexts: without any forecast, with a forecast A and with a forecast B. One of forecast A and B really contained seasonal forecast, the other only contained random forecasts taken from past climate. This placebo experiment, realised in a blind test, allowed us to calculate promising skill scores of the DMP based on seasonal forecast in comparison to a classical approach based on climatology, and to EPTG SGL current practice.

  13. Indian Summer Monsoon Sub-seasonal Low-Level Circulation Predictability and its Association with Rainfall in a Coupled Model

    KAUST Repository

    Sagalgile, Archana P.

    2017-10-26

    This study investigates predictability of the sub-seasonal Indian summer monsoon (ISM) circulation and its relation with rainfall variations in the coupled model National Centers for Environmental Prediction (NCEP) Climate Forecast System version 2 (CFSv2). Hindcasts based on CFSv2 for the period of 1982–2009 are used for detailed analysis. Though the model is capable of predicting the seasonal ISM rainfall at long lead months, the predication skill of the model for sub-seasonal rainfall in general is poor for short and long lead except for September. Rainfall over the ISM region/Indian Subcontinent is highly correlated with the low-level jet (LLJ) or Somali jet both in the observations and the model. The model displays improved skill in predicting LLJ as compared to precipitation in seasonal mean and September, whereas the model skill is poor for June and August. Detailed analysis reveals that the model LLJ variations throughout the season are overdependent on the El Niño-Southern Oscillation (ENSO) unlike in the observations. This is mainly responsible for the model’s low skill in predicting LLJ especially in July and August, which is the primary cause for the poor rainfall skill. Though LLJ is weak in September, the model skill is reasonably good because of its ENSO dependency both in model and the observations and which is contributed to the seasonal mean skill. Thus, to improve the skill of seasonal mean monsoon forecast, it is essential to improve the skill of individual months/sub-seasonal circulation and rainfall skill.

  14. Indian Summer Monsoon Sub-seasonal Low-Level Circulation Predictability and its Association with Rainfall in a Coupled Model

    KAUST Repository

    Sagalgile, Archana P.; Chowdary, Jasti S.; Srinivas, G.; Gnanaseelan, C.; Parekh, Anant; Attada, Raju; Singh, Prem

    2017-01-01

    This study investigates predictability of the sub-seasonal Indian summer monsoon (ISM) circulation and its relation with rainfall variations in the coupled model National Centers for Environmental Prediction (NCEP) Climate Forecast System version 2 (CFSv2). Hindcasts based on CFSv2 for the period of 1982–2009 are used for detailed analysis. Though the model is capable of predicting the seasonal ISM rainfall at long lead months, the predication skill of the model for sub-seasonal rainfall in general is poor for short and long lead except for September. Rainfall over the ISM region/Indian Subcontinent is highly correlated with the low-level jet (LLJ) or Somali jet both in the observations and the model. The model displays improved skill in predicting LLJ as compared to precipitation in seasonal mean and September, whereas the model skill is poor for June and August. Detailed analysis reveals that the model LLJ variations throughout the season are overdependent on the El Niño-Southern Oscillation (ENSO) unlike in the observations. This is mainly responsible for the model’s low skill in predicting LLJ especially in July and August, which is the primary cause for the poor rainfall skill. Though LLJ is weak in September, the model skill is reasonably good because of its ENSO dependency both in model and the observations and which is contributed to the seasonal mean skill. Thus, to improve the skill of seasonal mean monsoon forecast, it is essential to improve the skill of individual months/sub-seasonal circulation and rainfall skill.

  15. ATHENS SEASONAL VARIATION OF GROUND RESISTANCE PREDICTION USING NEURAL NETWORKS

    Directory of Open Access Journals (Sweden)

    S. Anbazhagan

    2015-10-01

    Full Text Available The objective in ground resistance is to attain the most minimal ground safety esteem conceivable that bodes well monetarily and physically. An application of artificial neural networks (ANN to presage and relegation has been growing rapidly due to sundry unique characteristics of ANN models. A decent forecast is able to capture the dubiousness associated with those ground resistance. A portion of the key instabilities are soil composition, moisture content, temperature, ground electrodes and spacing of the electrodes. Propelled by this need, this paper endeavors to develop a generalized regression neural network (GRNN to predict the ground resistance. The GRNN has a single design parameter and expeditious learning and efficacious modeling for nonlinear time series. The precision of the forecast is applied to the Athens seasonal variation of ground resistance that shows the efficacy of the proposed approach.

  16. Soil Moisture and Sea Surface Temperatures equally important for Land Climate in the Warm Season

    Science.gov (United States)

    Orth, R.; Seneviratne, S. I.

    2015-12-01

    Both sea surface temperatures (SSTs) and soil moisture (SM) are important drivers of climate variability over land. In this study we present a comprehensive comparison of SM versus SST impacts on land climate in the warm season. We perform ensemble experiments with the Community Earth System Model (CESM) where we set SM or SSTs to median conditions, respectively, to remove their inter-annual variability, whereby the other component - SST or SM - is still interactively computed. In contrast to earlier experiments performed with prescribed SSTs, our experiments suggest that SM is overall as important as SSTs for land climate, not only in the midlatitudes but also in the tropics and subtropics. Mean temperature and precipitation are reduced by 0.1-0.5 K and 0-0.2 mm, respectively, whereas their variability at different time scales decreases by 10-40% (temperature) and 0-10% (precipitation) when either SM or SSTs are prescribed. Also drought occurrence is affected, with mean changes in the maximum number of cumulative dry days of 0-0.75 days. Both SM and SST-induced changes are strongest for hot temperatures (up to 0.7 K, and 50%), extreme precipitation (up to 0.4 mm, and 20%), and strong droughts (up to 2 days). Local climate changes in response to removed SM variability are controlled - to first order - by the land-atmosphere coupling and the natural SM variability. SST-related changes are partly controlled by the relation of local temperature or precipitation with the El Niño-Southern Oscillation. Moreover removed SM or SST variabilities both induce remote effects by impacting the atmospheric circulation. Our results are similar for the present day and the end of the century. We investigate the inter-dependency between SM and SST and find a sufficient degree of independence for the purpose of this study. The robustness of our findings is shown by comparing the response of CESM to removed SM variability with four other global climate models. In summary, SM and SSTs

  17. Understanding the predictability of seasonal precipitation over northeast Brazil

    Science.gov (United States)

    Misra, Vasubandhu

    2006-05-01

    Using multiple long-term simulations of the Center for Ocean-Land-Atmosphere Studies (COLA) atmospheric general circulation model (AGCM) forced with observed sea surface temperature (SST), it is shown that the model has high skill in simulating the February-March-April (FMA) rainy season over northeast Brazil (Nordeste). Separate sensitivity experiments conducted with the same model that entails suppression of all variability except for the climatological annual cycle in SST over the Pacific and Atlantic Oceans reveal that this skill over Nordeste is sensitive to SST anomalies in the tropical Atlantic Ocean. However, the spatial pattern of SST anomalies in the tropical Atlantic Ocean that correlate with FMA Nordeste rainfall are in fact a manifestation of El Niño Southern Oscillation (ENSO) phenomenon in the Pacific Ocean. This study also analyzes the failure of the COLA AGCM in capturing the correct FMA precipitation anomalies over Nordeste in several years of the simulation. It is found that this failure occurs when the SST anomalies over the northern tropical Atlantic Ocean are large and not significantly correlated with contemporaneous SST anomalies over the eastern Pacific Ocean. In two of the relatively large ENSO years when the model failed to capture the correct signal of the interannual variability of precipitation over Nordeste, it was found that the meridional gradient of SST anomalies over the tropical Atlantic Ocean was inconsistent with the canonical development of ENSO. The analysis of the probabilistic skill of the model revealed that it has more skill in predicting flood years than drought. Furthermore, the model has no skill in predicting normal seasons. These model features are consistent with the model systematic errors.

  18. Creating Dynamically Downscaled Seasonal Climate Forecast and Climate Change Projection Information for the North American Monsoon Region Suitable for Decision Making Purposes

    Science.gov (United States)

    Castro, C. L.; Dominguez, F.; Chang, H.

    2010-12-01

    Current seasonal climate forecasts and climate change projections of the North American monsoon are based on the use of course-scale information from a general circulation model. The global models, however, have substantial difficulty in resolving the regional scale forcing mechanisms of precipitation. This is especially true during the period of the North American Monsoon in the warm season. Precipitation is driven primarily due to the diurnal cycle of convection, and this process cannot be resolve in coarse-resolution global models that have a relatively poor representation of terrain. Though statistical downscaling may offer a relatively expedient method to generate information more appropriate for the regional scale, and is already being used in the resource decision making processes in the Southwest U.S., its main drawback is that it cannot account for a non-stationary climate. Here we demonstrate the use of a regional climate model, specifically the Weather Research and Forecast (WRF) model, for dynamical downscaling of the North American Monsoon. To drive the WRF simulations, we use retrospective reforecasts from the Climate Forecast System (CFS) model, the operational model used at the U.S. National Center for Environmental Prediction, and three select “well performing” IPCC AR 4 models for the A2 emission scenario. Though relatively computationally expensive, the use of WRF as a regional climate model in this way adds substantial value in the representation of the North American Monsoon. In both cases, the regional climate model captures a fairly realistic and reasonable monsoon, where none exists in the driving global model, and captures the dominant modes of precipitation anomalies associated with ENSO and the Pacific Decadal Oscillation (PDO). Long-term precipitation variability and trends in these simulations is considered via the standardized precipitation index (SPI), a commonly used metric to characterize long-term drought. Dynamically

  19. How climate seasonality modifies drought duration and deficit

    NARCIS (Netherlands)

    Loon, van A.F.; Tijdeman, E.; Wanders, N.; Lanen, van H.A.J.; Teuling, A.J.; Uijlenhoet, R.

    2014-01-01

    Drought propagation through the terrestrial hydrological cycle is associated with a change in drought characteristics (duration and deficit), moving from precipitation via soil moisture to discharge. Here we investigate climate controls on drought propagation with a modeling experiment in 1271

  20. The effect of seasonal changes and climatic factors on suicide attempts of young people.

    Science.gov (United States)

    Akkaya-Kalayci, Türkan; Vyssoki, Benjamin; Winkler, Dietmar; Willeit, Matthaeus; Kapusta, Nestor D; Dorffner, Georg; Özlü-Erkilic, Zeliha

    2017-11-15

    Seasonal changes and climatic factors like ambient temperature, sunlight duration and rainfall can influence suicidal behavior. This study analyses the relationship between seasonal changes and climatic variations and suicide attempts in 2131 young patients in Istanbul, Turkey. In our study sample, there was an association between suicide attempts in youths and seasonal changes, as suicide attempts occurred most frequently during summer in females as well as in males. Furthermore, there was a positive correlation between the mean temperature over the past 10 days and temperature at the index day and suicide attempts in females. After seasonality effects were mathematically removed, the mean temperature 10 days before a suicide attempt remained significant in males only, indicating a possible short-term influence of temperature on suicide attempts. This study shows an association between suicide attempts of young people and climatic changes, in particular temperature changes as well as seasonal changes. Therefore, the influence of seasonal changes and climatic factors on young suicide attempters should get more attention in research to understand the biopsychosocial mechanisms playing a role in suicide attempts of young people. As suicide attempts most frequently occur in young people, further research is of considerable clinical importance.

  1. Evaluation of climate model aerosol seasonal and spatial variability

    CSIR Research Space (South Africa)

    Horowitz, HM

    2017-11-01

    Full Text Available , regional circulation transports dust from deserts in Iraq and southern Iran during summer and a mixture of fine pollution aerosols from the Persian Gulf throughout the year (Eck et al., 2008; Basart et al., 2009). The Izaña site has a different seasonal...

  2. Climate-driven seasonal geocenter motion during the GRACE period

    NARCIS (Netherlands)

    Zhang, Hongyue; Sun, Y.

    2018-01-01

    Annual cycles in the geocenter motion time series are primarily driven by mass changes in the Earth’s hydrologic system, which includes land hydrology, atmosphere, and oceans. Seasonal variations of the geocenter motion have been reliably determined according to Sun et al. (J Geophys Res Solid

  3. The potential value of seasonal forecasts in a changing climate

    CSIR Research Space (South Africa)

    Winsemius, HC

    2013-12-01

    Full Text Available -range Weather Forecasts (ECMWF) seasonal forecasting system. The focus is on the frequency of dry spells as well as the frequency of heat stress conditions expressed in the Temperature Heat Index. In areas where their frequency of occurrence increases...

  4. Climate Prediction Center (CPC) Palmer Drought and Crop Moisture Indices

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Climate Prediction Center (CPC) Palmer Drought Severity and Crop Moisture Indices are computed for the 344 U.S. Climate Divisions on a weekly basis based on a...

  5. Identifying and Assessing Gaps in Subseasonal to Seasonal Prediction Skill using the North American Multi-model Ensemble

    Science.gov (United States)

    Pegion, K.; DelSole, T. M.; Becker, E.; Cicerone, T.

    2016-12-01

    Predictability represents the upper limit of prediction skill if we had an infinite member ensemble and a perfect model. It is an intrinsic limit of the climate system associated with the chaotic nature of the atmosphere. Producing a forecast system that can make predictions very near to this limit is the ultimate goal of forecast system development. Estimates of predictability together with calculations of current prediction skill are often used to define the gaps in our prediction capabilities on subseasonal to seasonal timescales and to inform the scientific issues that must be addressed to build the next forecast system. Quantification of the predictability is also important for providing a scientific basis for relaying to stakeholders what kind of climate information can be provided to inform decision-making and what kind of information is not possible given the intrinsic predictability of the climate system. One challenge with predictability estimates is that different prediction systems can give different estimates of the upper limit of skill. How do we know which estimate of predictability is most representative of the true predictability of the climate system? Previous studies have used the spread-error relationship and the autocorrelation to evaluate the fidelity of the signal and noise estimates. Using a multi-model ensemble prediction system, we can quantify whether these metrics accurately indicate an individual model's ability to properly estimate the signal, noise, and predictability. We use this information to identify the best estimates of predictability for 2-meter temperature, precipitation, and sea surface temperature from the North American Multi-model Ensemble and compare with current skill to indicate the regions with potential for improving skill.

  6. On climate prediction: how much can we expect from climate memory?

    Science.gov (United States)

    Yuan, Naiming; Huang, Yan; Duan, Jianping; Zhu, Congwen; Xoplaki, Elena; Luterbacher, Jürg

    2018-03-01

    Slowing variability in climate system is an important source of climate predictability. However, it is still challenging for current dynamical models to fully capture the variability as well as its impacts on future climate. In this study, instead of simulating the internal multi-scale oscillations in dynamical models, we discussed the effects of internal variability in terms of climate memory. By decomposing climate state x(t) at a certain time point t into memory part M(t) and non-memory part ɛ (t) , climate memory effects from the past 30 years on climate prediction are quantified. For variables with strong climate memory, high variance (over 20% ) in x(t) is explained by the memory part M(t), and the effects of climate memory are non-negligible for most climate variables, but the precipitation. Regarding of multi-steps climate prediction, a power law decay of the explained variance was found, indicating long-lasting climate memory effects. The explained variances by climate memory can remain to be higher than 10% for more than 10 time steps. Accordingly, past climate conditions can affect both short (monthly) and long-term (interannual, decadal, or even multidecadal) climate predictions. With the memory part M(t) precisely calculated from Fractional Integral Statistical Model, one only needs to focus on the non-memory part ɛ (t) , which is an important quantity that determines climate predictive skills.

  7. An prediction and explanation of 'climatic swing

    Science.gov (United States)

    Barkin, Yury

    2010-05-01

    Introduction. In works of the author [1, 2] the mechanism has been offered and the scenario of formation of congelations and warming of the Earth and their inversion and asymmetric displays in opposite hemispheres has been described. These planetary thermal processes are connected with gravitational forced oscillations of the core-mantle system of the Earth, controlling and directing submission of heat in the top layers of the mantle and on a surface of the Earth. It is shown, that action of this mechanism should observed in various time scales. In particular significant changes of a climate should occur to the thousand-year periods, with the periods in tens and hundred thousand years. Thus excitation of system the core-mantle is caused by planetary secular orbital perturbations and by perturbations of the Earth rotation which as is known are characterized by significant amplitudes. But also in a short time scale the climate variations with the interannual and decade periods also should be observed, how dynamic consequences of the swing of the core-mantle system of the Earth with the same periods [3]. The fundamental phenomenon of secular polar drift of the core relatively to the viscous-elastic and changeable mantle [4] in last years has obtained convincing confirmations various geosciences. Reliable an attribute of influence of oscillations of the core on a variation of natural processes is their property of inversion when, for example, activity of process accrues in northern hemisphere and decreases in a southern hemisphere. Such contrast secular changes in northern and southern (N/S) hemispheres have been predicted on the base of geodynamic model [1] and revealed according to observations: from gravimetry measurements of a gravity [5]; in determination of a secular trend of a sea level, as global, and in northern and southern hemispheres [6, 7]; in redistribution of air masses [6, 8]; in geodetic measurements of changes of average radiuses of northern and

  8. Quercus pollen season dynamics in the Iberian peninsula: response to meteorological parameters and possible consequences of climate change.

    Science.gov (United States)

    Garcia-Mozo, Herminia; Galan, Carmen; Jato, Victoria; Belmonte, Jordina; de la Guardia, Consuelo; Fernandez, Delia; Gutierrez, Montserrat; Aira, M; Roure, Joan; Ruiz, Luis; Trigo, Mar; Dominguez-Vilches, Eugenio

    2006-01-01

    The main characteristics of the Quercus pollination season were studied in 14 different localities of the Iberian Peninsula from 1992-2004. Results show that Quercus flowering season has tended to start earlier in recent years, probably due to the increased temperatures in the pre-flowering period, detected at study sites over the second half of the 20th century. A Growing Degree Days forecasting model was used, together with future meteorological data forecast using the Regional Climate Model developed by the Hadley Meteorological Centre, in order to determine the expected advance in the start of Quercus pollination in future years. At each study site, airborne pollen curves presented a similar pattern in all study years, with different peaks over the season attributable in many cases to the presence of several species. High pollen concentrations were recorded, particularly at Mediterranean sites. This study also proposes forecasting models to predict both daily pollen values and annual pollen emission. All models were externally validated using data for 2001 and 2004, with acceptable results. Finally, the impact of the highly-likely climate change on Iberian Quercus pollen concentration values was studied by applying RCM meteorological data for different future years, 2025, 2050, 2075 and 2099. Results indicate that under a doubled CO(2) scenario at the end of the 21st century Quercus pollination season could start on average one month earlier and airborne pollen concentrations will increase by 50 % with respect to current levels, with higher values in Mediterranean inland areas.

  9. Prediction of meningococcal meningitis epidemics in western Africa by using climate information

    Science.gov (United States)

    YAKA, D. P.; Sultan, B.; Tarbangdo, F.; Thiaw, W. M.

    2013-12-01

    The variations of certain climatic parameters and the degradation of ecosystems, can affect human's health by influencing the transmission, the spatiotemporal repartition and the intensity of infectious diseases. It is mainly the case of meningococcal meningitis (MCM) whose epidemics occur particularly in Sahelo-Soudanian climatic area of Western Africa under quite particular climatic conditions. Meningococcal Meningitis (MCM) is a contagious infection disease due to the bacteria Neisseria meningitis. MCM epidemics occur worldwide but the highest incidence is observed in the "meningitis belt" of sub-Saharan Africa, stretching from Senegal to Ethiopia. In spite of standards, strategies of prevention and control of MCS epidemic from World Health Organization (WHO) and States, African Sahelo-Soudanian countries remain frequently afflicted by disastrous epidemics. In fact, each year, during the dry season (February-April), 25 to 250 thousands of cases are observed. Children under 15 are particularly affected. Among favourable conditions for the resurgence and dispersion of the disease, climatic conditions may be important inducing seasonal fluctuations in disease incidence and contributing to explain the spatial pattern of the disease roughly circumscribed to the ecological Sahelo-Sudanian band. In this study, we tried to analyse the relationships between climatic factors, ecosystems degradation and MCM for a better understanding of MCM epidemic dynamic and their prediction. We have shown that MCM epidemics, whether at the regional, national or local level, occur in a specific period of the year, mainly from January to May characterised by a dry, hot and sandy weather. We have identified both in situ (meteorological synoptic stations) and satellitales climatic variables (NCEP reanalysis dataset) whose seasonal variability is dominating in MCM seasonal transmission. Statistical analysis have measured the links between seasonal variation of certain climatic parameters

  10. Seasonal dependence of the predictable low-level circulation patterns over the tropical Indo-Pacific domain

    Science.gov (United States)

    Zhang, Tuantuan; Huang, Bohua; Yang, Song; Laohalertchai, Charoon

    2018-06-01

    The seasonal dependence of the prediction skill of 850-hPa monthly zonal wind over the tropical Indo-Pacific domain is examined using the ensemble reforecasts for 1983-2010 from the National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis and Reforecast (CFSRR) project. According to a maximum signal-to-noise empirical orthogonal function analysis, the most predictable patterns of atmospheric low-level circulation are associated with the developing and maturing phases of El Niño-Southern Oscillation (ENSO). The CFSv2 is capable of predicting these ENSO-related patterns up to 9-months in advance for all months, except for May-June when the effect of the spring barrier is strong. The other predictable climate processes associated with the low-level atmospheric circulation are more seasonally dependent. For winter and spring, the second most predictable patterns are associated with the ENSO decaying phase. Within these seasons, the monthly evolution of the predictable patterns is characterized by a southward shift of westerly wind anomalies, generated by the interaction between the annual cycle and the ENSO signals (i.e., the combination-mode). In general, the CFSv2 hindcast well predicts these patterns at least 5 months in advance for spring, while shows much lower skills for winter months. In summer, the second predictable patterns are associated with the western North Pacific (WNP) monsoon (i.e., the WNP anticyclone/cyclone) in short leads while associated with ENSO in longer leads (after 4-month lead). The second predictable patterns in fall are mainly associated with tropical Indian Ocean Dipole, which can be predicted 3 months in advance.

  11. Macroweather Predictions and Climate Projections using Scaling and Historical Observations

    Science.gov (United States)

    Hébert, R.; Lovejoy, S.; Del Rio Amador, L.

    2017-12-01

    There are two fundamental time scales that are pertinent to decadal forecasts and multidecadal projections. The first is the lifetime of planetary scale structures, about 10 days (equal to the deterministic predictability limit), and the second is - in the anthropocene - the scale at which the forced anthropogenic variability exceeds the internal variability (around 16 - 18 years). These two time scales define three regimes of variability: weather, macroweather and climate that are respectively characterized by increasing, decreasing and then increasing varibility with scale.We discuss how macroweather temperature variability can be skilfully predicted to its theoretical stochastic predictability limits by exploiting its long-range memory with the Stochastic Seasonal and Interannual Prediction System (StocSIPS). At multi-decadal timescales, the temperature response to forcing is approximately linear and this can be exploited to make projections with a Green's function, or Climate Response Function (CRF). To make the problem tractable, we exploit the temporal scaling symmetry and restrict our attention to global mean forcing and temperature response using a scaling CRF characterized by the scaling exponent H and an inner scale of linearity τ. An aerosol linear scaling factor α and a non-linear volcanic damping exponent ν were introduced to account for the large uncertainty in these forcings. We estimate the model and forcing parameters by Bayesian inference using historical data and these allow us to analytically calculate a median (and likely 66% range) for the transient climate response, and for the equilibrium climate sensitivity: 1.6K ([1.5,1.8]K) and 2.4K ([1.9,3.4]K) respectively. Aerosol forcing typically has large uncertainty and we find a modern (2005) forcing very likely range (90%) of [-1.0, -0.3] Wm-2 with median at -0.7 Wm-2. Projecting to 2100, we find that to keep the warming below 1.5 K, future emissions must undergo cuts similar to Representative

  12. Seasonal cycle of Martian climate : Experimental data and numerical simulation

    NARCIS (Netherlands)

    Rodin, A. V.; Willson, R. J.

    2006-01-01

    The most adequate theoretical method of investigating the present-day Martian climate is numerical simulation based on a model of general circulation of the atmosphere. First and foremost, such models encounter the greatest difficulties in description of aerosols and clouds, which in turn

  13. Do planetary seasons play a role in attaining stable climates?

    DEFF Research Database (Denmark)

    Olsen, Kasper Wibeck; Bohr, Jakob

    2018-01-01

    A simple phenomenological account for planetary climate instabilities is presented. The description is based on the standard model where the balance of incoming stellar radiation and outward thermal radiation is described by the effective planet temperature. Often, it is found to have three diffe...

  14. Diagnosing observed characteristics of the wet season across Africa to identify deficiencies in climate model simulations

    Science.gov (United States)

    Dunning, C.; Black, E.; Allan, R. P.

    2017-12-01

    The seasonality of rainfall over Africa plays a key role in determining socio-economic impacts for agricultural stakeholders, influences energy supply from hydropower, affects the length of the malaria transmission season and impacts surface water supplies. Hence, failure or delays of these rains can lead to significant socio-economic impacts. Diagnosing and interpreting interannual variability and long-term trends in seasonality, and analysing the physical driving mechanisms, requires a robust definition of African precipitation seasonality, applicable to both observational datasets and model simulations. Here we present a methodology for objectively determining the onset and cessation of multiple wet seasons across the whole of Africa. Compatibility with known physical drivers of African rainfall, consistency with indigenous methods, and generally strong agreement between satellite-based rainfall data sets confirm that the method is capturing the correct seasonal progression of African rainfall. Application of this method to observational datasets reveals that over East Africa cessation of the short rains is 5 days earlier in La Nina years, and the failure of the rains and subsequent humanitarian disaster is associated with shorter as well as weaker rainy seasons over this region. The method is used to examine the representation of the seasonality of African precipitation in CMIP5 model simulations. Overall, atmosphere-only and fully coupled CMIP5 historical simulations represent essential aspects of the seasonal cycle; patterns of seasonal progression of the rainy season are captured, for the most part mean model onset/ cessation dates agree with mean observational dates to within 18 days. However, unlike the atmosphere-only simulations, the coupled simulations do not capture the biannual regime over the southern West African coastline, linked to errors in Gulf of Guinea Sea Surface Temperature. Application to both observational and climate model datasets, and

  15. Predicting the Response of Electricity Load to Climate Change

    Energy Technology Data Exchange (ETDEWEB)

    Sullivan, Patrick [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Colman, Jesse [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Kalendra, Eric [National Renewable Energy Laboratory (NREL), Golden, CO (United States)

    2015-07-28

    Our purpose is to develop a methodology to quantify the impact of climate change on electric loads in the United States. We perform simple linear regression, assisted by geospatial smoothing, on paired temperature and load time-series to estimate the heating- and coolinginduced sensitivity to temperature across 300 transmission zones and 16 seasonal and diurnal time periods. The estimated load sensitivities can be coupled with climate scenarios to quantify the potential impact of climate change on load, with a primary application being long-term electricity scenarios. The method allows regional and seasonal differences in climate and load response to be reflected in the electricity scenarios. While the immediate product of this analysis was designed to mesh with the spatial and temporal resolution of a specific electricity model to enable climate change scenarios and analysis with that model, we also propose that the process could be applied for other models and purposes.

  16. Climate model biases in seasonality of continental water storage revealed by satellite gravimetry

    Science.gov (United States)

    Swenson, Sean; Milly, P.C.D.

    2006-01-01

    Satellite gravimetric observations of monthly changes in continental water storage are compared with outputs from five climate models. All models qualitatively reproduce the global pattern of annual storage amplitude, and the seasonal cycle of global average storage is reproduced well, consistent with earlier studies. However, global average agreements mask systematic model biases in low latitudes. Seasonal extrema of low‐latitude, hemispheric storage generally occur too early in the models, and model‐specific errors in amplitude of the low‐latitude annual variations are substantial. These errors are potentially explicable in terms of neglected or suboptimally parameterized water stores in the land models and precipitation biases in the climate models.

  17. Atmospheric modelling and prediction at time scales from days to seasons

    CSIR Research Space (South Africa)

    Landman, WA

    2010-09-01

    Full Text Available to seasonal forecasts, and produce multi-decadal climate change projections. This paper focuses on the shorter time-range from days to seasons. The conformal-cubic atmospheric model (CCAM) is an atmospheric global circulation model (AGCM) that can operate...

  18. Seasonal prediction of lightning activity in North Western Venezuela: Large-scale versus local drivers

    Science.gov (United States)

    Muñoz, Á. G.; Díaz-Lobatón, J.; Chourio, X.; Stock, M. J.

    2016-05-01

    The Lake Maracaibo Basin in North Western Venezuela has the highest annual lightning rate of any place in the world (~ 200 fl km- 2 yr- 1), whose electrical discharges occasionally impact human and animal lives (e.g., cattle) and frequently affect economic activities like oil and natural gas exploitation. Lightning activity is so common in this region that it has a proper name: Catatumbo Lightning (plural). Although short-term lightning forecasts are now common in different parts of the world, to the best of the authors' knowledge, seasonal prediction of lightning activity is still non-existent. This research discusses the relative role of both large-scale and local climate drivers as modulators of lightning activity in the region, and presents a formal predictability study at seasonal scale. Analysis of the Catatumbo Lightning Regional Mode, defined in terms of the second Empirical Orthogonal Function of monthly Lightning Imaging Sensor (LIS-TRMM) and Optical Transient Detector (OTD) satellite data for North Western South America, permits the identification of potential predictors at seasonal scale via a Canonical Correlation Analysis. Lightning activity in North Western Venezuela responds to well defined sea-surface temperature patterns (e.g., El Niño-Southern Oscillation, Atlantic Meridional Mode) and changes in the low-level meridional wind field that are associated with the Inter-Tropical Convergence Zone migrations, the Caribbean Low Level Jet and tropical cyclone activity, but it is also linked to local drivers like convection triggered by the topographic configuration and the effect of the Maracaibo Basin Nocturnal Low Level Jet. The analysis indicates that at seasonal scale the relative contribution of the large-scale drivers is more important than the local (basin-wide) ones, due to the synoptic control imposed by the former. Furthermore, meridional CAPE transport at 925 mb is identified as the best potential predictor for lightning activity in the Lake

  19. Impact of climate seasonality on catchment yield: A parameterization for commonly-used water balance formulas

    Science.gov (United States)

    de Lavenne, Alban; Andréassian, Vazken

    2018-03-01

    This paper examines the hydrological impact of the seasonality of precipitation and maximum evaporation: seasonality is, after aridity, a second-order determinant of catchment water yield. Based on a data set of 171 French catchments (where aridity ranged between 0.2 and 1.2), we present a parameterization of three commonly-used water balance formulas (namely, Turc-Mezentsev, Tixeront-Fu and Oldekop formulas) to account for seasonality effects. We quantify the improvement of seasonality-based parameterization in terms of the reconstitution of both catchment streamflow and water yield. The significant improvement obtained (reduction of RMSE between 9 and 14% depending on the formula) demonstrates the importance of climate seasonality in the determination of long-term catchment water balance.

  20. Seasonality of livebirths and climatic factors in Italian regions (1863-1933

    Directory of Open Access Journals (Sweden)

    Gabriele Ruiu

    2017-07-01

    Full Text Available Birth seasonality is a phenomenon that characterizes almost all the populations of the world. In spite of this, the causes underlying these seasonal fluctuations represent an as yet unsolved puzzle. Two main theoretical approaches have been proposed to explain birth seasonality. The first encompasses a social explanation and emphasizes the role of social, economic and cultural factors in determining the optimal moment (from a social perspective for conception (e.g., according to the cycle of agricultural workload, religious festivity, marriage seasonality, etc.. The second theoretical approach encompasses an environmental explanation and focuses on the role that climatic factors (e.g., temperature, rainfall, light intensity, etc. play in determining the optimal moment of conception from a biological perspective. Our paper may be collocated in the latter strand of the literature. The aim is to investigate the effects of temperature on conceptions, and subsequently on the seasonality of livebirths, while controlling for a possible social confounding effect, i.e. the seasonal pattern of marriage. To achieve this end, we empirically investigate the role of temperature as well as that of marriage seasonality in Italian regions for the period stretching from the Italian unification to the eve of World War II. We find that extreme temperatures (both cold and hot negatively affect the number of births. At the same time, marriage seasonality also seems to be an important explicative factor of the seasonal fluctuation of live births.

  1. Impacts of Climate Change on the Timing of the Production Season of Maple Syrup in Eastern Canada.

    Science.gov (United States)

    Houle, Daniel; Paquette, Alain; Côté, Benoît; Logan, Travis; Power, Hugues; Charron, Isabelle; Duchesne, Louis

    2015-01-01

    Maple syrup production is an important economic activity in north-eastern North-America. The beginning and length of the production season is linked to daily variation in temperature. There are increasing concerns about the potential impact of climatic change on this industry. Here, we used weekly data of syrup yield for the 1999-2011 period from 121 maple stands in 11 regions of Québec (Canada) to predict how the period of production may be impacted by climate warming. The date at which the production begins is highly variable between years with an average range of 36 days among the regions. However, the average start date for a given region, which ranged from Julian day 65 to 83, was highly predictable (r2 = 0.88) using the average temperature from January to April (TJ-A). A logistic model predicting the weekly presence or absence of production was also developed. Using the inputs of 77 future climate scenarios issued from global models, projections of future production timing were made based on average TJ-A and on the logistic model. The projections of both approaches were in very good agreement and suggest that the sap season will be displaced to occur 15-19 days earlier on average in the 2080-2100 period. The data also show that the displacement in time will not be accompanied by a greater between years variability in the beginning of the season. However, in the southern part of Québec, very short periods of syrup production due to unfavourable conditions in the spring will occur more frequently in the future although their absolute frequencies will remain low.

  2. Impacts of Climate Change on the Timing of the Production Season of Maple Syrup in Eastern Canada.

    Directory of Open Access Journals (Sweden)

    Daniel Houle

    Full Text Available Maple syrup production is an important economic activity in north-eastern North-America. The beginning and length of the production season is linked to daily variation in temperature. There are increasing concerns about the potential impact of climatic change on this industry. Here, we used weekly data of syrup yield for the 1999-2011 period from 121 maple stands in 11 regions of Québec (Canada to predict how the period of production may be impacted by climate warming. The date at which the production begins is highly variable between years with an average range of 36 days among the regions. However, the average start date for a given region, which ranged from Julian day 65 to 83, was highly predictable (r2 = 0.88 using the average temperature from January to April (TJ-A. A logistic model predicting the weekly presence or absence of production was also developed. Using the inputs of 77 future climate scenarios issued from global models, projections of future production timing were made based on average TJ-A and on the logistic model. The projections of both approaches were in very good agreement and suggest that the sap season will be displaced to occur 15-19 days earlier on average in the 2080-2100 period. The data also show that the displacement in time will not be accompanied by a greater between years variability in the beginning of the season. However, in the southern part of Québec, very short periods of syrup production due to unfavourable conditions in the spring will occur more frequently in the future although their absolute frequencies will remain low.

  3. Impacts of Climate Change on the Timing of the Production Season of Maple Syrup in Eastern Canada

    Science.gov (United States)

    Côté, Benoît; Logan, Travis; Power, Hugues; Charron, Isabelle; Duchesne, Louis

    2015-01-01

    Maple syrup production is an important economic activity in north-eastern North-America. The beginning and length of the production season is linked to daily variation in temperature. There are increasing concerns about the potential impact of climatic change on this industry. Here, we used weekly data of syrup yield for the 1999–2011 period from 121 maple stands in 11 regions of Québec (Canada) to predict how the period of production may be impacted by climate warming. The date at which the production begins is highly variable between years with an average range of 36 days among the regions. However, the average start date for a given region, which ranged from Julian day 65 to 83, was highly predictable (r2 = 0.88) using the average temperature from January to April (TJ-A). A logistic model predicting the weekly presence or absence of production was also developed. Using the inputs of 77 future climate scenarios issued from global models, projections of future production timing were made based on average TJ-A and on the logistic model. The projections of both approaches were in very good agreement and suggest that the sap season will be displaced to occur 15–19 days earlier on average in the 2080–2100 period. The data also show that the displacement in time will not be accompanied by a greater between years variability in the beginning of the season. However, in the southern part of Québec, very short periods of syrup production due to unfavourable conditions in the spring will occur more frequently in the future although their absolute frequencies will remain low. PMID:26682889

  4. Statistical prediction of seasonal discharge in the Naryn basin for water resources planning in Central Asia

    Science.gov (United States)

    Apel, Heiko; Gafurov, Abror; Gerlitz, Lars; Unger-Shayesteh, Katy; Vorogushyn, Sergiy; Merkushkin, Aleksandr; Merz, Bruno

    2016-04-01

    The semi-arid regions of Central Asia crucially depend on the water resources supplied by the mountainous areas of the Tien-Shan and Pamirs. During the summer months the snow and glacier melt water of the rivers originating in the mountains provides the only water resource available for agricultural production but also for water collection in reservoirs for energy production in winter months. Thus a reliable seasonal forecast of the water resources is crucial for a sustainable management and planning of water resources.. In fact, seasonal forecasts are mandatory tasks of national hydro-meteorological services in the region. Thus this study aims at a statistical forecast of the seasonal water availability, whereas the focus is put on the usage of freely available data in order to facilitate an operational use without data access limitations. The study takes the Naryn basin as a test case, at which outlet the Toktogul reservoir stores the discharge of the Naryn River. As most of the water originates form snow and glacier melt, a statistical forecast model should use data sets that can serve as proxy data for the snow masses and snow water equivalent in late spring, which essentially determines the bulk of the seasonal discharge. CRU climate data describing the precipitation and temperature in the basin during winter and spring was used as base information, which was complemented by MODIS snow cover data processed through ModSnow tool, discharge during the spring and also GRACE gravimetry anomalies. For the construction of linear forecast models monthly as well as multi-monthly means over the period January to April were used to predict the seasonal mean discharge of May-September at the station Uchterek. An automatic model selection was performed in multiple steps, whereas the best models were selected according to several performance measures and their robustness in a leave-one-out cross validation. It could be shown that the seasonal discharge can be predicted with

  5. Real-time prediction of rain-triggered lahars: incorporating seasonality and catchment recovery

    Science.gov (United States)

    Jones, Robbie; Manville, Vern; Peakall, Jeff; Froude, Melanie J.; Odbert, Henry M.

    2017-12-01

    Rain-triggered lahars are a significant secondary hydrological and geomorphic hazard at volcanoes where unconsolidated pyroclastic material produced by explosive eruptions is exposed to intense rainfall, often occurring for years to decades after the initial eruptive activity. Previous studies have shown that secondary lahar initiation is a function of rainfall parameters, source material characteristics and time since eruptive activity. In this study, probabilistic rain-triggered lahar forecasting models are developed using the lahar occurrence and rainfall record of the Belham River valley at the Soufrière Hills volcano (SHV), Montserrat, collected between April 2010 and April 2012. In addition to the use of peak rainfall intensity (PRI) as a base forecasting parameter, considerations for the effects of rainfall seasonality and catchment evolution upon the initiation of rain-triggered lahars and the predictability of lahar generation are also incorporated into these models. Lahar probability increases with peak 1 h rainfall intensity throughout the 2-year dataset and is higher under given rainfall conditions in year 1 than year 2. The probability of lahars is also enhanced during the wet season, when large-scale synoptic weather systems (including tropical cyclones) are more common and antecedent rainfall and thus levels of deposit saturation are typically increased. The incorporation of antecedent conditions and catchment evolution into logistic-regression-based rain-triggered lahar probability estimation models is shown to enhance model performance and displays the potential for successful real-time prediction of lahars, even in areas featuring strongly seasonal climates and temporal catchment recovery.

  6. Real-time prediction of rain-triggered lahars: incorporating seasonality and catchment recovery

    Directory of Open Access Journals (Sweden)

    R. Jones

    2017-12-01

    Full Text Available Rain-triggered lahars are a significant secondary hydrological and geomorphic hazard at volcanoes where unconsolidated pyroclastic material produced by explosive eruptions is exposed to intense rainfall, often occurring for years to decades after the initial eruptive activity. Previous studies have shown that secondary lahar initiation is a function of rainfall parameters, source material characteristics and time since eruptive activity. In this study, probabilistic rain-triggered lahar forecasting models are developed using the lahar occurrence and rainfall record of the Belham River valley at the Soufrière Hills volcano (SHV, Montserrat, collected between April 2010 and April 2012. In addition to the use of peak rainfall intensity (PRI as a base forecasting parameter, considerations for the effects of rainfall seasonality and catchment evolution upon the initiation of rain-triggered lahars and the predictability of lahar generation are also incorporated into these models. Lahar probability increases with peak 1 h rainfall intensity throughout the 2-year dataset and is higher under given rainfall conditions in year 1 than year 2. The probability of lahars is also enhanced during the wet season, when large-scale synoptic weather systems (including tropical cyclones are more common and antecedent rainfall and thus levels of deposit saturation are typically increased. The incorporation of antecedent conditions and catchment evolution into logistic-regression-based rain-triggered lahar probability estimation models is shown to enhance model performance and displays the potential for successful real-time prediction of lahars, even in areas featuring strongly seasonal climates and temporal catchment recovery.

  7. Direct observations of ice seasonality reveal changes in climate over the past 320–570 years

    Science.gov (United States)

    Sharma, Sapna; Magnuson, John J.; Batt, Ryan D.; Winslow, Luke; Korhonen, Johanna; Yasuyuki Aono,

    2016-01-01

    Lake and river ice seasonality (dates of ice freeze and breakup) responds sensitively to climatic change and variability. We analyzed climate-related changes using direct human observations of ice freeze dates (1443–2014) for Lake Suwa, Japan, and of ice breakup dates (1693–2013) for Torne River, Finland. We found a rich array of changes in ice seasonality of two inland waters from geographically distant regions: namely a shift towards later ice formation for Suwa and earlier spring melt for Torne, increasing frequencies of years with warm extremes, changing inter-annual variability, waning of dominant inter-decadal quasi-periodic dynamics, and stronger correlations of ice seasonality with atmospheric CO2 concentration and air temperature after the start of the Industrial Revolution. Although local factors, including human population growth, land use change, and water management influence Suwa and Torne, the general patterns of ice seasonality are similar for both systems, suggesting that global processes including climate change and variability are driving the long-term changes in ice seasonality.

  8. Developing the Capacity of Farmers to Understand and Apply Seasonal Climate Forecasts through Collaborative Learning Processes

    Science.gov (United States)

    Cliffe, Neil; Stone, Roger; Coutts, Jeff; Reardon-Smith, Kathryn; Mushtaq, Shahbaz

    2016-01-01

    Purpose: This paper documents and evaluates collaborative learning processes aimed at developing farmer's knowledge, skills and aspirations to use seasonal climate forecasting (SCF). Methodology: Thirteen workshops conducted in 2012 engaged over 200 stakeholders across Australian sugar production regions. Workshop design promoted participant…

  9. Impacts of climate change on the seasonality of low flows in 134 catchments in the river Rhine basin using an ensemble of bias-corrected regional climate simulations.

    NARCIS (Netherlands)

    Demirel, M.C.; Booij, Martijn J.; Hoekstra, Arjen Ysbert

    2013-01-01

    The impacts of climate change on the seasonality of low flows were analysed for 134 sub-catchments covering the River Rhine basin upstream of the Dutch-German border. Three seasonality indices for low flows were estimated, namely the seasonality ratio (SR), weighted mean occurrence day (WMOD) and

  10. Enhanced seasonal predictability of the summer mean temperature in Central Europe favored by new dominant weather patterns

    Science.gov (United States)

    Hoffmann, P.

    2018-04-01

    In this study two complementary approaches have been combined to estimate the reliability of the data-driven seasonal predictability of the meteorological summer mean temperature (T_{JJA}) over Europe. The developed model is based on linear regressions and uses early season predictors to estimate the target value T_{JJA}. We found for the Potsdam (Germany) climate station that the monthly standard deviations (σ) from January to April and the temperature mean ( m) in April are good predictors to describe T_{JJA} after 1990. However, before 1990 the model failed. The core region where this model works is the north-eastern part of Central Europe. We also analyzed long-term trends of monthly Hess/Brezowsky weather types as possible causes of the dynamical changes. In spring, a significant increase of the occurrences for two opposite weather patterns was found: Zonal Ridge across Central Europe (BM) and Trough over Central Europe (TRM). Both currently make up about 30% of the total alternating weather systems over Europe. Other weather types are predominantly decreasing or their trends are not significant. Thus, the predictability may be attributed to these two weather types where the difference between the two Z500 composite patterns is large. This also applies to the north-eastern part of Central Europe. Finally, the detected enhanced seasonal predictability over Europe is alarming, because severe side effects may occur. One of these are more frequent climate extremes in summer half-year.

  11. Strongly seasonal Proterozoic glacial climate in low palaeolatitudes: Radically different climate system on the pre-Ediacaran Earth

    Directory of Open Access Journals (Sweden)

    George E. Williams

    2016-07-01

    Full Text Available Proterozoic (pre-Ediacaran glaciations occurred under strongly seasonal climates near sea level in low palaeolatitudes. Metre-scale primary sand wedges in Cryogenian periglacial deposits are identical to those actively forming, through the infilling of seasonal (winter thermal contraction-cracks in permafrost by windblown sand, in present-day polar regions with a mean monthly air temperature range of 40 °C and mean annual air temperatures of −20 °C or lower. Varve-like rhythmites with dropstones in Proterozoic glacial successions are consistent with an active seasonal freeze–thaw cycle. The seasonal (annual oscillation of sea level recorded by tidal rhythmites in Cryogenian glacial successions indicates a significant seasonal cycle and extensive open seas. Palaeomagnetic data determined directly for Proterozoic glacial deposits and closely associated rocks indicate low palaeolatitudes: Cryogenian deposits in South Australia accumulated at ≤10°, most other Cryogenian deposits at 54° during Proterozoic low-latitude glaciations, whereby the equator would be cooler than the poles, on average, and global seasonality would be greatly amplified.

  12. Seasonal prediction of East Asian summer rainfall using a multi-model ensemble system

    Science.gov (United States)

    Ahn, Joong-Bae; Lee, Doo-Young; Yoo, Jin‑Ho

    2015-04-01

    Using the retrospective forecasts of seven state-of-the-art coupled models and their multi-model ensemble (MME) for boreal summers, the prediction skills of climate models in the western tropical Pacific (WTP) and East Asian region are assessed. The prediction of summer rainfall anomalies in East Asia is difficult, while the WTP has a strong correlation between model prediction and observation. We focus on developing a new approach to further enhance the seasonal prediction skill for summer rainfall in East Asia and investigate the influence of convective activity in the WTP on East Asian summer rainfall. By analyzing the characteristics of the WTP convection, two distinct patterns associated with El Niño-Southern Oscillation developing and decaying modes are identified. Based on the multiple linear regression method, the East Asia Rainfall Index (EARI) is developed by using the interannual variability of the normalized Maritime continent-WTP Indices (MPIs), as potentially useful predictors for rainfall prediction over East Asia, obtained from the above two main patterns. For East Asian summer rainfall, the EARI has superior performance to the East Asia summer monsoon index or each MPI. Therefore, the regressed rainfall from EARI also shows a strong relationship with the observed East Asian summer rainfall pattern. In addition, we evaluate the prediction skill of the East Asia reconstructed rainfall obtained by hybrid dynamical-statistical approach using the cross-validated EARI from the individual models and their MME. The results show that the rainfalls reconstructed from simulations capture the general features of observed precipitation in East Asia quite well. This study convincingly demonstrates that rainfall prediction skill is considerably improved by using a hybrid dynamical-statistical approach compared to the dynamical forecast alone. Acknowledgements This work was carried out with the support of Rural Development Administration Cooperative Research

  13. Future climate change enhances rainfall seasonality in a regional model of western Maritime Continent

    Science.gov (United States)

    Kang, Suchul; Im, Eun-Soon; Eltahir, Elfatih A. B.

    2018-03-01

    In this study, future changes in rainfall due to global climate change are investigated over the western Maritime Continent based on dynamically downscaled climate projections using the MIT Regional Climate Model (MRCM) with 12 km horizontal resolution. A total of nine 30-year regional climate projections driven by multi-GCMs projections (CCSM4, MPI-ESM-MR and ACCESS1.0) under multi-scenarios of greenhouse gases emissions (Historical: 1976-2005, RCP4.5 and RCP8.5: 2071-2100) from phase 5 of the Coupled Model Inter-comparison Project (CMIP5) are analyzed. Focusing on dynamically downscaled rainfall fields, the associated systematic biases originating from GCM and MRCM are removed based on observations using Parametric Quantile Mapping method in order to enhance the reliability of future projections. The MRCM simulations with bias correction capture the spatial patterns of seasonal rainfall as well as the frequency distribution of daily rainfall. Based on projected rainfall changes under both RCP4.5 and RCP8.5 scenarios, the ensemble of MRCM simulations project a significant decrease in rainfall over the western Maritime Continent during the inter-monsoon periods while the change in rainfall is not relevant during wet season. The main mechanism behind the simulated decrease in rainfall is rooted in asymmetries of the projected changes in seasonal dynamics of the meridional circulation along different latitudes. The sinking motion, which is marginally positioned in the reference simulation, is enhanced and expanded under global climate change, particularly in RCP8.5 scenario during boreal fall season. The projected enhancement of rainfall seasonality over the western Maritime Continent suggests increased risk of water stress for natural ecosystems as well as man-made water resources reservoirs.

  14. Effects of seasonal and climate variations on calves' thermal comfort and behaviour.

    Science.gov (United States)

    Tripon, Iulian; Cziszter, Ludovic Toma; Bura, Marian; Sossidou, Evangelia N

    2014-09-01

    The aim of this study was to measure the effect of season and climate variations on thermal comfort and behaviour of 6-month-old dairy calves housed in a semi-opened shelter to develop animal-based indicators for assessing animal thermal comfort. The ultimate purpose was to further exploit the use of those indicators to prevent thermal stress by providing appropriate care to the animals. Measurements were taken for winter and summer seasons. Results showed that season significantly influenced (P ≤ 0.01) the lying down behaviour of calves by reducing the time spent lying, from 679.9 min in winter to 554.1 min in summer. Moreover, season had a significant influence (P ≤ 0.01) on feeding behaviour. In detail, the total length of feeding periods was shorter in winter, 442.1 min in comparison to 543.5 min in summer. Time spent drinking increased significantly (P ≤ 0.001), from 11.9 min in winter to 26.9 min in summer. Furthermore, season had a significant influence (P ≤ 0.001) on self grooming behaviour which was 5.5 times longer in duration in winter than in summer (1,336 s vs 244 s). It was concluded that calves' thermal comfort is affected by seasonal and climate variations and that this can be assessed by measuring behaviour with animal-based indicators, such as lying down, resting, standing up, feeding, rumination, drinking and self grooming. The indicators developed may be a useful tool to prevent animal thermal stress by providing appropriate housing and handling to calves under seasonal and climate challenge.

  15. Amount, composition and seasonality of dissolved organic carbon and nitrogen export from agriculture in contrasting climates

    DEFF Research Database (Denmark)

    Graeber, Daniel; Meerhof, Mariana; Zwirnmann, Elke

    2014-01-01

    Agricultural catchments are potentially important but often neglected sources of dissolved organic matter (DOM), of which a large part is dissolved organic carbon (DOC) and nitrogen (DON). DOC is an important source of aquatic microbial respiration and DON may be an important source of nitrogen...... to aquatic ecosystems. However, there is still a lack of comprehensive studies on the amount, composition and seasonality of DOM export from agricultural catchments in different climates. The aim of our study was to assess the amount, composition and seasonality of DOM in a total of four streams in the wet......-temperate and subtropical climate of Denmark and Uruguay, respectively. In each climate, we investigated one stream with extensive agriculture (mostly pasture) and one stream with intensive agriculture (mostly intensively used arable land) in the catchment. We sampled each stream taking grab samples fortnightly for two...

  16. Can We Envision a Bettor's Guide to Climate Prediction Markets?

    Science.gov (United States)

    Trexler, M.

    2017-12-01

    It's one thing to set up a climate prediction market, it's another to find enough informed traders to make the market work. Climate bets could range widely, from purely scientific or atmospheric metrics, to bets that involve the interplay of science, policy, economic, and behavioral outcomes. For a topic as complex and politicized as climate change, a Bettor's Guide to Climate Predictions could substantially expand and diversify the pool of individuals trading in the market, increasing both its liquidity and decision-support value. The Climate Web is an on-line and publically accessible Beta version of such a Bettor's Guide, implementing the knowledge management adage: "if only we knew what we know." The Climate Web not only curates the key literature, news coverage, and websites relating to more than 100 climate topics, from extreme event exceedance curves to climate economics to climate risk scenarios, it extracts and links together thousands of ideas and graphics across all of those topics. The Climate Web integrates the many disciplinary silos that characterize today's often dysfunctional climate policy conversations, allowing rapid cross-silo exploration and understanding. As a Bettor's Guide it would allow prediction market traders to better research and understand their potential bets, and to quickly survey key thinking and uncertainties relating to those bets. The availability of such a Bettor's Guide to Climate Predictions should make traders willing to place more bets than they otherwise would, and should facilitate higher quality betting. The presentation will introduce the knowledge management dimensions and challenges of climate prediction markets, and introduce the Climate Web as one solution to those challenges.

  17. Climate modelling, uncertainty and responses to predictions of change

    International Nuclear Information System (INIS)

    Henderson-Sellers, A.

    1996-01-01

    Article 4.1(F) of the Framework Convention on Climate Change commits all parties to take climate change considerations into account, to the extent feasible, in relevant social, economic and environmental policies and actions and to employ methods such as impact assessments to minimize adverse effects of climate change. This could be achieved by, inter alia, incorporating climate change risk assessment into development planning processes, i.e. relating climatic change to issues of habitability and sustainability. Adaptation is an ubiquitous and beneficial natural and human strategy. Future adaptation (adjustment) to climate is inevitable at the least to decrease the vulnerability to current climatic impacts. An urgent issue is the mismatch between the predictions of global climatic change and the need for information on local to regional change in order to develop adaptation strategies. Mitigation efforts are essential since the more successful mitigation activities are, the less need there will be for adaptation responses. And, mitigation responses can be global (e.g. a uniform percentage reduction in greenhouse gas emissions) while adaptation responses will be local to regional in character and therefore depend upon confident predictions of regional climatic change. The dilemma facing policymakers is that scientists have considerable confidence in likely global climatic changes but virtually zero confidence in regional changes. Mitigation and adaptation strategies relevant to climatic change can most usefully be developed in the context of sound understanding of climate, especially the near-surface continental climate, permitting discussion of societally relevant issues. But, climate models can't yet deliver this type of regionally and locationally specific prediction and some aspects of current research even seem to indicate increased uncertainty. These topics are explored in this paper using the specific example of the prediction of land-surface climate changes

  18. Antarctic stratospheric ozone and seasonal predictability over southern Africa

    CSIR Research Space (South Africa)

    Engelbrecht, FA

    2015-09-01

    Full Text Available of the Atmospheric Model Intercomparison project (AMIP) was performed first, generating different ensemble members using a lagged-average forecasting approach. These simulations are shown to be skilful in representing southern African summer-season inter...

  19. Climate control loads prediction of electric vehicles

    International Nuclear Information System (INIS)

    Zhang, Ziqi; Li, Wanyong; Zhang, Chengquan; Chen, Jiangping

    2017-01-01

    Highlights: • A model of vehicle climate control loads is proposed based on experiments. • Main climate control loads of the modeled vehicle are quantitatively analyzed. • Range reductions of the modeled vehicle under different conditions are simulated. - Abstract: A new model of electric vehicle climate control loads is provided in this paper. The mathematical formulations of the major climate control loads are developed, and the coefficients of the formulations are experimentally determined. Then, the detailed climate control loads are analyzed, and the New European Driving Cycle (NEDC) range reductions due to these loads are calculated under different conditions. It is found that in an electric vehicle, the total climate control loads vary with the vehicle speed, HVAC mode and blower level. The ventilation load is the largest climate control load, followed by the solar radiation load. These two add up to more than 80% of total climate control load in summer. The ventilation load accounts for 70.7–83.9% of total heating load under the winter condition. The climate control loads will cause a 17.2–37.1% reduction of NEDC range in summer, and a 17.1–54.1% reduction in winter, compared to the AC off condition. The heat pump system has an advantage in range extension. A heat pump system with an average heating COP of 1.7 will extend the range by 7.6–21.1% based on the simulation conditions.

  20. Prediction of the Arctic Oscillation in Boreal Winter by Dynamical Seasonal Forecasting Systems

    Science.gov (United States)

    Kang, Daehyun; Lee, Myong-In; Im, Jungho; Kim, Daehyun; Kim, Hye-Mi; Kang, Hyun-Suk; Schubert, Siegfried D.; Arribas, Alberto; MacLachlan, Craig

    2014-01-01

    This study assesses the skill of boreal winter Arctic Oscillation (AO) predictions with state-of-the-art dynamical ensemble prediction systems (EPSs): GloSea4, CFSv2, GEOS-5, CanCM3, CanCM4, and CM2.1. Long-term reforecasts with the EPSs are used to evaluate how well they represent the AO and to assess the skill of both deterministic and probabilistic forecasts of the AO. The reforecasts reproduce the observed changes in the large-scale patterns of the Northern Hemispheric surface temperature, upper level wind, and precipitation associated with the different phases of the AO. The results demonstrate that most EPSs improve upon persistence skill scores for lead times up to 2 months in boreal winter, suggesting some potential for skillful prediction of the AO and its associated climate anomalies at seasonal time scales. It is also found that the skill of AO forecasts during the recent period (1997-2010) is higher than that of the earlier period (1983-1996).

  1. Recent changes in seasonal variations of climate within the range of northern caribou populations

    Directory of Open Access Journals (Sweden)

    Paul H. Whitfield

    2005-05-01

    Full Text Available The Arctic is one region where it is expected that the impacts of a globally changing climate will be readily observed. We present results that indicate that climate derivatives of potential significance to caribou changed during the past 50 years. Many temperature derivatives reflect the increasing overall temperature in the Arctic such as decreases in the number of days with low temperatures, increases in the number of days with thaw, and days with extremely warm temperatures. Other derivatives reflect changes in the precipitation regime such as days with heavy precipitation and number of days when rain fell on snow. Our results indicate that specific caribou herds from across the Arctic were subjected to different variations of these derivatives in different seasons in the recent past. Examination of temperature and precipitation at finer time-steps than annual or monthly means, shows that climatic variations in the region are neither consistent through the seasons nor across space. Decadal changes in seasonal patterns of temperature and precipitation are shown for selected herds. A process for assessing caribou-focused climate derivatives is proposed.

  2. The role of seasonal, climatic and meteorological conditions in modifying nuclear accident consequences

    International Nuclear Information System (INIS)

    Mueller, H.; Proehl, G.

    1989-01-01

    One of the most important factors which influence the ingestion doses after an accidental release of radionuclides is the season of the year at which the release occurs. This is demonstrated with some examples for German conditions. This seasonal effect depends strongly on the growing periods of the different plants. Therefore it is influenced by the climatic conditions which vary to a large degree in the different countries causing very different growing periods. The influence of the meteorological conditions during and after the passing of a radioactive cloud on the initial contamination of the plants is discussed

  3. Past and future climate change in the context of memorable seasonal extremes

    Directory of Open Access Journals (Sweden)

    T. Matthews

    2016-01-01

    Full Text Available It is thought that direct personal experience of extreme weather events could result in greater public engagement and policy response to climate change. Based on this premise, we present a set of future climate scenarios for Ireland communicated in the context of recent, observed extremes. Specifically, we examine the changing likelihood of extreme seasonal conditions in the long-term observational record, and explore how frequently such extremes might occur in a changed Irish climate according to the latest model projections. Over the period (1900–2014 records suggest a greater than 50-fold increase in the likelihood of the warmest recorded summer (1995, whilst the likelihood of the wettest winter (1994/95 and driest summer (1995 has respectively doubled since 1850. The most severe end-of-century climate model projections suggest that summers as cool as 1995 may only occur once every ∼7 years, whilst winters as wet as 1994/95 and summers as dry as 1995 may increase by factors of ∼8 and ∼10 respectively. Contrary to previous research, we find no evidence for increased wintertime storminess as the Irish climate warms, but caution that this conclusion may be an artefact of the metric employed. It is hoped that framing future climate scenarios in the context of extremes from living memory will help communicate the scale of the challenge climate change presents, and in so doing bridge the gap between climate scientists and wider society.

  4. Seasonal temperature variation influences climate suitability for dengue, chikungunya, and Zika transmission.

    Science.gov (United States)

    Huber, John H; Childs, Marissa L; Caldwell, Jamie M; Mordecai, Erin A

    2018-05-01

    Dengue, chikungunya, and Zika virus epidemics transmitted by Aedes aegypti mosquitoes have recently (re)emerged and spread throughout the Americas, Southeast Asia, the Pacific Islands, and elsewhere. Understanding how environmental conditions affect epidemic dynamics is critical for predicting and responding to the geographic and seasonal spread of disease. Specifically, we lack a mechanistic understanding of how seasonal variation in temperature affects epidemic magnitude and duration. Here, we develop a dynamic disease transmission model for dengue virus and Aedes aegypti mosquitoes that integrates mechanistic, empirically parameterized, and independently validated mosquito and virus trait thermal responses under seasonally varying temperatures. We examine the influence of seasonal temperature mean, variation, and temperature at the start of the epidemic on disease dynamics. We find that at both constant and seasonally varying temperatures, warmer temperatures at the start of epidemics promote more rapid epidemics due to faster burnout of the susceptible population. By contrast, intermediate temperatures (24-25°C) at epidemic onset produced the largest epidemics in both constant and seasonally varying temperature regimes. When seasonal temperature variation was low, 25-35°C annual average temperatures produced the largest epidemics, but this range shifted to cooler temperatures as seasonal temperature variation increased (analogous to previous results for diurnal temperature variation). Tropical and sub-tropical cities such as Rio de Janeiro, Fortaleza, and Salvador, Brazil; Cali, Cartagena, and Barranquilla, Colombia; Delhi, India; Guangzhou, China; and Manila, Philippines have mean annual temperatures and seasonal temperature ranges that produced the largest epidemics. However, more temperate cities like Shanghai, China had high epidemic suitability because large seasonal variation offset moderate annual average temperatures. By accounting for seasonal

  5. Seasonal temperature variation influences climate suitability for dengue, chikungunya, and Zika transmission.

    Directory of Open Access Journals (Sweden)

    John H Huber

    2018-05-01

    Full Text Available Dengue, chikungunya, and Zika virus epidemics transmitted by Aedes aegypti mosquitoes have recently (reemerged and spread throughout the Americas, Southeast Asia, the Pacific Islands, and elsewhere. Understanding how environmental conditions affect epidemic dynamics is critical for predicting and responding to the geographic and seasonal spread of disease. Specifically, we lack a mechanistic understanding of how seasonal variation in temperature affects epidemic magnitude and duration. Here, we develop a dynamic disease transmission model for dengue virus and Aedes aegypti mosquitoes that integrates mechanistic, empirically parameterized, and independently validated mosquito and virus trait thermal responses under seasonally varying temperatures. We examine the influence of seasonal temperature mean, variation, and temperature at the start of the epidemic on disease dynamics. We find that at both constant and seasonally varying temperatures, warmer temperatures at the start of epidemics promote more rapid epidemics due to faster burnout of the susceptible population. By contrast, intermediate temperatures (24-25°C at epidemic onset produced the largest epidemics in both constant and seasonally varying temperature regimes. When seasonal temperature variation was low, 25-35°C annual average temperatures produced the largest epidemics, but this range shifted to cooler temperatures as seasonal temperature variation increased (analogous to previous results for diurnal temperature variation. Tropical and sub-tropical cities such as Rio de Janeiro, Fortaleza, and Salvador, Brazil; Cali, Cartagena, and Barranquilla, Colombia; Delhi, India; Guangzhou, China; and Manila, Philippines have mean annual temperatures and seasonal temperature ranges that produced the largest epidemics. However, more temperate cities like Shanghai, China had high epidemic suitability because large seasonal variation offset moderate annual average temperatures. By accounting

  6. Validating predictions from climate envelope models.

    Directory of Open Access Journals (Sweden)

    James I Watling

    Full Text Available Climate envelope models are a potentially important conservation tool, but their ability to accurately forecast species' distributional shifts using independent survey data has not been fully evaluated. We created climate envelope models for 12 species of North American breeding birds previously shown to have experienced poleward range shifts. For each species, we evaluated three different approaches to climate envelope modeling that differed in the way they treated climate-induced range expansion and contraction, using random forests and maximum entropy modeling algorithms. All models were calibrated using occurrence data from 1967-1971 (t1 and evaluated using occurrence data from 1998-2002 (t2. Model sensitivity (the ability to correctly classify species presences was greater using the maximum entropy algorithm than the random forest algorithm. Although sensitivity did not differ significantly among approaches, for many species, sensitivity was maximized using a hybrid approach that assumed range expansion, but not contraction, in t2. Species for which the hybrid approach resulted in the greatest improvement in sensitivity have been reported from more land cover types than species for which there was little difference in sensitivity between hybrid and dynamic approaches, suggesting that habitat generalists may be buffered somewhat against climate-induced range contractions. Specificity (the ability to correctly classify species absences was maximized using the random forest algorithm and was lowest using the hybrid approach. Overall, our results suggest cautious optimism for the use of climate envelope models to forecast range shifts, but also underscore the importance of considering non-climate drivers of species range limits. The use of alternative climate envelope models that make different assumptions about range expansion and contraction is a new and potentially useful way to help inform our understanding of climate change effects on

  7. Validating predictions from climate envelope models

    Science.gov (United States)

    Watling, J.; Bucklin, D.; Speroterra, C.; Brandt, L.; Cabal, C.; Romañach, Stephanie S.; Mazzotti, Frank J.

    2013-01-01

    Climate envelope models are a potentially important conservation tool, but their ability to accurately forecast species’ distributional shifts using independent survey data has not been fully evaluated. We created climate envelope models for 12 species of North American breeding birds previously shown to have experienced poleward range shifts. For each species, we evaluated three different approaches to climate envelope modeling that differed in the way they treated climate-induced range expansion and contraction, using random forests and maximum entropy modeling algorithms. All models were calibrated using occurrence data from 1967–1971 (t1) and evaluated using occurrence data from 1998–2002 (t2). Model sensitivity (the ability to correctly classify species presences) was greater using the maximum entropy algorithm than the random forest algorithm. Although sensitivity did not differ significantly among approaches, for many species, sensitivity was maximized using a hybrid approach that assumed range expansion, but not contraction, in t2. Species for which the hybrid approach resulted in the greatest improvement in sensitivity have been reported from more land cover types than species for which there was little difference in sensitivity between hybrid and dynamic approaches, suggesting that habitat generalists may be buffered somewhat against climate-induced range contractions. Specificity (the ability to correctly classify species absences) was maximized using the random forest algorithm and was lowest using the hybrid approach. Overall, our results suggest cautious optimism for the use of climate envelope models to forecast range shifts, but also underscore the importance of considering non-climate drivers of species range limits. The use of alternative climate envelope models that make different assumptions about range expansion and contraction is a new and potentially useful way to help inform our understanding of climate change effects on species.

  8. Ensemble-based Regional Climate Prediction: Political Impacts

    Science.gov (United States)

    Miguel, E.; Dykema, J.; Satyanath, S.; Anderson, J. G.

    2008-12-01

    Accurate forecasts of regional climate, including temperature and precipitation, have significant implications for human activities, not just economically but socially. Sub Saharan Africa is a region that has displayed an exceptional propensity for devastating civil wars. Recent research in political economy has revealed a strong statistical relationship between year to year fluctuations in precipitation and civil conflict in this region in the 1980s and 1990s. To investigate how climate change may modify the regional risk of civil conflict in the future requires a probabilistic regional forecast that explicitly accounts for the community's uncertainty in the evolution of rainfall under anthropogenic forcing. We approach the regional climate prediction aspect of this question through the application of a recently demonstrated method called generalized scalar prediction (Leroy et al. 2009), which predicts arbitrary scalar quantities of the climate system. This prediction method can predict change in any variable or linear combination of variables of the climate system averaged over a wide range spatial scales, from regional to hemispheric to global. Generalized scalar prediction utilizes an ensemble of model predictions to represent the community's uncertainty range in climate modeling in combination with a timeseries of any type of observational data that exhibits sensitivity to the scalar of interest. It is not necessary to prioritize models in deriving with the final prediction. We present the results of the application of generalized scalar prediction for regional forecasts of temperature and precipitation and Sub Saharan Africa. We utilize the climate predictions along with the established statistical relationship between year-to-year rainfall variability in Sub Saharan Africa to investigate the potential impact of climate change on civil conflict within that region.

  9. Seasonal prediction of precipitation over Nigeria | Adeniyi | Journal ...

    African Journals Online (AJOL)

    While for the training and verification periods of 1986-2012, correlations are not gener-ally significant although some were found to be above the significant level of 5%. For the recon-structed seasonal precipitation, correlations of ten stations were found to be significant at 5 %, but at 10 % level, their number increased to 13.

  10. Seasonal prediction of Indian summer monsoon: Sensitivity to ...

    Indian Academy of Sciences (India)

    trations, land-snow cover and soil moisture, etc. The seasonal ... tested at T85L26 resolution, which is equivalent to .... units, soil columns, and plant functional types (PFT). The ... soil texture. ... monthly rain product TB42 is not used here for the.

  11. Seasonal prediction of Indian summer monsoon: Sensitivity to ...

    Indian Academy of Sciences (India)

    In the present study, the assessment of the Community Atmosphere Model (CAM) developed at National Centre for Atmospheric Research (NCAR) for seasonal forecasting of Indian Summer Monsoon (ISM) with different persistent SST is reported. Towards achieving the objective, 30-year model climatology has been ...

  12. Comparison of phenology models for predicting the onset of growing season over the Northern Hemisphere.

    Directory of Open Access Journals (Sweden)

    Yang Fu

    Full Text Available Vegetation phenology models are important for examining the impact of climate change on the length of the growing season and carbon cycles in terrestrial ecosystems. However, large uncertainties in present phenology models make accurate assessment of the beginning of the growing season (BGS a challenge. In this study, based on the satellite-based phenology product (i.e. the V005 MODIS Land Cover Dynamics (MCD12Q2 product, we calibrated four phenology models, compared their relative strength to predict vegetation phenology; and assessed the spatial pattern and interannual variability of BGS in the Northern Hemisphere. The results indicated that parameter calibration significantly influences the models' accuracy. All models showed good performance in cool regions but poor performance in warm regions. On average, they explained about 67% (the Growing Degree Day model, 79% (the Biome-BGC phenology model, 73% (the Number of Growing Days model and 68% (the Number of Chilling Days-Growing Degree Day model of the BGS variations over the Northern Hemisphere. There were substantial differences in BGS simulations among the four phenology models. Overall, the Biome-BGC phenology model performed best in predicting the BGS, and showed low biases in most boreal and cool regions. Compared with the other three models, the two-phase phenology model (NCD-GDD showed the lowest correlation and largest biases with the MODIS phenology product, although it could catch the interannual variations well for some vegetation types. Our study highlights the need for further improvements by integrating the effects of water availability, especially for plants growing in low latitudes, and the physiological adaptation of plants into phenology models.

  13. Comparison of phenology models for predicting the onset of growing season over the Northern Hemisphere.

    Science.gov (United States)

    Fu, Yang; Zhang, Haicheng; Dong, Wenjie; Yuan, Wenping

    2014-01-01

    Vegetation phenology models are important for examining the impact of climate change on the length of the growing season and carbon cycles in terrestrial ecosystems. However, large uncertainties in present phenology models make accurate assessment of the beginning of the growing season (BGS) a challenge. In this study, based on the satellite-based phenology product (i.e. the V005 MODIS Land Cover Dynamics (MCD12Q2) product), we calibrated four phenology models, compared their relative strength to predict vegetation phenology; and assessed the spatial pattern and interannual variability of BGS in the Northern Hemisphere. The results indicated that parameter calibration significantly influences the models' accuracy. All models showed good performance in cool regions but poor performance in warm regions. On average, they explained about 67% (the Growing Degree Day model), 79% (the Biome-BGC phenology model), 73% (the Number of Growing Days model) and 68% (the Number of Chilling Days-Growing Degree Day model) of the BGS variations over the Northern Hemisphere. There were substantial differences in BGS simulations among the four phenology models. Overall, the Biome-BGC phenology model performed best in predicting the BGS, and showed low biases in most boreal and cool regions. Compared with the other three models, the two-phase phenology model (NCD-GDD) showed the lowest correlation and largest biases with the MODIS phenology product, although it could catch the interannual variations well for some vegetation types. Our study highlights the need for further improvements by integrating the effects of water availability, especially for plants growing in low latitudes, and the physiological adaptation of plants into phenology models.

  14. Experimental prediction of severe droughts on seasonal to intra-annual time scales with GFDL High-Resolution Atmosphere Model

    Science.gov (United States)

    Yu, Z.; Lin, S.

    2011-12-01

    Regional heat waves and drought have major economic and societal impacts on regional and even global scales. For example, during and following the 2010-2011 La Nina period, severe droughts have been reported in many places around the world including China, the southern US, and the east Africa, causing severe hardship in China and famine in east Africa. In this study, we investigate the feasibility and predictability of severe spring-summer draught events, 3 to 6 months in advance with the 25-km resolution Geophysical Fluid Dynamics Laboratory High-Resolution Atmosphere Model (HiRAM), which is built as a seamless weather-climate model, capable of long-term climate simulations as well as skillful seasonal predictions (e.g., Chen and Lin 2011, GRL). We adopted a similar methodology and the same (HiRAM) model as in Chen and Lin (2011), which is used successfully for seasonal hurricane predictions. A series of initialized 7-month forecasts starting from Dec 1 are performed each year (5 members each) during the past decade (2000-2010). We will then evaluate the predictability of the severe drought events during this period by comparing model predictions vs. available observations. To evaluate the predictive skill, in this preliminary report, we will focus on the anomalies of precipitation, sea-level-pressure, and 500-mb height. These anomalies will be computed as the individual model prediction minus the mean climatology obtained by an independent AMIP-type "simulation" using observed SSTs (rather than using predictive SSTs in the forecasts) from the same model.

  15. Maritime Continent seasonal climate biases in AMIP experiments of the CMIP5 multimodel ensemble

    Science.gov (United States)

    Toh, Ying Ying; Turner, Andrew G.; Johnson, Stephanie J.; Holloway, Christopher E.

    2018-02-01

    The fidelity of 28 Coupled Model Intercomparison Project phase 5 (CMIP5) models in simulating mean climate over the Maritime Continent in the Atmospheric Model Intercomparison Project (AMIP) experiment is evaluated in this study. The performance of AMIP models varies greatly in reproducing seasonal mean climate and the seasonal cycle. The multi-model mean has better skill at reproducing the observed mean climate than the individual models. The spatial pattern of 850 hPa wind is better simulated than the precipitation in all four seasons. We found that model horizontal resolution is not a good indicator of model performance. Instead, a model's local Maritime Continent biases are somewhat related to its biases in the local Hadley circulation and global monsoon. The comparison with coupled models in CMIP5 shows that AMIP models generally performed better than coupled models in the simulation of the global monsoon and local Hadley circulation but less well at simulating the Maritime Continent annual cycle of precipitation. To characterize model systematic biases in the AMIP runs, we performed cluster analysis on Maritime Continent annual cycle precipitation. Our analysis resulted in two distinct clusters. Cluster I models are able to capture both the winter monsoon and summer monsoon shift, but they overestimate the precipitation; especially during the JJA and SON seasons. Cluster II models simulate weaker seasonal migration than observed, and the maximum rainfall position stays closer to the equator throughout the year. The tropics-wide properties of these clusters suggest a connection between the skill of simulating global properties of the monsoon circulation and the skill of simulating the regional scale of Maritime Continent precipitation.

  16. Climate change effect on Betula (birch) and Quercus (oak) pollen seasons in the United States

    Science.gov (United States)

    Zhang, Yong; Bielory, Leonard; Georgopoulos, Panos G.

    2014-07-01

    Climatic change is expected to affect the spatiotemporal patterns of airborne allergenic pollen, which has been found to act synergistically with common air pollutants, such as ozone, to cause allergic airway disease (AAD). Observed airborne pollen data from six stations from 1994 to 2011 at Fargo (North Dakota), College Station (Texas), Omaha (Nebraska), Pleasanton (California), Cherry Hill and Newark (New Jersey) in the US were studied to examine climate change effects on trends of annual mean and peak value of daily concentrations, annual production, season start, and season length of Betula (birch) and Quercus (oak) pollen. The growing degree hour (GDH) model was used to establish a relationship between start/end dates and differential temperature sums using observed hourly temperatures from surrounding meteorology stations. Optimum GDH models were then combined with meteorological information from the Weather Research and Forecasting (WRF) model, and land use land coverage data from the Biogenic Emissions Land use Database, version 3.1 (BELD3.1), to simulate start dates and season lengths of birch and oak pollen for both past and future years across the contiguous US (CONUS). For most of the studied stations, comparison of mean pollen indices between the periods of 1994-2000 and 2001-2011 showed that birch and oak trees were observed to flower 1-2 weeks earlier; annual mean and peak value of daily pollen concentrations tended to increase by 13.6 %-248 %. The observed pollen season lengths varied for birch and for oak across the different monitoring stations. Optimum initial date, base temperature, and threshold GDH for start date was found to be 1 March, 8 °C, and 1,879 h, respectively, for birch; 1 March, 5 °C, and 4,760 h, respectively, for oak. Simulation results indicated that responses of birch and oak pollen seasons to climate change are expected to vary for different regions.

  17. Timing of seasonal migration in mule deer: effects of climate, plant phenology, andlife-history characteristics

    OpenAIRE

    Monteith, Kevin L.; Bleich, Vernon C.; Stephenson, Thomas R.; Pierce, Becky M.; Conner, Mary M.; Klaver, Robert W.; Bowyer, R. Terry

    2011-01-01

    Phenological events of plants and animals are sensitive to climatic processes. Migration is a life-history event exhibited by most large herbivores living in seasonal environments, and is thought to occur in response to dynamics of forage and weather. Decisions regarding when to migrate, however, may be affected by differences in life-history characteristics of individuals. Long-term and intensive study of a population of mule deer (Odocoileus hemionus) in the Sierra Nevada, California, USA, ...

  18. Climate Prediction Center (CPC) U.S. Hazards Outlook

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Climate Prediction Center releases a US Hazards Outlook daily, Monday through Friday. The product highlights regions of anticipated hazardous weather during the...

  19. Climate Prediction Center (CPC) Madden-Julian Oscillation (MJO) Index

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Climate Prediction Center (CPC) Madden Julian Oscillation index (MJO) is a dataset that allows evaluation of the strength and phase of the MJO during the dataset...

  20. Seasonal predictions of equatorial Atlantic SST in a low-resolution CGCM with surface heat flux correction

    Science.gov (United States)

    Dippe, Tina; Greatbatch, Richard; Ding, Hui

    2016-04-01

    The dominant mode of interannual variability in tropical Atlantic sea surface temperatures (SSTs) is the Atlantic Niño or Zonal Mode. Akin to the El Niño-Southern Oscillation in the Pacific sector, it is able to impact the climate both of the adjacent equatorial African continent and remote regions. Due to heavy biases in the mean state climate of the equatorial-to-subtropical Atlantic, however, most state-of-the-art coupled global climate models (CGCMs) are unable to realistically simulate equatorial Atlantic variability. In this study, the Kiel Climate Model (KCM) is used to investigate the impact of a simple bias alleviation technique on the predictability of equatorial Atlantic SSTs. Two sets of seasonal forecasting experiments are performed: An experiment using the standard KCM (STD), and an experiment with additional surface heat flux correction (FLX) that efficiently removes the SST bias from simulations. Initial conditions for both experiments are generated by the KCM run in partially coupled mode, a simple assimilation technique that forces the KCM with observed wind stress anomalies and preserves SST as a fully prognostic variable. Seasonal predictions for both sets of experiments are run four times yearly for 1981-2012. Results: Heat flux correction substantially improves the simulated variability in the initialization runs for boreal summer and fall (June-October). In boreal spring (March-May), however, neither the initialization runs of the STD or FLX-experiments are able to capture the observed variability. FLX-predictions show no consistent enhancement of skill relative to the predictions of the STD experiment over the course of the year. The skill of persistence forecasts is hardly beat by either of the two experiments in any season, limiting the usefulness of the few forecasts that show significant skill. However, FLX-forecasts initialized in May recover skill in July and August, the peak season of the Atlantic Niño (anomaly correlation

  1. Climate-Agriculture-Modeling and Decision Tool for Disease (CAMDT-Disease) for seasonal climate forecast-based crop disease risk management in agriculture

    Science.gov (United States)

    Kim, K. H.; Lee, S.; Han, E.; Ines, A. V. M.

    2017-12-01

    Climate-Agriculture-Modeling and Decision Tool (CAMDT) is a decision support system (DSS) tool that aims to facilitate translations of probabilistic seasonal climate forecasts (SCF) to crop responses such as yield and water stress. Since CAMDT is a software framework connecting different models and algorithms with SCF information, it can be easily customized for different types of agriculture models. In this study, we replaced the DSSAT-CSM-Rice model originally incorporated in CAMDT with a generic epidemiological model, EPIRICE, to generate a seasonal pest outlook. The resulting CAMDT-Disease generates potential risks for selected fungal, viral, and bacterial diseases of rice over the next months by translating SCFs into agriculturally-relevant risk information. The integrated modeling procedure of CAMDT-Disease first disaggregates a given SCF using temporal downscaling methods (predictWTD or FResampler1), runs EPIRICE with the downscaled weather inputs, and finally visualizes the EPIRICE outputs as disease risk compared to that of the previous year and the 30-year-climatological average. In addition, the easy-to-use graphical user interface adopted from CAMDT allows users to simulate "what-if" scenarios of disease risks over different planting dates with given SCFs. Our future work includes the simulation of the effect of crop disease on yields through the disease simulation models with the DSSAT-CSM-Rice model, as disease remains one of the most critical yield-reducing factors in the field.

  2. Birth seasonality and offspring production in threatened neotropical primates related to climate

    Science.gov (United States)

    Wiederholt, R.; Post, E.

    2011-01-01

    Given the threatened status of many primate species, the impacts of global warming on primate reproduction and, consequently, population growth should be of concern. We examined relations between climatic variability and birth seasonality, offspring production, and infant sex ratios in two ateline primates, northern muriquis, and woolly monkeys. In both species, the annual birth season was delayed by dry conditions and El Ni??o years, and delayed birth seasons were linked to lower birth rates. Additionally, increased mean annual temperatures were associated with lower birth rates for northern muriquis. Offspring sex ratios varied with climatic conditions in both species, but in different ways: directly in woolly monkeys and indirectly in northern muriquis. Woolly monkeys displayed an increase in the proportion of males among offspring in association with El Ni??o events, whereas in northern muriquis, increases in the proportion of males among offspring were associated with delayed onset of the birth season, which itself was related, although weakly, to warm, dry conditions. These results illustrate that global warming, increased drought frequency, and changes in the frequency of El Ni??o events could limit primate reproductive output, threatening the persistence and recovery of ateline primate populations. ?? 2011 Blackwell Publishing Ltd.

  3. Influence of climate change on flood magnitude and seasonality in the Arga River catchment in Spain

    Science.gov (United States)

    Garijo, Carlos; Mediero, Luis

    2018-04-01

    Climate change projections suggest that extremes, such as floods, will modify their behaviour in the future. Detailed catchment-scale studies are needed to implement the European Union Floods Directive and give recommendations for flood management and design of hydraulic infrastructure. In this study, a methodology to quantify changes in future flood magnitude and seasonality due to climate change at a catchment scale is proposed. Projections of 24 global climate models are used, with 10 being downscaled by the Spanish Meteorological Agency (Agencia Estatal de Meteorología, AEMET) and 14 from the EURO-CORDEX project, under two representative concentration pathways (RCPs) 4.5 and 8.5, from the Fifth Assessment Report provided by the Intergovernmental Panel on Climate Change. Downscaled climate models provided by the AEMET were corrected in terms of bias. The HBV rainfall-runoff model was selected to simulate the catchment hydrological behaviour. Simulations were analysed through both annual maximum and peaks-over-threshold (POT) series. The results show a decrease in the magnitude of extreme floods for the climate model projections downscaled by the AEMET. However, results for the climate model projections downscaled by EURO-CORDEX show differing trends, depending on the RCP. A small decrease in the flood magnitude was noticed for the RCP 4.5, while an increase was found for the RCP 8.5. Regarding the monthly seasonality analysis performed by using the POT series, a delay in the flood timing from late-autumn to late-winter is identified supporting the findings of recent studies performed with observed data in recent decades.

  4. New England observed and predicted Julian day of maximum growing season stream/river temperature points

    Data.gov (United States)

    U.S. Environmental Protection Agency — The shapefile contains points with associated observed and predicted Julian day of maximum growing season stream/river temperatures in New England based on a spatial...

  5. New England observed and predicted growing season maximum stream/river temperature points

    Data.gov (United States)

    U.S. Environmental Protection Agency — The shapefile contains points with associated observed and predicted growing season maximum stream/river temperatures in New England based on a spatial statistical...

  6. Predictions of Tropospheric Zenithal Delay for South America : Seasonal Variability and Quality Evaluation

    Directory of Open Access Journals (Sweden)

    Luiz Augusto Toledo Machado

    2006-12-01

    Full Text Available The Zenithal Tropospheric Delay (Z TD is an important error source in the observable involved in the positioning methods using artificial satellite. Frequently, the Z TD influence in the positioning is minimized by applying empirical models. However, such models are not able to supply the precision required to some real time applications, such as navigation and steak out. In 2010 it will be implanted the new navigation and administration system of the air traffic, denominated CNS-ATM (Communication Navigation Surveillance - Air Traffic Management. In this new system the application of positioning techniques by satellites in the air traffic will be quite explored because they provide good precision in real time. The predictions of Z TD values from Numeric Weather Prediction (NWP, denominated dynamic modeling, is an alternative to model the atmospheric gases effects in the radio-frequency signals in real time. The Center for Weather Forecasting and Climate Studies (CPTEC has generated operationally prediction of Z TD values to South American Continent since March, 2004. The aims of the present paper are to investigate the Z TD seasonal variability and evaluate the quality of predicted Z TD values. One year of GPS data from Brazilian Continuous GPS Network (RBMC was used in this evaluation. The RMS values resulting from this evaluation were in the range of 4 to 11 cm. Considering the Z TDtemporal variability, the advantages provide by this modeling, the results obtained in this evaluation and the future improvements, this work shows that the dynamic modeling has great potential to become the most appropriate alternative to model Z TD in real time.

  7. Climate and tourism in the Black Forest during the warm season.

    Science.gov (United States)

    Endler, Christina; Matzarakis, Andreas

    2011-03-01

    Climate, climate change and tourism all interact. Part of the public discussion about climate change focusses on the tourism sector, with direct and indirect impacts being of equally high relevance. Climate and tourism are closely linked. Thus, climate is a very decisive factor in choices both of destination and of type of journey (active holidays, wellness, and city tours) in the tourism sector. However, whether choices about destinations or types of trip will alter with climate change is difficult to predict. Future climates can be simulated and projected, and the tendencies of climate parameters can be estimated using global and regional climate models. In this paper, the focus is on climate change in the mountainous regions of southwest Germany - the Black Forest. The Black Forest is one of the low mountain ranges where both winter and summer tourism are vulnerable to climate change due to its southern location; the strongest climatic changes are expected in areas covering the south and southwest of Germany. Moreover, as the choice of destination is highly dependent on good weather, a climatic assessment for tourism is essential. Thus, the aim of this study was to estimate climatic changes in mountainous regions during summer, especially for tourism and recreation. The assessment method was based on human-biometeorology as well as tourism-climatologic approaches. Regional climate simulations based on the regional climate model REMO were used for tourism-related climatic analyses. Emission scenarios A1B and B1 were considered for the time period 2021 to 2050, compared to the 30-year base period of 1971-2000, particularly for the warm period of the year, defined here as the months of March-November. In this study, we quantified the frequency, but not the means, of climate parameters. The study results show that global and regional warming is reflected in an increase in annual mean air temperature, especially in autumn. Changes in the spring show a slight negative

  8. Seasonal variation and climate change impact in Rainfall Erosivity across Europe

    Science.gov (United States)

    Panagos, Panos; Borrelli, Pasquale; Meusburger, Katrin; Alewell, Christine; Ballabio, Cristiano

    2017-04-01

    Rainfall erosivity quantifies the climatic effect on water erosion and is of high importance for soil scientists, land use planners, agronomists, hydrologists and environmental scientists in general. The rainfall erosivity combines the influence of rainfall duration, magnitude, frequency and intensity. Rainfall erosivity is calculated from a series of single storm events by multiplying the total storm kinetic energy with the measured maximum 30-minute rainfall intensity. This estimation requests high temporal resolution (e.g. 30 minutes) rainfall data for sufficiently long time periods (i.e. 20 years). The European Commission's Joint Research Centr(JRC) in collaboration with national/regional meteorological services and Environmental Institutions made an extensive data collection of high resolution rainfall data in the 28 Member States of the European Union plus Switzerland to estimate rainfall erosivity in Europe. This resulted in the Rainfall Erosivity Database on the European Scale (REDES) which included 1,675 stations. The interpolation of those point erosivity values with a Gaussian Process Regression (GPR) model has resulted in the first Rainfall Erosivity map of Europe (Science of the Total Environment, 511: 801-815). In 2016, REDES extended with a monthly component, which allowed developing monthly and seasonal erosivity maps and assessing rainfall erosivity both spatially and temporally for European Union and Switzerland. The monthly erosivity maps have been used to develop composite indicators that map both intra-annual variability and concentration of erosive events (Science of the Total Environment, 579: 1298-1315). Consequently, spatio-temporal mapping of rainfall erosivity permits to identify the months and the areas with highest risk of soil loss where conservation measures should be applied in different seasons of the year. Finally, the identification of the most erosive month allows recommending certain agricultural management practices (crop

  9. The interactive roles of mastery climate and performance climate in predicting intrinsic motivation.

    Science.gov (United States)

    Buch, R; Nerstad, C G L; Säfvenbom, R

    2017-02-01

    This study examined the interplay between perceived mastery and performance climates in predicting increased intrinsic motivation. The results of a two-wave longitudinal study comprising of 141 individuals from three military academies revealed a positive relationship between a perceived mastery climate and increased intrinsic motivation only for individuals who perceived a low performance climate. This finding suggests a positive relationship between a perceived mastery climate and increased intrinsic motivation only when combined with low perceptions of a performance climate. Hence, introducing a performance climate in addition to a mastery climate can be an undermining motivational strategy, as it attenuates the positive relationship between a mastery climate and increased intrinsic motivation. Implications for future research and practice are discussed. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

  10. Evaluating the applicability of using daily forecasts from seasonal prediction systems (SPSs) for agriculture: a case study of Nepal's Terai with the NCEP CFSv2

    Science.gov (United States)

    Jha, Prakash K.; Athanasiadis, Panos; Gualdi, Silvio; Trabucco, Antonio; Mereu, Valentina; Shelia, Vakhtang; Hoogenboom, Gerrit

    2018-03-01

    Ensemble forecasts from dynamic seasonal prediction systems (SPSs) have the potential to improve decision-making for crop management to help cope with interannual weather variability. Because the reliability of crop yield predictions based on seasonal weather forecasts depends on the quality of the forecasts, it is essential to evaluate forecasts prior to agricultural applications. This study analyses the potential of Climate Forecast System version 2 (CFSv2) in predicting the Indian summer monsoon (ISM) for producing meteorological variables relevant to crop modeling. The focus area was Nepal's Terai region, and the local hindcasts were compared with weather station and reanalysis data. The results showed that the CFSv2 model accurately predicts monthly anomalies of daily maximum and minimum air temperature (Tmax and Tmin) as well as incoming total surface solar radiation (Srad). However, the daily climatologies of the respective CFSv2 hindcasts exhibit significant systematic biases compared to weather station data. The CFSv2 is less capable of predicting monthly precipitation anomalies and simulating the respective intra-seasonal variability over the growing season. Nevertheless, the observed daily climatologies of precipitation fall within the ensemble spread of the respective daily climatologies of CFSv2 hindcasts. These limitations in the CFSv2 seasonal forecasts, primarily in precipitation, restrict the potential application for predicting the interannual variability of crop yield associated with weather variability. Despite these limitations, ensemble averaging of the simulated yield using all CFSv2 members after applying bias correction may lead to satisfactory yield predictions.

  11. Application of the North American Multi-Model Ensemble to seasonal water supply forecasting in the Great Lakes basin through the use of the Great Lakes Seasonal Climate Forecast Tool

    Science.gov (United States)

    Gronewold, A.; Apps, D.; Fry, L. M.; Bolinger, R.

    2017-12-01

    The U.S. Army Corps of Engineers (USACE) contribution to the internationally coordinated 6-month forecast of Great Lakes water levels relies on several water supply models, including a regression model relating a coming month's water supply to past water supplies, previous months' precipitation and temperature, and forecasted precipitation and temperature. Probabilistic forecasts of precipitation and temperature depicted in the Climate Prediction Center's seasonal outlook maps are considered to be standard for use in operational forecasting for seasonal time horizons, and have provided the basis for computing a coming month's precipitation and temperature for use in the USACE water supply regression models. The CPC outlook maps are a useful forecast product offering insight into interpretation of climate models through the prognostic discussion and graphical forecasts. However, recent evolution of USACE forecast procedures to accommodate automated data transfer and manipulation offers a new opportunity for direct incorporation of ensemble climate forecast data into probabilistic outlooks of water supply using existing models that have previously been implemented in a deterministic fashion. We will present results from a study investigating the potential for applying data from the North American Multi-Model Ensemble to operational water supply forecasts. The use of NMME forecasts is facilitated by a new, publicly available, Great Lakes Seasonal Climate Forecast Tool that provides operational forecasts of monthly average temperatures and monthly total precipitation summarized for each lake basin.

  12. Collaborative Research: Improving Decadal Prediction of Arctic Climate Variability and Change Using a Regional Arctic

    Energy Technology Data Exchange (ETDEWEB)

    Gutowski, William J. [Iowa State Univ., Ames, IA (United States)

    2017-12-28

    This project developed and applied a regional Arctic System model for enhanced decadal predictions. It built on successful research by four of the current PIs with support from the DOE Climate Change Prediction Program, which has resulted in the development of a fully coupled Regional Arctic Climate Model (RACM) consisting of atmosphere, land-hydrology, ocean and sea ice components. An expanded RACM, a Regional Arctic System Model (RASM), has been set up to include ice sheets, ice caps, mountain glaciers, and dynamic vegetation to allow investigation of coupled physical processes responsible for decadal-scale climate change and variability in the Arctic. RASM can have high spatial resolution (~4-20 times higher than currently practical in global models) to advance modeling of critical processes and determine the need for their explicit representation in Global Earth System Models (GESMs). The pan-Arctic region is a key indicator of the state of global climate through polar amplification. However, a system-level understanding of critical arctic processes and feedbacks needs further development. Rapid climate change has occurred in a number of Arctic System components during the past few decades, including retreat of the perennial sea ice cover, increased surface melting of the Greenland ice sheet, acceleration and thinning of outlet glaciers, reduced snow cover, thawing permafrost, and shifts in vegetation. Such changes could have significant ramifications for global sea level, the ocean thermohaline circulation and heat budget, ecosystems, native communities, natural resource exploration, and commercial transportation. The overarching goal of the RASM project has been to advance understanding of past and present states of arctic climate and to improve seasonal to decadal predictions. To do this the project has focused on variability and long-term change of energy and freshwater flows through the arctic climate system. The three foci of this research are: - Changes

  13. A conceptual prediction model for seasonal drought processes using atmospheric and oceanic standardized anomalies and its application to four recent severe regional drought events in China

    Science.gov (United States)

    Liu, Z.; LU, G.; He, H.; Wu, Z.; He, J.

    2017-12-01

    Reliable drought prediction is fundamental for seasonal water management. Considering that drought development is closely related to the spatio-temporal evolution of large-scale circulation patterns, we develop a conceptual prediction model of seasonal drought processes based on atmospheric/oceanic Standardized Anomalies (SA). It is essentially the synchronous stepwise regression relationship between 90-day-accumulated atmospheric/oceanic SA-based predictors and 3-month SPI updated daily (SPI3). It is forced with forecasted atmospheric and oceanic variables retrieved from seasonal climate forecast systems, and it can make seamless drought prediction for operational use after a year-to-year calibration. Simulation and prediction of four severe seasonal regional drought processes in China were forced with the NCEP/NCAR reanalysis datasets and the NCEP Climate Forecast System Version 2 (CFSv2) operationally forecasted datasets, respectively. With the help of real-time correction for operational application, model application during four recent severe regional drought events in China revealed that the model is good at development prediction but weak in severity prediction. In addition to weakness in prediction of drought peak, the prediction of drought relief is possible to be predicted as drought recession. This weak performance may be associated with precipitation-causing weather patterns during drought relief. Based on initial virtual analysis on predicted 90-day prospective SPI3 curves, it shows that the 2009/2010 drought in Southwest China and 2014 drought in North China can be predicted and simulated well even for the prospective 1-75 day. In comparison, the prospective 1-45 day may be a feasible and acceptable lead time for simulation and prediction of the 2011 droughts in Southwest China and East China, after which the simulated and predicted developments clearly change.

  14. Remotely sensed vegetation indices for seasonal crop yields predictions in the Czech Republic

    Science.gov (United States)

    Hlavinka, Petr; Semerádová, Daniela; Balek, Jan; Bohovic, Roman; Žalud, Zdeněk; Trnka, Miroslav

    2015-04-01

    Remotely sensed vegetation indices by satellites are valuable tool for vegetation conditions assessment also in the case of field crops. This study is based on the use of NDVI (Normalized Difference Vegetation Index) and EVI (Enhanced Vegetation Index) derived from MODIS (Moderate Resolution Imaging Spectroradiometer) aboard Terra satellite. Data available from the year 2000 were analyzed and tested for seasonal yields predictions within selected districts of the Czech Republic (Central Europe). Namely the yields of spring barley, winter wheat and oilseed winter rape during the period from 2000 to 2014 were assessed. Observed yields from 14 districts (NUTS 4) were collected and thus 210 seasons were included. Selected districts differ considerably in their soil fertility and terrain configuration and represent transect across various agroclimatic conditions (from warm and dry to relative cool and wet regions). Two approaches were tested: 1) using of composite remotely sensed data (available in 16 day time step) provided by the USGS (https://lpdaac.usgs.gov/); 2) using daily remotely sensed data in combination with originally developed smoothing method. The yields were successfully predicted based on established regression models (remotely sensed data used as independent parameter). Besides others the impact of severe drought episodes within vegetation were identified and yield reductions at district level predicted (even before harvest). As a result the periods with the best relationship between remotely sensed data and yields were identified. The impact of drought conditions as well as normal or above normal yields of field crops could be predicted by proposed method within study region up to 30 days prior to the harvest. It could be concluded that remotely sensed vegetation conditions assessment should be important part of early warning systems focused on drought. Such information should be widely available for various users (decision makers, farmers, etc.) in

  15. Improving the reliability of seasonal climate forecasts through empirical downscaling and multi-model considerations; presentation

    CSIR Research Space (South Africa)

    Landman, WA

    2012-11-01

    Full Text Available , discrimination and sharpness. We present seasonal prediction verification for the equatorial Pacific Ocean (where El Niño and La Niña events occur) sea-surface temperatures. The verification is done over a recent multi-decadal period for which hindcasts (re...

  16. Climate extremes and predicted warming threaten Mediterranean Holocene firs forests refugia.

    Science.gov (United States)

    Sánchez-Salguero, Raúl; Camarero, J Julio; Carrer, Marco; Gutiérrez, Emilia; Alla, Arben Q; Andreu-Hayles, Laia; Hevia, Andrea; Koutavas, Athanasios; Martínez-Sancho, Elisabet; Nola, Paola; Papadopoulos, Andreas; Pasho, Edmond; Toromani, Ervin; Carreira, José A; Linares, Juan C

    2017-11-21

    Warmer and drier climatic conditions are projected for the 21st century; however, the role played by extreme climatic events on forest vulnerability is still little understood. For example, more severe droughts and heat waves could threaten quaternary relict tree refugia such as Circum-Mediterranean fir forests (CMFF). Using tree-ring data and a process-based model, we characterized the major climate constraints of recent (1950-2010) CMFF growth to project their vulnerability to 21st-century climate. Simulations predict a 30% growth reduction in some fir species with the 2050s business-as-usual emission scenario, whereas growth would increase in moist refugia due to a longer and warmer growing season. Fir populations currently subjected to warm and dry conditions will be the most vulnerable in the late 21st century when climatic conditions will be analogous to the most severe dry/heat spells causing dieback in the late 20th century. Quantification of growth trends based on climate scenarios could allow defining vulnerability thresholds in tree populations. The presented predictions call for conservation strategies to safeguard relict tree populations and anticipate how many refugia could be threatened by 21st-century dry spells.

  17. Development and Evaluation of Season-ahead Precipitation and Streamflow Predictions for Sectoral Management in Western Ethiopia

    Science.gov (United States)

    Block, P. J.; Alexander, S.; WU, S.

    2017-12-01

    Skillful season-ahead predictions conditioned on local and large-scale hydro-climate variables can provide valuable knowledge to farmers and reservoir operators, enabling informed water resource allocation and management decisions. In Ethiopia, the potential for advancing agriculture and hydropower management, and subsequently economic growth, is substantial, yet evidence suggests a weak adoption of prediction information by sectoral audiences. To address common critiques, including skill, scale, and uncertainty, probabilistic forecasts are developed at various scales - temporally and spatially - for the Finchaa hydropower dam and the Koga agricultural scheme in an attempt to promote uptake and application. Significant prediction skill is evident across scales, particularly for statistical models. This raises questions regarding other potential barriers to forecast utilization at community scales, which are also addressed.

  18. Non-climatic factors and long-term, continental-scale changes in seasonally frozen ground

    Science.gov (United States)

    Shiklomanov, Nikolay I.

    2012-03-01

    Numerous studies indicate that the northern high latitudes are experiencing an unprecedented rate of environmental change, including an increase in air temperatures (e.g. Serreze and Francis 2006), reduction of snow cover (e.g. Brown and Robinson 2011), ecosystem transformations and land cover changes (e.g. Callaghan et al 2011). Many of the potential environmental impacts of global warming in the high latitudes are associated with frozen ground, which occupies about 55% of the unglaciated land area in the northern hemisphere and consists of both permafrost and seasonally frozen ground. Frozen soils have a tremendous impact on hydrologic, climatic and biologic systems. Periodic freezing and thawing promote changes in soil structure, affect the surface and subsurface water cycle, and regulate the availability of nutrients in the soil for plants and biota that depend upon them. Freezing and thawing cycles can affect the decomposition of organic substances in the soil and greenhouse gas exchange between the atmosphere and land surface. Significant efforts have been devoted to permafrost-related studies, including the establishment of standardized observations (e.g. Romanovsky et al 2010, Shiklomanov et al 2008), modeling (e.g. Riseborough et al 2008), and climate-related feedback processes (e.g. Schuur et al 2008). Despite its vast extent and importance, seasonally frozen ground has received much less attention. One of the major obstacles in assessing changes in seasonally frozen ground is the lack of long-term data. In general, observations on soil temperature and freeze propagation are available for a limited area and involve a relatively short time period, precluding assessment of long-term, climate-driven change. A few known exceptions include shallow soil temperature and freeze/thaw depth observations conducted as part of the standard hydrometeorological monitoring system in China (e.g. Zhao et al 2004) and the Soviet Union/Russia (e.g. Gilichinsky et al 2000

  19. Value of Seasonal Fuzzy-based Inflow Prediction in the Jucar River Basin

    Science.gov (United States)

    Pulido-Velazquez, M.; Macian-Sorribes, H.

    2016-12-01

    The development and application of climate services in Integrated Water Resources Management (IWRM) is said to add important benefits in terms of water use efficiency due to an increase ability to foresee future water availability. A method to evaluate the economic impact of these services is presented, based on the use of hydroeconomic modelling techniques (hydroeconomic simulation) to compare the net benefits from water use in the system with and without the inflow forecasting. The Jucar River Basin (Spain) has been used as case study. Operating rules currently applied in the basin were assessed using fuzzy rule-based (FRB) systems via a co-development process involving the system operators. These operating rules use as input variable the hydrological inflows in several sub-basins, which need to be foreseen by the system operators. The inflow forecasting mechanism to preview water availability in the irrigation season (May-September) relied on fuzzy regression in which future inflows were foreseen based on past inflows and rainfall in the basin. This approach was compared with the current use of the two past year inflows for projecting the future inflow. For each irrigation season, the previewed inflows were determined using both methods and their impact on the system operation assessed through a hydroeconomic DSS. Results show that the implementation of the fuzzy inflow forecasting system offers higher economic returns. Another advantage of the fuzzy approach regards to the uncertainty treatment using fuzzy numbers, which allow us to estimate the uncertainty range of the expected benefits. Consequently, we can use the fuzzy approach to estimate the uncertainty associated with both the prediction and the associated benefits.

  20. Enhancement of seasonal prediction of East Asian summer rainfall related to the western tropical Pacific convection

    Science.gov (United States)

    Lee, D. Y.; Ahn, J. B.; Yoo, J. H.

    2014-12-01

    The prediction skills of climate model simulations in the western tropical Pacific (WTP) and East Asian region are assessed using the retrospective forecasts of seven state-of-the-art coupled models and their multi-model ensemble (MME) for boreal summers (June-August) during the period 1983-2005, along with corresponding observed and reanalyzed data. The prediction of summer rainfall anomalies in East Asia is difficult, while the WTP has a strong correlation between model prediction and observation. We focus on developing a new approach to further enhance the seasonal prediction skill for summer rainfall in East Asia and investigate the influence of convective activity in the WTP on East Asian summer rainfall. By analyzing the characteristics of the WTP convection, two distinct patterns associated with El Niño-Southern Oscillation (ENSO) developing and decaying modes are identified. Based on the multiple linear regression method, the East Asia Rainfall Index (EARI) is developed by using the interannual variability of the normalized Maritime continent-WTP indices (MPIs), as potentially useful predictors for rainfall prediction over East Asia, obtained from the above two main patterns. For East Asian summer rainfall, the EARI has superior performance to the East Asia summer monsoon index (EASMI) or each MP index (MPI). Therefore, the regressed rainfall from EARI also shows a strong relationship with the observed East Asian summer rainfall pattern. In addition, we evaluate the prediction skill of the East Asia reconstructed rainfall obtained by statistical-empirical approach using the cross-validated EARI from the individual models and their MME. The results show that the rainfalls reconstructed from simulations capture the general features of observed precipitation in East Asia quite well. This study convincingly demonstrates that rainfall prediction skill is considerably improved by using the statistical-empirical method compared to the dynamical models

  1. Climatic extremes improve predictions of spatial patterns of tree species

    Science.gov (United States)

    Zimmermann, N.E.; Yoccoz, N.G.; Edwards, T.C.; Meier, E.S.; Thuiller, W.; Guisan, Antoine; Schmatz, D.R.; Pearman, P.B.

    2009-01-01

    Understanding niche evolution, dynamics, and the response of species to climate change requires knowledge of the determinants of the environmental niche and species range limits. Mean values of climatic variables are often used in such analyses. In contrast, the increasing frequency of climate extremes suggests the importance of understanding their additional influence on range limits. Here, we assess how measures representing climate extremes (i.e., interannual variability in climate parameters) explain and predict spatial patterns of 11 tree species in Switzerland. We find clear, although comparably small, improvement (+20% in adjusted D2, +8% and +3% in cross-validated True Skill Statistic and area under the receiver operating characteristics curve values) in models that use measures of extremes in addition to means. The primary effect of including information on climate extremes is a correction of local overprediction and underprediction. Our results demonstrate that measures of climate extremes are important for understanding the climatic limits of tree species and assessing species niche characteristics. The inclusion of climate variability likely will improve models of species range limits under future conditions, where changes in mean climate and increased variability are expected.

  2. Contingent valuation study of the benefits of seasonal climate forecasts for maize farmers in the Republic of Benin, West Africa

    Directory of Open Access Journals (Sweden)

    Cocou Jaurès Amegnaglo

    2017-04-01

    Full Text Available This study aims to assess the economic benefits of seasonal climate forecasts in West Africa based on a random survey of 354 maize farmers and to use the contingent valuation method. Results indicate that farmers need accurate seasonal climate forecasts between 1 and 2 months before the onset of rains. The most desirable dissemination channels are radio, local elders, local farmer meetings and extension agents. The most likely used farming strategies are change of: planting date, crop acreage, crop variety, and production intensification. The vast majority of farmers are willing to pay for seasonal climate forecasts, and the average annual economic value of seasonal climate forecasts are about USD 5492 for the 354 sampled farmers and USD 66.5 million dollar at the national level. Furthermore, benefits of seasonal climate forecasts are likely to increase with better access to farmer based organisation, to extension services, to financial services, to modern communication tools, intensity of use of fertilizer and with larger farm sizes. Seasonal climate forecasts are a source of improvement of farmers’ performance and the service should be integrated in extension programmes and in national agricultural development agenda.

  3. A Bayesian modelling method for post-processing daily sub-seasonal to seasonal rainfall forecasts from global climate models and evaluation for 12 Australian catchments

    Directory of Open Access Journals (Sweden)

    A. Schepen

    2018-03-01

    Full Text Available Rainfall forecasts are an integral part of hydrological forecasting systems at sub-seasonal to seasonal timescales. In seasonal forecasting, global climate models (GCMs are now the go-to source for rainfall forecasts. For hydrological applications however, GCM forecasts are often biased and unreliable in uncertainty spread, and calibration is therefore required before use. There are sophisticated statistical techniques for calibrating monthly and seasonal aggregations of the forecasts. However, calibration of seasonal forecasts at the daily time step typically uses very simple statistical methods or climate analogue methods. These methods generally lack the sophistication to achieve unbiased, reliable and coherent forecasts of daily amounts and seasonal accumulated totals. In this study, we propose and evaluate a Rainfall Post-Processing method for Seasonal forecasts (RPP-S, which is based on the Bayesian joint probability modelling approach for calibrating daily forecasts and the Schaake Shuffle for connecting the daily ensemble members of different lead times. We apply the method to post-process ACCESS-S forecasts for 12 perennial and ephemeral catchments across Australia and for 12 initialisation dates. RPP-S significantly reduces bias in raw forecasts and improves both skill and reliability. RPP-S forecasts are also more skilful and reliable than forecasts derived from ACCESS-S forecasts that have been post-processed using quantile mapping, especially for monthly and seasonal accumulations. Several opportunities to improve the robustness and skill of RPP-S are identified. The new RPP-S post-processed forecasts will be used in ensemble sub-seasonal to seasonal streamflow applications.

  4. A Bayesian modelling method for post-processing daily sub-seasonal to seasonal rainfall forecasts from global climate models and evaluation for 12 Australian catchments

    Science.gov (United States)

    Schepen, Andrew; Zhao, Tongtiegang; Wang, Quan J.; Robertson, David E.

    2018-03-01

    Rainfall forecasts are an integral part of hydrological forecasting systems at sub-seasonal to seasonal timescales. In seasonal forecasting, global climate models (GCMs) are now the go-to source for rainfall forecasts. For hydrological applications however, GCM forecasts are often biased and unreliable in uncertainty spread, and calibration is therefore required before use. There are sophisticated statistical techniques for calibrating monthly and seasonal aggregations of the forecasts. However, calibration of seasonal forecasts at the daily time step typically uses very simple statistical methods or climate analogue methods. These methods generally lack the sophistication to achieve unbiased, reliable and coherent forecasts of daily amounts and seasonal accumulated totals. In this study, we propose and evaluate a Rainfall Post-Processing method for Seasonal forecasts (RPP-S), which is based on the Bayesian joint probability modelling approach for calibrating daily forecasts and the Schaake Shuffle for connecting the daily ensemble members of different lead times. We apply the method to post-process ACCESS-S forecasts for 12 perennial and ephemeral catchments across Australia and for 12 initialisation dates. RPP-S significantly reduces bias in raw forecasts and improves both skill and reliability. RPP-S forecasts are also more skilful and reliable than forecasts derived from ACCESS-S forecasts that have been post-processed using quantile mapping, especially for monthly and seasonal accumulations. Several opportunities to improve the robustness and skill of RPP-S are identified. The new RPP-S post-processed forecasts will be used in ensemble sub-seasonal to seasonal streamflow applications.

  5. The Influence of Climatic Seasonality on the Diversity of Different Tropical Pollinator Groups

    Science.gov (United States)

    Abrahamczyk, Stefan; Kluge, Jürgen; Gareca, Yuvinka; Reichle, Steffen; Kessler, Michael

    2011-01-01

    Tropical South America is rich in different groups of pollinators, but the biotic and abiotic factors determining the geographical distribution of their species richness are poorly understood. We analyzed the species richness of three groups of pollinators (bees and wasps, butterflies, hummingbirds) in six tropical forests in the Bolivian lowlands along a gradient of climatic seasonality and precipitation ranging from 410 mm to 6250 mm. At each site, we sampled the three pollinator groups and their food plants twice for 16 days in both the dry and rainy seasons. The richness of the pollinator groups was related to climatic factors by linear regressions. Differences in species numbers between pollinator groups were analyzed by Wilcoxon tests for matched pairs and the proportion in species numbers between pollinator groups by correlation analyses. Species richness of hummingbirds was most closely correlated to the continuous availability of food, that of bees and wasps to the number of food plant species and flowers, and that of butterflies to air temperature. Only the species number of butterflies differed significantly between seasons. We were not able to find shifts in the proportion of species numbers of the different groups of pollinators along the study gradient. Thus, we conclude that the diversity of pollinator guilds is determined by group-specific factors and that the constant proportions in species numbers of the different pollinator groups constitute a general pattern. PMID:22073268

  6. The influence of climatic seasonality on the diversity of different tropical pollinator groups.

    Directory of Open Access Journals (Sweden)

    Stefan Abrahamczyk

    Full Text Available Tropical South America is rich in different groups of pollinators, but the biotic and abiotic factors determining the geographical distribution of their species richness are poorly understood. We analyzed the species richness of three groups of pollinators (bees and wasps, butterflies, hummingbirds in six tropical forests in the Bolivian lowlands along a gradient of climatic seasonality and precipitation ranging from 410 mm to 6250 mm. At each site, we sampled the three pollinator groups and their food plants twice for 16 days in both the dry and rainy seasons. The richness of the pollinator groups was related to climatic factors by linear regressions. Differences in species numbers between pollinator groups were analyzed by Wilcoxon tests for matched pairs and the proportion in species numbers between pollinator groups by correlation analyses. Species richness of hummingbirds was most closely correlated to the continuous availability of food, that of bees and wasps to the number of food plant species and flowers, and that of butterflies to air temperature. Only the species number of butterflies differed significantly between seasons. We were not able to find shifts in the proportion of species numbers of the different groups of pollinators along the study gradient. Thus, we conclude that the diversity of pollinator guilds is determined by group-specific factors and that the constant proportions in species numbers of the different pollinator groups constitute a general pattern.

  7. An Evaluation of the Predictability of Austral Summer Season Precipitation over South America.

    Science.gov (United States)

    Misra, Vasubandhu

    2004-03-01

    In this study predictability of austral summer seasonal precipitation over South America is investigated using a 12-yr set of a 3.5-month range (seasonal) and a 17-yr range (continuous multiannual) five-member ensemble integrations of the Center for Ocean Land Atmosphere Studies (COLA) atmospheric general circulation model (AGCM). These integrations were performed with prescribed observed sea surface temperature (SST); therefore, skill attained represents an estimate of the upper bound of the skill achievable by COLA AGCM with predicted SST. The seasonal runs outperform the multiannual model integrations both in deterministic and probabilistic skill. The simulation of the January February March (JFM) seasonal climatology of precipitation is vastly superior in the seasonal runs except over the Nordeste region where the multiannual runs show a marginal improvement. The teleconnection of the ensemble mean JFM precipitation over tropical South America with global contemporaneous observed sea surface temperature in the seasonal runs conforms more closely to observations than in the multiannual runs. Both the sets of runs clearly beat persistence in predicting the interannual precipitation anomalies over the Amazon River basin, Nordeste, South Atlantic convergence zone, and subtropical South America. However, both types of runs display poorer simulations over subtropical regions than the tropical areas of South America. The examination of probabilistic skill of precipitation supports the conclusions from deterministic skill analysis that the seasonal runs yield superior simulations than the multiannual-type runs.

  8. The origins of computer weather prediction and climate modeling

    International Nuclear Information System (INIS)

    Lynch, Peter

    2008-01-01

    Numerical simulation of an ever-increasing range of geophysical phenomena is adding enormously to our understanding of complex processes in the Earth system. The consequences for mankind of ongoing climate change will be far-reaching. Earth System Models are capable of replicating climate regimes of past millennia and are the best means we have of predicting the future of our climate. The basic ideas of numerical forecasting and climate modeling were developed about a century ago, long before the first electronic computer was constructed. There were several major practical obstacles to be overcome before numerical prediction could be put into practice. A fuller understanding of atmospheric dynamics allowed the development of simplified systems of equations; regular radiosonde observations of the free atmosphere and, later, satellite data, provided the initial conditions; stable finite difference schemes were developed; and powerful electronic computers provided a practical means of carrying out the prodigious calculations required to predict the changes in the weather. Progress in weather forecasting and in climate modeling over the past 50 years has been dramatic. In this presentation, we will trace the history of computer forecasting through the ENIAC integrations to the present day. The useful range of deterministic prediction is increasing by about one day each decade, and our understanding of climate change is growing rapidly as Earth System Models of ever-increasing sophistication are developed

  9. Evaluation of performance of seasonal precipitation prediction at regional scale over India

    Science.gov (United States)

    Mohanty, U. C.; Nageswararao, M. M.; Sinha, P.; Nair, A.; Singh, A.; Rai, R. K.; Kar, S. C.; Ramesh, K. J.; Singh, K. K.; Ghosh, K.; Rathore, L. S.; Sharma, R.; Kumar, A.; Dhekale, B. S.; Maurya, R. K. S.; Sahoo, R. K.; Dash, G. P.

    2018-03-01

    The seasonal scale precipitation amount is an important ingredient in planning most of the agricultural practices (such as a type of crops, and showing and harvesting schedules). India being an agroeconomic country, the seasonal scale prediction of precipitation is directly linked to the socioeconomic growth of the nation. At present, seasonal precipitation prediction at regional scale is a challenging task for the scientific community. In the present study, an attempt is made to develop multi-model dynamical-statistical approach for seasonal precipitation prediction at the regional scale (meteorological subdivisions) over India for four prominent seasons which are winter (from December to February; DJF), pre-monsoon (from March to May; MAM), summer monsoon (from June to September; JJAS), and post-monsoon (from October to December; OND). The present prediction approach is referred as extended range forecast system (ERFS). For this purpose, precipitation predictions from ten general circulation models (GCMs) are used along with the India Meteorological Department (IMD) rainfall analysis data from 1982 to 2008 for evaluation of the performance of the GCMs, bias correction of the model results, and development of the ERFS. An extensive evaluation of the performance of the ERFS is carried out with dependent data (1982-2008) as well as independent predictions for the period 2009-2014. In general, the skill of the ERFS is reasonably better and consistent for all the seasons and different regions over India as compared to the GCMs and their simple mean. The GCM products failed to explain the extreme precipitation years, whereas the bias-corrected GCM mean and the ERFS improved the prediction and well represented the extremes in the hindcast period. The peak intensity, as well as regions of maximum precipitation, is better represented by the ERFS than the individual GCMs. The study highlights the improvement of forecast skill of the ERFS over 34 meteorological subdivisions

  10. The Urgent Need for Improved Climate Models and Predictions

    Science.gov (United States)

    Goddard, Lisa; Baethgen, Walter; Kirtman, Ben; Meehl, Gerald

    2009-09-01

    An investment over the next 10 years of the order of US$2 billion for developing improved climate models was recommended in a report (http://wcrp.wmo.int/documents/WCRP_WorldModellingSummit_Jan2009.pdf) from the May 2008 World Modelling Summit for Climate Prediction, held in Reading, United Kingdom, and presented by the World Climate Research Programme. The report indicated that “climate models will, as in the past, play an important, and perhaps central, role in guiding the trillion dollar decisions that the peoples, governments and industries of the world will be making to cope with the consequences of changing climate.” If trillions of dollars are going to be invested in making decisions related to climate impacts, an investment of $2 billion, which is less than 0.1% of that amount, to provide better climate information seems prudent. One example of investment in adaptation is the World Bank's Climate Investment Fund, which has drawn contributions of more than $6 billion for work on clean technologies and adaptation efforts in nine pilot countries and two pilot regions. This is just the beginning of expenditures on adaptation efforts by the World Bank and other mechanisms, focusing on only a small fraction of the nations of the world and primarily aimed at anticipated anthropogenic climate change. Moreover, decisions are being made now, all around the world—by individuals, companies, and governments—that affect people and their livelihoods today, not just 50 or more years in the future. Climate risk management, whether related to projects of the scope of the World Bank's or to the planning and decisions of municipalities, will be best guided by meaningful climate information derived from observations of the past and model predictions of the future.

  11. Climate-induced seasonal changes in smallmouth bass growth rate potential at the southern range extent

    Science.gov (United States)

    Middaugh, Christopher R.; Kessinger, Brin; Magoulick, Daniel D.

    2018-01-01

    Temperature increases due to climate change over the coming century will likely affect smallmouth bass (Micropterus dolomieu) growth in lotic systems at the southern extent of their native range. However, the thermal response of a stream to warming climate conditions could be affected by the flow regime of each stream, mitigating the effects on smallmouth bass populations. We developed bioenergetics models to compare change in smallmouth bass growth rate potential (GRP) from present to future projected monthly stream temperatures across two flow regimes: runoff and groundwater-dominated. Seasonal differences in GRP between stream types were then compared. The models were developed for fourteen streams within the Ozark–Ouachita Interior Highlands in Arkansas, Oklahoma and Missouri, USA, which contain smallmouth bass. In our simulations, smallmouth bass mean GRP during summer months decreased by 0.005 g g−1 day−1 in runoff streams and 0.002 g g−1 day−1 in groundwater streams by the end of century. Mean GRP during winter, fall and early spring increased under future climate conditions within both stream types (e.g., 0.00019 g g−1 day−1 in runoff and 0.0014 g g−1 day−1 in groundwater streams in spring months). We found significant differences in change in GRP between runoff and groundwater streams in three seasons in end-of-century simulations (spring, summer and fall). Potential differences in stream temperature across flow regimes could be an important habitat component to consider when investigating effects of climate change as fishes from various flow regimes that are relatively close geographically could be affected differently by warming climate conditions.

  12. Dynamical seasonal prediction of Southern African summer precipitation

    CSIR Research Space (South Africa)

    Yuan, C

    2014-01-01

    Full Text Available Pacific as predictors. More recently, they were replaced by two- and one-tiered dynamical 75 forecast systems, but raw model outputs, such as geopotential height at 850 hPa, are often 76 statistically downscaled to achieve better prediction skills... above-normal years, all have a distinct La Niña signal in the tropical Pacific, and 315 among six successfully predicted below-normal years, all but the 2000/2001 austral summer 316 have a distinct El Niño signal. As a result, composites of SST...

  13. Fire, climate and vegetation linkages in the Bolivian Chiquitano seasonally dry tropical forest.

    Science.gov (United States)

    Power, M J; Whitney, B S; Mayle, F E; Neves, D M; de Boer, E J; Maclean, K S

    2016-06-05

    South American seasonally dry tropical forests (SDTFs) are critically endangered, with only a small proportion of their original distribution remaining. This paper presents a 12 000 year reconstruction of climate change, fire and vegetation dynamics in the Bolivian Chiquitano SDTF, based upon pollen and charcoal analysis, to examine the resilience of this ecosystem to drought and fire. Our analysis demonstrates a complex relationship between climate, fire and floristic composition over multi-millennial time scales, and reveals that moisture variability is the dominant control upon community turnover in this ecosystem. Maximum drought during the Early Holocene, consistent with regional drought reconstructions, correlates with a period of significant fire activity between 8000 and 7000 cal yr BP which resulted in a decrease in SDTF diversity. As fire activity declined but severe regional droughts persisted through the Middle Holocene, SDTFs, including Anadenanthera and Astronium, became firmly established in the Bolivian lowlands. The trend of decreasing fire activity during the last two millennia promotes the idea among forest ecologists that SDTFs are threatened by fire. Our analysis shows that the Chiquitano seasonally dry biome has been more resilient to Holocene changes in climate and fire regime than previously assumed, but raises questions over whether this resilience will continue in the future under increased temperatures and drought coupled with a higher frequency anthropogenic fire regime.This article is part of the themed issue 'The interaction of fire and mankind'. © 2016 The Author(s).

  14. Association of Climatic Variability, Vector Population and Malarial Disease in District of Visakhapatnam, India: A Modeling and Prediction Analysis.

    Science.gov (United States)

    Srimath-Tirumula-Peddinti, Ravi Chandra Pavan Kumar; Neelapu, Nageswara Rao Reddy; Sidagam, Naresh

    2015-01-01

    Malarial incidence, severity, dynamics and distribution of malaria are strongly determined by climatic factors, i.e., temperature, precipitation, and relative humidity. The objectives of the current study were to analyse and model the relationships among climate, vector and malaria disease in district of Visakhapatnam, India to understand malaria transmission mechanism (MTM). Epidemiological, vector and climate data were analysed for the years 2005 to 2011 in Visakhapatnam to understand the magnitude, trends and seasonal patterns of the malarial disease. Statistical software MINITAB ver. 14 was used for performing correlation, linear and multiple regression analysis. Perennial malaria disease incidence and mosquito population was observed in the district of Visakhapatnam with peaks in seasons. All the climatic variables have a significant influence on disease incidence as well as on mosquito populations. Correlation coefficient analysis, seasonal index and seasonal analysis demonstrated significant relationships among climatic factors, mosquito population and malaria disease incidence in the district of Visakhapatnam, India. Multiple regression and ARIMA (I) models are best suited models for modeling and prediction of disease incidences and mosquito population. Predicted values of average temperature, mosquito population and malarial cases increased along with the year. Developed MTM algorithm observed a major MTM cycle following the June to August rains and occurring between June to September and minor MTM cycles following March to April rains and occurring between March to April in the district of Visakhapatnam. Fluctuations in climatic factors favored an increase in mosquito populations and thereby increasing the number of malarial cases. Rainfall, temperatures (20°C to 33°C) and humidity (66% to 81%) maintained a warmer, wetter climate for mosquito growth, parasite development and malaria transmission. Changes in climatic factors influence malaria directly by

  15. Seasonal rainfall predictability over the Lake Kariba catchment area ...

    African Journals Online (AJOL)

    Retroactive forecasts are produced for lead times of up to 5 months and probabilistic forecast performances evaluated for extreme rainfall thresholds of the 25th and 75th percentile values of the climatological record. The verification of the retroactive forecasts shows that rainfall over the catchment is predictable at extended ...

  16. Atmospheric modelling for seasonal prediction at the CSIR

    CSIR Research Space (South Africa)

    Landman, WA

    2014-10-01

    Full Text Available re-forecasts) made at lead-times which are the result of forcing the CCAM with predicted SST (while the sea-ice remains specified as climatological values) in order to determine how the model can be expected to perform under real-time operational...

  17. Prediction of South China sea level using seasonal ARIMA models

    Science.gov (United States)

    Fernandez, Flerida Regine; Po, Rodolfo; Montero, Neil; Addawe, Rizavel

    2017-11-01

    Accelerating sea level rise is an indicator of global warming and poses a threat to low-lying places and coastal countries. This study aims to fit a Seasonal Autoregressive Integrated Moving Average (SARIMA) model to the time series obtained from the TOPEX and Jason series of satellite radar altimetries of the South China Sea from the year 2008 to 2015. With altimetric measurements taken in a 10-day repeat cycle, monthly averages of the satellite altimetry measurements were taken to compose the data set used in the study. SARIMA models were then tried and fitted to the time series in order to find the best-fit model. Results show that the SARIMA(1,0,0)(0,1,1)12 model best fits the time series and was used to forecast the values for January 2016 to December 2016. The 12-month forecast using SARIMA(1,0,0)(0,1,1)12 shows that the sea level gradually increases from January to September 2016, and decreases until December 2016.

  18. Statistical prediction of Late Miocene climate

    Digital Repository Service at National Institute of Oceanography (India)

    Fernandes, A.A; Gupta, S.M.

    by making certain simplifying assumptions; for example in modelling ocean 4 currents, the geostrophic approximation is made. In case of statistical prediction no such a priori assumption need be made. statistical prediction comprises of using observed data... the number of equations. In this case the equations are overdetermined, and therefore one has to look for a solution that best fits the sample data in a least squares sense. To this end we express the sample .... (2.1)+ ry = y + data as follows: n L c. (x...

  19. The Impact of Climate Change on the Duration and Division of Flood Season in the Fenhe River Basin, China

    Directory of Open Access Journals (Sweden)

    Hejia Wang

    2016-03-01

    Full Text Available This study analyzes the duration and division of the flood season in the Fenhe River Basin over the period of 1957–2014 based on daily precipitation data collected from 14 meteorological stations. The Mann–Kendall detection, the multiscale moving t-test, and the Fisher optimal partition methods are used to evaluate the impact of climate change on flood season duration and division. The results show that the duration of the flood season has extended in 1975–2014 compared to that in 1957–1974. Specifically, the onset date of the flood season has advanced 15 days, whereas the retreat date of the flood season remains almost the same. The flood season of the Fenhe River Basin can be divided into three stages, and the variations in the onset and retreat dates of each stage are also examined. Corresponding measures are also proposed to better utilize the flood resources to adapt to the flood season variations.

  20. Refuge behaviour from outdoor thermal environmental stress and seasonal differences of thermal sense in tropical urban climate

    Science.gov (United States)

    Kurazumi, Y.; Ishii, J.; Fukagawa, K.; Kondo, E.; Aruninta, A.

    2017-12-01

    Thermal sensation affects body temperature regulation. As a starting point for behavioral body temperature regulation taken to improve from a poor thermal environment to a more pleasant environment, thermal sense of thermal environment stimulus is important. The poupose of this sutudy is to use the outdoor thermal environment evaluation index ETFe to quantify effects on thermal sensations of the human body of a tropical region climate with small annual temperature differences, and to examine seasonal differences in thermal sensation. It was found temperature preferences were lower in the winter season than in the dry season, and that a tolerance for higher temperatures in the dry season than in the winter season. It was found effects of seasonal differences of the thermal environment appear in quantitative changes in thermal sensations. It was found that effects of seasonal differences of the thermal environment do not greatly affect quantitative changes in thermal comfort.

  1. Seasonal prediction skill of ECMWF System 4 and NCEP CFSv2 retrospective forecast for the Northern Hemisphere Winter

    International Nuclear Information System (INIS)

    Kim, Hye-Mi; Webster, Peter J.; Curry, Judith A.

    2012-01-01

    The seasonal prediction skill for the Northern Hemisphere winter is assessed using retrospective predictions (1982-2010) from the ECMWF System 4 (Sys4) and National Center for Environmental Prediction (NCEP) CFS version 2 (CFSv2) coupled atmosphere-ocean seasonal climate prediction systems. Sys4 shows a cold bias in the equatorial Pacific but a warm bias is found in the North Pacific and part of the North Atlantic. The CFSv2 has strong warm bias from the cold tongue region of the eastern Pacific to the equatorial central Pacific and cold bias in broad areas over the North Pacific and the North Atlantic. A cold bias in the Southern Hemisphere is common in both reforecasts. In addition, excessive precipitation is found in the equatorial Pacific, the equatorial Indian Ocean and the western Pacific in Sys4, and in the South Pacific, the southern Indian Ocean and the western Pacific in CFSv2. A dry bias is found for both modeling systems over South America and northern Australia. The mean prediction skill of 2 meter temperature (2mT) and precipitation anomalies are greater over the tropics than the extra-tropics and also greater over ocean than land. The prediction skill of tropical 2mT and precipitation is greater in strong El Nino Southern Oscillation (ENSO) winters than in weak ENSO winters. Both models predict the year-to-year ENSO variation quite accurately, although sea surface temperature trend bias in CFSv2 over the tropical Pacific results in lower prediction skill for the CFSv2 relative to the Sys4. Both models capture the main ENSO teleconnection pattern of strong anomalies over the tropics, the North Pacific and the North America. However, both models have difficulty in forecasting the year-to-year winter temperature variability over the US and northern Europe. (orig.)

  2. Seasonal prediction skill of ECMWF System 4 and NCEP CFSv2 retrospective forecast for the Northern Hemisphere Winter

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Hye-Mi; Webster, Peter J.; Curry, Judith A. [Georgia Institute of Technology, School of Earth and Atmospheric Science, Atlanta, GA (United States)

    2012-12-15

    The seasonal prediction skill for the Northern Hemisphere winter is assessed using retrospective predictions (1982-2010) from the ECMWF System 4 (Sys4) and National Center for Environmental Prediction (NCEP) CFS version 2 (CFSv2) coupled atmosphere-ocean seasonal climate prediction systems. Sys4 shows a cold bias in the equatorial Pacific but a warm bias is found in the North Pacific and part of the North Atlantic. The CFSv2 has strong warm bias from the cold tongue region of the eastern Pacific to the equatorial central Pacific and cold bias in broad areas over the North Pacific and the North Atlantic. A cold bias in the Southern Hemisphere is common in both reforecasts. In addition, excessive precipitation is found in the equatorial Pacific, the equatorial Indian Ocean and the western Pacific in Sys4, and in the South Pacific, the southern Indian Ocean and the western Pacific in CFSv2. A dry bias is found for both modeling systems over South America and northern Australia. The mean prediction skill of 2 meter temperature (2mT) and precipitation anomalies are greater over the tropics than the extra-tropics and also greater over ocean than land. The prediction skill of tropical 2mT and precipitation is greater in strong El Nino Southern Oscillation (ENSO) winters than in weak ENSO winters. Both models predict the year-to-year ENSO variation quite accurately, although sea surface temperature trend bias in CFSv2 over the tropical Pacific results in lower prediction skill for the CFSv2 relative to the Sys4. Both models capture the main ENSO teleconnection pattern of strong anomalies over the tropics, the North Pacific and the North America. However, both models have difficulty in forecasting the year-to-year winter temperature variability over the US and northern Europe. (orig.)

  3. Changes in seasonal climate outpace compensatory density-dependence in eastern brook trout

    Science.gov (United States)

    Bassar, Ronald D.; Letcher, Benjamin H.; Nislow, Keith H.; Whiteley, Andrew R.

    2016-01-01

    Understanding how multiple extrinsic (density-independent) factors and intrinsic (density-dependent) mechanisms influence population dynamics has become increasingly urgent in the face of rapidly changing climates. It is particularly unclear how multiple extrinsic factors with contrasting effects among seasons are related to declines in population numbers and changes in mean body size and whether there is a strong role for density-dependence. The primary goal of this study was to identify the roles of seasonal variation in climate driven environmental direct effects (mean stream flow and temperature) versus density-dependence on population size and mean body size in eastern brook trout (Salvelinus fontinalis). We use data from a 10-year capture-mark-recapture study of eastern brook trout in four streams in Western Massachusetts, USA to parameterize a discrete-time population projection model. The model integrates matrix modeling techniques used to characterize discrete population structures (age, habitat type and season) with integral projection models (IPMs) that characterize demographic rates as continuous functions of organismal traits (in this case body size). Using both stochastic and deterministic analyses we show that decreases in population size are due to changes in stream flow and temperature and that these changes are larger than what can be compensated for through density-dependent responses. We also show that the declines are due mostly to increasing mean stream temperatures decreasing the survival of the youngest age class. In contrast, increases in mean body size over the same period are the result of indirect changes in density with a lesser direct role of climate-driven environmental change.

  4. Association with humans and seasonality interact to reverse predictions for animal space use.

    Science.gov (United States)

    Laver, Peter N; Alexander, Kathleen A

    2018-01-01

    Variation in animal space use reflects fitness trade-offs associated with ecological constraints. Associated theories such as the metabolic theory of ecology and the resource dispersion hypothesis generate predictions about what drives variation in animal space use. But, metabolic theory is usually tested in macro-ecological studies and is seldom invoked explicitly in within-species studies. Full evaluation of the resource dispersion hypothesis requires testing in more species. Neither have been evaluated in the context of anthropogenic landscape change. In this study, we used data for banded mongooses ( Mungos mungo ) in northeastern Botswana, along a gradient of association with humans, to test for effects of space use drivers predicted by these theories. We used Bayesian parameter estimation and inference from linear models to test for seasonal differences in space use metrics and to model seasonal effects of space use drivers. Results suggest that space use is strongly associated with variation in the level of overlap that mongoose groups have with humans. Seasonality influences this association, reversing seasonal space use predictions historically-accepted by ecologists. We found support for predictions of the metabolic theory when moderated by seasonality, by association with humans and by their interaction. Space use of mongooses living in association with humans was more concentrated in the dry season than the wet season, when historically-accepted ecological theory predicted more dispersed space use. Resource richness factors such as building density were associated with space use only during the dry season. We found negligible support for predictions of the resource dispersion hypothesis in general or for metabolic theory where seasonality and association with humans were not included. For mongooses living in association with humans, space use was not associated with patch dispersion or group size over both seasons. In our study, living in association

  5. Greenhouse Gas Induced Changes in the Seasonal Cycle of the Amazon Basin in Coupled Climate-Vegetation Regional Model

    Directory of Open Access Journals (Sweden)

    Flavio Justino

    2016-01-01

    Full Text Available Previous work suggests that changes in seasonality could lead to a 70% reduction in the extent of the Amazon rainforest. The primary cause of the dieback of the rainforest is a lengthening of the dry season due to a weakening of the large-scale tropical circulation. Here we examine these changes in the seasonal cycle. Under present day conditions the Amazon climate is characterized by a zonal separation of the dominance of the annual and semi-annual seasonal cycles. This behavior is strongly modified under greenhouse warming conditions, with the annual cycle becoming dominant throughout the Amazon basin, increasing differences between the dry and wet seasons. In particular, there are substantial changes in the annual cycle of temperature due to the increase in the temperature of the warmest month, but the lengthening of the dry season is believed to be particularly important for vegetation-climate feedbacks. Harmonic analysis performed to regional climate model simulations yields results that differ from the global climate model that it is forced from, with the regional model being more sensitive to changes in the seasonal cycle.

  6. Seasonal climate variation and caribou availability: Modeling sequential movement using satellite-relocation data

    Science.gov (United States)

    Nicolson, Craig; Berman, Matthew; West, Colin Thor; Kofinas, Gary P.; Griffith, Brad; Russell, Don; Dugan, Darcy

    2013-01-01

    Livelihood systems that depend on mobile resources must constantly adapt to change. For people living in permanent settlements, environmental changes that affect the distribution of a migratory species may reduce the availability of a primary food source, with the potential to destabilize the regional social-ecological system. Food security for Arctic indigenous peoples harvesting barren ground caribou (Rangifer tarandus granti) depends on movement patterns of migratory herds. Quantitative assessments of physical, ecological, and social effects on caribou distribution have proven difficult because of the significant interannual variability in seasonal caribou movement patterns. We developed and evaluated a modeling approach for simulating the distribution of a migratory herd throughout its annual cycle over a multiyear period. Beginning with spatial and temporal scales developed in previous studies of the Porcupine Caribou Herd of Canada and Alaska, we used satellite collar locations to compute and analyze season-by-season probabilities of movement of animals between habitat zones under two alternative weather conditions for each season. We then built a set of transition matrices from these movement probabilities, and simulated the sequence of movements across the landscape as a Markov process driven by externally imposed seasonal weather states. Statistical tests showed that the predicted distributions of caribou were consistent with observed distributions, and significantly correlated with subsistence harvest levels for three user communities. Our approach could be applied to other caribou herds and could be adapted for simulating the distribution of other ungulates and species with similarly large interannual variability in the use of their range.

  7. Impacts of climate change on rainfall, seasonal flooding, and evapotranspiration in the Okavango Delta, Botswana

    Science.gov (United States)

    Konecky, B. L.; Noone, D.; Mosimanyana, E.; Gondwe, M.

    2016-12-01

    The Okavango Delta in northern Botswana is one of the world's richest biodiversity hotspots. A UNESCO World Heritage Site, the Delta is known for its unique annual flood pulse, whereby the wetland and its neighboring river systems are inundated with waters that travel nearly 1000 km before reaching this subtropical, semi-arid destination. The livelihoods of northern Botswana's ecosystems and human populations rely on these floods to supplement the short and variable rainy season, which in many years is too minimal to ameliorate regional drought. However, anthropogenic climate change is reducing the amount of water that reaches the delta by increasing evaporation from soils and rivers, and transpiration by vegetation, during its long transit to Botswana. Future changes in rainfall patterns, extreme events, and increased upstream water use could exacerbate this water stress. Unfortunately, it remains difficult to assess the impacts of climate change on the delta because few data exist to constrain its complex climatic and seasonal water cycling regimes. This study presents a novel characterization of the water cycle in and around the Okavango Delta based on a survey of free-flowing surface waters, stagnant pools, precipitation, and groundwater carried out during the 2016 rainy and early-flood season. We use stable isotope and water quality data to assess local moisture sources, transport, evaporation, wetland flushing, and land-atmosphere exchanges, all of which are subject to change under global warming. We find a strong evaporation gradient and a progressive flushing of stagnant swamp waters along the northeastern and northwestern channels of the Delta. The evaporation gradient is more limited in nearby rivers with more limited wetlands. We contrast results with a survey of the Delta performed in the 1970's in order to assess changes over the past 40 years. Since some of these changes may arise from rainfall supply, we also present new analysis of rainfall moisture

  8. Seasonal variation in the incidence of preeclampsia and eclampsia in tropical climatic conditions

    Directory of Open Access Journals (Sweden)

    Subramaniam Vidya

    2007-10-01

    Full Text Available Abstract Background Observational studies have demonstrated various correlations between hypertensive disorders of pregnancy and different weather parameters. We aim to study if a correlation exists between the incidence of eclampsia and pre-eclampsia and various weather parameters in the tropical coastal city of Mumbai which has the distinction of having relatively uniform meteorological variables all throughout the year, except for the monsoon season. Methods We retrospectively analysed data from a large maternity centre in Mumbai, India over a period of 36 months from March 1993 to February 1996, recording the incidence of preeclampsia and eclampsia. Meteorological data was acquired from the regional meteorological centre recording the monthly average temperature, humidity, barometric pressure and rainfall during the study period. Study period was then divided into two climate conditions: monsoon season (June to August and dry season September to May. The incidence of preeclampsia and eclampsia and the meteorological differences between the two seasons were compared. Results Over a 36-month period, a total of 29562 deliveries were recorded, of which 1238 patients developed preeclampsia (4.18% and 34 developed eclampsia (0.11%. The incidence of preeclampsia did not differ between the monsoon and the dry season (4.3% vs. 4.15%, p = 0.5. The incidence of eclampsia was significantly higher in the monsoon (0.2% vs. 0.08%, p = 0.01. The monsoon was significantly cooler (median maximum temperature 30.7°C vs. 32.3°C, p = 0.01, more humid (median relative humidity 85% vs. 70%, p = 0.0008, and received higher rainfall (median 504.9 mm vs. 0.3 mm, p = 0.0002 than the rest of the year. The median barometric pressure (1005 mb during the monsoon season was significantly lower than the rest of the year (1012 mb, p Conclusion In the tropical climate of Mumbai, the incidence of eclampsia is significantly higher in monsoon, when the weather is cooler and

  9. Response of seasonal soil freeze depth to climate change across China

    Science.gov (United States)

    Peng, Xiaoqing; Zhang, Tingjun; Frauenfeld, Oliver W.; Wang, Kang; Cao, Bin; Zhong, Xinyue; Su, Hang; Mu, Cuicui

    2017-05-01

    The response of seasonal soil freeze depth to climate change has repercussions for the surface energy and water balance, ecosystems, the carbon cycle, and soil nutrient exchange. Despite its importance, the response of soil freeze depth to climate change is largely unknown. This study employs the Stefan solution and observations from 845 meteorological stations to investigate the response of variations in soil freeze depth to climate change across China. Observations include daily air temperatures, daily soil temperatures at various depths, mean monthly gridded air temperatures, and the normalized difference vegetation index. Results show that soil freeze depth decreased significantly at a rate of -0.18 ± 0.03 cm yr-1, resulting in a net decrease of 8.05 ± 1.5 cm over 1967-2012 across China. On the regional scale, soil freeze depth decreases varied between 0.0 and 0.4 cm yr-1 in most parts of China during 1950-2009. By investigating potential climatic and environmental driving factors of soil freeze depth variability, we find that mean annual air temperature and ground surface temperature, air thawing index, ground surface thawing index, and vegetation growth are all negatively associated with soil freeze depth. Changes in snow depth are not correlated with soil freeze depth. Air and ground surface freezing indices are positively correlated with soil freeze depth. Comparing these potential driving factors of soil freeze depth, we find that freezing index and vegetation growth are more strongly correlated with soil freeze depth, while snow depth is not significant. We conclude that air temperature increases are responsible for the decrease in seasonal freeze depth. These results are important for understanding the soil freeze-thaw dynamics and the impacts of soil freeze depth on ecosystem and hydrological process.

  10. Greenhouse Gas Induced Changes in the Seasonal Cycle of the Amazon Basin in Coupled Climate-Vegetation Regional Model

    OpenAIRE

    Flavio Justino; Frode Stordal; Edward K. Vizy; Kerry H. Cook; Marcos P. S. Pereira

    2016-01-01

    Previous work suggests that changes in seasonality could lead to a 70% reduction in the extent of the Amazon rainforest. The primary cause of the dieback of the rainforest is a lengthening of the dry season due to a weakening of the large-scale tropical circulation. Here we examine these changes in the seasonal cycle. Under present day conditions the Amazon climate is characterized by a zonal separation of the dominance of the annual and semi-annual seasonal cycles. This behavior is strongly ...

  11. Impact of lower stratospheric ozone on seasonal prediction systems

    Directory of Open Access Journals (Sweden)

    Kelebogile Mathole

    2014-03-01

    Full Text Available We conducted a comparison of trends in lower stratospheric temperatures and summer zonal wind fields based on 27 years of reanalysis data and output from hindcast simulations using a coupled ocean-atmospheric general circulation model (OAGCM. Lower stratospheric ozone in the OAGCM was relaxed to the observed climatology and increasing greenhouse gas concentrations were neglected. In the reanalysis, lower stratospheric ozone fields were better represented than in the OAGCM. The spring lower stratospheric/ upper tropospheric cooling in the polar cap observed in the reanalysis, which is caused by a direct ozone depletion in the past two decades and is in agreement with previous studies, did not appear in the OAGCM. The corresponding summer tropospheric response also differed between data sets. In the reanalysis, a statistically significant poleward trend of the summer jet position was found, whereas no such trend was found in the OAGCM. Furthermore, the jet position in the reanalysis exhibited larger interannual variability than that in the OAGCM. We conclude that these differences are caused by the absence of long-term lower stratospheric ozone changes in the OAGCM. Improper representation or non-inclusion of such ozone variability in a prediction model could adversely affect the accuracy of the predictability of summer rainfall forecasts over South Africa.

  12. Prediction Markets and Beliefs about Climate: Results from Agent-Based Simulations

    Science.gov (United States)

    Gilligan, J. M.; John, N. J.; van der Linden, M.

    2015-12-01

    Climate scientists have long been frustrated by persistent doubts a large portion of the public expresses toward the scientific consensus about anthropogenic global warming. The political and ideological polarization of this doubt led Vandenbergh, Raimi, and Gilligan [1] to propose that prediction markets for climate change might influence the opinions of those who mistrust the scientific community but do trust the power of markets.We have developed an agent-based simulation of a climate prediction market in which traders buy and sell future contracts that will pay off at some future year with a value that depends on the global average temperature at that time. The traders form a heterogeneous population with different ideological positions, different beliefs about anthropogenic global warming, and different degrees of risk aversion. We also vary characteristics of the market, including the topology of social networks among the traders, the number of traders, and the completeness of the market. Traders adjust their beliefs about climate according to the gains and losses they and other traders in their social network experience. This model predicts that if global temperature is predominantly driven by greenhouse gas concentrations, prediction markets will cause traders' beliefs to converge toward correctly accepting anthropogenic warming as real. This convergence is largely independent of the structure of the market and the characteristics of the population of traders. However, it may take considerable time for beliefs to converge. Conversely, if temperature does not depend on greenhouse gases, the model predicts that traders' beliefs will not converge. We will discuss the policy-relevance of these results and more generally, the use of agent-based market simulations for policy analysis regarding climate change, seasonal agricultural weather forecasts, and other applications.[1] MP Vandenbergh, KT Raimi, & JM Gilligan. UCLA Law Rev. 61, 1962 (2014).

  13. The Roles of Climate Change and Climate Variability in the 2017 Atlantic Hurricane Season

    Science.gov (United States)

    Lim, Young-Kwon; Schubert, Siegfried D.; Kovach, Robin; Molod, Andrea M.; Pawson, Steven

    2018-01-01

    The 2017 hurricane season was extremely active with six major hurricanes, the third most on record. The sea-surface temperatures (SSTs) over the eastern Main Development Region (EMDR), where many tropical cyclones (TCs) developed during active months of August/September, were approximately 0.96 degrees Centigrade above the 1901-2017 average (warmest on record): about 0.42 degrees Centigrade from a long-term upward trend and the rest (around 80 percent) attributed to the Atlantic Meridional Mode (AMM). The contribution to the SST from the North Atlantic Oscillation over the EMDR was a weak warming, while that from ENSO was negligible. Nevertheless, ENSO, the NAO, and the AMM all contributed to favorable wind shear conditions, while the AMM also produced enhanced atmospheric instability. Compared with the strong hurricane years of 2005-2010, the ocean heat content (OHC) during 2017 was larger across the tropics, with higher SST anomalies over the EMDR and Caribbean Sea. On the other hand, the dynamical/thermodynamical atmospheric conditions, while favorable for enhanced TC activity, were less prominent than in 2005-2010 across the tropics. The results suggest that unusually warm SST in the EMDR together with the long fetch of the resulting storms in the presence of record-breaking OHC were key factors in driving the strong TC activity in 2017.

  14. Short Term Prediction of PM10 Concentrations Using Seasonal Time Series Analysis

    Directory of Open Access Journals (Sweden)

    Hamid Hazrul Abdul

    2016-01-01

    Full Text Available Air pollution modelling is one of an important tool that usually used to make short term and long term prediction. Since air pollution gives a big impact especially to human health, prediction of air pollutants concentration is needed to help the local authorities to give an early warning to people who are in risk of acute and chronic health effects from air pollution. Finding the best time series model would allow prediction to be made accurately. This research was carried out to find the best time series model to predict the PM10 concentrations in Nilai, Negeri Sembilan, Malaysia. By considering two seasons which is wet season (north east monsoon and dry season (south west monsoon, seasonal autoregressive integrated moving average model were used to find the most suitable model to predict the PM10 concentrations in Nilai, Negeri Sembilan by using three error measures. Based on AIC statistics, results show that ARIMA (1, 1, 1 × (1, 0, 012 is the most suitable model to predict PM10 concentrations in Nilai, Negeri Sembilan.

  15. Modelling seasonal effects of temperature and precipitation on honey bee winter mortality in a temperate climate.

    Science.gov (United States)

    Switanek, Matthew; Crailsheim, Karl; Truhetz, Heimo; Brodschneider, Robert

    2017-02-01

    Insect pollinators are essential to global food production. For this reason, it is alarming that honey bee (Apis mellifera) populations across the world have recently seen increased rates of mortality. These changes in colony mortality are often ascribed to one or more factors including parasites, diseases, pesticides, nutrition, habitat dynamics, weather and/or climate. However, the effect of climate on colony mortality has never been demonstrated. Therefore, in this study, we focus on longer-term weather conditions and/or climate's influence on honey bee winter mortality rates across Austria. Statistical correlations between monthly climate variables and winter mortality rates were investigated. Our results indicate that warmer and drier weather conditions in the preceding year were accompanied by increased winter mortality. We subsequently built a statistical model to predict colony mortality using temperature and precipitation data as predictors. Our model reduces the mean absolute error between predicted and observed colony mortalities by 9% and is statistically significant at the 99.9% confidence level. This is the first study to show clear evidence of a link between climate variability and honey bee winter mortality. Copyright © 2016 British Geological Survey, NERC. Published by Elsevier B.V. All rights reserved.

  16. Multi-century cool- and warm-season rainfall reconstructions for Australia's major climatic regions

    Science.gov (United States)

    Freund, Mandy; Henley, Benjamin J.; Karoly, David J.; Allen, Kathryn J.; Baker, Patrick J.

    2017-11-01

    Australian seasonal rainfall is strongly affected by large-scale ocean-atmosphere climate influences. In this study, we exploit the links between these precipitation influences, regional rainfall variations, and palaeoclimate proxies in the region to reconstruct Australian regional rainfall between four and eight centuries into the past. We use an extensive network of palaeoclimate records from the Southern Hemisphere to reconstruct cool (April-September) and warm (October-March) season rainfall in eight natural resource management (NRM) regions spanning the Australian continent. Our bi-seasonal rainfall reconstruction aligns well with independent early documentary sources and existing reconstructions. Critically, this reconstruction allows us, for the first time, to place recent observations at a bi-seasonal temporal resolution into a pre-instrumental context, across the entire continent of Australia. We find that recent 30- and 50-year trends towards wetter conditions in tropical northern Australia are highly unusual in the multi-century context of our reconstruction. Recent cool-season drying trends in parts of southern Australia are very unusual, although not unprecedented, across the multi-century context. We also use our reconstruction to investigate the spatial and temporal extent of historical drought events. Our reconstruction reveals that the spatial extent and duration of the Millennium Drought (1997-2009) appears either very much below average or unprecedented in southern Australia over at least the last 400 years. Our reconstruction identifies a number of severe droughts over the past several centuries that vary widely in their spatial footprint, highlighting the high degree of diversity in historical droughts across the Australian continent. We document distinct characteristics of major droughts in terms of their spatial extent, duration, intensity, and seasonality. Compared to the three largest droughts in the instrumental period (Federation Drought

  17. Multi-century cool- and warm-season rainfall reconstructions for Australia's major climatic regions

    Directory of Open Access Journals (Sweden)

    M. Freund

    2017-11-01

    Full Text Available Australian seasonal rainfall is strongly affected by large-scale ocean–atmosphere climate influences. In this study, we exploit the links between these precipitation influences, regional rainfall variations, and palaeoclimate proxies in the region to reconstruct Australian regional rainfall between four and eight centuries into the past. We use an extensive network of palaeoclimate records from the Southern Hemisphere to reconstruct cool (April–September and warm (October–March season rainfall in eight natural resource management (NRM regions spanning the Australian continent. Our bi-seasonal rainfall reconstruction aligns well with independent early documentary sources and existing reconstructions. Critically, this reconstruction allows us, for the first time, to place recent observations at a bi-seasonal temporal resolution into a pre-instrumental context, across the entire continent of Australia. We find that recent 30- and 50-year trends towards wetter conditions in tropical northern Australia are highly unusual in the multi-century context of our reconstruction. Recent cool-season drying trends in parts of southern Australia are very unusual, although not unprecedented, across the multi-century context. We also use our reconstruction to investigate the spatial and temporal extent of historical drought events. Our reconstruction reveals that the spatial extent and duration of the Millennium Drought (1997–2009 appears either very much below average or unprecedented in southern Australia over at least the last 400 years. Our reconstruction identifies a number of severe droughts over the past several centuries that vary widely in their spatial footprint, highlighting the high degree of diversity in historical droughts across the Australian continent. We document distinct characteristics of major droughts in terms of their spatial extent, duration, intensity, and seasonality. Compared to the three largest droughts in the instrumental

  18. Understanding the Response of Photosynthetic Metabolism in Tropical Forests to Seasonal Climate Variations. Final Report

    Energy Technology Data Exchange (ETDEWEB)

    Dye, Dennis [U.S. Geological Survey, Menlo Park, CA (United States); Ivanov, Valeriy [Univ. of Michigan, Ann Arbor, MI (United States); Saleska, Scott [Univ. of Arizona, Tucson, AZ (United States); Huete, Alfredo [Univ. of Arizona, Tucson, AZ (United States); Univ. of Technology, Sydney NSW (Australia)

    2017-03-31

    This U.S-Brazil collaboration for GOAmazon has investigated a deceptively simple question: what controls the response of photosynthesis in Amazon tropical forests to seasonal variations in climate? In the past this question has been difficult to answer with modern earth system process models. We hypothesized that observed dry season increases in photosynthetic capacity are controlled by the phenology of leaf flush and litter fall, from which the seasonal pattern of LAI emerges. Our results confirm this hypothesis (Wu et al., 2016). Synthesis of data collected throughout the 3-year project period continues through December 31, 2017 under no-cost extensions granted to the project teams at University of Michigan and University of Arizona (Award 2). The USGS component (Award 1) ceased on the final date of the project performance period, December 31, 2016. This report summarizes the overall activities and achievements of the project, and constitutes the final project report for the USGS component. The University of Michigan will submit a separate final report that includes additional results and deliverables achieved during the period of their and the University of Arizona’s no-cost extension, which will end on December 31, 2017.

  19. The effects of season and meteorology on human mortality in tropical climates: a systematic review.

    Science.gov (United States)

    Burkart, Katrin; Khan, Md Mobarak Hossain; Schneider, Alexandra; Breitner, Susanne; Langner, Marcel; Krämer, Alexander; Endlicher, Wilfried

    2014-07-01

    Research in the field of atmospheric science and epidemiology has long recognized the health effects of seasonal and meteorological conditions. However, little scientific knowledge exists to date about the impacts of atmospheric parameters on human mortality in tropical regions. Working within the scope of this systematic review, this investigation conducted a literature search using different databases; original research articles were chosen according to pre-defined inclusion and exclusion criteria. Both seasonal and meteorological effects were considered. The findings suggest that high amounts of rainfall and increasing temperatures cause a seasonal excess in infectious disease mortality and are therefore relevant in regions and populations in which such diseases are prevalent. On the contrary, moderately low and very high temperatures exercise an adverse effect on cardio-respiratory mortality and shape the mortality pattern in areas and sub-groups in which these diseases are dominant. Atmospheric effects were subject to population-specific factors such as age and socio-economic status and differed between urban and rural areas. The consequences of climate change as well as environmental, epidemiological and social change (e.g., emerging non-communicable diseases, ageing of the population, urbanization) suggest a growing relevance of heat-related excess mortality in tropical regions. © The Author 2014. Published by Oxford University Press on behalf of Royal Society of Tropical Medicine and Hygiene. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  20. Effects of Global Warming on Predatory Bugs Supported by Data Across Geographic and Seasonal Climatic Gradients

    Science.gov (United States)

    Schuldiner-Harpaz, Tarryn; Coll, Moshe

    2013-01-01

    Global warming may affect species abundance and distribution, as well as temperature-dependent morphometric traits. In this study, we first used historical data to document changes in Orius (Heteroptera: Anthocoridae) species assemblage and individual morphometric traits over the past seven decades in Israel. We then tested whether these changes could have been temperature driven by searching for similar patterns across seasonal and geographic climatic gradients in a present survey. The historical records indicated a shift in the relative abundance of dominant Orius species; the relative abundance of O. albidipennis, a desert-adapted species, increased while that of O. laevigatus decreased in recent decades by 6 and 10–15 folds, respectively. These shifts coincided with an overall increase of up to 2.1°C in mean daily temperatures over the last 25 years in Israel. Similar trends were found in contemporary data across two other climatic gradients, seasonal and geographic; O. albidipennis dominated Orius assemblages under warm conditions. Finally, specimens collected in the present survey were significantly smaller than those from the 1980’s, corresponding to significantly smaller individuals collected now during warmer than colder seasons. Taken together, results provide strong support to the hypothesis that temperature is the most likely driver of the observed shifts in species composition and body sizes because (1) historical changes in both species assemblage and body size were associated with rising temperatures in the study region over the last few decades; and (2) similar changes were observed as a result of contemporary drivers that are associated with temperature. PMID:23805249

  1. Predicting vulnerabilities of North American shorebirds to climate change.

    Directory of Open Access Journals (Sweden)

    Hector Galbraith

    Full Text Available Despite an increase in conservation efforts for shorebirds, there are widespread declines of many species of North American shorebirds. We wanted to know whether these declines would be exacerbated by climate change, and whether relatively secure species might become at-risk species. Virtually all of the shorebird species breeding in the USA and Canada are migratory, which means climate change could affect extinction risk via changes on the breeding, wintering, and/or migratory refueling grounds, and that ecological synchronicities could be disrupted at multiple sites. To predict the effects of climate change on shorebird extinction risks, we created a categorical risk model complementary to that used by Partners-in-Flight and the U.S. Shorebird Conservation Plan. The model is based on anticipated changes in breeding, migration, and wintering habitat, degree of dependence on ecological synchronicities, migration distance, and degree of specialization on breeding, migration, or wintering habitat. We evaluated 49 species, and for 3 species we evaluated 2 distinct populations each, and found that 47 (90% taxa are predicted to experience an increase in risk of extinction. No species was reclassified into a lower-risk category, although 6 species had at least one risk factor decrease in association with climate change. The number of species that changed risk categories in our assessment is sensitive to how much of an effect of climate change is required to cause the shift, but even at its least sensitive, 20 species were at the highest risk category for extinction. Based on our results it appears that shorebirds are likely to be highly vulnerable to climate change. Finally, we discuss both how our approach can be integrated with existing risk assessments and potential future directions for predicting change in extinction risk due to climate change.

  2. Sub-seasonal Predictability of Heavy Precipitation Events: Implication for Real-time Flood Management in Iran

    Science.gov (United States)

    Najafi, H.; Shahbazi, A.; Zohrabi, N.; Robertson, A. W.; Mofidi, A.; Massah Bavani, A. R.

    2016-12-01

    Each year, a number of high impact weather events occur worldwide. Since any level of predictability at sub-seasonal to seasonal timescale is highly beneficial to society, international efforts is now on progress to promote reliable Ensemble Prediction Systems for monthly forecasts within the WWRP/WCRP initiative (S2S) project and North American Multi Model Ensemble (NMME). For water resources managers in the face of extreme events, not only can reliable forecasts of high impact weather events prevent catastrophic losses caused by floods but also contribute to benefits gained from hydropower generation and water markets. The aim of this paper is to analyze the predictability of recent severe weather events over Iran. Two recent heavy precipitations are considered as an illustration to examine whether S2S forecasts can be used for developing flood alert systems especially where large cascade of dams are in operation. Both events have caused major damages to cities and infrastructures. The first severe precipitation was is in the early November 2015 when heavy precipitation (more than 50 mm) occurred in 2 days. More recently, up to 300 mm of precipitation is observed within less than a week in April 2016 causing a consequent flash flood. Over some stations, the observed precipitation was even more than the total annual mean precipitation. To analyze the predictive capability, ensemble forecasts from several operational centers including (European Centre for Medium-Range Weather Forecasts (ECMWF) system, Climate Forecast System Version 2 (CFSv2) and Chinese Meteorological Center (CMA) are evaluated. It has been observed that significant changes in precipitation anomalies were likely to be predicted days in advance. The next step will be to conduct thorough analysis based on comparing multi-model outputs over the full hindcast dataset developing real-time high impact weather prediction systems.

  3. Climate Prediction Center (CPC) Rainfall Estimator (RFE) for Africa

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — As of January 1, 2001, RFE version 2.0 has been implemented by NOAA?s Climate Prediction Center. Created by Ping-Ping Xie, this replaces RFE 1.0 the previous...

  4. Canadian snow and sea ice: assessment of snow, sea ice, and related climate processes in Canada's Earth system model and climate-prediction system

    Science.gov (United States)

    Kushner, Paul J.; Mudryk, Lawrence R.; Merryfield, William; Ambadan, Jaison T.; Berg, Aaron; Bichet, Adéline; Brown, Ross; Derksen, Chris; Déry, Stephen J.; Dirkson, Arlan; Flato, Greg; Fletcher, Christopher G.; Fyfe, John C.; Gillett, Nathan; Haas, Christian; Howell, Stephen; Laliberté, Frédéric; McCusker, Kelly; Sigmond, Michael; Sospedra-Alfonso, Reinel; Tandon, Neil F.; Thackeray, Chad; Tremblay, Bruno; Zwiers, Francis W.

    2018-04-01

    The Canadian Sea Ice and Snow Evolution (CanSISE) Network is a climate research network focused on developing and applying state-of-the-art observational data to advance dynamical prediction, projections, and understanding of seasonal snow cover and sea ice in Canada and the circumpolar Arctic. This study presents an assessment from the CanSISE Network of the ability of the second-generation Canadian Earth System Model (CanESM2) and the Canadian Seasonal to Interannual Prediction System (CanSIPS) to simulate and predict snow and sea ice from seasonal to multi-decadal timescales, with a focus on the Canadian sector. To account for observational uncertainty, model structural uncertainty, and internal climate variability, the analysis uses multi-source observations, multiple Earth system models (ESMs) in Phase 5 of the Coupled Model Intercomparison Project (CMIP5), and large initial-condition ensembles of CanESM2 and other models. It is found that the ability of the CanESM2 simulation to capture snow-related climate parameters, such as cold-region surface temperature and precipitation, lies within the range of currently available international models. Accounting for the considerable disagreement among satellite-era observational datasets on the distribution of snow water equivalent, CanESM2 has too much springtime snow mass over Canada, reflecting a broader northern hemispheric positive bias. Biases in seasonal snow cover extent are generally less pronounced. CanESM2 also exhibits retreat of springtime snow generally greater than observational estimates, after accounting for observational uncertainty and internal variability. Sea ice is biased low in the Canadian Arctic, which makes it difficult to assess the realism of long-term sea ice trends there. The strengths and weaknesses of the modelling system need to be understood as a practical tradeoff: the Canadian models are relatively inexpensive computationally because of their moderate resolution, thus enabling their

  5. Climate drift of AMOC, North Atlantic salinity and arctic sea ice in CFSv2 decadal predictions

    Science.gov (United States)

    Huang, Bohua; Zhu, Jieshun; Marx, Lawrence; Wu, Xingren; Kumar, Arun; Hu, Zeng-Zhen; Balmaseda, Magdalena A.; Zhang, Shaoqing; Lu, Jian; Schneider, Edwin K.; Kinter, James L., III

    2015-01-01

    There are potential advantages to extending operational seasonal forecast models to predict decadal variability but major efforts are required to assess the model fidelity for this task. In this study, we examine the North Atlantic climate simulated by the NCEP Climate Forecast System, version 2 (CFSv2), using a set of ensemble decadal hindcasts and several 30-year simulations initialized from realistic ocean-atmosphere states. It is found that a substantial climate drift occurs in the first few years of the CFSv2 hindcasts, which represents a major systematic bias and may seriously affect the model's fidelity for decadal prediction. In particular, it is noted that a major reduction of the upper ocean salinity in the northern North Atlantic weakens the Atlantic meridional overturning circulation (AMOC) significantly. This freshening is likely caused by the excessive freshwater transport from the Arctic Ocean and weakened subtropical water transport by the North Atlantic Current. A potential source of the excessive freshwater is the quick melting of sea ice, which also causes unrealistically thin ice cover in the Arctic Ocean. Our sensitivity experiments with adjusted sea ice albedo parameters produce a sustainable ice cover with realistic thickness distribution. It also leads to a moderate increase of the AMOC strength. This study suggests that a realistic freshwater balance, including a proper sea ice feedback, is crucial for simulating the North Atlantic climate and its variability.

  6. Computational data sciences for assessment and prediction of climate extremes

    Science.gov (United States)

    Ganguly, A. R.

    2011-12-01

    Climate extremes may be defined inclusively as severe weather events or large shifts in global or regional weather patterns which may be caused or exacerbated by natural climate variability or climate change. This area of research arguably represents one of the largest knowledge-gaps in climate science which is relevant for informing resource managers and policy makers. While physics-based climate models are essential in view of non-stationary and nonlinear dynamical processes, their current pace of uncertainty reduction may not be adequate for urgent stakeholder needs. The structure of the models may in some cases preclude reduction of uncertainty for critical processes at scales or for the extremes of interest. On the other hand, methods based on complex networks, extreme value statistics, machine learning, and space-time data mining, have demonstrated significant promise to improve scientific understanding and generate enhanced predictions. When combined with conceptual process understanding at multiple spatiotemporal scales and designed to handle massive data, interdisciplinary data science methods and algorithms may complement or supplement physics-based models. Specific examples from the prior literature and our ongoing work suggests how data-guided improvements may be possible, for example, in the context of ocean meteorology, climate oscillators, teleconnections, and atmospheric process understanding, which in turn can improve projections of regional climate, precipitation extremes and tropical cyclones in an useful and interpretable fashion. A community-wide effort is motivated to develop and adapt computational data science tools for translating climate model simulations to information relevant for adaptation and policy, as well as for improving our scientific understanding of climate extremes from both observed and model-simulated data.

  7. Initializing decadal climate predictions over the North Atlantic region

    Science.gov (United States)

    Matei, Daniela Mihaela; Pohlmann, Holger; Jungclaus, Johann; Müller, Wolfgang; Haak, Helmuth; Marotzke, Jochem

    2010-05-01

    Decadal climate prediction aims to predict the internally-generated decadal climate variability in addition to externally-forced climate change signal. In order to achieve this it is necessary to start the predictions from the current climate state. In this study we investigate the forecast skill of the North Atlantic decadal climate predictions using two different ocean initialization strategies. First we apply an assimilation of ocean synthesis data provided by the GECCO project (Köhl and Stammer, 2008) as initial conditions for the coupled model ECHAM5/MPI-OM. Hindcast experiments are then performed over the period 1952-2001. An alternative approach is one in which the subsurface ocean temperature and salinity are diagnosed from an ensemble of ocean model runs forced by the NCEP-NCAR atmospheric reanalyzes for the period 1948-2007, then nudge into the coupled model to produce initial conditions for the hindcast experiments. An anomaly coupling scheme is used in both approaches to avoid the hindcast drift and the associated initial shock. Differences between the two assimilation approaches are discussed by comparing them with the observational data in key regions and processes. We asses the skill of the initialized decadal hindcast experiments against the prediction skill of the non-initialized hindcasts simulation. We obtain an overview of the regions with the highest predictability from the regional distribution of the anomaly correlation coefficients and RMSE for the SAT. For the first year the hindcast skill is increased over almost all ocean regions in the NCEP-forced approach. This increase in the hindcast skill for the 1 year lead time is somewhat reduced in the GECCO approach. At lead time 5yr and 10yr, the skill enhancement is still found over the North Atlantic and North Pacific regions. We also consider the potential predictability of the Atlantic Meridional Overturning Circulation (AMOC) and Nordic Seas Overflow by comparing the predicted values to

  8. Robustness of a multiple-use reservoir to seasonal runoff shifts associated with climate change

    International Nuclear Information System (INIS)

    Lettenmaier, D.P.; Brettman, K.L.

    1990-05-01

    Although much remains to be learned about long-term climate change associated with anthropogenic increases in concentrations of the so-called ''greenhouse gases,'' such as carbon dioxide and methane, there is a general consensus that some global warming will result from past and present emissions. In the western United States, the dominant hydrologic effect of such warming, aside from any accompanying changes in precipitation, would be to reduce winter snow accumulations in mountainous headwaters regions. To assess the robustness of reservoir operation to such shifts in seasonal runoff, simulations were developed of monthly runoff for the American River, Washington, using the National Weather Service River Forecast System. The American River is presently unregulated; however, we tested the performance of hypothetical reservoirs with capacity of 0.25 and 0.50 of the mean annual flow for a range of annual temperature changes from 0.0 (present climate) to 4.0 degree C. We considered a multiple-purpose reservoir system operated for water supply ad hydropower, with minimum releases required for fisheries enhancement. In addition to evaluating the sensitivity of water supply, low flow, and hydropower performance using a heuristic operating rule, the relative performance of the system under present and altered climates was evaluated using an optimization algorithm, extended linear quadratic Gaussian control. This paper reports the results of hydrologic simulations for the American River, Washington. 13 refs., 8 figs

  9. Growing Season Conditions Mediate the Dependence of Aspen on Redistributed Snow Under Climate Change.

    Science.gov (United States)

    Soderquist, B.; Kavanagh, K.; Link, T. E.; Seyfried, M. S.; Strand, E. K.

    2016-12-01

    Precipitation regimes in many semiarid ecosystems are becoming increasingly dominated by winter rainfall as a result of climate change. Across these regions, snowpack plays a vital role in the distribution and timing of soil moisture availability. Rising temperatures will result in a more uniform distribution of soil moisture, advanced spring phenology, and prolonged growing seasons. Productive and wide ranging tree species like aspen, Populus tremuloides, may experience increased vulnerability to drought and mortality resulting from both reduced snowpack and increased evaporative demand during the growing season. We simulated the net primary production (NPP) of aspen stands spanning the rain:snow transition zone in the Reynolds Creek Critical Zone Observatory (RCCZO) in southwest Idaho, USA. Within the RCCZO, the total amount of precipitation has remained unchanged over the past 50 years, however the percentage of the precipitation falling as snow has declined by approximately 4% per decade at mid-elevation sites. The biogeochemical process model Biome-BGC was used to simulate aspen NPP at three stands located directly below snowdrifts that provide melt water late into the spring. After adjusting precipitation inputs to account for the redistribution of snow, we assessed climate change impacts on future aspen productivity. Mid-century (2046-2065) aspen NPP was simulated using temperature projections from a multi-model average under high emission conditions using the Multivariate Adaptive Constructed Analogs (MACA) data set. While climate change simulations indicated over a 20% decrease in annual NPP for some years, NPP rates for other mid-century years remained relatively unchanged due to variations in growing season conditions. Mid-century years with the largest decreases in NPP typically showed increased spring transpiration rates resulting from earlier leaf flush combined with warmer spring conditions. During these years, the onset of drought stress occurred

  10. Circular Migration as Climate Change Adaptation: Reconceptualising New Zealand´s and Australia’s Seasonal Worker Programs

    Directory of Open Access Journals (Sweden)

    Christine Brickenstein

    2013-12-01

    Full Text Available This paper looks into an aspect of adaptation, namely the role of the circular migration as climate change adaptation. It focuses on two of the Pacific region’s recently well -known seasonal labor schemes, Namely Australia’s Seasonal Workers Program (SWP and New Zealand ‘s recognized Seasonal Employer Scheme (RSE, and asks if beyond the current goals the schemes May be reconceptualsed as adaptation programs responsive not only towards developmental and economic Concerns but the wider (and interconnected With the first two climate change challenges. According to MacDermott and Opeskin, labor mobility schemes, for the sending country focus on the “development perspective “such as (a Employment Opportunities, (b Regular benefits of Remittances and (c skills enhancement, while receiving countries country can meet the challenges posed by labor shortages in seasonal industries where “a reliable workforce is lacking”.

  11. Effect of climate change on seasonal monsoon in Asia and its impact on the variability of monsoon rainfall in Southeast Asia

    Directory of Open Access Journals (Sweden)

    Yen Yi Loo

    2015-11-01

    Full Text Available Global warming and climate change is one of the most extensively researched and discussed topical issues affecting the environment. Although there are enough historical evidence to support the theory that climate change is a natural phenomenon, many research scientists are widely in agreement that the increase in temperature in the 20th century is anthropologically related. The associated effects are the variability of rainfall and cyclonic patterns that are being observed globally. In Southeast Asia the link between global warming and the seasonal atmospheric flow during the monsoon seasons shows varying degree of fuzziness. This study investigates the impact of climate change on the seasonality of monsoon Asia and its effect on the variability of monsoon rainfall in Southeast Asia. The comparison of decadal variation of precipitation and temperature anomalies before the 1970s found general increases which were mostly varying. But beyond the 1970s, global precipitation anomalous showed increases that almost corresponded with increases in global temperature anomalies for the same period. There are frequent changes and a shift westward of the Indian summer monsoon. Although precipitation is observed to be 70% below normal levels, in some areas the topography affects the intensity of rainfall. These shifting phenomenon of other monsoon season in the region are impacting on the variability of rainfall and the onset of monsoons in Southeast Asia and is predicted to delay for 15 days the onset of the monsoon in the future. The variability of monsoon rainfall in the SEA region is observed to be decadal and the frequency and intensity of intermittent flooding of some areas during the monsoon season have serious consequences on the human, financial, infrastructure and food security of the region.

  12. Seasonal Climate Variation and Caribou Availability: Modeling Sequential Movement Using Satellite-Relocation Data

    Directory of Open Access Journals (Sweden)

    Craig Nicolson

    2013-06-01

    Full Text Available Livelihood systems that depend on mobile resources must constantly adapt to change. For people living in permanent settlements, environmental changes that affect the distribution of a migratory species may reduce the availability of a primary food source, with the potential to destabilize the regional social-ecological system. Food security for Arctic indigenous peoples harvesting barren ground caribou (Rangifer tarandus granti depends on movement patterns of migratory herds. Quantitative assessments of physical, ecological, and social effects on caribou distribution have proven difficult because of the significant interannual variability in seasonal caribou movement patterns. We developed and evaluated a modeling approach for simulating the distribution of a migratory herd throughout its annual cycle over a multiyear period. Beginning with spatial and temporal scales developed in previous studies of the Porcupine Caribou Herd of Canada and Alaska, we used satellite collar locations to compute and analyze season-by-season probabilities of movement of animals between habitat zones under two alternative weather conditions for each season. We then built a set of transition matrices from these movement probabilities, and simulated the sequence of movements across the landscape as a Markov process driven by externally imposed seasonal weather states. Statistical tests showed that the predicted distributions of caribou were consistent with observed distributions, and significantly correlated with subsistence harvest levels for three user communities. Our approach could be applied to other caribou herds and could be adapted for simulating the distribution of other ungulates and species with similarly large interannual variability in the use of their range.

  13. Nonverbal interpersonal attunement and extravert personality predict outcome of light treatment in seasonal affective disorder

    NARCIS (Netherlands)

    Geerts, E; Kouwert, E; Bouhuys, N; Meesters, Y; Jansen, J

    We investigated whether personality and nonverbal interpersonal processes can predict the subsequent response to light treatment in seasonal affective disorder (SAD) patients. In 60 SAD patients, Neuroticism and Extraversion were assessed prior to light treatment (4 days with 30 min of 10.000 lux).

  14. South African mid-summer seasonal rainfall prediction performance by a coupled ocean-atmosphere model

    CSIR Research Space (South Africa)

    Landman, WA

    2011-01-01

    Full Text Available . 2000; Goddard and Mason, 2002). Such a so-called two-tiered procedure to predict the outcome of the rainfall season has been employed in South Africa for a number of years already (e.g., Landman et al., 2001). The advent of fully coupled ocean...

  15. Predicting Climate-sensitive Infectious Diseases: Development of a Federal Science Plan and the Path Forward

    Science.gov (United States)

    Trtanj, J.; Balbus, J. M.; Brown, C.; Shimamoto, M. M.

    2017-12-01

    The transmission and spread of infectious diseases, especially vector-borne diseases, water-borne diseases and zoonosis, are influenced by short and long-term climate factors, in conjunction with numerous other drivers. Public health interventions, including vaccination, vector control programs, and outreach campaigns could be made more effective if the geographic range and timing of increased disease risk could be more accurately targeted, and high risk areas and populations identified. While some progress has been made in predictive modeling for transmission of these diseases using climate and weather data as inputs, they often still start after the first case appears, the skill of those models remains limited, and their use by public health officials infrequent. And further, predictions with lead times of weeks, months or seasons are even rarer, yet the value of acting early holds the potential to save more lives, reduce cost and enhance both economic and national security. Information on high-risk populations and areas for infectious diseases is also potentially useful for the federal defense and intelligence communities as well. The US Global Change Research Program, through its Interagency Group on Climate Change and Human Health (CCHHG), has put together a science plan that pulls together federal scientists and programs working on predictive modeling of climate-sensitive diseases, and draws on academic and other partners. Through a series of webinars and an in-person workshop, the CCHHG has convened key federal and academic stakeholders to assess the current state of science and develop an integrated science plan to identify data and observation systems needs as well as a targeted research agenda for enhancing predictive modeling. This presentation will summarize the findings from this effort and engage AGU members on plans and next steps to improve predictive modeling for infectious diseases.

  16. Verification of ECMWF System 4 for seasonal hydrological forecasting in a northern climate

    Science.gov (United States)

    Bazile, Rachel; Boucher, Marie-Amélie; Perreault, Luc; Leconte, Robert

    2017-11-01

    Hydropower production requires optimal dam and reservoir management to prevent flooding damage and avoid operation losses. In a northern climate, where spring freshet constitutes the main inflow volume, seasonal forecasts can help to establish a yearly strategy. Long-term hydrological forecasts often rely on past observations of streamflow or meteorological data. Another alternative is to use ensemble meteorological forecasts produced by climate models. In this paper, those produced by the ECMWF (European Centre for Medium-Range Forecast) System 4 are examined and bias is characterized. Bias correction, through the linear scaling method, improves the performance of the raw ensemble meteorological forecasts in terms of continuous ranked probability score (CRPS). Then, three seasonal ensemble hydrological forecasting systems are compared: (1) the climatology of simulated streamflow, (2) the ensemble hydrological forecasts based on climatology (ESP) and (3) the hydrological forecasts based on bias-corrected ensemble meteorological forecasts from System 4 (corr-DSP). Simulated streamflow computed using observed meteorological data is used as benchmark. Accounting for initial conditions is valuable even for long-term forecasts. ESP and corr-DSP both outperform the climatology of simulated streamflow for lead times from 1 to 5 months depending on the season and watershed. Integrating information about future meteorological conditions also improves monthly volume forecasts. For the 1-month lead time, a gain exists for almost all watersheds during winter, summer and fall. However, volume forecasts performance for spring varies from one watershed to another. For most of them, the performance is close to the performance of ESP. For longer lead times, the CRPS skill score is mostly in favour of ESP, even if for many watersheds, ESP and corr-DSP have comparable skill. Corr-DSP appears quite reliable but, in some cases, under-dispersion or bias is observed. A more complex bias

  17. Using subseasonal-to-seasonal (S2S) extreme rainfall forecasts for extended-range flood prediction in Australia

    Science.gov (United States)

    White, C. J.; Franks, S. W.; McEvoy, D.

    2015-06-01

    Meteorological and hydrological centres around the world are looking at ways to improve their capacity to be able to produce and deliver skilful and reliable forecasts of high-impact extreme rainfall and flooding events on a range of prediction timescales (e.g. sub-daily, daily, multi-week, seasonal). Making improvements to extended-range rainfall and flood forecast models, assessing forecast skill and uncertainty, and exploring how to apply flood forecasts and communicate their benefits to decision-makers are significant challenges facing the forecasting and water resources management communities. This paper presents some of the latest science and initiatives from Australia on the development, application and communication of extreme rainfall and flood forecasts on the extended-range "subseasonal-to-seasonal" (S2S) forecasting timescale, with a focus on risk-based decision-making, increasing flood risk awareness and preparedness, capturing uncertainty, understanding human responses to flood forecasts and warnings, and the growing adoption of "climate services". The paper also demonstrates how forecasts of flood events across a range of prediction timescales could be beneficial to a range of sectors and society, most notably for disaster risk reduction (DRR) activities, emergency management and response, and strengthening community resilience. Extended-range S2S extreme flood forecasts, if presented as easily accessible, timely and relevant information are a valuable resource to help society better prepare for, and subsequently cope with, extreme flood events.

  18. Using subseasonal-to-seasonal (S2S extreme rainfall forecasts for extended-range flood prediction in Australia

    Directory of Open Access Journals (Sweden)

    C. J. White

    2015-06-01

    Full Text Available Meteorological and hydrological centres around the world are looking at ways to improve their capacity to be able to produce and deliver skilful and reliable forecasts of high-impact extreme rainfall and flooding events on a range of prediction timescales (e.g. sub-daily, daily, multi-week, seasonal. Making improvements to extended-range rainfall and flood forecast models, assessing forecast skill and uncertainty, and exploring how to apply flood forecasts and communicate their benefits to decision-makers are significant challenges facing the forecasting and water resources management communities. This paper presents some of the latest science and initiatives from Australia on the development, application and communication of extreme rainfall and flood forecasts on the extended-range "subseasonal-to-seasonal" (S2S forecasting timescale, with a focus on risk-based decision-making, increasing flood risk awareness and preparedness, capturing uncertainty, understanding human responses to flood forecasts and warnings, and the growing adoption of "climate services". The paper also demonstrates how forecasts of flood events across a range of prediction timescales could be beneficial to a range of sectors and society, most notably for disaster risk reduction (DRR activities, emergency management and response, and strengthening community resilience. Extended-range S2S extreme flood forecasts, if presented as easily accessible, timely and relevant information are a valuable resource to help society better prepare for, and subsequently cope with, extreme flood events.

  19. Decadal climate prediction with a refined anomaly initialisation approach

    Science.gov (United States)

    Volpi, Danila; Guemas, Virginie; Doblas-Reyes, Francisco J.; Hawkins, Ed; Nichols, Nancy K.

    2017-03-01

    In decadal prediction, the objective is to exploit both the sources of predictability from the external radiative forcings and from the internal variability to provide the best possible climate information for the next decade. Predicting the climate system internal variability relies on initialising the climate model from observational estimates. We present a refined method of anomaly initialisation (AI) applied to the ocean and sea ice components of the global climate forecast model EC-Earth, with the following key innovations: (1) the use of a weight applied to the observed anomalies, in order to avoid the risk of introducing anomalies recorded in the observed climate, whose amplitude does not fit in the range of the internal variability generated by the model; (2) the AI of the ocean density, instead of calculating it from the anomaly initialised state of temperature and salinity. An experiment initialised with this refined AI method has been compared with a full field and standard AI experiment. Results show that the use of such refinements enhances the surface temperature skill over part of the North and South Atlantic, part of the South Pacific and the Mediterranean Sea for the first forecast year. However, part of such improvement is lost in the following forecast years. For the tropical Pacific surface temperature, the full field initialised experiment performs the best. The prediction of the Arctic sea-ice volume is improved by the refined AI method for the first three forecast years and the skill of the Atlantic multidecadal oscillation is significantly increased compared to a non-initialised forecast, along the whole forecast time.

  20. A new classification of large-scale climate regimes around the Tibetan Plateau based on seasonal circulation patterns

    Directory of Open Access Journals (Sweden)

    Xin-Gang Dai

    2017-03-01

    Full Text Available This study aims to develop a large-scale climate classification for investigating the characteristics of the climate regimes around the Tibetan Plateau based on seasonal precipitation, moisture transport and moisture divergence using in situ observations and ERA40 reanalysis data. The results indicate that the climate can be attributed to four regimes around the Plateau. They situate in East Asia, South Asia, Central Asia and the semi-arid zone in northern Central Asia throughout the dryland of northwestern China, in addition to the Köppen climate classification. There are different collocations of seasonal temperature and precipitation: 1 in phase for the East and South Asia monsoon regimes, 2 anti-phase for the Central Asia regime, 3 out-of-phase for the westerly regime. The seasonal precipitation concentrations are coupled with moisture divergence, i.e., moisture convergence coincides with the Asian monsoon zone and divergence appears over the Mediterranean-like arid climate region and westerly controlled area in the warm season, while it reverses course in the cold season. In addition, moisture divergence is associated with meridional moisture transport. The northward/southward moisture transport corresponds to moisture convergence/divergence, indicating that the wet and dry seasons are, to a great extent, dominated by meridional moisture transport in these regions. The climate mean southward transport results in the dry-cold season of the Asian monsoon zone and the dry-warm season, leading to desertification or land degradation in Central Asia and the westerly regime zone. The mean-wind moisture transport (MMT is the major contributor to total moisture transport, while persistent northward transient eddy moisture transport (TEMT plays a key role in dry season precipitation, especially in the Asian monsoon zone. The persistent TEMT divergence is an additional mechanism of the out-of-phase collocation in the westerly regime zone. In addition

  1. Projected climate change impacts and short term predictions on staple crops in Sub-Saharan Africa

    Science.gov (United States)

    Mereu, V.; Spano, D.; Gallo, A.; Carboni, G.

    2013-12-01

    Agriculture in Sub-Saharan Africa (SSA) drives the economy of many African countries and it is mainly rain-fed agriculture used for subsistence. Increasing temperatures, changed precipitation patterns and more frequent droughts may lead to a substantial decrease of crop yields. The projected impacts of future climate change on agriculture are expected to be significant and extensive in the SSA due to the shortening of the growing seasons and the increasing of water-stress risk. Differences in Agro-Ecological Zones and geographical characteristics of SSA influence the diverse impacts of climate change, which can greatly differ across the continent and within countries. The vulnerability of African Countries to climate change is aggravated by the low adaptive capacity of the continent, due to the increasing of its population, the widespread poverty, and other social factors. In this contest, the assessment of climate change impact on agricultural sector has a particular interest to stakeholder and policy makers, in order to identify specific agricultural sectors and Agro-Ecological Zones that could be more vulnerable to changes in climatic conditions and to develop the most appropriate policies to cope with these threats. For these reasons, the evaluation of climate change impacts for key crops in SSA was made exploring climate uncertainty and focusing on short period monitoring, which is particularly useful for food security and risk management analysis. The DSSAT-CSM (Decision Support System for Agrotechnology Transfer - Cropping System Model) software, version 4.5 was used for the analysis. Crop simulation models included in DSSAT-CSM are tools that allow to simulate physiological process of crop growth, development and production, by combining genetic crop characteristics and environmental (soil and weather) conditions. For each selected crop, the models were used, after a parameterization phase, to evaluate climate change impacts on crop phenology and production

  2. Predictable and unpredictable modes of seasonal mean precipitation over Northeast China

    Science.gov (United States)

    Ying, Kairan; Frederiksen, Carsten S.; Zhao, Tianbao; Zheng, Xiaogu; Xiong, Zhe; Yi, Xue; Li, Chunxiang

    2018-04-01

    This study investigates the patterns of interannual variability that arise from the potentially predictable (slow) and unpredictable (intraseasonal) components of seasonal mean precipitation over Northeast (NE) China, using observations from a network of 162 meteorological stations for the period 1961-2014. A variance decomposition method is applied to identify the sources of predictability, as well as the sources of prediction uncertainty, for January-February-March (JFM), April-May-June (AMJ), July-August-September (JAS) and October-November-December (OND). The averaged potential predictability (ratio of slow to total variance) of NE China precipitation has the highest value of 0.32 during JAS and lowest value of 0.1 in AMJ. Possible sources of seasonal prediction for the leading predictable precipitation EOF modes come from the SST anomalies in the Japan Sea, as well as the North Atlantic during JFM, the Indian Ocean SST in AMJ, and the eastern tropical Pacific SST in JAS and OND. The prolonged linear trend, which is seen in the principal component time series of the leading predictable mode in JFM and OND, may also serve as a source of predictability. The Polar-Eurasia and Northern Annular Mode atmospheric teleconnection patterns are closely connected with the leading and the second predictable mode of JAS, respectively. The Hadley cell circulation is closely related to the leading predictable mode of OND. The leading/second unpredictable precipitation modes for all these four seasons show a similar monopole/dipole structure, and can be largely attributed to the intraseasonal variabilities of the atmosphere.

  3. Collaborative Proposal: Improving Decadal Prediction of Arctic Climate Variability and Change Using a Regional Arctic System Model (RASM)

    Energy Technology Data Exchange (ETDEWEB)

    Maslowski, Wieslaw [Naval Postgraduate School, Monterey, CA (United States)

    2016-10-17

    This project aims to develop, apply and evaluate a regional Arctic System model (RASM) for enhanced decadal predictions. Its overarching goal is to advance understanding of the past and present states of arctic climate and to facilitate improvements in seasonal to decadal predictions. In particular, it will focus on variability and long-term change of energy and freshwater flows through the arctic climate system. The project will also address modes of natural climate variability as well as extreme and rapid climate change in a region of the Earth that is: (i) a key indicator of the state of global climate through polar amplification and (ii) which is undergoing environmental transitions not seen in instrumental records. RASM will readily allow the addition of other earth system components, such as ecosystem or biochemistry models, thus allowing it to facilitate studies of climate impacts (e.g., droughts and fires) and of ecosystem adaptations to these impacts. As such, RASM is expected to become a foundation for more complete Arctic System models and part of a model hierarchy important for improving climate modeling and predictions.

  4. Predicting climate change impacts on polar bear litter size.

    Science.gov (United States)

    Molnár, Péter K; Derocher, Andrew E; Klanjscek, Tin; Lewis, Mark A

    2011-02-08

    Predicting the ecological impacts of climate warming is critical for species conservation. Incorporating future warming into population models, however, is challenging because reproduction and survival cannot be measured for yet unobserved environmental conditions. In this study, we use mechanistic energy budget models and data obtainable under current conditions to predict polar bear litter size under future conditions. In western Hudson Bay, we predict climate warming-induced litter size declines that jeopardize population viability: ∼28% of pregnant females failed to reproduce for energetic reasons during the early 1990s, but 40-73% could fail if spring sea ice break-up occurs 1 month earlier than during the 1990s, and 55-100% if break-up occurs 2 months earlier. Simultaneously, mean litter size would decrease by 22-67% and 44-100%, respectively. The expected timeline for these declines varies with climate-model-specific sea ice predictions. Similar litter size declines may occur in over one-third of the global polar bear population.

  5. Spatial Models for Prediction and Early Warning of Aedes aegypti Proliferation from Data on Climate Change and Variability in Cuba.

    Science.gov (United States)

    Ortiz, Paulo L; Rivero, Alina; Linares, Yzenia; Pérez, Alina; Vázquez, Juan R

    2015-04-01

    Climate variability, the primary expression of climate change, is one of the most important environmental problems affecting human health, particularly vector-borne diseases. Despite research efforts worldwide, there are few studies addressing the use of information on climate variability for prevention and early warning of vector-borne infectious diseases. Show the utility of climate information for vector surveillance by developing spatial models using an entomological indicator and information on predicted climate variability in Cuba to provide early warning of danger of increased risk of dengue transmission. An ecological study was carried out using retrospective and prospective analyses of time series combined with spatial statistics. Several entomological and climatic indicators were considered using complex Bultó indices -1 and -2. Moran's I spatial autocorrelation coefficient specified for a matrix of neighbors with a radius of 20 km, was used to identify the spatial structure. Spatial structure simulation was based on simultaneous autoregressive and conditional autoregressive models; agreement between predicted and observed values for number of Aedes aegypti foci was determined by the concordance index Di and skill factor Bi. Spatial and temporal distributions of populations of Aedes aegypti were obtained. Models for describing, simulating and predicting spatial patterns of Aedes aegypti populations associated with climate variability patterns were put forward. The ranges of climate variability affecting Aedes aegypti populations were identified. Forecast maps were generated for the municipal level. Using the Bultó indices of climate variability, it is possible to construct spatial models for predicting increased Aedes aegypti populations in Cuba. At 20 x 20 km resolution, the models are able to provide warning of potential changes in vector populations in rainy and dry seasons and by month, thus demonstrating the usefulness of climate information for

  6. Rain-season trends in precipitation and their effect in different climate regions of China during 1961-2008

    International Nuclear Information System (INIS)

    Song Yanling; Achberger, Christine; Linderholm, Hans W

    2011-01-01

    Using high-quality precipitation data from 524 stations, the trends of a set of precipitation variables during the main rain season (May-September) from 1961 to 2008 for different climate regions in China were analysed. However, different characteristics were displayed in different regions of China. In most temperate monsoon regions (north-eastern China), total rain-season precipitation and precipitation days showed decreasing trends; positive tendencies in precipitation intensity were, however, noted for most stations in this region. It is suggested that the decrease in rain-season precipitation is mainly related to there being fewer rain days and a change towards drier conditions in north-eastern China, and as a result, the available water resources have been negatively affected in the temperate monsoon regions. In most subtropical and tropical monsoon climate regions (south-eastern China), the rain-season precipitation and precipitation days (11-50, with > 50 mm) showed slightly positive trends. However, precipitation days with ≤ 10 mm decreased in these regions. Changes towards wetter conditions in this area, together with more frequent heavy rainfall events causing floods, have a severe impact on peoples' lives and socio-economic development. In general, the rain-season precipitation, precipitation days and rain-season precipitation intensity had all increased in the temperate continental and plateau/mountain regions of western China. This increase in rain-season precipitation has been favourable to pasture growth.

  7. Estimation of Seasonal Efficiency of Sochi Resort Climate Therapy by Means of Psychologic Testing of Patients with Cardiometabolic Pathology

    OpenAIRE

    Irina N. Sorochinskaya; Andrei V. Chernyshev

    2012-01-01

    Cardiovascular diseases are major reasons for population mortality in majority of countries, including Russia. Metabolic syndrome is considered to be one of the main pathologic states, leading to enhancement of atherogenesis, ischemic heart diseases and cerebrovascular diseases. Physical methods, including resort treatment play great role in metabolic syndrome prevention and treatment. Climate therapy depends on resort climate and season and is a major component of resort treatment. Psycholog...

  8. In-Season Yield Prediction of Cabbage with a Hand-Held Active Canopy Sensor.

    Science.gov (United States)

    Ji, Rongting; Min, Ju; Wang, Yuan; Cheng, Hu; Zhang, Hailin; Shi, Weiming

    2017-10-08

    Efficient and precise yield prediction is critical to optimize cabbage yields and guide fertilizer application. A two-year field experiment was conducted to establish a yield prediction model for cabbage by using the Greenseeker hand-held optical sensor. Two cabbage cultivars (Jianbao and Pingbao) were used and Jianbao cultivar was grown for 2 consecutive seasons but Pingbao was only grown in the second season. Four chemical nitrogen application rates were implemented: 0, 80, 140, and 200 kg·N·ha -1 . Normalized difference vegetation index (NDVI) was collected 20, 50, 70, 80, 90, 100, 110, 120, 130, and 140 days after transplanting (DAT). Pearson correlation analysis and regression analysis were performed to identify the relationship between the NDVI measurements and harvested yields of cabbage. NDVI measurements obtained at 110 DAT were significantly correlated to yield and explained 87-89% and 75-82% of the cabbage yield variation of Jianbao cultivar over the two-year experiment and 77-81% of the yield variability of Pingbao cultivar. Adjusting the yield prediction models with CGDD (cumulative growing degree days) could make remarkable improvement to the accuracy of the prediction model and increase the determination coefficient to 0.82, while the modification with DFP (days from transplanting when GDD > 0) values did not. The integrated exponential yield prediction equation was better than linear or quadratic functions and could accurately make in-season estimation of cabbage yields with different cultivars between years.

  9. Varied representation of the West Pacific pattern in multiple dynamical seasonal predictions of APCC-MME

    Science.gov (United States)

    Lee, Yun-Young

    2017-04-01

    West Pacific (WP) teleconnection pattern is one of the well-known primary modes of boreal winter low-frequency variability (LFV) resolved in 500 hPa geopotential height and its phase and amplitude strongly influence regional weather conditions including temperature and rainfall extremes [Baxter and Nigam, 2015; Hsu and Wallace, 1985; Linkin and Nigam, 2008; Mo and Livezey, 1986; Thompson and Wallace, 1998; Wallace and Gutzler, 1981]. This study primary aims to evaluate individual 11 GCMs seasonal hindcasts employed as members of multi-model ensemble (MME) produced in APEC Climate Center (APCC) in representing WP. For the extensive and comprehensive evaluation, this study applied seven verification metrics in three scopes: (a) temporal representation of observed indices, (b) spatial mode separation in the Northern Hemisphere (NH), and (c) regional mode isolated in the preset longitudinal domain. Verification results display quite large inter-model spread. Some models mimic observed index variability while others display large bias of index variability compared to climatology. Basic north-south dipole pattern is mostly well reproduced in both rotated and unrotated loading modes. However, each individual seasonal forecast model exhibits slightly different behavior (e.g. amplification/weakening, zonal and meridional shift, downstream extension and so forth) in representing spatial structure of WP. When taking all 7 metrics into account, one Europe (CMCC) model, one Oceania (POAMA) model and two North America (NASA and NCEP) models are classified as relatively good performers while PNU is classified as a matchless poor performer out of 11. Least WP representing skill of PNU is sort of consistent with the largest bias of NH total variability. This study further tries to examine winter mean biases of individual models and figure out how mean bias is linked to WP representation in model world. Model bias of winter climatology is investigated focusing on six large scale

  10. Disruption of the European climate seasonal clock in a warming world

    Science.gov (United States)

    Cattiaux, J.; Cassou, C.

    2015-12-01

    Strength and inland penetration of the oceanic westerly flow over Europe control a large part of the temperature variability over most of the continent. Reduced westerlies, linked to high-pressure anomalies over Scandinavia, induce cold conditions in winter and warm conditions in summer. Here we propose to define the onset of these two seasons as the calendar day where the daily circulation/temperature relationship over Western Europe switches sign. According to this meteorologically-based metrics assessed from several observational datasets, we provide robust evidence for an earlier summer onset by ~10 days between the 1960s and 2000s. Results from model ensemble simulations dedicated to detection-attribution show that this calendar advance is incompatible with the sole internal climate variability and can be attributed to anthropogenic forcings. Late winter snow disappearance over Eastern Europe affects cold air intrusion to the West when easterlies blow, and is mainly responsible for the observed present-day and near-future summer advance. Our findings agree with phenological-based trends (earlier spring events) reported for many living species over Europe, for which they provide a novel dynamical interpretation beyond the traditionally evoked global warming effect. Based on business-as-usual scenario, a seasonal shift of ~25 days is expected by 2100 for summer onset, while no clear signal arises for winter onset.

  11. Predicting seasonal variations in coastal seabird habitats in the English Channel and the Bay of Biscay

    Science.gov (United States)

    Virgili, A.; Lambert, C.; Pettex, E.; Dorémus, G.; Van Canneyt, O.; Ridoux, V.

    2017-07-01

    Seabirds, like all animals, have to live in suitable habitats to fulfil their energetic needs for both somatic and reproductive growth and maintenance. Apart from migration trips, all coastal seabirds are linked to the coast, because they need to return daily to land for resting or breeding. Their use of marine habitats strongly depends on their biology, but also on environmental conditions, and can be described using habitat models. This study aimed to: (1) identify the processes that mostly influence seabird distributions along the coasts of the English Channel and the Bay of Biscay; (2) determine seasonal variations of these processes, (3) provide prediction maps that describe the species distributions. We collected data of coastal seabird sightings from aerial surveys carried out in the English Channel and the eastern North Atlantic in the winter 2011-2012 and summer 2012. We classified seabirds into morphological groups and described their habitats using physiographic and oceanographic variables in Generalised Additive Models (GAMs). Finally, we produced maps of predicted distributions by season for each group. The distributions of coastal seabirds were essentially determined by the distance to the nearest coast, with a weaker influence of oceanographic variables. The nature of the substrate, sand or rock, combined with the timing of reproduction, also contributed to determine seasonal at-sea distributions for some species. The highest densities were predicted near the coast, particularly in bays and estuaries for strictly coastal species with possible variations depending on the season. From this study, we were able to predict the seasonal distribution of the studied species according to varying environmental parameters that changed over time, allowing us to understand better their behaviour and ecology.

  12. Analysis of climatic variations in seasonal precipitation and temperature in Salamanca (Spain); Analisis de las variaciones climaticas en series estacionales de temperatura y precipitacion en Salamanca (Espana)

    Energy Technology Data Exchange (ETDEWEB)

    Garcia Casado, A.; Encinas, A.H.; Rodriguez Puebla, C. [Dpto. de Fisica General y de la Atmosfera Universidad de Salamanca, Salamanca (Spain)

    1996-12-31

    This paper describes the seasonal precipitation and temperature variability in Salamanca. The objectives of the study are: to determine the climate signals on inter annual time-scale within the time series; to redefine the series as a function of the significant oscillation components and to predict local precipitation and temperature variables. The methods used are spectral analysis to obtain the periods of the significant components, linear and nonlinear regression models to obtain the analytical functions that best fit the data. (Author) 14 refs.

  13. Selenium deficiency risk predicted to increase under future climate change.

    Science.gov (United States)

    Jones, Gerrad D; Droz, Boris; Greve, Peter; Gottschalk, Pia; Poffet, Deyan; McGrath, Steve P; Seneviratne, Sonia I; Smith, Pete; Winkel, Lenny H E

    2017-03-14

    Deficiencies of micronutrients, including essential trace elements, affect up to 3 billion people worldwide. The dietary availability of trace elements is determined largely by their soil concentrations. Until now, the mechanisms governing soil concentrations have been evaluated in small-scale studies, which identify soil physicochemical properties as governing variables. However, global concentrations of trace elements and the factors controlling their distributions are virtually unknown. We used 33,241 soil data points to model recent (1980-1999) global distributions of Selenium (Se), an essential trace element that is required for humans. Worldwide, up to one in seven people have been estimated to have low dietary Se intake. Contrary to small-scale studies, soil Se concentrations were dominated by climate-soil interactions. Using moderate climate-change scenarios for 2080-2099, we predicted that changes in climate and soil organic carbon content will lead to overall decreased soil Se concentrations, particularly in agricultural areas; these decreases could increase the prevalence of Se deficiency. The importance of climate-soil interactions to Se distributions suggests that other trace elements with similar retention mechanisms will be similarly affected by climate change.

  14. A conceptual prediction model for seasonal drought processes using atmospheric and oceanic standardized anomalies: application to regional drought processes in China

    Science.gov (United States)

    Liu, Zhenchen; Lu, Guihua; He, Hai; Wu, Zhiyong; He, Jian

    2018-01-01

    Reliable drought prediction is fundamental for water resource managers to develop and implement drought mitigation measures. Considering that drought development is closely related to the spatial-temporal evolution of large-scale circulation patterns, we developed a conceptual prediction model of seasonal drought processes based on atmospheric and oceanic standardized anomalies (SAs). Empirical orthogonal function (EOF) analysis is first applied to drought-related SAs at 200 and 500 hPa geopotential height (HGT) and sea surface temperature (SST). Subsequently, SA-based predictors are built based on the spatial pattern of the first EOF modes. This drought prediction model is essentially the synchronous statistical relationship between 90-day-accumulated atmospheric-oceanic SA-based predictors and SPI3 (3-month standardized precipitation index), calibrated using a simple stepwise regression method. Predictor computation is based on forecast atmospheric-oceanic products retrieved from the NCEP Climate Forecast System Version 2 (CFSv2), indicating the lead time of the model depends on that of CFSv2. The model can make seamless drought predictions for operational use after a year-to-year calibration. Model application to four recent severe regional drought processes in China indicates its good performance in predicting seasonal drought development, despite its weakness in predicting drought severity. Overall, the model can be a worthy reference for seasonal water resource management in China.

  15. 20th-Century Climate Change over Africa: Seasonal Variation in Hydroclimate Trends and Sahara Desert Extent

    Science.gov (United States)

    Nigam, S.; Thomas, N. P.

    2017-12-01

    Twentieth-century trends in seasonal temperature and precipitation over the African continent are analyzed from observational data sets and historical climate simulations. Given the agricultural economy of the continent, a seasonal perspective is adopted as it is more pertinent than an annual-average one which can mask off-setting but agriculturally-sensitive seasonal hydroclimate variations. Examination of linear trends in seasonal surface air temperature (SAT) shows that heat stress has increased in several regions, including Sudan and Northern Africa where largest SAT trends occur in the warm season. Broadly speaking, the northern continent has warmed more than the southern one in all seasons. Precipitation trends are varied but notable declining trends are found in the countries along the Gulf of Guinea, especially in the source region of Niger river in West Africa, and in the Congo river basin. Rainfall over the African Great Lakes - one of the largest freshwater repositories - has however increased. We show that the Sahara Desert has expanded significantly over the 20th century - by 12-20% depending on the season. The desert expanded southward in summer, reflecting retreat of the northern edge of the Sahel rainfall belt; and to the north in winter, indicating potential impact of the widening of the Tropics. Specific mechanisms driving the expansion in each season are investigated. Finally, this observational analysis is used to evaluate the state-of-the-art climate models from a comparison of the 20th-century hydroclimate trends with those manifest in historical climate simulations. The evaluation shows that modeling regional hydroclimate change over the Africa continent remains challenging.

  16. A Standardized Evaluation System for Decadal Climate Prediction

    Science.gov (United States)

    Kadow, C.; Cubasch, U.

    2012-12-01

    The evaluation of decadal prediction systems is a scientific challenge as well as a technical challenge in the climate research. The major project MiKlip (www.fona-miklip.de) for medium-term climate prediction funded by the Federal Ministry of Education and Research in Germany (BMBF) has the aim to create a model system that can provide reliable decadal forecasts on climate and weather. The model system to be developed will be novel in several aspects, with great challenges for the methodology development. This concerns especially the determination of the initial conditions, the inclusion into the model of processes relevant to decadal predictions, the increase of the spatial resolution through regionalisation, the improvement or adjustment of statistical post-processing, and finally the synthesis and validation of the entire model system. Therefore, a standardized evaluation system will be part of the MiKlip system to validate it - developed by the project 'Integrated data and evaluation system for decadal scale prediction' (INTEGRATION). The presentation gives an overview of the different linkages of such a project, shows the different development stages and gives an outlook for users and possible end users in climate service. The technical interface combines all projects inside of MiKlip and invites them to participate in a common evaluation system. The system design and the validation strategy from a standalone tool in the beginning to a user friendly web based system using GRID technologies to an integrated part of the operational MiKlip system for industry and society will give the opportunity to enhance the MiKlip strategy. First results of different possibilities of such a system will be shown to present the scientific background through Taylor diagrams, ensemble skill scores and e.g. climatological means to show the usability and possibilities of MiKlip and the INTEGRATION project.

  17. Toward seamless weather-climate and environmental prediction

    Science.gov (United States)

    Brunet, Gilbert

    2016-04-01

    Over the last decade or so, predicting the weather, climate and atmospheric composition has emerged as one of the most important areas of scientific endeavor. This is partly because the remarkable increase in skill of current weather forecasts has made society more and more dependent on them day to day for a whole range of decision making. And it is partly because climate change is now widely accepted and the realization is growing rapidly that it will affect every person in the world profoundly, either directly or indirectly. One of the important endeavors of our societies is to remain at the cutting-edge of modelling and predicting the evolution of the fully coupled environmental system: atmosphere (weather and composition), oceans, land surface (physical and biological), and cryosphere. This effort will provide an increasingly accurate and reliable service across all the socio-economic sectors that are vulnerable to the effects of adverse weather and climatic conditions, whether now or in the future. This emerging challenge was at the center of the World Weather Open Science Conference (Montreal, 2014).The outcomes of the conference are described in the World Meteorological Organization (WMO) book: Seamless Prediction of the Earth System: from Minutes to Months, (G. Brunet, S. Jones, P. Ruti Eds., WMO-No. 1156, 2015). It is freely available on line at the WMO website. We will discuss some of the outcomes of the conference for the WMO World Weather Research Programme (WWRP) and Global Atmospheric Watch (GAW) long term goals and provide examples of seamless modelling and prediction across a range of timescales at convective and sub-kilometer scales for regional coupled forecasting applications at Environment and Climate Change Canada (ECCC).

  18. [Temporal and spatial change of climate resources and meteorological disasters under climate change during winter crop growing season in Guangdong Province, China.

    Science.gov (United States)

    Wang, Hua; Chen, Hui Hua; Tang, Li Sheng; Wang, Juan Huai; Tang, Hai Yan

    2018-01-01

    Trend analysis method was applied to analyze the general variation characteristics of the climate resources and meteorological disasters of growing season of the winter planting in Guangdong before (1961-1996) and after climate warming (1997-2015). Percentile method was employed to determine thresholds for extreme cold and drought in major planting regions, and the characteristics of extreme disasters since climate warming were analyzed. The results showed that, by comparing 1997-2015 with 1961-1996, the heat value in winter growing season increased significantly. The belt with a higher heat value, where the average temperature was ≥15 ℃ and accumulated temperature was ≥2200 ℃·d, covered the main winter production regions as Shaoguan, Zhanjiang, Maoming, Huizhou, Meizhou and Guangzhou. Meanwhile, the precipitation witnessed a slight increase. The regions with precipitations of 250-350 mm included Zhanjiang, Maoming, Huizhou, Guangzhou and Meizhou. Chilling injury in the winter planting season in the regions decreased, the belt with an accumulated chilling of climate resources and the occurrence law of meteorological disasters in growing season.

  19. Seasonal variations of Saanen goat milk composition and the impact of climatic conditions.

    Science.gov (United States)

    Kljajevic, Nemanja V; Tomasevic, Igor B; Miloradovic, Zorana N; Nedeljkovic, Aleksandar; Miocinovic, Jelena B; Jovanovic, Snezana T

    2018-01-01

    The aim of this research was to investigate the effect of climatic conditions and their impact on seasonal variations of physico-chemical characteristics of Saanen goat milk produced over a period of 4 years. Lactation period (early, mid and late) and year were considered as factors that influence physico-chemical composition of milk. Pearson's coefficient of correlation was calculated between the physico-chemical characteristics of milk (fat, proteins, lactose, non-fat dry matter, density, freezing point, pH, titrable acidity) and climatic condition parameters (air temperature, temperature humidity index-THI, solar radiation duration, relative humidity). Results showed that all physico-chemical characteristics of Saanen goat milk varied significantly throughout the lactation period and years. The decrease of fat, protein, non-fat dry matter and lactose content in goat milk during the mid-lactation period was more pronounced than was previously reported in the literature. The highest values for these characteristics were recorded in the late lactation period. Observed variations were explained by negative correlation between THI and the physico-chemical characteristics of Saanen goat milk. This indicated that Saanen goats were very prone to heat stress, which implied the decrease of physico-chemical characteristics during hot summers.

  20. Improved Seasonal Prediction of European Summer Temperatures With New Five-Layer Soil-Hydrology Scheme

    Science.gov (United States)

    Bunzel, Felix; Müller, Wolfgang A.; Dobrynin, Mikhail; Fröhlich, Kristina; Hagemann, Stefan; Pohlmann, Holger; Stacke, Tobias; Baehr, Johanna

    2018-01-01

    We evaluate the impact of a new five-layer soil-hydrology scheme on seasonal hindcast skill of 2 m temperatures over Europe obtained with the Max Planck Institute Earth System Model (MPI-ESM). Assimilation experiments from 1981 to 2010 and 10-member seasonal hindcasts initialized on 1 May each year are performed with MPI-ESM in two soil configurations, one using a bucket scheme and one a new five-layer soil-hydrology scheme. We find the seasonal hindcast skill for European summer temperatures to improve with the five-layer scheme compared to the bucket scheme and investigate possible causes for these improvements. First, improved indirect soil moisture assimilation allows for enhanced soil moisture-temperature feedbacks in the hindcasts. Additionally, this leads to improved prediction of anomalies in the 500 hPa geopotential height surface, reflecting more realistic atmospheric circulation patterns over Europe.

  1. Uncertainties in predicting rice yield by current crop models under a wide range of climatic conditions

    NARCIS (Netherlands)

    Li, T.; Hasegawa, T.; Yin, X.; Zhu, Y.; Boote, K.; Adam, M.; Bregaglio, S.; Buis, S.; Confalonieri, R.; Fumoto, T.; Gaydon, D.; Marcaida III, M.; Nakagawa, H.; Oriol, P.; Ruane, A.C.; Ruget, F.; Singh, B.; Singh, U.; Tang, L.; Yoshida, H.; Zhang, Z.; Bouman, B.

    2015-01-01

    Predicting rice (Oryza sativa) productivity under future climates is important for global food security. Ecophysiological crop models in combination with climate model outputs are commonly used in yield prediction, but uncertainties associated with crop models remain largely unquantified. We

  2. Seasonal Variability of Aragonite Saturation State in the North Pacific Ocean Predicted by Multiple Linear Regression

    Science.gov (United States)

    Kim, T. W.; Park, G. H.

    2014-12-01

    Seasonal variation of aragonite saturation state (Ωarag) in the North Pacific Ocean (NPO) was investigated, using multiple linear regression (MLR) models produced from the PACIFICA (Pacific Ocean interior carbon) dataset. Data within depth ranges of 50-1200m were used to derive MLR models, and three parameters (potential temperature, nitrate, and apparent oxygen utilization (AOU)) were chosen as predictor variables because these parameters are associated with vertical mixing, DIC (dissolved inorganic carbon) removal and release which all affect Ωarag in water column directly or indirectly. The PACIFICA dataset was divided into 5° × 5° grids, and a MLR model was produced in each grid, giving total 145 independent MLR models over the NPO. Mean RMSE (root mean square error) and r2 (coefficient of determination) of all derived MLR models were approximately 0.09 and 0.96, respectively. Then the obtained MLR coefficients for each of predictor variables and an intercept were interpolated over the study area, thereby making possible to allocate MLR coefficients to data-sparse ocean regions. Predictability from the interpolated coefficients was evaluated using Hawaiian time-series data, and as a result mean residual between measured and predicted Ωarag values was approximately 0.08, which is less than the mean RMSE of our MLR models. The interpolated MLR coefficients were combined with seasonal climatology of World Ocean Atlas 2013 (1° × 1°) to produce seasonal Ωarag distributions over various depths. Large seasonal variability in Ωarag was manifested in the mid-latitude Western NPO (24-40°N, 130-180°E) and low-latitude Eastern NPO (0-12°N, 115-150°W). In the Western NPO, seasonal fluctuations of water column stratification appeared to be responsible for the seasonal variation in Ωarag (~ 0.5 at 50 m) because it closely followed temperature variations in a layer of 0-75 m. In contrast, remineralization of organic matter was the main cause for the seasonal

  3. Evaluation of climate model aerosol seasonal and spatial variability over Africa using AERONET

    Science.gov (United States)

    Horowitz, Hannah M.; Garland, Rebecca M.; Thatcher, Marcus; Landman, Willem A.; Dedekind, Zane; van der Merwe, Jacobus; Engelbrecht, Francois A.

    2017-11-01

    The sensitivity of climate models to the characterization of African aerosol particles is poorly understood. Africa is a major source of dust and biomass burning aerosols and this represents an important research gap in understanding the impact of aerosols on radiative forcing of the climate system. Here we evaluate the current representation of aerosol particles in the Conformal Cubic Atmospheric Model (CCAM) with ground-based remote retrievals across Africa, and additionally provide an analysis of observed aerosol optical depth at 550 nm (AOD550 nm) and Ångström exponent data from 34 Aerosol Robotic Network (AERONET) sites. Analysis of the 34 long-term AERONET sites confirms the importance of dust and biomass burning emissions to the seasonal cycle and magnitude of AOD550 nm across the continent and the transport of these emissions to regions outside of the continent. In general, CCAM captures the seasonality of the AERONET data across the continent. The magnitude of modeled and observed multiyear monthly average AOD550 nm overlap within ±1 standard deviation of each other for at least 7 months at all sites except the Réunion St Denis Island site (Réunion St. Denis). The timing of modeled peak AOD550 nm in southern Africa occurs 1 month prior to the observed peak, which does not align with the timing of maximum fire counts in the region. For the western and northern African sites, it is evident that CCAM currently overestimates dust in some regions while others (e.g., the Arabian Peninsula) are better characterized. This may be due to overestimated dust lifetime, or that the characterization of the soil for these areas needs to be updated with local information. The CCAM simulated AOD550 nm for the global domain is within the spread of previously published results from CMIP5 and AeroCom experiments for black carbon, organic carbon, and sulfate aerosols. The model's performance provides confidence for using the model to estimate large-scale regional impacts

  4. Evaluation of climate model aerosol seasonal and spatial variability over Africa using AERONET

    Directory of Open Access Journals (Sweden)

    H. M. Horowitz

    2017-11-01

    Full Text Available The sensitivity of climate models to the characterization of African aerosol particles is poorly understood. Africa is a major source of dust and biomass burning aerosols and this represents an important research gap in understanding the impact of aerosols on radiative forcing of the climate system. Here we evaluate the current representation of aerosol particles in the Conformal Cubic Atmospheric Model (CCAM with ground-based remote retrievals across Africa, and additionally provide an analysis of observed aerosol optical depth at 550 nm (AOD550 nm and Ångström exponent data from 34 Aerosol Robotic Network (AERONET sites. Analysis of the 34 long-term AERONET sites confirms the importance of dust and biomass burning emissions to the seasonal cycle and magnitude of AOD550 nm across the continent and the transport of these emissions to regions outside of the continent. In general, CCAM captures the seasonality of the AERONET data across the continent. The magnitude of modeled and observed multiyear monthly average AOD550 nm overlap within ±1 standard deviation of each other for at least 7 months at all sites except the Réunion St Denis Island site (Réunion St. Denis. The timing of modeled peak AOD550 nm in southern Africa occurs 1 month prior to the observed peak, which does not align with the timing of maximum fire counts in the region. For the western and northern African sites, it is evident that CCAM currently overestimates dust in some regions while others (e.g., the Arabian Peninsula are better characterized. This may be due to overestimated dust lifetime, or that the characterization of the soil for these areas needs to be updated with local information. The CCAM simulated AOD550 nm for the global domain is within the spread of previously published results from CMIP5 and AeroCom experiments for black carbon, organic carbon, and sulfate aerosols. The model's performance provides confidence for using the model to estimate

  5. Climatic potential for tourism in the Black Forest, Germany — winter season

    Science.gov (United States)

    Endler, Christina; Matzarakis, Andreas

    2011-05-01

    Climate change, whether natural or human-caused, will have an impact on human life, including recreation and tourism among other things. In this study, methods from biometeorology and tourism climatology are used to assess the effect of a changed climate on tourism and recreation in particular. The study area is the Black Forest mountainous region of south-west Germany, which is well known for its tourist and recreational assets. Climate model projections for the 2021-2050 period based on REMO-UBA simulations with a high spatial resolution of 10 km are compared to a 30-year reference period (1971-2000) using the IPCC emission scenarios A1B and B1. The results show that the mean winter air temperature will increase by up to 1.8°C, which is the most pronounced warming compared to the other seasons. The annual precipitation amount will increase marginally by 5% in the A1B scenario and 10% in the B1 scenario. Winter precipitation contributes about 10% (A1B) and 30% (B1) to variations in annual precipitation. Although the results show that winter precipitation will increase slightly, snow days affecting skiing will be reduced on average by approximately 40% due to regional warming. Cold stress will be reduced on average by up to 25%. The result is that the thermal environment will be advanced, and warmer winters are likely to lead to an upward altitudinal shift of ski resorts and winter sport activities, thus displacing land-use currently dedicated to nature conservation.

  6. The Effects of Climate Change on Variability of the Growing Seasons in the Elbe River Lowland, Czech Republic

    Czech Academy of Sciences Publication Activity Database

    Potopová, V.; Zahradníček, Pavel; Türkot, L.; Štěpánek, Petr; Soukup, J.

    2015-01-01

    Roč. 2015, č. 546920 (2015), s. 546920 ISSN 1687-9309 R&D Projects: GA MŠk(CZ) EE2.3.20.0248 Institutional support: RVO:67179843 Keywords : Central Europe * extremes * climate change * growing seasons * Elbe River Lowland Subject RIV: DG - Athmosphere Sciences, Meteorology Impact factor: 1.107, year: 2015

  7. Thermal comfort of people in the hot and humid area of China-impacts of season, climate, and thermal history.

    Science.gov (United States)

    Zhang, Y; Chen, H; Wang, J; Meng, Q

    2016-10-01

    We conducted a climate chamber study on the thermal comfort of people in the hot and humid area of China. Sixty subjects from naturally ventilated buildings and buildings with split air conditioners participated in the study, and identical experiments were conducted in a climate chamber in both summer and winter. Psychological and physiological responses were observed over a wide range of conditions, and the impacts of season, climate, and thermal history on human thermal comfort were analyzed. Seasonal and climatic heat acclimatization was confirmed, but they were found to have no significant impacts on human thermal sensation and comfort. The outdoor thermal history was much less important than the indoor thermal history in regard to human thermal sensation, and the indoor thermal history in all seasons of a year played a key role in shaping the subjects' sensations in a wide range of thermal conditions. A warmer indoor thermal history in warm seasons produced a higher neutral temperature, a lower thermal sensitivity, and lower thermal sensations in warm conditions. The comfort and acceptable conditions were identified for people in the hot and humid area of China. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

  8. Avian population consequences of climate change are most severe for long-distance migrants in seasonal habitats.

    NARCIS (Netherlands)

    Both, C.; Turnhout, van C.A.M.; Bijlsma, R.G.; Siepel, H.; Strien, van A.J.; Foppen, R.P.B.

    2010-01-01

    One consequence of climate change is an increasing mismatch between timing of food requirements and food availability. Such a mismatch is primarily expected in avian long-distance migrants because of their complex annual cycle, and in habitats with a seasonal food peak. Here we show that

  9. Avian population consequences of climate change are most severe for long-distance migrants in seasonal habitats

    NARCIS (Netherlands)

    Both, Christiaan; Van Turnhout, Chris A. M.; Bijlsma, Rob G.; Siepel, Henk; Van Strien, Arco J.; Foppen, Ruud P. B.

    2010-01-01

    One consequence of climate change is an increasing mismatch between timing of food requirements and food availability. Such a mismatch is primarily expected in avian long-distance migrants because of their complex annual cycle, and in habitats with a seasonal food peak. Here we show that

  10. Seasonal Climate Signals in Multiple Tree-Ring Parameters: A Pilot Study of Pinus ponderosa in the Columbia River Basin

    Science.gov (United States)

    Dannenberg, M.; Wise, E. K.; Keung, J. H.

    2014-12-01

    Proxy-based reconstructions of past climate have played an integral role in assessments of historical climate change, and tree-ring widths (TRW) have a long history of use in this paleoclimate research due to their annual resolution, widespread availability, and sensitivity of growth processes to variation in temperature and water availability. Increasingly, studies have shown that additional tree-ring metrics—including earlywood and latewood widths (EW and LW, respectively), maximum latewood density, and the intensity of reflected blue light from latewood (BI)—can provide additional information on seasonal climatic variability that is not present in TRW alone due to different processes that affect growth in different parts of the growing season. Studies of these additional tree-ring metrics highlight their utility in climate reconstructions, but to date they have mostly been limited to a few tree species and regions. Here, we extend the range of previous studies on alternative tree-ring metrics by evaluating the seasonal climate signals in TRW, EW, LW, and BI of Pinus ponderosa at six semiarid sites surrounding the Columbia River basin in the U.S. Pacific Northwest (PNW). Cores from each site were cross-dated and EW, LW, and TRW were measured using standard dendrochronological procedures. BI was obtained using a high-resolution flatbed scanner and CooRecorder software. To evaluate the unique climate processes and seasonalities contributing to different dendrochronological metrics, monthly temperature and precipitation from each site were obtained from the PRISM climate model and were correlated with each of the tree-ring metrics using the MATLAB program SEASCORR. We also evaluate the potential of using multiple tree-ring metrics (rather than a single proxy) in reconstructions of precipitation in the PNW. Initial results suggest that 1) tree growth at each site is water-limited but with substantial differences among the sites in the strength and seasonality of

  11. A Seasonal Time-Series Model Based on Gene Expression Programming for Predicting Financial Distress

    Directory of Open Access Journals (Sweden)

    Ching-Hsue Cheng

    2018-01-01

    Full Text Available The issue of financial distress prediction plays an important and challenging research topic in the financial field. Currently, there have been many methods for predicting firm bankruptcy and financial crisis, including the artificial intelligence and the traditional statistical methods, and the past studies have shown that the prediction result of the artificial intelligence method is better than the traditional statistical method. Financial statements are quarterly reports; hence, the financial crisis of companies is seasonal time-series data, and the attribute data affecting the financial distress of companies is nonlinear and nonstationary time-series data with fluctuations. Therefore, this study employed the nonlinear attribute selection method to build a nonlinear financial distress prediction model: that is, this paper proposed a novel seasonal time-series gene expression programming model for predicting the financial distress of companies. The proposed model has several advantages including the following: (i the proposed model is different from the previous models lacking the concept of time series; (ii the proposed integrated attribute selection method can find the core attributes and reduce high dimensional data; and (iii the proposed model can generate the rules and mathematical formulas of financial distress for providing references to the investors and decision makers. The result shows that the proposed method is better than the listing classifiers under three criteria; hence, the proposed model has competitive advantages in predicting the financial distress of companies.

  12. A Seasonal Time-Series Model Based on Gene Expression Programming for Predicting Financial Distress

    Science.gov (United States)

    2018-01-01

    The issue of financial distress prediction plays an important and challenging research topic in the financial field. Currently, there have been many methods for predicting firm bankruptcy and financial crisis, including the artificial intelligence and the traditional statistical methods, and the past studies have shown that the prediction result of the artificial intelligence method is better than the traditional statistical method. Financial statements are quarterly reports; hence, the financial crisis of companies is seasonal time-series data, and the attribute data affecting the financial distress of companies is nonlinear and nonstationary time-series data with fluctuations. Therefore, this study employed the nonlinear attribute selection method to build a nonlinear financial distress prediction model: that is, this paper proposed a novel seasonal time-series gene expression programming model for predicting the financial distress of companies. The proposed model has several advantages including the following: (i) the proposed model is different from the previous models lacking the concept of time series; (ii) the proposed integrated attribute selection method can find the core attributes and reduce high dimensional data; and (iii) the proposed model can generate the rules and mathematical formulas of financial distress for providing references to the investors and decision makers. The result shows that the proposed method is better than the listing classifiers under three criteria; hence, the proposed model has competitive advantages in predicting the financial distress of companies. PMID:29765399

  13. A Seasonal Time-Series Model Based on Gene Expression Programming for Predicting Financial Distress.

    Science.gov (United States)

    Cheng, Ching-Hsue; Chan, Chia-Pang; Yang, Jun-He

    2018-01-01

    The issue of financial distress prediction plays an important and challenging research topic in the financial field. Currently, there have been many methods for predicting firm bankruptcy and financial crisis, including the artificial intelligence and the traditional statistical methods, and the past studies have shown that the prediction result of the artificial intelligence method is better than the traditional statistical method. Financial statements are quarterly reports; hence, the financial crisis of companies is seasonal time-series data, and the attribute data affecting the financial distress of companies is nonlinear and nonstationary time-series data with fluctuations. Therefore, this study employed the nonlinear attribute selection method to build a nonlinear financial distress prediction model: that is, this paper proposed a novel seasonal time-series gene expression programming model for predicting the financial distress of companies. The proposed model has several advantages including the following: (i) the proposed model is different from the previous models lacking the concept of time series; (ii) the proposed integrated attribute selection method can find the core attributes and reduce high dimensional data; and (iii) the proposed model can generate the rules and mathematical formulas of financial distress for providing references to the investors and decision makers. The result shows that the proposed method is better than the listing classifiers under three criteria; hence, the proposed model has competitive advantages in predicting the financial distress of companies.

  14. Characterization of the Dynamics of Climate Systems and Identification of Missing Mechanisms Impacting the Long Term Predictive Capabilities of Global Climate Models Utilizing Dynamical Systems Approaches to the Analysis of Observed and Modeled Climate

    Energy Technology Data Exchange (ETDEWEB)

    Bhatt, Uma S. [Univ. of Alaska, Fairbanks, AK (United States). Dept. of Atmospheric Sciences; Wackerbauer, Renate [Univ. of Alaska, Fairbanks, AK (United States). Dept. of Physics; Polyakov, Igor V. [Univ. of Alaska, Fairbanks, AK (United States). Dept. of Atmospheric Sciences; Newman, David E. [Univ. of Alaska, Fairbanks, AK (United States). Dept. of Physics; Sanchez, Raul E. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Fusion Energy Division; Univ. Carlos III de Madrid (Spain)

    2015-11-13

    The goal of this research was to apply fractional and non-linear analysis techniques in order to develop a more complete characterization of climate change and variability for the oceanic, sea ice and atmospheric components of the Earth System. This research applied two measures of dynamical characteristics of time series, the R/S method of calculating the Hurst exponent and Renyi entropy, to observational and modeled climate data in order to evaluate how well climate models capture the long-term dynamics evident in observations. Fractional diffusion analysis was applied to ARGO ocean buoy data to quantify ocean transport. Self organized maps were applied to North Pacific sea level pressure and analyzed in ways to improve seasonal predictability for Alaska fire weather. This body of research shows that these methods can be used to evaluate climate models and shed light on climate mechanisms (i.e., understanding why something happens). With further research, these methods show promise for improving seasonal to longer time scale forecasts of climate.

  15. Decadal Recruitment and Mortality of Ponderosa pine Predicted for the 21st Century Under five Downscaled Climate Change Scenarios

    Science.gov (United States)

    Ironside, K. E.; Cole, K. L.; Eischeid, J. K.; Garfin, G. M.; Shaw, J. D.; Cobb, N. S.

    2008-12-01

    Ponderosa pine (Pinus ponderosa var. scopulorum) is the dominant conifer in higher elevation regions of the southwestern United States. Because this species is so prominent, southwestern montane ecosystems will be significantly altered if this species is strongly affected by future climate changes. These changes could be highly challenging for land management agencies. In order to model the consequences of future climates, 20th Century recruitment events and mortality for ponderosa pine were characterized using measures of seasonal water balance (precipitation - potential evapotranspiration). These relationships, assuming they will remain unchanged, were then used to predict 21st Century changes in ponderosa pine occurrence in the southwest. Twenty-one AR4 IPCC General Circulation Model (GCM) A1B simulation results were ranked on their ability to simulate the later 20th Century (1950-2000 AD) precipitation seasonality, spatial patterns, and quantity in the western United States. Among the top ranked GCMs, five were selected for downscaling to a 4 km grid that represented a range in predictions in terms of changes in water balance. Predicted decadal changes in southwestern ponderosa pine for the 21st Century for these five climate change scenarios were calculated using a multiple quadratic logistic regression model. Similar models of other western tree species (Pinus edulis, Yucca brevifolia) predicted severe contractions, especially in the southern half of their ranges. However, the results for Ponderosa pine suggested future expansions throughout its range to both higher and lower elevations, as well as very significant expansions northward.

  16. The Predictability of Dry-Season Precipitation in Tropical West Africa

    Science.gov (United States)

    Knippertz, P.; Davis, J.; Fink, A. H.

    2012-04-01

    Precipitation during the boreal winter dry season in tropical West Africa is rare but occasionally connected to high-impacts for the local population. Previous work has shown that these events are usually connected to a trough over northwestern Africa, an extensive cloud plume on its eastern side, unusual precipitation at the northern and western fringes of the Sahara, and reduced surface pressure over the southern Sahara and Sahel, which allows an inflow of moist southerlies from the Gulf of Guinea to feed the unusual dry-season rainfalls. These results also suggest that the extratropical influence enhances the predictability of these events on the synoptic timescale. Here we further investigate this question for the 11 dry seasons (November-March) 1998/99-2008/09 using rainfall estimates from TRMM (Tropical Rainfall Measuring Mission) and GPCP (Global Precipitation Climatology Project), and operational ensemble predictions from the European Centre for Medium-Range Forecasts (ECMWF). All fields are averaged over the study area 7.5-15°N, 10°W-10°E that spans most of southern West Africa. For each 0000 UTC analysis time, the daily precipitation estimates are accumulated to pentads and compared with 120-hour predictions starting at the same time. Compared to TRMM, the ensemble mean shows a weak positive bias, whereas there is a substantial negative bias with regard to GPCP. Temporal correlations reach a high value of 0.8 for both datasets, showing similar synoptic variability despite the differences in total amount. Standard probabilistic evaluation methods such as relative operating characteristic (ROC) diagrams indicate remarkably good reliability, resolution and skill, particularly for lower precipitation thresholds. Not surprisingly, forecasts cluster at low probabilities for higher thresholds, but the reliability and ROC score are still reasonably high. The results show that global ensemble prediction systems are capable to predict dry-season rainfall events

  17. Prediction of East African Seasonal Rainfall Using Simplex Canonical Correlation Analysis.

    Science.gov (United States)

    Ntale, Henry K.; Yew Gan, Thian; Mwale, Davison

    2003-06-01

    A linear statistical model, canonical correlation analysis (CCA), was driven by the Nelder-Mead simplex optimization algorithm (called CCA-NMS) to predict the standardized seasonal rainfall totals of East Africa at 3-month lead time using SLP and SST anomaly fields of the Indian and Atlantic Oceans combined together by 24 simplex optimized weights, and then `reduced' by the principal component analysis. Applying the optimized weights to the predictor fields produced better March-April-May (MAM) and September-October-November (SON) seasonal rain forecasts than a direct application of the same, unweighted predictor fields to CCA at both calibration and validation stages. Northeastern Tanzania and south-central Kenya had the best SON prediction results with both validation correlation and Hanssen-Kuipers skill scores exceeding +0.3. The MAM season was better predicted in the western parts of East Africa. The CCA correlation maps showed that low SON rainfall in East Africa is associated with cold SSTs off the Somali coast and the Benguela (Angola) coast, and low MAM rainfall is associated with a buildup of low SSTs in the Indian Ocean adjacent to East Africa and the Gulf of Guinea.

  18. Climate change and predicting soil loss from rainfall

    Science.gov (United States)

    Kinnell, Peter

    2017-04-01

    Conceptually, rainfall has a certain capacity to cause soil loss from an eroding area while soil surfaces have a certain resistance to being eroded by rainfall. The terms "rainfall erosivity' and "soil erodibility" are frequently used to encapsulate the concept and in the Revised Universal Soil Loss Equation (RUSLE), the most widely used soil loss prediction equation in the world, average annual values of the R "erosivity" factor and the K "erodibility" factor provide a basis for accounting for variation in rainfall erosion associated with geographic variations of climate and soils. In many applications of RUSLE, R and K are considered to be independent but in reality they are not. In RUSLE2, provision has been made to take account of the fact that K values determined using soil physical factors have to be adjusted for variations in climate because runoff is not directly included as a factor in determining R. Also, the USLE event erosivity index EI30 is better related to accounting for event sediment concentration than event soil loss. While the USLE-M, a modification of the USLE which includes runoff as a factor in determining the event erosivity index provides better estimates of event soil loss when event runoff is known, runoff prediction provides a challenge to modelling event soil loss as climate changes

  19. Seasonal dynamics and micro-climatic preference of two Alpine endemic hypogean beetles

    Directory of Open Access Journals (Sweden)

    Stefano Mammola

    2015-09-01

    Full Text Available Hypogean beetles generally live in stable environments, characterized by constant temperature and high relative humidity. Changes in the underground microclimatic conditions generally induce local migrations of the beetles through the hypogean environment in search of suitable microhabitats. We studied the seasonal dynamics and the micro-climatic preference of two Alpine endemic hypogean beetles - Sphodropsis ghilianii (Coleoptera, Carabidae and Dellabeffaella roccae (Coleoptera, Cholevidae - in the hypogean complex of Pugnetto (Graian Alps, Italy. We surveyed the two species for one year, using baited pitfall traps and measuring temperature and humidity along the two main caves. We used logistic regression mixed models (GLMMs to relate the presence of the two species to several variables, namely microclimate (seasonality, temperature, and humidity, subjacency and cave length. In addition, we tested the attractive power of the bait on the two species. The thermic optimum for S. ghilianii was found to be around 7°C, with an increasing probability of finding the species in the vicinity of the cave entrance during summer, autumn and spring. The species migrates inside the cave in winter, in response to the drop in the mean daily temperature and in the relative humidity occurring in the outer parts of the cave. On the contrary, D. roccae showed a significant preference for the deeper sections of the cave, characterized by an almost constant temperature of 9°C in air saturated with water vapour. Males and females individuals of both species were found to be equally affected by the environmental variables included in the analysis. We also provided information on the life history of the two species and methodological insights about the use of the bait in the traps

  20. Evaluating uncertainties in regional climate simulations over South America at the seasonal scale

    Energy Technology Data Exchange (ETDEWEB)

    Solman, Silvina A. [Centro de Investigaciones del Mar y la Atmosfera CIMA/CONICET-UBA, DCAO/FCEN, UMI-IFAECI/CNRS, CIMA-Ciudad Universitaria, Buenos Aires (Argentina); Pessacg, Natalia L. [Centro Nacional Patagonico (CONICET), Puerto Madryn, Chubut (Argentina)

    2012-07-15

    This work focuses on the evaluation of different sources of uncertainty affecting regional climate simulations over South America at the seasonal scale, using the MM5 model. The simulations cover a 3-month period for the austral spring season. Several four-member ensembles were performed in order to quantify the uncertainty due to: the internal variability; the definition of the regional model domain; the choice of physical parameterizations and the selection of physical parameters within a particular cumulus scheme. The uncertainty was measured by means of the spread among individual members of each ensemble during the integration period. Results show that the internal variability, triggered by differences in the initial conditions, represents the lowest level of uncertainty for every variable analyzed. The geographic distribution of the spread among ensemble members depends on the variable: for precipitation and temperature the largest spread is found over tropical South America while for the mean sea level pressure the largest spread is located over the southeastern Atlantic Ocean, where large synoptic-scale activity occurs. Using nudging techniques to ingest the boundary conditions reduces dramatically the internal variability. The uncertainty due to the domain choice displays a similar spatial pattern compared with the internal variability, except for the mean sea level pressure field, though its magnitude is larger all over the model domain for every variable. The largest spread among ensemble members is found for the ensemble in which different combinations of physical parameterizations are selected. The perturbed physics ensemble produces a level of uncertainty slightly larger than the internal variability. This study suggests that no matter what the source of uncertainty is, the geographical distribution of the spread among members of the ensembles is invariant, particularly for precipitation and temperature. (orig.)

  1. Predicted Changes in Climatic Niche and Climate Refugia of Conservation Priority Salamander Species in the Northeastern United States

    Directory of Open Access Journals (Sweden)

    William B. Sutton

    2014-12-01

    Full Text Available Global climate change represents one of the most extensive and pervasive threats to wildlife populations. Amphibians, specifically salamanders, are particularly susceptible to the effects of changing climates due to their restrictive physiological requirements and low vagility; however, little is known about which landscapes and species are vulnerable to climate change. Our study objectives included, (1 evaluating species-specific predictions (based on 2050 climate projections and vulnerabilities to climate change and (2 using collective species responses to identify areas of climate refugia for conservation priority salamanders in the northeastern United States. All evaluated salamander species were projected to lose a portion of their climatic niche. Averaged projected losses ranged from 3%–100% for individual species, with the Cow Knob Salamander (Plethodon punctatus, Cheat Mountain Salamander (Plethodon nettingi, Shenandoah Mountain Salamander (Plethodon virginia, Mabee’s Salamander (Ambystoma mabeei, and Streamside Salamander (Ambystoma barbouri predicted to lose at least 97% of their landscape-scale climatic niche. The Western Allegheny Plateau was predicted to lose the greatest salamander climate refugia richness (i.e., number of species with a climatically-suitable niche in a landscape patch, whereas the Central Appalachians provided refugia for the greatest number of species during current and projected climate scenarios. Our results can be used to identify species and landscapes that are likely to be further affected by climate change and potentially resilient habitats that will provide consistent climatic conditions in the face of environmental change.

  2. Effects of ocean initial perturbation on developing phase of ENSO in a coupled seasonal prediction model

    Science.gov (United States)

    Lee, Hyun-Chul; Kumar, Arun; Wang, Wanqiu

    2018-03-01

    Coupled prediction systems for seasonal and inter-annual variability in the tropical Pacific are initialized from ocean analyses. In ocean initial states, small scale perturbations are inevitably smoothed or distorted by the observational limits and data assimilation procedures, which tends to induce potential ocean initial errors for the El Nino-Southern Oscillation (ENSO) prediction. Here, the evolution and effects of ocean initial errors from the small scale perturbation on the developing phase of ENSO are investigated by an ensemble of coupled model predictions. Results show that the ocean initial errors at the thermocline in the western tropical Pacific grow rapidly to project on the first mode of equatorial Kelvin wave and propagate to the east along the thermocline. In boreal spring when the surface buoyancy flux weakens in the eastern tropical Pacific, the subsurface errors influence sea surface temperature variability and would account for the seasonal dependence of prediction skill in the NINO3 region. It is concluded that the ENSO prediction in the eastern tropical Pacific after boreal spring can be improved by increasing the observational accuracy of subsurface ocean initial states in the western tropical Pacific.

  3. The Asian-Bering-North American teleconnection: seasonality, maintenance, and climate impact on North America

    Science.gov (United States)

    Yu, Bin; Lin, H.; Wu, Z. W.; Merryfield, W. J.

    2018-03-01

    The Asian-Bering-North American (ABNA) teleconnection index is constructed from the normalized 500-hPa geopotential field by excluding the Pacific-North American pattern contribution. The ABNA pattern features a zonally elongated wavetrain originating from North Asia and flowing downstream across Bering Sea and Strait towards North America. The large-scale teleconnection is a year-round phenomenon that displays strong seasonality with the peak variability in winter. North American surface temperature and temperature extremes, including warm days and nights as well as cold days and nights, are significantly controlled by this teleconnection. The ABNA pattern has an equivalent barotropic structure in the troposphere and is supported by synoptic-scale eddy forcing in the upper troposphere. Its associated sea surface temperature anomalies exhibit a horseshoe-shaped structure in the North Pacific, most prominent in winter, which is driven by atmospheric circulation anomalies. The snow cover anomalies over the West Siberian plain and Central Siberian Plateau in autumn and spring and over southern Siberia in winter may act as a forcing influence on the ABNA pattern. The snow forcing influence in winter and spring can be traced back to the preceding season, which provides a predictability source for this teleconnection and for North American temperature variability. The ABNA associated energy budget is dominated by surface longwave radiation anomalies year-round, with the temperature anomalies supported by anomalous downward longwave radiation and damped by upward longwave radiation at the surface.

  4. Cod Gadus morhua and climate change: processes, productivity and prediction

    DEFF Research Database (Denmark)

    Brander, Keith

    2010-01-01

    the causes. Investigation of cod Gadus morhua populations across the whole North Atlantic Ocean has shown large-scale patterns of change in productivity due to lower individual growth and condition, caused by large-scale climate forcing. If a population is being heavily exploited then a drop in productivity......Environmental factors act on individual fishes directly and indirectly. The direct effects on rates and behaviour can be studied experimentally and in the field, particularly with the advent of ever smarter tags for tracking fishes and their environment. Indirect effects due to changes in food......, predators, parasites and diseases are much more difficult to estimate and predict. Climate can affect all life-history stages through direct and indirect processes and although the consequences in terms of growth, survival and reproductive output can be monitored, it is often difficult to determine...

  5. From Fall to Spring, or Spring to Fall? Seasonal Cholera Transmission Cycles and Implications for Climate Change

    Science.gov (United States)

    Akanda, A. S.; Jutla, A. S.; Huq, A.; Colwell, R.; Islam, S.; WE Reason

    2010-12-01

    Cholera remains a major public health threat in many developing countries around the world. The striking seasonality and the annual recurrence of this infectious disease in endemic areas continues to be of considerable interest to scientists and public health workers. Despite major advances in the ecological, and microbiological understanding of Vibrio cholerae, the causative agent, the role of underlying macro-scale hydroclimatic processes in propagating the disease in different seasons and years is not well understood. The incidence of cholera in the Bengal Delta region, the ‘native homeland’ of cholera, shows distinct biannual peaks in the southern floodplains, as opposed to single annual peaks in coastal areas and the northern parts of Bangladesh, as well as other cholera-endemic regions in the world. A coupled analysis of the regional hydroclimate and cholera incidence reveals a strong association of the spatio-temporal variability of incidence peaks with seasonal processes and extreme events. At a seasonal scale, the cycles indicate a spring-fall transmission pattern, contrary to the prevalent notion of a fall-spring transmission cycle. We show that the asymmetric seasonal hydroclimatology affects regional cholera dynamics by providing a coastal growth environment for bacteria in spring, while propagating transmission to fall by flooding. This seasonal interpretation of the progression of cholera has important implications, for formulating effective cholera intervention and mitigation efforts through improved water management and understanding the impacts of changing climate patterns on seasonal cholera transmission. (Water Environental Research Education Actionable Solutions Network)

  6. A review of multimodel superensemble forecasting for weather, seasonal climate, and hurricanes

    Science.gov (United States)

    Krishnamurti, T. N.; Kumar, V.; Simon, A.; Bhardwaj, A.; Ghosh, T.; Ross, R.

    2016-06-01

    This review provides a summary of work in the area of ensemble forecasts for weather, climate, oceans, and hurricanes. This includes a combination of multiple forecast model results that does not dwell on the ensemble mean but uses a unique collective bias reduction procedure. A theoretical framework for this procedure is provided, utilizing a suite of models that is constructed from the well-known Lorenz low-order nonlinear system. A tutorial that includes a walk-through table and illustrates the inner workings of the multimodel superensemble's principle is provided. Systematic errors in a single deterministic model arise from a host of features that range from the model's initial state (data assimilation), resolution, representation of physics, dynamics, and ocean processes, local aspects of orography, water bodies, and details of the land surface. Models, in their diversity of representation of such features, end up leaving unique signatures of systematic errors. The multimodel superensemble utilizes as many as 10 million weights to take into account the bias errors arising from these diverse features of multimodels. The design of a single deterministic forecast models that utilizes multiple features from the use of the large volume of weights is provided here. This has led to a better understanding of the error growths and the collective bias reductions for several of the physical parameterizations within diverse models, such as cumulus convection, planetary boundary layer physics, and radiative transfer. A number of examples for weather, seasonal climate, hurricanes and sub surface oceanic forecast skills of member models, the ensemble mean, and the superensemble are provided.

  7. Use of optical sensor for in-season nitrogen management and grain yield prediction in maize

    Directory of Open Access Journals (Sweden)

    Bandhu Raj Baral

    2015-12-01

    Full Text Available Precision agriculture technologies have developed optical sensors which can determine plant’s normalized difference vegetation index (NDVI.To evaluate the relationship between maize grain yield and early season NDVI readings, an experiment was conducted at farm land of National Maize Research Program, Rampur, Chitwan during winter season of 2012. Eight different levels of N 0, 30, 60, 90, 120, 150, 180 and 210 kg N/ha were applied for hybrid maize RML 32 × RML 17 to study grain yield response and NDVI measurement. Periodic NDVI was measured at 10 days interval from 55 days after sowing (DAS to 115 DAS by using Green seeker hand held crop sensor. Periodic NDVI measurement taken at a range of growing degree days (GDD was critical for predicting grain yield potential. Poor exponential relationship existed between NDVI from early reading measured before 208 GDD (55 DAS and grain yield. At the 261GDD (65DAS a strong relationship (R2 = 0.70 was achieved between NDVI and grain yield. Later sensor measurements after 571 GDD (95DAS failed to distinguish variation in green biomass as a result of canopy closure. N level had significantly influenced on NDVI reading, measured grain yield, calculated in season estimated yield (INSEY, predicted yield with added N (YPN, response index (RI and grain N demand. Measuring NDVI reading by GDD (261–571 GDD allow a practical window of opportunity for side dress N applications. This study showed that yield potential in maize could be accurately predicted in season with NDVI measured with the Green Seeker crop sensor.

  8. Preseason Aerobic Fitness Predicts In-Season Injury and Illness in Female Youth Athletes.

    Science.gov (United States)

    Watson, Andrew; Brickson, Stacey; Brooks, M Alison; Dunn, Warren

    2017-09-01

    Although preseason aerobic fitness has been suggested as a modifiable risk factor for injury in adult athletes, the relationship between aerobic fitness, injury, and illness in youth athletes is unknown. To determine whether preseason aerobic fitness predicts in-season injury and illness risk in female adolescent soccer players. Case-control study; Level of evidence, 3. Fifty-four female adolescent soccer players underwent preseason evaluation to determine years of experience, body mass index (BMI), maximal aerobic capacity (VO 2max ), and time to exhaustion (T max ) during cycle ergometer testing. All injuries and illnesses during the subsequent 20-week season were recorded. Variables were compared between individuals with and without a self-reported injury and individuals with and without a self-reported illness. Separate Poisson regression models were developed to predict number of injuries and illnesses for each individual by use of age, years of experience, BMI, VO 2max , and T max. Twenty-eight injuries and 38 illnesses in 23 individuals were recorded during the season. Although not a statistically significant finding, individuals who reported an in-season injury had lower VO 2max than those who did not (54.9 ± 7.3 vs 58.3 ± 8.5 mL/kg/min, P = .13). Individuals who reported an illness had significantly lower VO 2max than those who did not (54.5 ± 9.9 vs 58.8 ± 6.2 mL/kg/min, P = .014). With the Poisson regression models, VO 2max was a significant predictor of both injury (odds ratio [OR], 0.95; P = .046) and illness (OR, 0.94; P = .009), while no significant relationships were identified between injury or illness and age, years of experience, T max , or BMI (all P > .05). Among adolescent female soccer players, greater preseason aerobic fitness is associated with a reduced risk of in-season injury and illness. Off-season intervention to promote aerobic fitness may help reduce the risk of lost time during the season due to injury and illness.

  9. Predicting Seagrass Occurrence in a Changing Climate Using Random Forests

    Science.gov (United States)

    Aydin, O.; Butler, K. A.

    2017-12-01

    Seagrasses are marine plants that can quickly sequester vast amounts of carbon (up to 100 times more and 12 times faster than tropical forests). In this work, we present an integrated GIS and machine learning approach to build a data-driven model of seagrass presence-absence. We outline a random forest approach that avoids the prevalence bias in many ecological presence-absence models. One of our goals is to predict global seagrass occurrence from a spatially limited training sample. In addition, we conduct a sensitivity study which investigates the vulnerability of seagrass to changing climate conditions. We integrate multiple data sources including fine-scale seagrass data from MarineCadastre.gov and the recently available globally extensive publicly available Ecological Marine Units (EMU) dataset. These data are used to train a model for seagrass occurrence along the U.S. coast. In situ oceans data are interpolated using Empirical Bayesian Kriging (EBK) to produce globally extensive prediction variables. A neural network is used to estimate probable future values of prediction variables such as ocean temperature to assess the impact of a warming climate on seagrass occurrence. The proposed workflow can be generalized to many presence-absence models.

  10. [Characteristics and adaptation of seasonal drought in southern China under the background of climate change. V. Seasonal drought characteristics division and assessment in southern China].

    Science.gov (United States)

    Huang, Wan-Hua; Sui, Yue; Yang, Xiao-Guang; Dai, Shu-Wei; Li, Mao-Song

    2013-10-01

    Zoning seasonal drought based on the study of drought characteristics can provide theoretical basis for formulating drought mitigation plans and improving disaster reduction technologies in different arid zones under global climate change. Based on the National standard of meteorological drought indices and agricultural drought indices and the 1959-2008 meteorological data from 268 meteorological stations in southern China, this paper analyzed the climatic background and distribution characteristics of seasonal drought in southern China, and made a three-level division of seasonal drought in this region by the methods of combining comprehensive factors and main factors, stepwise screening indices, comprehensive disaster analysis, and clustering analysis. The first-level division was with the annual aridity index and seasonal aridity index as the main indices and with the precipitation during entire year and main crop growing season as the auxiliary indices, dividing the southern China into four primary zones, including semi-arid zone, sub-humid zone, humid zone, and super-humid zone. On this basis, the four primary zones were subdivided into nine second-level zones, including one semi-arid area-temperate-cold semi-arid hilly area in Sichuan-Yunnan Plateau, three sub-humid areas of warm sub-humid area in the north of the Yangtze River, warm-tropical sub-humid area in South China, and temperate-cold sub-humid plateau area in Southwest China, three humid areas of temperate-tropical humid area in the Yangtze River Basin, warm-tropical humid area in South China, and warm humid hilly area in Southwest China, and two super-humid areas of warm-tropical super-humid area in South China and temperate-cold super-humid hilly area in the south of the Yangtze River and Southwest China. According to the frequency and intensity of multiple drought indices, the second-level zones were further divided into 29 third-level zones. The distribution of each seasonal drought zone was

  11. A study of the effect of seasonal climatic factors on the electrical resistivity response of three experimental graves

    Science.gov (United States)

    Jervis, John R.; Pringle, Jamie K.

    2014-09-01

    Electrical resistivity surveys have proven useful for locating clandestine graves in a number of forensic searches. However, some aspects of grave detection with resistivity surveys remain imperfectly understood. One such aspect is the effect of seasonal changes in climate on the resistivity response of graves. In this study, resistivity survey data collected over three years over three simulated graves were analysed in order to assess how the graves' resistivity anomalies varied seasonally and when they could most easily be detected. Thresholds were used to identify anomalies, and the ‘residual volume' of grave-related anomalies was calculated as the area bounded by the relevant thresholds multiplied by the anomaly's average value above the threshold. The residual volume of a resistivity anomaly associated with a buried pig cadaver showed evidence of repeating annual patterns and was moderately correlated with the soil moisture budget. This anomaly was easiest to detect between January and April each year, after prolonged periods of high net gain in soil moisture. The resistivity response of a wrapped cadaver was more complex, although it also showed evidence of seasonal variation during the third year after burial. We suggest that the observed variation in the graves' resistivity anomalies was caused by seasonal change in survey data noise levels, which was in turn influenced by the soil moisture budget. It is possible that similar variations occur elsewhere for sites with seasonal climate variations and this could affect successful detection of other subsurface features. Further research to investigate how different climates and soil types affect seasonal variation in grave-related resistivity anomalies would be useful.

  12. Assessing the vulnerability of economic sectors to climate variability to improve the usability of seasonal to decadal climate forecasts in Europe - a preliminary concept

    Science.gov (United States)

    Funk, Daniel

    2015-04-01

    Climate variability poses major challenges for decision-makers in climate-sensitive sectors. Seasonal to decadal (S2D) forecasts provide potential value for management decisions especially in the context of climate change where information from present or past climatology loses significance. However, usable and decision-relevant tailored climate forecasts are still sparse for Europe and successful examples of application require elaborate and individual producer-user interaction. The assessment of sector-specific vulnerabilities to critical climate conditions at specific temporal scale will be a great step forward to increase the usability and efficiency of climate forecasts. A concept for a sector-specific vulnerability assessment (VA) to climate variability is presented. The focus of this VA is on the provision of usable vulnerability information which can be directly incorporated in decision-making processes. This is done by developing sector-specific climate-impact-decision-pathways and the identification of their specific time frames using data from both bottom-up and top-down approaches. The structure of common VA's for climate change related issues is adopted which envisages the determination of exposure, sensitivity and coping capacity. However, the application of the common vulnerability components within the context of climate service application poses some fundamental considerations: Exposure - the effect of climate events on the system of concern may be modified and delayed due to interconnected systems (e.g. catchment). The critical time-frame of a climate event or event sequence is dependent on system-internal thresholds and initial conditions. But also on decision-making processes which require specific lead times of climate information to initiate respective coping measures. Sensitivity - in organizational systems climate may pose only one of many factors relevant for decision making. The scope of "sensitivity" in this concept comprises both the

  13. Seasonal and Interannual Trends in Largest Cholera Endemic Megacity: Water Sustainability - Climate - Health Challenges in Dhaka, Bangladesh

    Science.gov (United States)

    Akanda, Ali S.; Jutla, Antarpreet; Faruque, Abu S. G.; Huq, Anwar; Colwell, Rita R.

    2014-05-01

    The last three decades of surveillance data shows a drastic increase of cholera prevalence in the largest cholera-endemic city in the world - Dhaka, Bangladesh. Emerging megacities in the region, especially those located in coastal areas also remain vulnerable to large scale drivers of cholera outbreaks. However, there has not been any systematic study on linking long-term disease trends with related changes in natural or societal variables. Here, we analyze the 30-year dynamics of urban cholera prevalence in Dhaka with changes in climatic or anthropogenic forcings: regional hydrology, flooding, water usage, changes in distribution systems, population growth and density in urban settlements, as well as shifting climate patterns and frequency of natural disasters. An interesting change is observed in the seasonal trends of cholera prevalence; while an endemic upward trend is seen in the dry season, the post-monsoon trend is epidemic in nature. In addition, the trend in the pre-monsoon dry season is significantly stronger than the post-monsoon wet season; and thus spring is becoming the dominant cholera season of the year. Evidence points to growing urbanization and rising population in unplanned settlements along the city peripheries. The rapid pressure of growth has led to an unsustainable and potentially disastrous situation with negligible-to-poor water and sanitation systems compounded by changing climatic patterns and increasing number of extreme weather events. Growing water scarcity in the dry season and lack of sustainable water and sanitation infrastructure for urban settlements have increased endemicity of cholera outbreaks in spring, while record flood events and prolonged post-monsoon inundation have contributed to increased epidemic outbreaks in fall. We analyze our findings with the World Health Organization recommended guidelines and investigate large scale water sustainability challenges in the context of climatic and anthropogenic changes in the

  14. Asian summer monsoon prediction in ECMWF System 4 and NCEP CFSv2 retrospective seasonal forecasts

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Hye-Mi; Webster, Peter J.; Curry, Judith A.; Toma, Violeta E. [Georgia Institute of Technology, School of Earth and Atmospheric Science, Atlanta, GA (United States)

    2012-12-15

    The seasonal prediction skill of the Asian summer monsoon is assessed using retrospective predictions (1982-2009) from the ECMWF System 4 (SYS4) and NCEP CFS version 2 (CFSv2) seasonal prediction systems. In both SYS4 and CFSv2, a cold bias of sea-surface temperature (SST) is found over the equatorial Pacific, North Atlantic, Indian Oceans and over a broad region in the Southern Hemisphere relative to observations. In contrast, a warm bias is found over the northern part of North Pacific and North Atlantic. Excessive precipitation is found along the ITCZ, equatorial Atlantic, equatorial Indian Ocean and the maritime continent. The southwest monsoon flow and the Somali Jet are stronger in SYS4, while the south-easterly trade winds over the tropical Indian Ocean, the Somali Jet and the subtropical northwestern Pacific high are weaker in CFSv2 relative to the reanalysis. In both systems, the prediction of SST, precipitation and low-level zonal wind has greatest skill in the tropical belt, especially over the central and eastern Pacific where the influence of El Nino-Southern Oscillation (ENSO) is dominant. Both modeling systems capture the global monsoon and the large-scale monsoon wind variability well, while at the same time performing poorly in simulating monsoon precipitation. The Asian monsoon prediction skill increases with the ENSO amplitude, although the models simulate an overly strong impact of ENSO on the monsoon. Overall, the monsoon predictive skill is lower than the ENSO skill in both modeling systems but both systems show greater predictive skill compared to persistence. (orig.)

  15. Diagnostic Analysis of Contributions of the North Atlantic Subtropical High to Sub-Seasonal Predictability of Summertime Drought and Flood in the Eastern US.

    Science.gov (United States)

    Carter, E.; Steinschneider, S.

    2017-12-01

    The North Atlantic Subtropical High (NASH)—a semi-permanent anticyclone established under the descending branch of the Hadley cell in the subtropical North Atlantic Ocean—has been identified as a primary driver of summertime precipitation in the Eastern US. Characteristic patterns of precipitation across the region are associated with variations in the location and intensity of the NASH western ridge, and recently these characteristics have been identified as potential sources of predictability for precipitation at sub-seasonal to seasonal (S2S) timescales. In this work, we show that the location and intensity of the NASH also directly control atmospheric variables that contribute to variability in moisture demand (PET), such as surface wind speed and solar insolation. Using surface climate and NASH characteristics from observational and reanalysis datasets, we will examine the relationship between NASH intensity and the location of its western ridge and both precipitation and PET at various seasonal to sub-seasonal (S2S) timescales. Particular focus will be given to how the strength of these relationships varies across space and through time. The goal is to identify the S2S time-scales where the NASH contributes to maximum predictability of atmospheric drivers of both agricultural drought and regional flooding. Results will help support the development of both drought and flood forecasting tools for different regions in the Eastern United States.

  16. New climate on the Earth: understanding, predicting, reacting

    International Nuclear Information System (INIS)

    Le Treut, H.

    2009-01-01

    The objective of the Copenhagen meeting was to recast the Kyoto protocol, to widen it to all countries, to find a global agreement for the aid to vulnerable populations and for the abatement of greenhouse gases both from industrialized and emerging countries, including the USA and China. Scientific research has revealed the huge complexity of the climate machine and the difficulty to predict its evolution. What will be the sea level in 2100, the pressure on coastal areas, the expansion of desertification, the evolution of glaciers? Today no quantification is possible but it is demonstrated that our greenhouse gas emissions are responsible for the climate change, that this change is already irreversible and will affect all natural environments, and that a warming up greater than 2 deg. C will make climate evolution out of control. In this book, the author lists the actions to implement urgently: significantly reducing greenhouse gas emissions, implementing energy saving policies, limiting fossil fuels consumption, developing alternate energies, capturing and sequestering the CO 2 of thermal plants. We just have few decades in front of us to reduce the extent of the changes in progress and to be prepared to face the ensuing new inequalities. (J.S.)

  17. Predicting unprecedented dengue outbreak using imported cases and climatic factors in Guangzhou, 2014.

    Directory of Open Access Journals (Sweden)

    Shaowei Sang

    2015-05-01

    Full Text Available Dengue is endemic in more than 100 countries, mainly in tropical and subtropical regions, and the incidence has increased 30-fold in the past 50 years. The situation of dengue in China has become more and more severe, with an unprecedented dengue outbreak hitting south China in 2014. Building a dengue early warning system is therefore urgent and necessary for timely and effective response.In the study we developed a time series Poisson multivariate regression model using imported dengue cases, local minimum temperature and accumulative precipitation to predict the dengue occurrence in four districts of Guangzhou, China. The time series data were decomposed into seasonal, trend and remainder components using a seasonal-trend decomposition procedure based on loess (STL. The time lag of climatic factors included in the model was chosen based on Spearman correlation analysis. Autocorrelation, seasonality and long-term trend were controlled in the model. A best model was selected and validated using Generalized Cross Validation (GCV score and residual test. The data from March 2006 to December 2012 were used to develop the model while the data from January 2013 to September 2014 were employed to validate the model. Time series Poisson model showed that imported cases in the previous month, minimum temperature in the previous month and accumulative precipitation with three month lags could project the dengue outbreaks occurred in 2013 and 2014 after controlling the autocorrelation, seasonality and long-term trend.Together with the sole transmission vector Aedes albopictus, imported cases, monthly minimum temperature and monthly accumulative precipitation may be used to develop a low-cost effective early warning system.

  18. Predicting plant invasions under climate change: are species distribution models validated by field trials?

    Science.gov (United States)

    Sheppard, Christine S; Burns, Bruce R; Stanley, Margaret C

    2014-09-01

    Climate change may facilitate alien species invasion into new areas, particularly for species from warm native ranges introduced into areas currently marginal for temperature. Although conclusions from modelling approaches and experimental studies are generally similar, combining the two approaches has rarely occurred. The aim of this study was to validate species distribution models by conducting field trials in sites of differing suitability as predicted by the models, thus increasing confidence in their ability to assess invasion risk. Three recently naturalized alien plants in New Zealand were used as study species (Archontophoenix cunninghamiana, Psidium guajava and Schefflera actinophylla): they originate from warm native ranges, are woody bird-dispersed species and of concern as potential weeds. Seedlings were grown in six sites across the country, differing both in climate and suitability (as predicted by the species distribution models). Seedling growth and survival were recorded over two summers and one or two winter seasons, and temperature and precipitation were monitored hourly at each site. Additionally, alien seedling performances were compared to those of closely related native species (Rhopalostylis sapida, Lophomyrtus bullata and Schefflera digitata). Furthermore, half of the seedlings were sprayed with pesticide, to investigate whether enemy release may influence performance. The results showed large differences in growth and survival of the alien species among the six sites. In the more suitable sites, performance was frequently higher compared to the native species. Leaf damage from invertebrate herbivory was low for both alien and native seedlings, with little evidence that the alien species should have an advantage over the native species because of enemy release. Correlations between performance in the field and predicted suitability of species distribution models were generally high. The projected increase in minimum temperature and reduced

  19. Conjunctival provocation tests: a predictive factor for patients' seasonal allergic rhinoconjunctivitis symptoms.

    Science.gov (United States)

    Kruse, Kristian; Gerwin, Eva; Eichel, Andrea; Shah-Hosseini, Kija; Mösges, Ralph

    2015-01-01

    No parameters currently exist that can reliably predict the impact of preseasonal immunotherapy on the symptoms occurring during the season. The purpose of our studies was to prove a correlation between preseasonal conjunctival allergen challenge and coseasonal primary clinical endpoints using the total combined score, ie, a combination of symptoms and medication score, as the primary outcome parameter. Twelve weeks before both the birch and the grass pollen seasons, 2 separate prospective, double-blind, randomized, controlled studies were conducted followed by posttrial observations for each study during the active season. In the studies, patients who reacted to conjunctival allergen challenge were treated with sublingual immunotherapy tablets that contain either birch and/or alder or grass pollen allergoids. In all, 158 patients were included in the grass and 160 in the tree pollen study; of these, 100 and 109 patients, respectively, took part in the posttrial observations. When comparing patients with and without a positive reaction in the final conjunctival allergen challenge, the results revealed a significant difference in the total combined score (grass: P < .001; birch: P = .025). The same applied to the rescue medication score (P = .005; P = .025). A significant difference regarding the rhinoconjunctivitis symptom score was shown in the grass pollen study (P = .002), and the difference of well days was significant in the tree pollen study (P = .049). When comparing patients based on their reaction to allergen challenge after immunotherapy, each study leads to similarly significant results. Therefore, conjunctival allergen challenge can be used effectively as a parameter to predict allergic rhinoconjunctivitis symptoms during the season in patients treated with preseasonal sublingual immunotherapy tablets. Whether this can be transferred to untreated patients needs to be determined. Copyright © 2015 American Academy of Allergy, Asthma & Immunology

  20. Climate Prediction Center - El Niño/La Niña Home

    Science.gov (United States)

    Weather Service NWS logo - Click to go to the NWS home page Climate Prediction Center Site Map News Information CPC Web Team HOME > El Niño/La Niña Forecasts Current U.S. Climate Outlook SST Forecasts Temperature and Anomalies NOAA/ National Weather Service National Centers for Environmental Prediction Climate

  1. Seasonal variation in AF-related admissions to a coronary care unit in a "hot" climate: fact or fiction?

    Science.gov (United States)

    Kiu, Andrew; Horowitz, John D; Stewart, Simon

    2004-01-01

    Seasonal variations in atrial fibrillation (AF)-related morbidity and mortality have been demonstrated in "cold" northern European climates, but there are few data describing such a phenomenon in a "hot" climate. To examine the pattern of AF-related admissions to a coronary care unit (CCU) in South Australia operating within a Mediterranean climate, and to determine potential differences according to mean daily temperatures. PATIENT COHORT AND METHODS: A total of 144 admissions to the CCU during the 30 hottest and coldest days (60 days in total) during the calendar year 2001 were analyzed in respect to the absolute number of admissions and the profile of those admitted during "hot" and "cold" days. Overall, there were significantly more admissions to the CCU on "cold" as opposed to "hot" days (90 vs 54 patients in 30 days, P < or = .001). Of the 24 patients found to be in AF on presentation to hospital, 18 (75%) were admitted on cold days (P < .05). Alternatively, during "hot" days, patients were more likely to be diagnosed with unstable angina rather than acute myocardial infarction (46% vs 30%, P = .07) with proportionately fewer patients in AF at the time (11% vs 20%, P = NS). These preliminary data suggest that the phenomenon of seasonal variations in AF-related morbidity extend beyond colder climates to hotter climates with sufficiently large relative (as opposed to absolute) changes in ambient temperatures during the year.

  2. Skilful seasonal forecasts of streamflow over Europe?

    Science.gov (United States)

    Arnal, Louise; Cloke, Hannah L.; Stephens, Elisabeth; Wetterhall, Fredrik; Prudhomme, Christel; Neumann, Jessica; Krzeminski, Blazej; Pappenberger, Florian

    2018-04-01

    This paper considers whether there is any added value in using seasonal climate forecasts instead of historical meteorological observations for forecasting streamflow on seasonal timescales over Europe. A Europe-wide analysis of the skill of the newly operational EFAS (European Flood Awareness System) seasonal streamflow forecasts (produced by forcing the Lisflood model with the ECMWF System 4 seasonal climate forecasts), benchmarked against the ensemble streamflow prediction (ESP) forecasting approach (produced by forcing the Lisflood model with historical meteorological observations), is undertaken. The results suggest that, on average, the System 4 seasonal climate forecasts improve the streamflow predictability over historical meteorological observations for the first month of lead time only (in terms of hindcast accuracy, sharpness and overall performance). However, the predictability varies in space and time and is greater in winter and autumn. Parts of Europe additionally exhibit a longer predictability, up to 7 months of lead time, for certain months within a season. In terms of hindcast reliability, the EFAS seasonal streamflow hindcasts are on average less skilful than the ESP for all lead times. The results also highlight the potential usefulness of the EFAS seasonal streamflow forecasts for decision-making (measured in terms of the hindcast discrimination for the lower and upper terciles of the simulated streamflow). Although the ESP is the most potentially useful forecasting approach in Europe, the EFAS seasonal streamflow forecasts appear more potentially useful than the ESP in some regions and for certain seasons, especially in winter for almost 40 % of Europe. Patterns in the EFAS seasonal streamflow hindcast skill are however not mirrored in the System 4 seasonal climate hindcasts, hinting at the need for a better understanding of the link between hydrological and meteorological variables on seasonal timescales, with the aim of improving climate

  3. Multi-scale enhancement of climate prediction over land by improving the model sensitivity to vegetation variability

    Science.gov (United States)

    Alessandri, A.; Catalano, F.; De Felice, M.; Hurk, B. V. D.; Doblas-Reyes, F. J.; Boussetta, S.; Balsamo, G.; Miller, P. A.

    2017-12-01

    Here we demonstrate, for the first time, that the implementation of a realistic representation of vegetation in Earth System Models (ESMs) can significantly improve climate simulation and prediction across multiple time-scales. The effective sub-grid vegetation fractional coverage vary seasonally and at interannual time-scales in response to leaf-canopy growth, phenology and senescence. Therefore it affects biophysical parameters such as the surface resistance to evapotranspiration, albedo, roughness lenght, and soil field capacity. To adequately represent this effect in the EC-Earth ESM, we included an exponential dependence of the vegetation cover on the Leaf Area Index.By comparing two sets of simulations performed with and without the new variable fractional-coverage parameterization, spanning from centennial (20th Century) simulations and retrospective predictions to the decadal (5-years), seasonal (2-4 months) and weather (4 days) time-scales, we show for the first time a significant multi-scale enhancement of vegetation impacts in climate simulation and prediction over land. Particularly large effects at multiple time scales are shown over boreal winter middle-to-high latitudes over Canada, West US, Eastern Europe, Russia and eastern Siberia due to the implemented time-varying shadowing effect by tree-vegetation on snow surfaces. Over Northern Hemisphere boreal forest regions the improved representation of vegetation-cover consistently correct the winter warm biases, improves the climate change sensitivity, the decadal potential predictability as well as the skill of forecasts at seasonal and weather time-scales. Significant improvements of the prediction of 2m temperature and rainfall are also shown over transitional land surface hot spots. Both the potential predictability at decadal time-scale and seasonal-forecasts skill are enhanced over Sahel, North American Great Plains, Nordeste Brazil and South East Asia, mainly related to improved performance in

  4. Late Noachian Icy Highlands climate model: Exploring the possibility of transient melting and fluvial/lacustrine activity through peak annual and seasonal temperatures

    Science.gov (United States)

    Palumbo, Ashley M.; Head, James W.; Wordsworth, Robin D.

    2018-01-01

    The nature of the Late Noachian climate of Mars remains one of the outstanding questions in the study of the evolution of martian geology and climate. Despite abundant evidence for flowing water (valley networks and open/closed basin lakes), climate models have had difficulties reproducing mean annual surface temperatures (MAT) > 273 K in order to generate the ;warm and wet; climate conditions presumed to be necessary to explain the observed fluvial and lacustrine features. Here, we consider a ;cold and icy; climate scenario, characterized by MAT ∼225 K and snow and ice distributed in the southern highlands, and ask: Does the formation of the fluvial and lacustrine features require continuous ;warm and wet; conditions, or could seasonal temperature variation in a ;cold and icy; climate produce sufficient summertime ice melting and surface runoff to account for the observed features? To address this question, we employ the 3D Laboratoire de Météorologie Dynamique global climate model (LMD GCM) for early Mars and (1) analyze peak annual temperature (PAT) maps to determine where on Mars temperatures exceed freezing in the summer season, (2) produce temperature time series at three valley network systems and compare the duration of the time during which temperatures exceed freezing with seasonal temperature variations in the Antarctic McMurdo Dry Valleys (MDV) where similar fluvial and lacustrine features are observed, and (3) perform a positive-degree-day analysis to determine the annual volume of meltwater produced through this mechanism, estimate the necessary duration that this process must repeat to produce sufficient meltwater for valley network formation, and estimate whether runoff rates predicted by this mechanism are comparable to those required to form the observed geomorphology of the valley networks. When considering an ambient CO2 atmosphere, characterized by MAT ∼225 K, we find that: (1) PAT can exceed the melting point of water (>273 K) in

  5. Can phenological models predict tree phenology accurately under climate change conditions?

    Science.gov (United States)

    Chuine, Isabelle; Bonhomme, Marc; Legave, Jean Michel; García de Cortázar-Atauri, Inaki; Charrier, Guillaume; Lacointe, André; Améglio, Thierry

    2014-05-01

    The onset of the growing season of trees has been globally earlier by 2.3 days/decade during the last 50 years because of global warming and this trend is predicted to continue according to climate forecast. The effect of temperature on plant phenology is however not linear because temperature has a dual effect on bud development. On one hand, low temperatures are necessary to break bud dormancy, and on the other hand higher temperatures are necessary to promote bud cells growth afterwards. Increasing phenological changes in temperate woody species have strong impacts on forest trees distribution and productivity, as well as crops cultivation areas. Accurate predictions of trees phenology are therefore a prerequisite to understand and foresee the impacts of climate change on forests and agrosystems. Different process-based models have been developed in the last two decades to predict the date of budburst or flowering of woody species. They are two main families: (1) one-phase models which consider only the ecodormancy phase and make the assumption that endodormancy is always broken before adequate climatic conditions for cell growth occur; and (2) two-phase models which consider both the endodormancy and ecodormancy phases and predict a date of dormancy break which varies from year to year. So far, one-phase models have been able to predict accurately tree bud break and flowering under historical climate. However, because they do not consider what happens prior to ecodormancy, and especially the possible negative effect of winter temperature warming on dormancy break, it seems unlikely that they can provide accurate predictions in future climate conditions. It is indeed well known that a lack of low temperature results in abnormal pattern of bud break and development in temperate fruit trees. An accurate modelling of the dormancy break date has thus become a major issue in phenology modelling. Two-phases phenological models predict that global warming should delay

  6. Growing season boundary layer climate and surface exchanges in a subarctic lichen woodland

    Science.gov (United States)

    Fitzjarrald, David R.; Moore, Kathleen E.

    1994-01-01

    Between June and August 1990, observations were made at two surface micrometeorological towers near Schefferville Quebec (54 deg 52 min N, 66 deg 40.5 min W), one in a fen and one in the subarctic lichen woodland, and at four surface climatological stations. Data from these surface stations were supplemented by regular radiosonde launches. Supporting measurements of radiative components and soil temperatures allowed heat and moisture balances to be obtained at two sites. The overall surface meteorological experiment design and results of micrometeorological observations made on a 30-m tower in the lichen woodland are presented here. Seasonal variation in the heat and water vapor transport characteristics illustrate the marked effect of the late summer climatological shift in air mass type. During the first half of the summer, average valley sidewalls only 100 m high are sufficient to channel winds along the valley in the entire convective boundary layer. Channeling effects at the surface, known for some time at the long-term climate station in Schefferville, are observed both at ridge top and in the valley, possibly the response of the flow to the NW-SE orientation of valleys in the region. Diurnal surface temperature amplitude at ridge top (approximately equal to 10 C) was found to be half that observed in the valley. Relatively large differences in precipitation among these stations and the climatological station at Schefferville airport were observed and attributed to the local topography. Eddy correlation observations of the heat, moisture and momentum transports were obtained from a 30-m tower above a sparse (approximately equal to 616 stems/ha) black spruce lichen woodland. Properties of the turbulent surface boundary layer agree well with previous wind tunnel studies over idealized rough surfaces. Daytime Bowen ratios of 2.5-3 are larger than those reported in previous studies. Surface layer flux data quality was assessed by looking at the surface layer heat

  7. Heat requirement for the onset of the Olea europaea L. pollen season in several sites in Andalusia and the effect of the expected future climate change

    Science.gov (United States)

    Galán, C.; García-Mozo, H.; Vázquez, L.; Ruiz, L.; Guardia, C. Díaz; Trigo, M. M.

    2005-01-01

    Olives are one of the largest crops in the Mediterranean region, especially in Andalusia, in southern Spain. A thermal model has been developed for forecasting the start of the olive tree pollen season at five localities in Andalusia: Cordoba, Priego, Jaen, Granada and Malaga using airborne pollen and meteorological data from 1982 to 2001. Threshold temperatures varied between 5°C and 12.5°C depending on bio-geographical characteristics. The external validity of the results was tested using the data for the year 2002 as an independent variable and it confirmed the model’s accuracy with only a few days difference from predicted values. All the localities had increasingly earlier start dates during the study period. This could confirm that olive flower phenology can be considered as a sensitive indicator of the effects of climate fluctuations in the Mediterranean area. The theoretical impact of the predicted climatic warming on the olive’s flowering phenology at the end of the century is also proposed by applying Regional Climate Model data. A general advance, from 1 to 3 weeks could be expected, although this advance will be more pronounced in mid-altitude inland areas.

  8. Effects of a Changing Climate on Seasonal Variation in Natural Recharge of Unconfined Coastal Aquifers

    Science.gov (United States)

    Antonellini, Marco; Nella Mollema, Pauline

    2013-04-01

    continuously throughout the year result in thicker freshwater lenses than models with the same amount of potential recharge applied discontinuously. This difference between the discontinuous and the continuous model is relatively small in areas where the total annual recharge is low (Wellington NZ, Ravenna IT, Ameland NL) but in places with Monsoon-dominated climate as Mumbai, the difference is large. Under the IPCC A1b climate scenario, only Tokyo and Singapore appear to change from a continuous to a discontinuous recharge regime whereas in the other locations there is merely a change in the amount of annual recharge, mostly reducing the size of the freshwater lenses (Ameland, Mekong, Mumbai, Hong Kong and Ravenna). In low latitudes settings such as Mumbai, Mekong Delta, and Hong Kong, this change is more dramatic with large losses of freshwater. This study shows that it is important to consider seasonal variations in temperature and precipitation in water resources management in the coastal zone, especially in view of climatic change.

  9. Predictability of Western Himalayan river flow: melt seasonal inflow into Bhakra Reservoir in northern India

    Directory of Open Access Journals (Sweden)

    I. Pal

    2013-06-01

    Full Text Available Snowmelt-dominated streamflow of the Western Himalayan rivers is an important water resource during the dry pre-monsoon spring months to meet the irrigation and hydropower needs in northern India. Here we study the seasonal prediction of melt-dominated total inflow into the Bhakra Dam in northern India based on statistical relationships with meteorological variables during the preceding winter. Total inflow into the Bhakra Dam includes the Satluj River flow together with a flow diversion from its tributary, the Beas River. Both are tributaries of the Indus River that originate from the Western Himalayas, which is an under-studied region. Average measured winter snow volume at the upper-elevation stations and corresponding lower-elevation rainfall and temperature of the Satluj River basin were considered as empirical predictors. Akaike information criteria (AIC and Bayesian information criteria (BIC were used to select the best subset of inputs from all the possible combinations of predictors for a multiple linear regression framework. To test for potential issues arising due to multicollinearity of the predictor variables, cross-validated prediction skills of the best subset were also compared with the prediction skills of principal component regression (PCR and partial least squares regression (PLSR techniques, which yielded broadly similar results. As a whole, the forecasts of the melt season at the end of winter and as the melt season commences were shown to have potential skill for guiding the development of stochastic optimization models to manage the trade-off between irrigation and hydropower releases versus flood control during the annual fill cycle of the Bhakra Reservoir, a major energy and irrigation source in the region.

  10. Using Seasonal Climate Forecasts to Guide Disaster Management: The Red Cross Experience during the 2008 West Africa Floods

    Directory of Open Access Journals (Sweden)

    Arame Tall

    2012-01-01

    Full Text Available In 2008, the seasonal forecast issued at the Seasonal Climate Outlook Forum for West Africa (PRESAO announced a high risk of above-normal rainfall for the July–September rainy season. With probabilities for above-normal rainfall of 0.45, this forecast indicated noteworthy increases in the risk of heavy rainfall. When this information reached the International Federation of Red Cross and Red Crescent Societies (IFRC West and Central Africa Office, it led to significant changes in the organization’s flood response operations. The IFRC regional office requested funds in advance of anticipated floods, prepositioned disaster relief items in strategic locations across West Africa to benefit up to 9,500 families, updated its flood contingency plans, and alerted vulnerable communities and decision-makers across the region. This forecast-based preparedness resulted in a decrease in the number of lives, property, and livelihoods lost to floods, compared to just one year prior in 2007 when similar floods claimed above 300 lives in the region. This article demonstrates how a science-based early warning informed decisions and saved lives by triggering action in anticipation of forecast events. It analyses what it took to move decision-makers to action, based on seasonal climate information, and to overcome traditional barriers to the uptake of seasonal climate information in the region, providing evidence that these barriers can be overcome. While some institutional, communication and technical barriers were addressed in 2008, many challenges remain. Scientists and humanitarians need to build more common ground.

  11. Season-specific climate signal and reconstruction from a new tree-ring network in the southwestern U.S

    Science.gov (United States)

    Griffin, D.; Woodhouse, C. A.; Meko, D. M.; Stahle, D. W.; Faulstich, H.; Leavitt, S. W.; Touchan, R.; Castro, C. L.; Carrillo, C.

    2011-12-01

    Our research group has updated existing tree-ring collections from over 50 sampling sites in the southwestern U.S. The new and archived specimens, carefully dated with dendrochronology, have been analyzed for width variations of "earlywood" and "latewood." These are the two components of annual rings in conifers that form in spring and summer, respectively. The network of primary tree-ring data has been used to develop a suite of well-replicated chronologies that extend through the 2008 growing season and are sensitive to the season-specific climate variability of the Southwest. Correlation function analysis indicates that the earlywood chronologies are closely related to cool season (October-April) precipitation variability and the chronologies derived from latewood are generally sensitive to precipitation and temperature conditions during the warm season (June-August). These proxy data originate from biological organisms and are not without bias; however, they do constitute a new means for evaluating the recent paleoclimatic history of the North American summer monsoon. The monsoon is a major component of the region's climate, impacting social and environmental systems and delivering up to 60% of the annual precipitation in the southwestern U.S. We have developed latewood-based retrodictions of monsoon precipitation that explain over half of the variance in the instrumental record, pass standard verification tests, and point to periods of persistent drought and wetness during the last 300-500 years. These reconstructions are being used to evaluate the monsoon's long-term spatiotemporal variability and its relationship to cool season climate and the major modes of ocean-atmosphere variability.

  12. Prediction of rainfall anomalies during the dry to wet transition season over the Southern Amazonia using machine learning tools

    Science.gov (United States)

    Shan, X.; Zhang, K.; Zhuang, Y.; Fu, R.; Hong, Y.

    2017-12-01

    Seasonal prediction of rainfall during the dry-to-wet transition season in austral spring (September-November) over southern Amazonia is central for improving planting crops and fire mitigation in that region. Previous studies have identified the key large-scale atmospheric dynamic and thermodynamics pre-conditions during the dry season (June-August) that influence the rainfall anomalies during the dry to wet transition season over Southern Amazonia. Based on these key pre-conditions during dry season, we have evaluated several statistical models and developed a Neural Network based statistical prediction system to predict rainfall during the dry to wet transition for Southern Amazonia (5-15°S, 50-70°W). Multivariate Empirical Orthogonal Function (EOF) Analysis is applied to the following four fields during JJA from the ECMWF Reanalysis (ERA-Interim) spanning from year 1979 to 2015: geopotential height at 200 hPa, surface relative humidity, convective inhibition energy (CIN) index and convective available potential energy (CAPE), to filter out noise and highlight the most coherent spatial and temporal variations. The first 10 EOF modes are retained for inputs to the statistical models, accounting for at least 70% of the total variance in the predictor fields. We have tested several linear and non-linear statistical methods. While the regularized Ridge Regression and Lasso Regression can generally capture the spatial pattern and magnitude of rainfall anomalies, we found that that Neural Network performs best with an accuracy greater than 80%, as expected from the non-linear dependence of the rainfall on the large-scale atmospheric thermodynamic conditions and circulation. Further tests of various prediction skill metrics and hindcasts also suggest this Neural Network prediction approach can significantly improve seasonal prediction skill than the dynamic predictions and regression based statistical predictions. Thus, this statistical prediction system could have

  13. Abiotic and seasonal control of soil-produced CO2 efflux in karstic ecosystems located in Oceanic and Mediterranean climates

    Science.gov (United States)

    Garcia-Anton, Elena; Cuezva, Soledad; Fernandez-Cortes, Angel; Alvarez-Gallego, Miriam; Pla, Concepcion; Benavente, David; Cañaveras, Juan Carlos; Sanchez-Moral, Sergio

    2017-09-01

    This study characterizes the processes involved in seasonal CO2 exchange between soils and shallow underground systems and explores the contribution of the different biotic and abiotic sources as a function of changing weather conditions. We spatially and temporally investigated five karstic caves across the Iberian Peninsula, which presented different microclimatic, geologic and geomorphologic features. The locations present Mediterranean and Oceanic climates. Spot air sampling of CO2 (g) and δ13CO2 in the caves, soils and outside atmospheric air was periodically conducted. The isotopic ratio of the source contribution enhancing the CO2 concentration was calculated using the Keeling model. We compared the isotopic ratio of the source in the soil (δ13Cs-soil) with that in the soil-underground system (δ13Cs-system). Although the studied field sites have different features, we found common seasonal trends in their values, which suggests a climatic control over the soil air CO2 and the δ13CO2 of the sources of CO2 in the soil (δ13Cs-soil) and the system (δ13Cs-system). The roots respiration and soil organic matter degradation are the main source of CO2 in underground environments, and the inlet of the gas is mainly driven by diffusion and advection. Drier and warmer conditions enhance soil-exterior CO2 interchange, reducing the CO2 concentration and increasing the δ13CO2 of the soil air. Moreover, the isotopic ratio of the source of CO2 in both the soil and the system tends to heavier values throughout the dry and warm season. We conclude that seasonal variations of soil CO2 concentration and its 13C/12C isotopic ratio are mainly regulated by thermo-hygrometric conditions. In cold and wet seasons, the increase of soil moisture reduces soil diffusivity and allows the storage of CO2 in the subsoil. During dry and warm seasons, the evaporation of soil water favours diffusive and advective transport of soil-derived CO2 to the atmosphere. The soil CO2 diffusion is

  14. Prediction of Indian Summer-Monsoon Onset Variability: A Season in Advance.

    Science.gov (United States)

    Pradhan, Maheswar; Rao, A Suryachandra; Srivastava, Ankur; Dakate, Ashish; Salunke, Kiran; Shameera, K S

    2017-10-27

    Monsoon onset is an inherent transient phenomenon of Indian Summer Monsoon and it was never envisaged that this transience can be predicted at long lead times. Though onset is precipitous, its variability exhibits strong teleconnections with large scale forcing such as ENSO and IOD and hence may be predictable. Despite of the tremendous skill achieved by the state-of-the-art models in predicting such large scale processes, the prediction of monsoon onset variability by the models is still limited to just 2-3 weeks in advance. Using an objective definition of onset in a global coupled ocean-atmosphere model, it is shown that the skillful prediction of onset variability is feasible under seasonal prediction framework. The better representations/simulations of not only the large scale processes but also the synoptic and intraseasonal features during the evolution of monsoon onset are the comprehensions behind skillful simulation of monsoon onset variability. The changes observed in convection, tropospheric circulation and moisture availability prior to and after the onset are evidenced in model simulations, which resulted in high hit rate of early/delay in monsoon onset in the high resolution model.

  15. Seasonal rainfall prediction skill over South Africa: one- versus two-tiered forecasting systems

    CSIR Research Space (South Africa)

    Landman, WA

    2012-04-01

    Full Text Available - able (e.g., Klopper et al. 1998; Landman and Goddard 2002; Reason and Rouault 2005, Tennant and Hewitson 2002). This knowledge led to the development of objective operational seasonal forecasting systems for South Africa, but only as recently... as the 1990s (e.g., Jury 1996; Jury et al. 1999; Landman and Mason 1999; Mason 1998). Although the prediction problem over southern Africa was also addressed by modelers from outside the region (e.g., Barnston et al. 1996), the South African...

  16. A linear regression model for predicting PNW estuarine temperatures in a changing climate

    Science.gov (United States)

    Pacific Northwest coastal regions, estuaries, and associated ecosystems are vulnerable to the potential effects of climate change, especially to changes in nearshore water temperature. While predictive climate models simulate future air temperatures, no such projections exist for...

  17. prediction of the impacts of climate changes on the stream flow

    African Journals Online (AJOL)

    HOD

    Soil and Water Assessment Tool, (SWAT) model was used to predict the impacts of Climate Change on Ajali River watershed ... Climate is the synthesis of atmospheric conditions characteristic of a .... generator available in the SWAT model.

  18. Deficiencies and possibilities for long-lead coupled climate prediction of the Western North Pacific-East Asian summer monsoon

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Sun-Seon; Ha, Kyung-Ja [Pusan National University, Division of Earth Environmental System, Busan (Korea, Republic of); Lee, June-Yi; Wang, Bin [University of Hawaii, Department of Meteorology and International Pacific Research Center, Honolulu, HI (United States); Schemm, Jae Kyung E. [Climate Prediction Center/NCEP, Camp Springs, MD (United States)

    2011-03-15

    Long-lead prediction of waxing and waning of the Western North Pacific (WNP)-East Asian (EA) summer monsoon (WNP-EASM) precipitation is a major challenge in seasonal time-scale climate prediction. In this study, deficiencies and potential for predicting the WNP-EASM precipitation and circulation one or two seasons ahead were examined using retrospective forecast data for the 26-year period of 1981-2006 from two operational couple models which are the National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS) and the Bureau of Meteorology Research Center (BMRC) Predictive Ocean-Atmosphere Model for Australia (POAMA). While both coupled models have difficulty in predicting summer mean precipitation anomalies over the region of interest, even for a 0-month lead forecast, they are capable of predicting zonal wind anomalies at 850 hPa several months ahead and, consequently, satisfactorily predict summer monsoon circulation indices for the EA region (EASMI) and for the WNP region (WNPSMI). It should be noted that the two models' multi-model ensemble (MME) reaches 0.40 of the correlation skill for the EASMI with a January initial condition and 0.75 for the WNPSMI with a February initial condition. Further analysis indicates that prediction reliability of the EASMI is related not only to the preceding El Nino and Southern Oscillation (ENSO) but also to simultaneous local SST variability. On other hand, better prediction of the WNPSMI is accompanied by a more realistic simulation of lead-lag relationship between the index and ENSO. It should also be noted that current coupled models have difficulty in capturing the interannual variability component of the WNP-EASM system which is not correlated with typical ENSO variability. To improve the long-lead seasonal prediction of the WNP-EASM precipitation, a statistical postprocessing was developed based on the multiple linear regression method. The method utilizes the MME prediction of the EASMI and

  19. Incorporating residual temperature and specific humidity in predicting weather-dependent warm-season electricity consumption

    Science.gov (United States)

    Guan, Huade; Beecham, Simon; Xu, Hanqiu; Ingleton, Greg

    2017-02-01

    Climate warming and increasing variability challenges the electricity supply in warm seasons. A good quantitative representation of the relationship between warm-season electricity consumption and weather condition provides necessary information for long-term electricity planning and short-term electricity management. In this study, an extended version of cooling degree days (ECDD) is proposed for better characterisation of this relationship. The ECDD includes temperature, residual temperature and specific humidity effects. The residual temperature is introduced for the first time to reflect the building thermal inertia effect on electricity consumption. The study is based on the electricity consumption data of four multiple-street city blocks and three office buildings. It is found that the residual temperature effect is about 20% of the current-day temperature effect at the block scale, and increases with a large variation at the building scale. Investigation of this residual temperature effect provides insight to the influence of building designs and structures on electricity consumption. The specific humidity effect appears to be more important at the building scale than at the block scale. A building with high energy performance does not necessarily have low specific humidity dependence. The new ECDD better reflects the weather dependence of electricity consumption than the conventional CDD method.

  20. A critical analysis of climatic influences on indoor radon concentrations: Implications for seasonal correction.

    Science.gov (United States)

    Groves-Kirkby, Christopher J; Crockett, Robin G M; Denman, Antony R; Phillips, Paul S

    2015-10-01

    Although statistically-derived national Seasonal Correction Factors (SCFs) are conventionally used to convert sub-year radon concentration measurements to an annual mean, it has recently been suggested that external temperature could be used to derive local SCFs for short-term domestic measurements. To validate this approach, hitherto unanalysed radon and temperature data from an environmentally-stable location were analysed. Radon concentration and internal temperature were measured over periods totalling 1025 days during an overall period of 1762 days, the greatest continuous sampling period being 334 days, with corresponding meteorological data collected at a weather station 10 km distant. Mean daily, monthly and annual radon concentrations and internal temperatures were calculated. SCFs derived using monthly mean radon concentration, external temperature and internal-external temperature-difference were cross-correlated with each other and with published UK domestic SCF sets. Relatively good correlation exists between SCFs derived from radon concentration and internal-external temperature difference but correlation with external temperature, was markedly poorer. SCFs derived from external temperature correlate very well with published SCF tabulations, confirming that the complexity of deriving SCFs from temperature data may be outweighed by the convenience of using either of the existing domestic SCF tabulations. Mean monthly radon data fitted to a 12-month sinusoid showed reasonable correlation with many of the annual climatic parameter profiles, exceptions being atmospheric pressure, rainfall and internal temperature. Introducing an additional 6-month sinusoid enhanced correlation with these three parameters, the other correlations remaining essentially unchanged. Radon latency of the order of months in moisture-related parameters suggests that the principal driver for radon is total atmospheric moisture content rather than relative humidity. Copyright

  1. Seasonal Climate Associated with Major Shipping Routes in the North Pacific and North Atlantic

    Directory of Open Access Journals (Sweden)

    Jau-Ming Chen

    2014-01-01

    Full Text Available The major shipping routes in the North Pacific (NP and North Atlantic (NA are analyzed via ship-reported records compiled by the International Comprehensive Ocean-Atmosphere Data Set (ICOADS. The shipping route seasonal characteristics and associated climatic features are also examined. In the NP, the dominant cross-basin route takes a great-circle path between East Asia and North America along 54°N north of the Aleutian Islands throughout the year. This route penetrates the Aleutian low center where ocean waves and winds are relatively weaker than those in the low¡¦s southern section south of 50°N. Moreover, the Earth¡¦s spherical shape makes a higher-latitude route shorter in navigational distance across the NP than a lower-latitude route. Two additional mid-latitude routes through the 40° - 50°N region appear in summer when the Aleutian low vanishes. In the NA, the major shipping routes form an X-shaped pattern in the oceans south of 40°N to connect North America/the Panama Canal and the Mediterranean Sea/the British Isles and Europe. These major shipping routes are far from the influence of the Icelandic low and thus are used throughout the year due to the stability in marine conditions and their general efficiency. A third and more zonal route appears to the north of the X-shaped routes in the 40° - 50°N region. Weak influence from the Icelandic low on marine conditions during summer and spring means that more ships take this route in summer and spring than in winter and fall.

  2. Seasonal response of biomass growth and allocation of a boreal bioenergy crop (Phalaris arundinacea L.) to climate change

    Energy Technology Data Exchange (ETDEWEB)

    Chang Zhang

    2013-06-01

    in the growing season. Compared to CON, ET and ETC increased LMF and SMF, and decreased RMF over the whole growing season under NW and HW. Under LW, ET and ETC decreased LMF and increased RMF throughout the growing season, and increased SMF in early periods and then decreased later in the growing season. EC decreased the LMF and SMF and increased the RMF over the growing season but did not significantly affect the seasonal biomass allocation pattern between plant organs. The LMF was higher and the RMF was lower throughout the growing season in response to the higher groundwater level, while the effect of groundwater level on the SMF depended on the developmental phase of the plants. Our results show that climatic treatments affected biomass growth and biomass allocation to each of the three plant organs, while the direction and extent of climate-related changes in biomass growth and allocation depended on the availability of groundwater. The influence of groundwater level appeared to be crucial for the carbon gain regarding the production of RCG biomass for energy purposes and the concurrent sequestration of carbon in soils under changing climates in the mire sites used to cultivate RCG. (orig.)

  3. Sub-seasonal predictability of water scarcity at global and local scale

    Science.gov (United States)

    Wanders, N.; Wada, Y.; Wood, E. F.

    2016-12-01

    Forecasting the water demand and availability for agriculture and energy production has been neglected in previous research, partly due to the fact that most large-scale hydrological models lack the skill to forecast human water demands at sub-seasonal time scale. We study the potential of a sub-seasonal water scarcity forecasting system for improved water management decision making and improved estimates of water demand and availability. We have generated 32 years of global sub-seasonal multi-model water availability, demand and scarcity forecasts. The quality of the forecasts is compared to a reference forecast derived from resampling historic weather observations. The newly developed system has been evaluated for both the global scale and in a real-time local application in the Sacramento valley for the Trinity, Shasta and Oroville reservoirs, where the water demand for agriculture and hydropower is high. On the global scale we find that the reference forecast shows high initial forecast skill (up to 8 months) for water scarcity in the eastern US, Central Asia and Sub-Saharan Africa. Adding dynamical sub-seasonal forecasts results in a clear improvement for most regions in the world, increasing the forecasts' lead time by 2 or more months on average. The strongest improvements are found in the US, Brazil, Central Asia and Australia. For the Sacramento valley we can accurately predict anomalies in the reservoir inflow, hydropower potential and the downstream irrigation water demand 6 months in advance. This allow us to forecast potential water scarcity in the Sacramento valley and adjust the reservoir management to prevent deficits in energy or irrigation water availability. The newly developed forecast system shows that it is possible to reduce the vulnerability to upcoming water scarcity events and allows optimization of the distribution of the available water between the agricultural and energy sector half a year in advance.

  4. Non-stationary analysis of dry spells in monsoon season of Senegal River Basin using data from Regional Climate Models (RCMs)

    Science.gov (United States)

    Giraldo Osorio, J. D.; García Galiano, S. G.

    2012-07-01

    SummaryThe Senegal River Basin, located in West Africa, has been affected by several droughts since the end of the 1960s. In its valley, which is densely populated and highly vulnerable to climate variability and water availability, agricultural activities provide the livelihood for thousands of people. Increasing the knowledge about plausible trends of drought events will allow to improve the adaptation and mitigation measures in order to build "adaptive capacity" to climate change in West Africa. An innovative methodology for the non-stationary analysis of droughts events, which allows the prediction of regional trends associated to several return periods, is presented. The analyses were based on Regional Climate Models (RCMs) provided by the European ENSEMBLES project for West Africa, together with observed data. A non-stationary behaviour of the annual series of maximum length of dry spells (AMDSL) in the monsoon season is reflected in temporal changes in mean and variance. The non-stationary nature of hydrometeorological series, due to climate change and anthropogenic activities, is the main criticism to traditional frequency analysis. Therefore, in this paper, the modelling tool GAMLSS (Generalized Additive Models for Location, Scale and Shape), is applied to develop regional probability density functions (pdfs) fitted to AMDSL series for the monsoon season in the Senegal River Basin. The skills of RCMs in the representation of maximum length of dry spells observed for the period 1970-1990, are evaluated considering observed data. Based on the results obtained, a first selection of the RCMs with which to apply GAMLSS to the AMDSL series identified, for the time period 1970-2050, is made. The results of GAMLSS analysis exhibit divergent trends, with different value ranges for parameters of probability distributions being detected. Therefore, in the second stage of the paper, regional pdfs are constructed using bootstrapping distributions based on probabilistic

  5. ECMWF seasonal forecast system 3 and its prediction of sea surface temperature

    Energy Technology Data Exchange (ETDEWEB)

    Stockdale, Timothy N.; Anderson, David L.T.; Balmaseda, Magdalena A.; Ferranti, Laura; Mogensen, Kristian; Palmer, Timothy N.; Molteni, Franco; Vitart, Frederic [ECMWF, Reading (United Kingdom); Doblas-Reyes, Francisco [ECMWF, Reading (United Kingdom); Institut Catala de Ciencies del Clima (IC3), Barcelona (Spain)

    2011-08-15

    The latest operational version of the ECMWF seasonal forecasting system is described. It shows noticeably improved skill for sea surface temperature (SST) prediction compared with previous versions, particularly with respect to El Nino related variability. Substantial skill is shown for lead times up to 1 year, although at this range the spread in the ensemble forecast implies a loss of predictability large enough to account for most of the forecast error variance, suggesting only moderate scope for improving long range El Nino forecasts. At shorter ranges, particularly 3-6 months, skill is still substantially below the model-estimated predictability limit. SST forecast skill is higher for more recent periods than earlier ones. Analysis shows that although various factors can affect scores in particular periods, the improvement from 1994 onwards seems to be robust, and is most plausibly due to improvements in the observing system made at that time. The improvement in forecast skill is most evident for 3-month forecasts starting in February, where predictions of NINO3.4 SST from 1994 to present have been almost without fault. It is argued that in situations where the impact of model error is small, the value of improved observational data can be seen most clearly. Significant skill is also shown in the equatorial Indian Ocean, although predictive skill in parts of the tropical Atlantic are relatively poor. SST forecast errors can be especially high in the Southern Ocean. (orig.)

  6. Variability, trends, and predictability of seasonal sea ice retreat and advance in the Chukchi Sea

    Science.gov (United States)

    Serreze, Mark C.; Crawford, Alex D.; Stroeve, Julienne C.; Barrett, Andrew P.; Woodgate, Rebecca A.

    2016-10-01

    As assessed over the period 1979-2014, the date that sea ice retreats to the shelf break (150 m contour) of the Chukchi Sea has a linear trend of -0.7 days per year. The date of seasonal ice advance back to the shelf break has a steeper trend of about +1.5 days per year, together yielding an increase in the open water period of 80 days. Based on detrended time series, we ask how interannual variability in advance and retreat dates relate to various forcing parameters including radiation fluxes, temperature and wind (from numerical reanalyses), and the oceanic heat inflow through the Bering Strait (from in situ moorings). Of all variables considered, the retreat date is most strongly correlated (r ˜ 0.8) with the April through June Bering Strait heat inflow. After testing a suite of statistical linear models using several potential predictors, the best model for predicting the date of retreat includes only the April through June Bering Strait heat inflow, which explains 68% of retreat date variance. The best model predicting the ice advance date includes the July through September inflow and the date of retreat, explaining 67% of advance date variance. We address these relationships by discussing heat balances within the Chukchi Sea, and the hypothesis of oceanic heat transport triggering ocean heat uptake and ice-albedo feedback. Developing an operational prediction scheme for seasonal retreat and advance would require timely acquisition of Bering Strait heat inflow data. Predictability will likely always be limited by the chaotic nature of atmospheric circulation patterns.

  7. Impact of climatic change on alpine ecosystems: inference and prediction

    Directory of Open Access Journals (Sweden)

    Nigel G. Yoccoz

    2011-01-01

    Full Text Available Alpine ecosystems will be greatly impacted by climatic change, but other factors, such as land use and invasive species, are likely to play an important role too. Climate can influence ecosystems at several levels. We describe some of them, stressing methodological approaches and available data. Climate can modify species phenology, such as flowering date of plants and hatching date in insects. It can also change directly population demography (survival, reproduction, dispersal, and therefore species distribution. Finally it can effect interactions among species – snow cover for example can affect the success of some predators. One characteristic of alpine ecosystems is the presence of snow cover, but surprisingly the role played by snow is relatively poorly known, mainly for logistical reasons. Even if we have made important progress regarding the development of predictive models, particularly so for distribution of alpine plants, we still need to set up observational and experimental networks which properly take into account the variability of alpine ecosystems and of their interactions with climate.Les écosystèmes alpins vont être grandement influencés par les changements climatiques à venir, mais d’autres facteurs, tels que l’utilisation des terres ou les espèces invasives, pourront aussi jouer un rôle important. Le climat peut influencer les écosystèmes à différents niveaux, et nous en décrivons certains, en mettant l’accent sur les méthodes utilisées et les données disponibles. Le climat peut d’abord modifier la phénologie des espèces, comme la date de floraison des plantes ou la date d’éclosion des insectes. Il peut ensuite affecter directement la démographie des espèces (survie, reproduction, dispersion et donc à terme leur répartition. Il peut enfin agir sur les interactions entre espèces – le couvert neigeux par exemple modifie le succès de certains prédateurs. Une caractéristique des

  8. Atlas : A library for numerical weather prediction and climate modelling

    Science.gov (United States)

    Deconinck, Willem; Bauer, Peter; Diamantakis, Michail; Hamrud, Mats; Kühnlein, Christian; Maciel, Pedro; Mengaldo, Gianmarco; Quintino, Tiago; Raoult, Baudouin; Smolarkiewicz, Piotr K.; Wedi, Nils P.

    2017-11-01

    The algorithms underlying numerical weather prediction (NWP) and climate models that have been developed in the past few decades face an increasing challenge caused by the paradigm shift imposed by hardware vendors towards more energy-efficient devices. In order to provide a sustainable path to exascale High Performance Computing (HPC), applications become increasingly restricted by energy consumption. As a result, the emerging diverse and complex hardware solutions have a large impact on the programming models traditionally used in NWP software, triggering a rethink of design choices for future massively parallel software frameworks. In this paper, we present Atlas, a new software library that is currently being developed at the European Centre for Medium-Range Weather Forecasts (ECMWF), with the scope of handling data structures required for NWP applications in a flexible and massively parallel way. Atlas provides a versatile framework for the future development of efficient NWP and climate applications on emerging HPC architectures. The applications range from full Earth system models, to specific tools required for post-processing weather forecast products. The Atlas library thus constitutes a step towards affordable exascale high-performance simulations by providing the necessary abstractions that facilitate the application in heterogeneous HPC environments by promoting the co-design of NWP algorithms with the underlying hardware.

  9. Climate Based Predictability of Oil Palm Tree Yield in Malaysia.

    Science.gov (United States)

    Oettli, Pascal; Behera, Swadhin K; Yamagata, Toshio

    2018-02-02

    The influence of local conditions and remote climate modes on the interannual variability of oil palm fresh fruit bunches (FFB) total yields in Malaysia and two major regions (Peninsular Malaysia and Sabah/Sarawak) is explored. On a country scale, the state of sea-surface temperatures (SST) in the tropical Pacific Ocean during the previous boreal winter is found to influence the regional climate. When El Niño occurs in the Pacific Ocean, rainfall in Malaysia reduces but air temperature increases, generating a high level of water stress for palm trees. As a result, the yearly production of FFB becomes lower than that of a normal year since the water stress during the boreal spring has an important impact on the total annual yields of FFB. Conversely, La Niña sets favorable conditions for palm trees to produce more FFB by reducing chances of water stress risk. The region of the Leeuwin current also seems to play a secondary role through the Ningaloo Niño/ Niña in the interannual variability of FFB yields. Based on these findings, a linear model is constructed and its ability to reproduce the interannual signal is assessed. This model has shown some skills in predicting the total FFB yield.

  10. Integrated model for predicting rice yield with climate change

    Science.gov (United States)

    Park, Jin-Ki; Das, Amrita; Park, Jong-Hwa

    2018-04-01

    Rice is the chief agricultural product and one of the primary food source. For this reason, it is of pivotal importance for worldwide economy and development. Therefore, in a decision-support-system both for the farmers and in the planning and management of the country's economy, forecasting yield is vital. However, crop yield, which is a dependent of the soil-bio-atmospheric system, is difficult to represent in statistical language. This paper describes a novel approach for predicting rice yield using artificial neural network, spatial interpolation, remote sensing and GIS methods. Herein, the variation in the yield is attributed to climatic parameters and crop health, and the normalized difference vegetation index from MODIS is used as an indicator of plant health and growth. Due importance was given to scaling up the input parameters using spatial interpolation and GIS and minimising the sources of error in every step of the modelling. The low percentage error (2.91) and high correlation (0.76) signifies the robust performance of the proposed model. This simple but effective approach is then used to estimate the influence of climate change on South Korean rice production. As proposed in the RCP8.5 scenario, an upswing in temperature may increase the rice yield throughout South Korea.

  11. Estimation of Seasonal Efficiency of Sochi Resort Climate Therapy by Means of Psychologic Testing of Patients with Cardiometabolic Pathology

    Directory of Open Access Journals (Sweden)

    Irina N. Sorochinskaya

    2012-11-01

    Full Text Available Cardiovascular diseases are major reasons for population mortality in majority of countries, including Russia. Metabolic syndrome is considered to be one of the main pathologic states, leading to enhancement of atherogenesis, ischemic heart diseases and cerebrovascular diseases. Physical methods, including resort treatment play great role in metabolic syndrome prevention and treatment. Climate therapy depends on resort climate and season and is a major component of resort treatment. Psychological testing showed that combined resort treatment, using climate therapy of patients with stable effort angina at Sochi Health-resort is more efficient in autumn and of patients with metabolic syndrome in summer. The findings have been confirmed by clinic-functional indicators.

  12. Phenological cues intrinsic in indigenous knowledge systems for forecasting seasonal climate in the Delta State of Nigeria

    Science.gov (United States)

    Fitchett, Jennifer M.; Ebhuoma, Eromose

    2017-12-01

    Shifts in the timing of phenological events in plants and animals are cited as one of the most robust bioindicators of climate change. Much effort has thus been placed on the collection of phenological datasets, the quantification of the rates of phenological shifts and the association of these shifts with recorded meteorological data. These outputs are of value both in tracking the severity of climate change and in facilitating more robust management approaches in forestry and agriculture to changing climatic conditions. However, such an approach requires meteorological and phenological records spanning multiple decades. For communities in the Delta State of Nigeria, small-scale farming communities do not have access to meteorological records, and the dissemination of government issued daily to seasonal forecasts has only taken place in recent years. Their ability to survive inter-annual to inter-decadal climatic variability and longer-term climatic change has thus relied on well-entrenched indigenous knowledge systems (IKS). An analysis of the environmental cues that are used to infer the timing and amount of rainfall by farmers from three communities in the Delta State reveals a reliance on phenological events, including the croaking of frogs, the appearance of red millipedes and the emergence of fresh rubber tree and cassava leaves. These represent the first recorded awareness of phenology within the Delta State of Nigeria, and a potentially valuable source of phenological data. However, the reliance of these indicators is of concern given the rapid phenological shifts occurring in response to climate change.

  13. Seasonal climate signals from multiple tree ring metrics: A case study of Pinus ponderosa in the upper Columbia River Basin

    Science.gov (United States)

    Dannenberg, Matthew P.; Wise, Erika K.

    2016-04-01

    Projected changes in the seasonality of hydroclimatic regimes are likely to have important implications for water resources and terrestrial ecosystems in the U.S. Pacific Northwest. The tree ring record, which has frequently been used to position recent changes in a longer-term context, typically relies on signals embedded in the total ring width of tree rings. Additional climatic inferences at a subannual temporal scale can be made using alternative tree ring metrics such as earlywood and latewood widths and the density of tree ring latewood. Here we examine seasonal precipitation and temperature signals embedded in total ring width, earlywood width, adjusted latewood width, and blue intensity chronologies from a network of six Pinus ponderosa sites in and surrounding the upper Columbia River Basin of the U.S. Pacific Northwest. We also evaluate the potential for combining multiple tree ring metrics together in reconstructions of past cool- and warm-season precipitation. The common signal among all metrics and sites is related to warm-season precipitation. Earlywood and latewood widths differ primarily in their sensitivity to conditions in the year prior to growth. Total and earlywood widths from the lowest elevation sites also reflect cool-season moisture. Effective correlation analyses and composite-plus-scale tests suggest that combining multiple tree ring metrics together may improve reconstructions of warm-season precipitation. For cool-season precipitation, total ring width alone explains more variance than any other individual metric or combination of metrics. The composite-plus-scale tests show that variance-scaled precipitation reconstructions in the upper Columbia River Basin may be asymmetric in their ability to capture extreme events.

  14. U.S. Annual/Seasonal Climate Normals (1981-2010)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The U.S. Annual Climate Normals for 1981 to 2010 are 30-year averages of meteorological parameters that provide users with many tools to understand typical climate...

  15. Atmospheric Rivers in Europe: impacts, predictability, and future climate scenarios

    Science.gov (United States)

    Ramos, A. M.; Tome, R.; Sousa, P. M.; Liberato, M. L. R.; Lavers, D.; Trigo, R. M.

    2017-12-01

    In recent years a strong relationship has been found between Atmospheric Rivers (ARs) and extreme precipitation and floods across western Europe, with some regions having 8 of their top 10 annual maxima precipitation events related to ARs. In the particular case of the Iberian Peninsula, the association between ARs and extreme precipitation days in the western river basins is noteworthy, while for the eastern and southern basins the impact of ARs is reduced. An automated ARs detection algorithm is used for the North Atlantic Ocean Basin, allowing the identification of major ARs affecting western European coasts in the present climate and under different climate change scenarios. We have used both reanalyzes and six General Circulation models under three climate scenarios (the control simulation, the RCP4.5 and RCP8.5 scenarios). The western coast of Europe was divided into five domains, namely the Iberian Peninsula, France, UK, Southern Scandinavia and the Netherlands, and Northern Scandinavia. It was found that there is an increase in the vertically integrated horizontal water transport which led to an increase in the AR frequency, a result more visible in the high emission scenarios (RCP8.5) for the 2074-2099 period. Since ARs are associated with high impact weather, it is important to study their predictability. This assessment was performed with the ECMWF ensemble forecasts up to 10 days for winters 2013/14, 2014/15 and 2015/16 for events that made landfall in the Iberian Peninsula. We show the model's potential added value to detect upcoming ARs events, which is particularly useful to predict potential hydrometeorological extremes. AcknowledgementsThis work was supported by the project FORLAND - Hydrogeomorphologic risk in Portugal: driving forces and application for land use planning [PTDC / ATPGEO / 1660/2014] funded by the Portuguese Foundation for Science and Technology (FCT), Portugal. A. M. Ramos was also supported by a FCT postdoctoral grant (FCT

  16. Seasonal Differences in Climatic Controls of Vegetation Growth in the Beijing-Tianjin Sand Source Region of China.

    Science.gov (United States)

    Wang, H.

    2017-12-01

    Seasonal differences in climatic controls of vegetation growth in the Beijing-Tianjin Sand Source Region of China Bin He1 , Haiyan Wan11 State Key Laboratory of Earth Surface Processes and Resource Ecology, College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China Corresponding author: Bin He, email addresses: hebin@bnu.edu.cnPhone:+861058806506, Address: Beijing Normal University, College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China. Email addresses of co-authors: wanghaiyan@mail.bnu.edu.cnABSTRACTLaunched in 2000, the Beiing-Tainjin Sand Source Controlling Project (BTSSCP) is an ecological restoration project intended to prevent desertification in China. Evidence from multiple sources has confirmed increases in vegetation growth in the BTSSCP region since the initiation of the project. Precipitation and related soil moisture conditions typically are considered to be the main drivers of vegetation growth in this arid region. However, by investigating the relationships between vegetation growth and corresponding climatic factors, we identified seasonal variation in the climatic constraints of vegetation growth. In spring, vegetation growth is stimulated mainly by elevated temperature, whereas precipitation is the lead driver of summer greening. In autumn, positive effects of both temperature and precipitation on vegetation growth were observed. Furthermore, strong biosphere-atmosphere interactions were observed in this region. Spring warming promotes vegetation growth, but also reduces soil moisture. Summer greening has a strong cooling effect on land surface temperature. These results indicate that 1) precipitation-based projections of vegetation growth may be misleading; and 2) the ecological and environment consequences of ecological projects should be comprehensively evaluated. KEYWORDS: vegetation growth, climatic drivers, seasonal variation, BTSSCP

  17. Using dendrometer and dendroclimatology data to predict the growth response of Douglas-fir to climate change in the Pacific Northwest, USA

    Science.gov (United States)

    Altered seasonal climate patterns towards hotter, drier summers through the 21st century resulting from global climate change could affect the growth of coniferous forests in the Pacific Northwest (PNW) region of North America. The seasonal effects of temperature, precipitation,...

  18. Seasonal-to-decadal predictions with the ensemble Kalman filter and the Norwegian Earth System Model: a twin experiment

    Directory of Open Access Journals (Sweden)

    Francois Counillon

    2014-03-01

    Full Text Available Here, we firstly demonstrate the potential of an advanced flow dependent data assimilation method for performing seasonal-to-decadal prediction and secondly, reassess the use of sea surface temperature (SST for initialisation of these forecasts. We use the Norwegian Climate Prediction Model (NorCPM, which is based on the Norwegian Earth System Model (NorESM and uses the deterministic ensemble Kalman filter to assimilate observations. NorESM is a fully coupled system based on the Community Earth System Model version 1, which includes an ocean, an atmosphere, a sea ice and a land model. A numerically efficient coarse resolution version of NorESM is used. We employ a twin experiment methodology to provide an upper estimate of predictability in our model framework (i.e. without considering model bias of NorCPM that assimilates synthetic monthly SST data (EnKF-SST. The accuracy of EnKF-SST is compared to an unconstrained ensemble run (FREE and ensemble predictions made with near perfect (i.e. microscopic SST perturbation initial conditions (PERFECT. We perform 10 cycles, each consisting of a 10-yr assimilation phase, followed by a 10-yr prediction. The results indicate that EnKF-SST improves sea level, ice concentration, 2 m atmospheric temperature, precipitation and 3-D hydrography compared to FREE. Improvements for the hydrography are largest near the surface and are retained for longer periods at depth. Benefits in salinity are retained for longer periods compared to temperature. Near-surface improvements are largest in the tropics, while improvements at intermediate depths are found in regions of large-scale currents, regions of deep convection, and at the Mediterranean Sea outflow. However, the benefits are often small compared to PERFECT, in particular, at depth suggesting that more observations should be assimilated in addition to SST. The EnKF-SST system is also tested for standard ocean circulation indices and demonstrates decadal

  19. Does objective cluster analysis serve as a useful precursor to seasonal precipitation prediction at local scale? Application to western Ethiopia

    Science.gov (United States)

    Zhang, Ying; Moges, Semu; Block, Paul

    2018-01-01

    Prediction of seasonal precipitation can provide actionable information to guide management of various sectoral activities. For instance, it is often translated into hydrological forecasts for better water resources management. However, many studies assume homogeneity in precipitation across an entire study region, which may prove ineffective for operational and local-level decisions, particularly for locations with high spatial variability. This study proposes advancing local-level seasonal precipitation predictions by first conditioning on regional-level predictions, as defined through objective cluster analysis, for western Ethiopia. To our knowledge, this is the first study predicting seasonal precipitation at high resolution in this region, where lives and livelihoods are vulnerable to precipitation variability given the high reliance on rain-fed agriculture and limited water resources infrastructure. The combination of objective cluster analysis, spatially high-resolution prediction of seasonal pre