WorldWideScience

Sample records for climate modelling variability

  1. Interpolation of climate variables and temperature modeling

    Science.gov (United States)

    Samanta, Sailesh; Pal, Dilip Kumar; Lohar, Debasish; Pal, Babita

    2012-01-01

    Geographic Information Systems (GIS) and modeling are becoming powerful tools in agricultural research and natural resource management. This study proposes an empirical methodology for modeling and mapping of the monthly and annual air temperature using remote sensing and GIS techniques. The study area is Gangetic West Bengal and its neighborhood in the eastern India, where a number of weather systems occur throughout the year. Gangetic West Bengal is a region of strong heterogeneous surface with several weather disturbances. This paper also examines statistical approaches for interpolating climatic data over large regions, providing different interpolation techniques for climate variables' use in agricultural research. Three interpolation approaches, like inverse distance weighted averaging, thin-plate smoothing splines, and co-kriging are evaluated for 4° × 4° area, covering the eastern part of India. The land use/land cover, soil texture, and digital elevation model are used as the independent variables for temperature modeling. Multiple regression analysis with standard method is used to add dependent variables into regression equation. Prediction of mean temperature for monsoon season is better than winter season. Finally standard deviation errors are evaluated after comparing the predicted temperature and observed temperature of the area. For better improvement, distance from the coastline and seasonal wind pattern are stressed to be included as independent variables.

  2. Multi-Wheat-Model Ensemble Responses to Interannual Climate Variability

    Science.gov (United States)

    Ruane, Alex C.; Hudson, Nicholas I.; Asseng, Senthold; Camarrano, Davide; Ewert, Frank; Martre, Pierre; Boote, Kenneth J.; Thorburn, Peter J.; Aggarwal, Pramod K.; Angulo, Carlos

    2016-01-01

    We compare 27 wheat models' yield responses to interannual climate variability, analyzed at locations in Argentina, Australia, India, and The Netherlands as part of the Agricultural Model Intercomparison and Improvement Project (AgMIP) Wheat Pilot. Each model simulated 1981e2010 grain yield, and we evaluate results against the interannual variability of growing season temperature, precipitation, and solar radiation. The amount of information used for calibration has only a minor effect on most models' climate response, and even small multi-model ensembles prove beneficial. Wheat model clusters reveal common characteristics of yield response to climate; however models rarely share the same cluster at all four sites indicating substantial independence. Only a weak relationship (R2 0.24) was found between the models' sensitivities to interannual temperature variability and their response to long-termwarming, suggesting that additional processes differentiate climate change impacts from observed climate variability analogs and motivating continuing analysis and model development efforts.

  3. Effect of Flux Adjustments on Temperature Variability in Climate Models

    International Nuclear Information System (INIS)

    It has been suggested that ''flux adjustments'' in climate models suppress simulated temperature variability. If true, this might invalidate the conclusion that at least some of observed temperature increases since 1860 are anthropogenic, since this conclusion is based in part on estimates of natural temperature variability derived from flux-adjusted models. We assess variability of surface air temperatures in 17 simulations of internal temperature variability submitted to the Coupled Model Intercomparison Project. By comparing variability in flux-adjusted vs. non-flux adjusted simulations, we find no evidence that flux adjustments suppress temperature variability in climate models; other, largely unknown, factors are much more important in determining simulated temperature variability. Therefore the conclusion that at least some of observed temperature increases are anthropogenic cannot be questioned on the grounds that it is based in part on results of flux-adjusted models. Also, reducing or eliminating flux adjustments would probably do little to improve simulations of temperature variability

  4. Multi-wheat-model ensemble responses to interannual climatic variability

    DEFF Research Database (Denmark)

    Ruane, A C; Hudson, N I; Asseng, S;

    2016-01-01

    We compare 27 wheat models' yield responses to interannual climate variability, analyzed at locations in Argentina, Australia, India, and The Netherlands as part of the Agricultural Model Intercomparison and Improvement Project (AgMIP) Wheat Pilot. Each model simulated 1981–2010 grain yield, and we...... evaluate results against the interannual variability of growing season temperature, precipitation, and solar radiation. The amount of information used for calibration has only a minor effect on most models' climate response, and even small multi-model ensembles prove beneficial. Wheat model clusters reveal...... common characteristics of yield response to climate; however models rarely share the same cluster at all four sites indicating substantial independence. Only a weak relationship (R2 ≤ 0.24) was found between the models' sensitivities to interannual temperature variability and their response to long...

  5. Modeling key processes causing climate change and variability

    Energy Technology Data Exchange (ETDEWEB)

    Henriksson, S.

    2013-09-01

    Greenhouse gas warming, internal climate variability and aerosol climate effects are studied and the importance to understand these key processes and being able to separate their influence on the climate is discussed. Aerosol-climate model ECHAM5-HAM and the COSMOS millennium model consisting of atmospheric, ocean and carbon cycle and land-use models are applied and results compared to measurements. Topics at focus are climate sensitivity, quasiperiodic variability with a period of 50-80 years and variability at other timescales, climate effects due to aerosols over India and climate effects of northern hemisphere mid- and high-latitude volcanic eruptions. The main findings of this work are (1) pointing out the remaining challenges in reducing climate sensitivity uncertainty from observational evidence, (2) estimates for the amplitude of a 50-80 year quasiperiodic oscillation in global mean temperature ranging from 0.03 K to 0.17 K and for its phase progression as well as the synchronising effect of external forcing, (3) identifying a power law shape S(f) {proportional_to} f-{alpha} for the spectrum of global mean temperature with {alpha} {approx} 0.8 between multidecadal and El Nino timescales with a smaller exponent in modelled climate without external forcing, (4) separating aerosol properties and climate effects in India by season and location (5) the more efficient dispersion of secondary sulfate aerosols than primary carbonaceous aerosols in the simulations, (6) an increase in monsoon rainfall in northern India due to aerosol light absorption and a probably larger decrease due to aerosol dimming effects and (7) an estimate of mean maximum cooling of 0.19 K due to larger northern hemisphere mid- and high-latitude volcanic eruptions. The results could be applied or useful in better isolating the human-caused climate change signal, in studying the processes further and in more detail, in decadal climate prediction, in model evaluation and in emission policy

  6. Multi-wheat-model ensemble responses to interannual climate variability

    NARCIS (Netherlands)

    Ruane, Alex C.; Hudson, Nicholas I.; Asseng, Senthold; Camarrano, Davide; Ewert, Frank; Martre, Pierre; Boote, Kenneth J.; Thorburn, Peter J.; Aggarwal, Pramod K.; Angulo, Carlos; Basso, Bruno; Bertuzzi, Patrick; Biernath, Christian; Brisson, Nadine; Challinor, Andrew J.; Doltra, Jordi; Gayler, Sebastian; Goldberg, Richard; Grant, Robert F.; Heng, Lee; Hooker, Josh; Hunt, Leslie A.; Ingwersen, Joachim; Izaurralde, Roberto C.; Kersebaum, Kurt Christian; Kumar, Soora Naresh; Müller, Christoph; Nendel, Claas; O'Leary, Garry; Olesen, Jørgen E.; Osborne, Tom M.; Palosuo, Taru; Priesack, Eckart; Ripoche, Dominique; Rötter, Reimund P.; Semenov, Mikhail A.; Shcherbak, Iurii; Steduto, Pasquale; Stöckle, Claudio O.; Stratonovitch, Pierre; Streck, Thilo; Supit, Iwan; Tao, Fulu; Travasso, Maria; Waha, Katharina; Wallach, Daniel; White, Jeffrey W.; Wolf, Joost

    2016-01-01

    We compare 27 wheat models' yield responses to interannual climate variability, analyzed at locations in Argentina, Australia, India, and The Netherlands as part of the Agricultural Model Intercomparison and Improvement Project (AgMIP) Wheat Pilot. Each model simulated 1981-2010 grain yield, and

  7. Modeling Surgery: A New Way Toward Understanding Earth Climate Variability

    Institute of Scientific and Technical Information of China (English)

    WU Lixin; LIU Zhengyu; Robert Gallimore; Michael Notaro; Robert Jacob

    2005-01-01

    A new modeling concept, referred to as Modeling Surgery, has been recently developed at University of Wisconsin-Madison. It is specifically designed to diagnose coupled feedbacks between different climate components as well as climatic teleconnections within a specific component through systematically modifying the coupling configurations and teleconnective pathways. It thus provides a powerful means for identifying the causes and mechanisms of low-frequency variability in the Earth's climate system. In this paper, we will give a short review of our recent progress in this new area.

  8. Climate variability and climate change

    International Nuclear Information System (INIS)

    Changes of variability with climate change are likely to have a substantial impact on vegetation and society, rivaling the importance of changes in the mean values themselves. A variety of paleoclimate and future climate simulations performed with the GISS global climate model is used to assess how the variabilities of temperature and precipitation are altered as climate warms or cools. In general, as climate warms, temperature variability decreases due to reductions in the latitudinal temperature gradient and precipitation variability increases together with the intensity of the hydrologic cycle. If future climate projections are accurate, the reduction in temperature variability will be minimized by the rapid change in mean temperatures, but the hydrologic variability will be amplified by increased evapotranspiration. Greater hydrologic variability would appear to pose a potentially severe problem for the next century. 19 refs.; 3 figs.; 2 tabs

  9. Climate variability and climate change

    International Nuclear Information System (INIS)

    Changes of variability with climate change are likely to have a substantial impact on vegetation and society, rivaling the importance of changes in the mean values themselves. A variety of paleoclimate and future climate simulations performed with the GISS global climate model is used to assess how the variabilities of temperature and precipitation are altered as climate warms or cools. In general, as climate warms, temperature variability decreases due to reductions in the latitudinal temperature gradient and precipitation variability increases together with the intensity of the hydrologic cycle. If future climate projections are accurate, the reduction in temperature variability will be minimized by the rapid change in mean temperatures, but the hydrologic variability will be amplified by increased evapotranspiration. Greater hydrologic variability would appear to pose a potentially severe problem for the next century

  10. Modelling impact of climate variability on rainfed groundnut

    OpenAIRE

    Gadgil, Sulochana; Rao, Seshagiri PR; Sridhar, S.

    1999-01-01

    We present here a heuristic model for the indirect impact of climate variability via the triggering of pests/diseases/weeds for rainfed groundnut over the Anantapur region. A simple hydrological model is used to determine the soil moisture for the rainfall pattern, in any given year. The criteria for determining when specific farming operations, such as ploughing and sowing, are performed are defined in terms of the soil moisture, on the basis of the farming practices in the region. With the ...

  11. Decadal Variability of Clouds and Comparison with Climate Model Simulations

    Science.gov (United States)

    Su, H.; Shen, T. J.; Jiang, J. H.; Yung, Y. L.

    2014-12-01

    An apparent climate regime shift occurred around 1998/1999, when the steady increase of global-mean surface temperature appeared to hit a hiatus. Coherent decadal variations are found in atmospheric circulation and hydrological cycles. Using 30-year cloud observations from the International Satellite Cloud Climatology Project, we examine the decadal variability of clouds and associated cloud radiative effects on surface warming. Empirical Orthogonal Function analysis is performed. After removing the seasonal cycle and ENSO signal in the 30-year data, we find that the leading EOF modes clearly represent a decadal variability in cloud fraction, well correlated with the indices of Pacific Decadal Oscillation (PDO) and Atlantic Multidecadal Oscillation (AMO). The cloud radiative effects associated with decadal variations of clouds suggest a positive cloud feedback, which would reinforce the global warming hiatus by a net cloud cooling after 1998/1999. Climate model simulations driven by observed sea surface temperature are compared with satellite observed cloud decadal variability. Copyright:

  12. Interannual climate variability seen in the Pliocene Model Intercomparison Project

    Directory of Open Access Journals (Sweden)

    C. M. Brierley

    2014-09-01

    Full Text Available Following proxy observations of weakened temperature gradients along the Equator in the early Pliocene, there has been much speculation about Pliocene climate variability. A major advance for our knowledge about the later Pliocene has been the coordination of modelling efforts through the Pliocene Model Intercomparison Project (PlioMIP. Here the changes in interannual modes of sea surface temperature variability will be presented across PlioMIP. Previously model ensembles have shown little consensus in the response of the El Niño–Southern Oscillation (ENSO to imposed forcings – either for the past or future. The PlioMIP ensemble, however, shows surprising agreement with eight models simulating reduced variability and only one model indicating no change. The Pliocene's robustly weaker ENSO also saw a shift to lower frequencies. Model ensembles focussed at a wide variety of forcing scenarios have not yet shown this level of coherency. Nonetheless the PlioMIP ensemble does not show a robust response of either ENSO flavour or sea surface temperature variability in the Tropical Indian and North Pacific Oceans. Existing suggestions of ENSO properties linked to changes in zonal temperature gradient, seasonal cycle and the elevation of the Andes Mountains are investigated, yet prove insufficient to explain the coherent response. The reason for this surprisingly coherent signal warrants further investigation.

  13. Attributing Sources of Variability in Regional Climate Model Experiments

    Science.gov (United States)

    Kaufman, C. G.; Sain, S. R.

    2008-12-01

    Variability in regional climate model (RCM) projections may be due to a number of factors, including the choice of RCM itself, the boundary conditions provided by a driving general circulation model (GCM), and the choice of emission scenario. We describe a new statistical methodology, Gaussian Process ANOVA, which allows us to decompose these sources of variability while also taking account of correlations in the output across space. Our hierarchical Bayesian framework easily allows joint inference about high probability envelopes for the functions, as well as decompositions of total variance that vary over the domain of the functions. These may be used to create maps illustrating the magnitude of each source of variability across the domain of the regional model. We use this method to analyze temperature and precipitation data from the Prudence Project, an RCM intercomparison project in which RCMs were crossed with GCM forcings and scenarios in a designed experiment. This work was funded by the North American Regional Climate Change Assessment Program (NARCCAP).

  14. Influence of climate model variability on projected Arctic shipping futures

    Science.gov (United States)

    Stephenson, Scott R.; Smith, Laurence C.

    2015-11-01

    Though climate models exhibit broadly similar agreement on key long-term trends, they have significant temporal and spatial differences due to intermodel variability. Such variability should be considered when using climate models to project the future marine Arctic. Here we present multiple scenarios of 21st-century Arctic marine access as driven by sea ice output from 10 CMIP5 models known to represent well the historical trend and climatology of Arctic sea ice. Optimal vessel transits from North America and Europe to the Bering Strait are estimated for two periods representing early-century (2011-2035) and mid-century (2036-2060) conditions under two forcing scenarios (RCP 4.5/8.5), assuming Polar Class 6 and open-water vessels with medium and no ice-breaking capability, respectively. Results illustrate that projected shipping viability of the Northern Sea Route (NSR) and Northwest Passage (NWP) depends critically on model choice. The eastern Arctic will remain the most reliably accessible marine space for trans-Arctic shipping by mid-century, while outcomes for the NWP are particularly model-dependent. Omitting three models (GFDL-CM3, MIROC-ESM-CHEM, and MPI-ESM-MR), our results would indicate minimal NWP potential even for routes from North America. Furthermore, the relative importance of the NSR will diminish over time as the number of viable central Arctic routes increases gradually toward mid-century. Compared to vessel class, climate forcing plays a minor role. These findings reveal the importance of model choice in devising projections for strategic planning by governments, environmental agencies, and the global maritime industry.

  15. North Atlantic climate variability in coupled models and data

    Directory of Open Access Journals (Sweden)

    S. K. Kravtsov

    2008-01-01

    Full Text Available We show that the observed zonally averaged jet in the Northern Hemisphere atmosphere exhibits two spatial patterns with broadband variability in the decadal and inter-decadal range; these patterns are consistent with an important role of local, mid-latitude ocean–atmosphere coupling. A key aspect of this behaviour is the fundamentally nonlinear bi-stability of the atmospheric jet's latitudinal position, which enables relatively small sea-surface temperature anomalies associated with ocean processes to affect the large-scale atmospheric winds. The wind anomalies induce, in turn, complex three-dimensional anomalies in the ocean's main thermocline; in particular, they may be responsible for recently reported cooling of the upper ocean. Both observed modes of variability, decadal and inter-decadal, have been found in our intermediate climate models. One mode resembles North Atlantic tri-polar sea-surface temperature (SST patterns described elsewhere. The other mode, with mono-polar SST pattern, is novel; its key aspects include interaction of oceanic turbulence with the large-scale oceanic flow. To the extent these anomalies exist, the interpretation of observed climate variability in terms of natural and human-induced changes will be affected. Coupled mid-latitude ocean-atmosphere modes do, however, suggest some degree of predictability is possible.

  16. Towards multi-resolution global climate modeling with ECHAM6-FESOM. Part II: climate variability

    Science.gov (United States)

    Rackow, T.; Goessling, H. F.; Jung, T.; Sidorenko, D.; Semmler, T.; Barbi, D.; Handorf, D.

    2016-06-01

    This study forms part II of two papers describing ECHAM6-FESOM, a newly established global climate model with a unique multi-resolution sea ice-ocean component. While part I deals with the model description and the mean climate state, here we examine the internal climate variability of the model under constant present-day (1990) conditions. We (1) assess the internal variations in the model in terms of objective variability performance indices, (2) analyze variations in global mean surface temperature and put them in context to variations in the observed record, with particular emphasis on the recent warming slowdown, (3) analyze and validate the most common atmospheric and oceanic variability patterns, (4) diagnose the potential predictability of various climate indices, and (5) put the multi-resolution approach to the test by comparing two setups that differ only in oceanic resolution in the equatorial belt, where one ocean mesh keeps the coarse ~1° resolution applied in the adjacent open-ocean regions and the other mesh is gradually refined to ~0.25°. Objective variability performance indices show that, in the considered setups, ECHAM6-FESOM performs overall favourably compared to five well-established climate models. Internal variations of the global mean surface temperature in the model are consistent with observed fluctuations and suggest that the recent warming slowdown can be explained as a once-in-one-hundred-years event caused by internal climate variability; periods of strong cooling in the model (`hiatus' analogs) are mainly associated with ENSO-related variability and to a lesser degree also to PDO shifts, with the AMO playing a minor role. Common atmospheric and oceanic variability patterns are simulated largely consistent with their real counterparts. Typical deficits also found in other models at similar resolutions remain, in particular too weak non-seasonal variability of SSTs over large parts of the ocean and episodic periods of almost absent

  17. A modelling methodology for assessing the impact of climate variability and climatic change on hydroelectric generation

    International Nuclear Information System (INIS)

    A new methodology relating basic climatic variables to hydroelectric generation was developed. The methodology can be implemented in large or small basins with any number of hydro plants. The method was applied to the Sacramento, Eel and Russian river basins in northern California where more than 100 hydroelectric plants are located. The final model predicts the availability of hydroelectric generation for the entire basin provided present and near past climate conditions, with about 90% accuracy. The results can be used for water management purposes or for analyzing the effect of climate variability on hydrogeneration availability in the basin. A wide range of results can be obtained depending on the climate change scenario used. (Author)

  18. Enhancements to modeling regional climate response and global variability; FINAL

    International Nuclear Information System (INIS)

    Efforts during this grant period focused on three main considerations: (a) developing and testing various climate scenarios with SEAM, a newly created model (b) model reconstruction efforts to speed up computations and (c) optimum realization statistics

  19. Assessment of climate change impacts on climate variables using probabilistic ensemble modeling and trend analysis

    Science.gov (United States)

    Safavi, Hamid R.; Sajjadi, Sayed Mahdi; Raghibi, Vahid

    2016-08-01

    Water resources in snow-dependent regions have undergone significant changes due to climate change. Snow measurements in these regions have revealed alarming declines in snowfall over the past few years. The Zayandeh-Rud River in central Iran chiefly depends on winter falls as snow for supplying water from wet regions in high Zagrous Mountains to the downstream, (semi-)arid, low-lying lands. In this study, the historical records (baseline: 1971-2000) of climate variables (temperature and precipitation) in the wet region were chosen to construct a probabilistic ensemble model using 15 GCMs in order to forecast future trends and changes while the Long Ashton Research Station Weather Generator (LARS-WG) was utilized to project climate variables under two A2 and B1 scenarios to a future period (2015-2044). Since future snow water equivalent (SWE) forecasts by GCMs were not available for the study area, an artificial neural network (ANN) was implemented to build a relationship between climate variables and snow water equivalent for the baseline period to estimate future snowfall amounts. As a last step, homogeneity and trend tests were performed to evaluate the robustness of the data series and changes were examined to detect past and future variations. Results indicate different characteristics of the climate variables at upstream stations. A shift is observed in the type of precipitation from snow to rain as well as in its quantities across the subregions. The key role in these shifts and the subsequent side effects such as water losses is played by temperature.

  20. Beyond a climate-centric view of plant distribution: edaphic variables add value to distribution models.

    Directory of Open Access Journals (Sweden)

    Frieda Beauregard

    Full Text Available Both climatic and edaphic conditions determine plant distribution, however many species distribution models do not include edaphic variables especially over large geographical extent. Using an exceptional database of vegetation plots (n = 4839 covering an extent of ∼55,000 km2, we tested whether the inclusion of fine scale edaphic variables would improve model predictions of plant distribution compared to models using only climate predictors. We also tested how well these edaphic variables could predict distribution on their own, to evaluate the assumption that at large extents, distribution is governed largely by climate. We also hypothesized that the relative contribution of edaphic and climatic data would vary among species depending on their growth forms and biogeographical attributes within the study area. We modelled 128 native plant species from diverse taxa using four statistical model types and three sets of abiotic predictors: climate, edaphic, and edaphic-climate. Model predictive accuracy and variable importance were compared among these models and for species' characteristics describing growth form, range boundaries within the study area, and prevalence. For many species both the climate-only and edaphic-only models performed well, however the edaphic-climate models generally performed best. The three sets of predictors differed in the spatial information provided about habitat suitability, with climate models able to distinguish range edges, but edaphic models able to better distinguish within-range variation. Model predictive accuracy was generally lower for species without a range boundary within the study area and for common species, but these effects were buffered by including both edaphic and climatic predictors. The relative importance of edaphic and climatic variables varied with growth forms, with trees being more related to climate whereas lower growth forms were more related to edaphic conditions. Our study

  1. Characterizing climate predictability and model response variability from multiple initial condition and multi-model ensembles

    CERN Document Server

    Kumar, Devashish

    2016-01-01

    Climate models are thought to solve boundary value problems unlike numerical weather prediction, which is an initial value problem. However, climate internal variability (CIV) is thought to be relatively important at near-term (0-30 year) prediction horizons, especially at higher resolutions. The recent availability of significant numbers of multi-model (MME) and multi-initial condition (MICE) ensembles allows for the first time a direct sensitivity analysis of CIV versus model response variability (MRV). Understanding the relative agreement and variability of MME and MICE ensembles for multiple regions, resolutions, and projection horizons is critical for focusing model improvements, diagnostics, and prognosis, as well as impacts, adaptation, and vulnerability studies. Here we find that CIV (MICE agreement) is lower (higher) than MRV (MME agreement) across all spatial resolutions and projection time horizons for both temperature and precipitation. However, CIV dominates MRV over higher latitudes generally an...

  2. Natural climate variability and future climate policy

    Science.gov (United States)

    Ricke, Katharine L.; Caldeira, Ken

    2014-05-01

    Large ensemble climate modelling experiments demonstrate the large role natural variability plays in local climate on a multi-decadal timescale. Variability in local weather and climate influences individual beliefs about climate change. To the extent that support for climate mitigation policies is determined by citizens' local experiences, natural variability will strongly influence the timescale for implementation of such policies. Under a number of illustrative threshold criteria for both national and international climate action, we show that variability-driven uncertainty about local change, even in the face of a well-constrained estimate of global change, can potentially delay the time to policy implementation by decades. Because several decades of greenhouse gas emissions can have a large impact on long-term climate outcomes, there is substantial risk associated with climate policies driven by consensus among individuals who are strongly influenced by local weather conditions.

  3. Effect of climate variables on cocoa black pod incidence in Sabah using ARIMAX model

    Science.gov (United States)

    Ling Sheng Chang, Albert; Ramba, Haya; Mohd. Jaaffar, Ahmad Kamil; Kim Phin, Chong; Chong Mun, Ho

    2016-06-01

    Cocoa black pod disease is one of the major diseases affecting the cocoa production in Malaysia and also around the world. Studies have shown that the climate variables have influenced the cocoa black pod disease incidence and it is important to quantify the black pod disease variation due to the effect of climate variables. Application of time series analysis especially auto-regressive moving average (ARIMA) model has been widely used in economics study and can be used to quantify the effect of climate variables on black pod incidence to forecast the right time to control the incidence. However, ARIMA model does not capture some turning points in cocoa black pod incidence. In order to improve forecasting performance, other explanatory variables such as climate variables should be included into ARIMA model as ARIMAX model. Therefore, this paper is to study the effect of climate variables on the cocoa black pod disease incidence using ARIMAX model. The findings of the study showed ARIMAX model using MA(1) and relative humidity at lag 7 days, RHt - 7 gave better R square value compared to ARIMA model using MA(1) which could be used to forecast the black pod incidence to assist the farmers determine timely application of fungicide spraying and culture practices to control the black pod incidence.

  4. Mediterranean climate modelling: variability and climate change scenarios; Modelisation climatique du Bassin mediterraneen: variabilite et scenarios de changement climatique

    Energy Technology Data Exchange (ETDEWEB)

    Somot, S

    2005-12-15

    Air-sea fluxes, open-sea deep convection and cyclo-genesis are studied in the Mediterranean with the development of a regional coupled model (AORCM). It accurately simulates these processes and their climate variabilities are quantified and studied. The regional coupling shows a significant impact on the number of winter intense cyclo-genesis as well as on associated air-sea fluxes and precipitation. A lower inter-annual variability than in non-coupled models is simulated for fluxes and deep convection. The feedbacks driving this variability are understood. The climate change response is then analysed for the 21. century with the non-coupled models: cyclo-genesis decreases, associated precipitation increases in spring and autumn and decreases in summer. Moreover, a warming and salting of the Mediterranean as well as a strong weakening of its thermohaline circulation occur. This study also concludes with the necessity of using AORCMs to assess climate change impacts on the Mediterranean. (author)

  5. Combining data sources to characterise climatic variability for hydrological modelling in high mountain catchments

    Science.gov (United States)

    Pritchard, David; Fowler, Hayley; Bardossy, Andras; O'Donnell, Greg; Forsythe, Nathan

    2016-04-01

    Robust hydrological modelling of high mountain catchments to support water resources management depends critically on the accuracy of climatic input data. However, the hydroclimatological complexity and sparse measurement networks typically characteristic of these environments present significant challenges for determining the structure of spatial and temporal variability in key climatic variables. Focusing on the Upper Indus Basin (UIB), this research explores how different data sources can be combined in order to characterise climatic patterns and related uncertainties at the scales required in hydrological modelling. Analysis of local observations with respect to underlying climatic processes and variability is extended relative to previous studies in this region, which forms a basis for evaluating the domains of applicability and potential insights associated with selected remote sensing and reanalysis products. As part of this, the information content of recent high resolution simulations for understanding climatic patterns is assessed, with particular reference to the High Asia Refined Analysis (HAR). A strategy for integrating these different data sources to obtain plausible realisations of the distributed climatic fields needed for hydrological modelling is developed on the basis of this analysis, which provides a platform for exploring uncertainties arising from potential biases and other sources of error. The interaction between uncertainties in climatic input data and alternative approaches to process parameterisation in hydrological and cryospheric modelling is explored.

  6. Sensitivity analysis of a forest gap model concerning current and future climate variability

    Energy Technology Data Exchange (ETDEWEB)

    Lasch, P.; Suckow, F.; Buerger, G.; Lindner, M.

    1998-07-01

    The ability of a forest gap model to simulate the effects of climate variability and extreme events depends on the temporal resolution of the weather data that are used and the internal processing of these data for growth, regeneration and mortality. The climatological driving forces of most current gap models are based on monthly means of weather data and their standard deviations, and long-term monthly means are used for calculating yearly aggregated response functions for ecological processes. In this study, the results of sensitivity analyses using the forest gap model FORSKA{sub -}P and involving climate data of different resolutions, from long-term monthly means to daily time series, including extreme events, are presented for the current climate and for a climate change scenario. The model was applied at two sites with differing soil conditions in the federal state of Brandenburg, Germany. The sensitivity of the model concerning climate variations and different climate input resolutions is analysed and evaluated. The climate variability used for the model investigations affected the behaviour of the model substantially. (orig.)

  7. Climate-induced interannual variability of marine primary and export production in three global coupled climate carbon cycle models

    Directory of Open Access Journals (Sweden)

    B. Schneider

    2008-04-01

    Full Text Available Fully coupled climate carbon cycle models are sophisticated tools that are used to predict future climate change and its impact on the land and ocean carbon cycles. These models should be able to adequately represent natural variability, requiring model validation by observations. The present study focuses on the ocean carbon cycle component, in particular the spatial and temporal variability in net primary productivity (PP and export production (EP of particulate organic carbon (POC. Results from three coupled climate carbon cycle models (IPSL, MPIM, NCAR are compared with observation-based estimates derived from satellite measurements of ocean colour and results from inverse modelling (data assimilation. Satellite observations of ocean colour have shown that temporal variability of PP on the global scale is largely dominated by the permanently stratified, low-latitude ocean (Behrenfeld et al., 2006 with stronger stratification (higher sea surface temperature; SST being associated with negative PP anomalies. Results from all three coupled models confirm the role of the low-latitude, permanently stratified ocean for anomalies in globally integrated PP, but only one model (IPSL also reproduces the inverse relationship between stratification (SST and PP. An adequate representation of iron and macronutrient co-limitation of phytoplankton growth in the tropical ocean has shown to be the crucial mechanism determining the capability of the models to reproduce observed interactions between climate and PP.

  8. Recent variability of the solar spectral irradiance and its impact on climate modelling

    CERN Document Server

    Ermolli, I; de Wit, T Dudok; Krivova, N A; Tourpali, K; Weber, M; Unruh, Y C; Gray, L; Langematz, U; Pilewskie, P; Rozanov, E; Schmutz, W; Shapiro, A; Solanki, S K; Woods, T N

    2013-01-01

    The lack of long and reliable time series of solar spectral irradiance (SSI) measurements makes an accurate quantification of solar contributions to recent climate change difficult. Whereas earlier SSI observations and models provided a qualitatively consistent picture of the SSI variability, recent measurements by the SORCE satellite suggest a significantly stronger variability in the ultraviolet (UV) spectral range and changes in the visible and near-infrared (NIR) bands in anti-phase with the solar cycle. A number of recent chemistry-climate model (CCM) simulations have shown that this might have significant implications on the Earth's atmosphere. Motivated by these results, we summarize here our current knowledge of SSI variability and its impact on Earth's climate. We present a detailed overview of existing SSI measurements and provide thorough comparison of models available to date. SSI changes influence the Earth's atmosphere, both directly, through changes in shortwave (SW) heating and therefore, temp...

  9. Rainfall variability and extremes over southern Africa: Assessment of a climate model to reproduce daily extremes

    Science.gov (United States)

    Williams, C. J. R.; Kniveton, D. R.; Layberry, R.

    2009-04-01

    It is increasingly accepted that that any possible climate change will not only have an influence on mean climate but may also significantly alter climatic variability. A change in the distribution and magnitude of extreme rainfall events (associated with changing variability), such as droughts or flooding, may have a far greater impact on human and natural systems than a changing mean. This issue is of particular importance for environmentally vulnerable regions such as southern Africa. The subcontinent is considered especially vulnerable to and ill-equipped (in terms of adaptation) for extreme events, due to a number of factors including extensive poverty, famine, disease and political instability. Rainfall variability and the identification of rainfall extremes is a function of scale, so high spatial and temporal resolution data are preferred to identify extreme events and accurately predict future variability. The majority of previous climate model verification studies have compared model output with observational data at monthly timescales. In this research, the assessment of ability of a state of the art climate model to simulate climate at daily timescales is carried out using satellite derived rainfall data from the Microwave Infra-Red Algorithm (MIRA). This dataset covers the period from 1993-2002 and the whole of southern Africa at a spatial resolution of 0.1 degree longitude/latitude. The ability of a climate model to simulate current climate provides some indication of how much confidence can be applied to its future predictions. In this paper, simulations of current climate from the UK Meteorological Office Hadley Centre's climate model, in both regional and global mode, are firstly compared to the MIRA dataset at daily timescales. This concentrates primarily on the ability of the model to simulate the spatial and temporal patterns of rainfall variability over southern Africa. Secondly, the ability of the model to reproduce daily rainfall extremes will

  10. Analysis of inter-variable relations in regional climate model output

    Science.gov (United States)

    Wilcke, Renate; Chandler, Richard

    2015-04-01

    The topic of physical consistency and inter-variable relations of climate model output, in particular when applying statistical downscaling and bias correction to single variables, is widely discussed in the climate impact modelling and climate impact communities. Many situations require the consideration of several climate variables simultaneously, as a result of which it is also necessary to check that the inter-variable dependence structure is simulated realistically by the RCMs. Given that it is common practice to bias-adjust RCM outputs so as to improve their properties with respect to the distribution of variables taken individually, it is also of interest to determine whether inter-variable relationships are affected by empirical bias adjustment procedures such as quantile mapping, that are applied separately to each variable. A pragmatic reason to look at this is, if bias-adjusted outputs are to be used in impacts studies, it is necessary to check that the inter-variable relationships are realistic. A more fundamental reason is, that RCMs are physically based and, before bias correction, their outputs should therefore ideally be physically consistent. However, an empirical bias adjustment procedure has the potential to break the physical consistency, thereby removing one of the strongest justifications for using RCMs in the first place. Based on these considerations, the study aims to answer two questions. The first is to assess the inter-variable relationships in a suite of RCM outputs in more detail than has previously been attempted, by examining conditional probability densities instead of correlations. The second is to quantify the extent to which these conditional densities are distorted by an empirical bias adjustment procedure. The results can be used both to evaluate the ability of current RCMs (bias-adjusted or not) to provide useful information for climate change impact assessments; and also to determine the viability of quantile mapping as a

  11. Assessment of a climate model to reproduce rainfall variability and extremes over Southern Africa

    Science.gov (United States)

    Williams, C. J. R.; Kniveton, D. R.; Layberry, R.

    2010-01-01

    It is increasingly accepted that any possible climate change will not only have an influence on mean climate but may also significantly alter climatic variability. A change in the distribution and magnitude of extreme rainfall events (associated with changing variability), such as droughts or flooding, may have a far greater impact on human and natural systems than a changing mean. This issue is of particular importance for environmentally vulnerable regions such as southern Africa. The sub-continent is considered especially vulnerable to and ill-equipped (in terms of adaptation) for extreme events, due to a number of factors including extensive poverty, famine, disease and political instability. Rainfall variability and the identification of rainfall extremes is a function of scale, so high spatial and temporal resolution data are preferred to identify extreme events and accurately predict future variability. The majority of previous climate model verification studies have compared model output with observational data at monthly timescales. In this research, the assessment of ability of a state of the art climate model to simulate climate at daily timescales is carried out using satellite-derived rainfall data from the Microwave Infrared Rainfall Algorithm (MIRA). This dataset covers the period from 1993 to 2002 and the whole of southern Africa at a spatial resolution of 0.1° longitude/latitude. This paper concentrates primarily on the ability of the model to simulate the spatial and temporal patterns of present-day rainfall variability over southern Africa and is not intended to discuss possible future changes in climate as these have been documented elsewhere. Simulations of current climate from the UK Meteorological Office Hadley Centre's climate model, in both regional and global mode, are firstly compared to the MIRA dataset at daily timescales. Secondly, the ability of the model to reproduce daily rainfall extremes is assessed, again by a comparison with

  12. North Atlantic multidecadal variability in coupled climate models - Mechanisms and responses

    OpenAIRE

    Medhaug, Iselin

    2011-01-01

    Global atmosphere-ocean general circulation models have been used to investigate mechanisms controlling the North Atlantic low-frequency variability, with the focus on the Atlantic Meridional Overturning Circulation, the Subpolar Gyre dynamics and the North Atlantic basin scale sea surface temperatures, called the Atlantic Multidecadal Oscillation. The understanding of the dynamics of, and mechanisms behind the variability in these components of the climate system are of great ...

  13. Surface air temperature variability in global climate models

    CERN Document Server

    Davy, Richard

    2012-01-01

    New results from the Coupled Model Inter-comparison Project phase 5 (CMIP5) and multiple global reanalysis datasets are used to investigate the relationship between the mean and standard deviation in the surface air temperature. A combination of a land-sea mask and orographic filter were used to investigate the geographic region with the strongest correlation and in all cases this was found to be for low-lying over-land locations. This result is consistent with the expectation that differences in the effective heat capacity of the atmosphere are an important factor in determining the surface air temperature response to forcing.

  14. The Contribution of Internal and Model Variabilities to the Uncertainty in CMIP5 Decadal Climate Predictions

    CERN Document Server

    Strobach, Ehud

    2015-01-01

    Decadal climate predictions, which are initialized with observed conditions, are characterized by two main sources of uncertainties--internal and model variabilities. Using an ensemble of climate model simulations from the CMIP5 decadal experiments, we quantified the total uncertainty associated with these predictions and the relative importance of each source. Annual and monthly averages of the surface temperature and wind components were considered. We show that different definitions of the anomaly results in different conclusions regarding the variance of the ensemble members. However, some features of the uncertainty are common to all the measures we considered. We found that over decadal time scales, there is no considerable increase in the uncertainty with time. The model variability is more sensitive to the annual cycle than the internal variability. This, in turn, results in a maximal uncertainty during the winter in the northern hemisphere. The uncertainty of the surface temperature prediction is dom...

  15. The mechanism of multidecadal variability in the Arctic and North Atlantic in climate model INMCM4

    International Nuclear Information System (INIS)

    Data from a 500-year preindustrial control run of climate model INMCM4 show distinct climate variability in the Arctic and North Atlantic with a period of 35–50 years. The variability can be seen as anomalies of upper ocean density that appear in the Arctic and propagate to the North Atlantic. The density gradient in a northeast–southwest direction alternates with the density gradient in a northwest–southeast direction. A positive density anomaly in the Arctic is associated with a positive salinity anomaly, a positive surface temperature anomaly and a reduction of sea ice in the Barents and Kara Seas. The nature of the variability is a vertical advection of density by thermal currents similar to that proposed in Dijkstra et al (2008 Phil. Trans. R. Soc. A 366). The cycle of model variability shows that after a negative anomaly of density in the northwest Atlantic, one should expect warming in the Arctic in 5–10 years. The ensemble of decadal predictions with climate model INMCM4 starting from 1995 shows that warming in the western Arctic and especially in the Barents Sea observed in 1996–2010 can be reproduced by eight of ten ensemble members. Arctic climate predictability in this case is associated with a proposed mechanism of a 35–50 year North Atlantic–Arctic oscillation. (letter)

  16. Cloud feedback on climate change and variability

    Science.gov (United States)

    Zhou, C.; Dessler, A. E.; Yang, P.

    2014-12-01

    Cloud feedback on climate change and variability follow similar mechanism in climate models, and the magnitude of cloud feedback on climate change and variability are well correlated among models. Therefore, the cloud feedback on short-term climate fluctuations correlates with the equilibrium climate sensitivity in climate models. Using this correlation and the observed short-term climate feedback, we infer a climate sensitivity of ~2.9K. The cloud response to inter-annual surface warming is generally consistent in observations and climate models, except for the tropical boundary-layer low clouds.

  17. Changes in atmospheric variability in a glacial climate and the impacts on proxy data: a model intercomparison

    Directory of Open Access Journals (Sweden)

    F. S. R. Pausata

    2009-09-01

    Full Text Available Using four different climate models, we investigate sea level pressure variability in the extratropical North Atlantic in the preindustrial climate (1750 AD and at the Last Glacial Maximum (LGM, 21 kyrs before present in order to understand how changes in atmospheric circulation can affect signals recorded in climate proxies.

    In general, the models exhibit a significant reduction in interannual variance of sea level pressure at the LGM compared to pre-industrial simulations and this reduction is concentrated in winter. For the preindustrial climate, all models feature a similar leading mode of sea level pressure variability that resembles the leading mode of variability in the instrumental record: the North Atlantic Oscillation (NAO. In contrast, the leading mode of sea level pressure variability at the LGM is model dependent, but in each model different from that in the preindustrial climate. In each model, the leading (NAO-like mode of variability explains a smaller fraction of the variance and also less absolute variance at the LGM than in the preindustrial climate.

    The models show that the relationship between atmospheric variability and surface climate (temperature and precipitation variability change in different climates. Results are model-specific, but indicate that proxy signals at the LGM may be misinterpreted if changes in the spatial pattern and seasonality of surface climate variability are not taken into account.

  18. Climate variability and vulnerability to climate change: a review

    OpenAIRE

    Thornton, Philip K.; Polly J Ericksen; Herrero, Mario; Challinor, Andrew J.

    2014-01-01

    The focus of the great majority of climate change impact studies is on changes in mean climate. In terms of climate model output, these changes are more robust than changes in climate variability. By concentrating on changes in climate means, the full impacts of climate change on biological and human systems are probably being seriously underestimated. Here, we briefly review the possible impacts of changes in climate variability and the frequency of extreme events on biological and food syst...

  19. Assessment of climate change impacts on rainfall using large scale climate variables and downscaling models – A case study

    Indian Academy of Sciences (India)

    Azadeh Ahmadi; Ali Moridi; Elham Kakaei Lafdani; Ghasem Kianpisheh

    2014-10-01

    Many of the applied techniques in water resources management can be directly or indirectly influenced by hydro-climatology predictions. In recent decades, utilizing the large scale climate variables as predictors of hydrological phenomena and downscaling numerical weather ensemble forecasts has revolutionized the long-lead predictions. In this study, two types of rainfall prediction models are developed to predict the rainfall of the Zayandehrood dam basin located in the central part of Iran. The first seasonal model is based on large scale climate signals data around the world. In order to determine the inputs of the seasonal rainfall prediction model, the correlation coefficient analysis and the new Gamma Test (GT) method are utilized. Comparison of modelling results shows that the Gamma test method improves the Nash–Sutcliffe efficiency coefficient of modelling performance as 8% and 10% for dry and wet seasons, respectively. In this study, Support Vector Machine (SVM) model for predicting rainfall in the region has been used and its results are compared with the benchmark models such as K-nearest neighbours (KNN) and Artificial Neural Network (ANN). The results show better performance of the SVM model at testing stage. In the second model, statistical downscaling model (SDSM) as a popular downscaling tool has been used. In this model, using the outputs from GCM, the rainfall of Zayandehrood dam is projected under two climate change scenarios. Most effective variables have been identified among 26 predictor variables. Comparison of the results of the two models shows that the developed SVM model has lesser errors in monthly rainfall estimation. The results show that the rainfall in the future wet periods are more than historical values and it is lower than historical values in the dry periods. The highest monthly uncertainty of future rainfall occurs in March and the lowest in July.

  20. Modes of climate variability under different background conditions: concepts, data, modelling

    Science.gov (United States)

    Lohmann, G.

    2011-12-01

    Through its nonlinear dynamics and involvement in past abrupt climate shifts the thermohaline circulation represents a key element for the understanding of rapid climate changes. By applying various statistical techniques on surface temperature data, several variability modes on decadal to millenial timescales are identified. The distinction between the modes provides a frame for interpreting past abrupt climate changes. Abrupt shifts associated to the ocean circulation are detected around 1970 and the last millenium, i.e. the medieval warm period. Such oscillations are analyzed for longer time scales covering the last glacial-interglacial cycle. During the Holocene such events seem to be Poisson distributed indicating for an internal mode. Statistical-conceptual and dynamical model concepts are proposed and tested for millenial to orbital time scales, showing the dominant role of the ocean circulation. New GCM model results indicate a strong sensitivity of long-term variability on background conditions. A transition from full glacial (with a strongly stratified ocean) to interglacial conditions is attempted. Finally, climate sensitivity on glacial-interglacial and shorter time scales will be evaluated using SST Alkenone data and GCM simulations. It is shown that the models underestimate the climate sensitivity as compared to the data by a factor of 3. It is argued that the models possibly underestimate the response to obliquity forcing.

  1. Rainfall variability over southern Africa: an overview of current research using satellite and climate model data

    Science.gov (United States)

    Williams, C.; Kniveton, D.; Layberry, R.

    2009-04-01

    It is increasingly accepted that any possible climate change will not only have an influence on mean climate but may also significantly alter climatic variability. A change in the distribution and magnitude of extreme rainfall events (associated with changing variability), such as droughts or flooding, may have a far greater impact on human and natural systems than a changing mean. This issue is of particular importance for environmentally vulnerable regions such as southern Africa. The subcontinent is considered especially vulnerable to and ill-equipped (in terms of adaptation) for extreme events, due to a number of factors including extensive poverty, famine, disease and political instability. Rainfall variability is a function of scale, so high spatial and temporal resolution data are preferred to identify extreme events and accurately predict future variability. In this research, satellite-derived rainfall data are used as a basis for undertaking model experiments using a state-of-the-art climate model, run at both high and low spatial resolution. Once the model's ability to reproduce extremes has been assessed, idealised regions of sea surface temperature (SST) anomalies are used to force the model, with the overall aim of investigating the ways in which SST anomalies influence rainfall extremes over southern Africa. In this paper, a brief overview is given of the authors' research to date, pertaining to southern African rainfall. This covers (i) a description of present-day rainfall variability over southern Africa; (ii) a comparison of model simulated daily rainfall with the satellite-derived dataset; (iii) results from sensitivity testing of the model's domain size; and (iv) results from the idealised SST experiments.

  2. The spread amongst ENSEMBLES regional scenarios: regional climate models, driving general circulation models and interannual variability

    Energy Technology Data Exchange (ETDEWEB)

    Deque, M.; Somot, S. [Meteo-France, Centre National de Recherches Meteorologiques, CNRS/GAME, Toulouse Cedex 01 (France); Sanchez-Gomez, E. [Cerfacs/CNRS, SUC URA1875, Toulouse Cedex 01 (France); Goodess, C.M. [University of East Anglia, Climatic Research Unit, Norwich (United Kingdom); Jacob, D. [Max Planck Institute for Meteorology, Hamburg (Germany); Lenderink, G. [KNMI, Postbus 201, De Bilt (Netherlands); Christensen, O.B. [Danish Meteorological Institute, Copenhagen Oe (Denmark)

    2012-03-15

    Various combinations of thirteen regional climate models (RCM) and six general circulation models (GCM) were used in FP6-ENSEMBLES. The response to the SRES-A1B greenhouse gas concentration scenario over Europe, calculated as the difference between the 2021-2050 and the 1961-1990 means can be viewed as an expected value about which various uncertainties exist. Uncertainties are measured here by variance explained for temperature and precipitation changes over eight European sub-areas. Three sources of uncertainty can be evaluated from the ENSEMBLES database. Sampling uncertainty is due to the fact that the model climate is estimated as an average over a finite number of years (30) despite a non-negligible interannual variability. Regional model uncertainty is due to the fact that the RCMs use different techniques to discretize the equations and to represent sub-grid effects. Global model uncertainty is due to the fact that the RCMs have been driven by different GCMs. Two methods are presented to fill the many empty cells of the ENSEMBLES RCM x GCM matrix. The first one is based on the same approach as in FP5-PRUDENCE. The second one uses the concept of weather regimes to attempt to separate the contribution of the GCM and the RCM. The variance of the climate response is analyzed with respect to the contribution of the GCM and the RCM. The two filling methods agree that the main contributor to the spread is the choice of the GCM, except for summer precipitation where the choice of the RCM dominates the uncertainty. Of course the implication of the GCM to the spread varies with the region, being maximum in the South-western part of Europe, whereas the continental parts are more sensitive to the choice of the RCM. The third cause of spread is systematically the interannual variability. The total uncertainty about temperature is not large enough to mask the 2021-2050 response which shows a similar pattern to the one obtained for 2071-2100 in PRUDENCE. The uncertainty

  3. Climate Variability Program

    Science.gov (United States)

    Halpern, David (Editor)

    2002-01-01

    The Annual Report of the Climate Variability Program briefly describes research activities of Principal Investigators who are funded by NASA's Earth Science Enterprise Research Division. The report is focused on the year 2001. Utilization of satellite observations is a singularity of research on climate science and technology at JPL (Jet Propulsion Laboratory). Research at JPL has two foci: generate new knowledge and develop new technology.

  4. Multi-model ensemble analysis of Pacific and Atlantic SST variability in unperturbed climate simulations

    Science.gov (United States)

    Zanchettin, D.; Bothe, O.; Rubino, A.; Jungclaus, J. H.

    2016-08-01

    We assess internally-generated climate variability expressed by a multi-model ensemble of unperturbed climate simulations. We focus on basin-scale annual-average sea surface temperatures (SSTs) from twenty multicentennial pre-industrial control simulations contributing to the fifth phase of the Coupled Model Intercomparison Project. Ensemble spatial patterns of regional modes of variability and ensemble (cross-)wavelet-based phase-frequency diagrams of corresponding paired indices summarize the ensemble characteristics of inter-basin and regional-to-global SST interactions on a broad range of timescales. Results reveal that tropical and North Pacific SSTs are a source of simulated interannual global SST variability. The North Atlantic-average SST fluctuates in rough co-phase with the global-average SST on multidecadal timescales, which makes it difficult to discern the Atlantic Multidecadal Variability (AMV) signal from the global signal. The two leading modes of tropical and North Pacific SST variability converge towards co-phase in the multi-model ensemble, indicating that the Pacific Decadal Oscillation (PDO) results from a combination of tropical and extra-tropical processes. No robust inter- or multi-decadal inter-basin SST interaction arises from our ensemble analysis between the Pacific and Atlantic oceans, though specific phase-locked fluctuations occur between Pacific and Atlantic modes of SST variability in individual simulations and/or periods within individual simulations. The multidecadal modulation of PDO by the AMV identified in observations appears to be a recurrent but not typical feature of ensemble-simulated internal variability. Understanding the mechanism(s) and circumstances favoring such inter-basin SST phasing and related uncertainties in their simulated representation could help constraining uncertainty in decadal climate predictions.

  5. Comparisons of model simulations of climate variability with data, Task 2. [Progress report

    Energy Technology Data Exchange (ETDEWEB)

    1990-12-31

    Significant progress has been made in our investigations aimed at diagnosing low frequency variations of climate in General Circulation Models. We have analyzed three versions of the Oregon State University General Circulation Model (OSU GCM). These are: (1) the Slab Model in which the ocean is treated as a static heat reservoir of fixed depth, (2) the coupled upper ocean-atmosphere model in which the ocean dynamics are calculated in two layers of variable depths representing the mixed layers and the thermocline; this model is referred to OSU2 in the following discussion, and (3) the coupled full ocean-atmosphere model in which the ocean is represented by six layers of variable depth; this model is referred to as OSU6 GCM in the discussion.

  6. Comparisons of model simulations of climate variability with data, Task 2

    Energy Technology Data Exchange (ETDEWEB)

    1990-01-01

    Significant progress has been made in our investigations aimed at diagnosing low frequency variations of climate in General Circulation Models. We have analyzed three versions of the Oregon State University General Circulation Model (OSU GCM). These are: (1) the Slab Model in which the ocean is treated as a static heat reservoir of fixed depth, (2) the coupled upper ocean-atmosphere model in which the ocean dynamics are calculated in two layers of variable depths representing the mixed layers and the thermocline; this model is referred to OSU2 in the following discussion, and (3) the coupled full ocean-atmosphere model in which the ocean is represented by six layers of variable depth; this model is referred to as OSU6 GCM in the discussion.

  7. Modeling bulk canopy resistance from climatic variables for predicting hourly evapotranspiration of maize and buckwheat

    Science.gov (United States)

    Yan, Haofang; Shi, Haibin; Hiroki, Oue; Zhang, Chuan; Xue, Zhu; Cai, Bin; Wang, Guoqing

    2015-06-01

    This study presents models for predicting hourly canopy resistance ( r c) and evapotranspiration (ETc) based on Penman-Monteith approach. The micrometeorological data and ET c were observed during maize and buckwheat growing seasons in 2006 and 2009 in China and Japan, respectively. The proposed models of r c were developed by a climatic resistance ( r *) that depends on climatic variables. Non-linear relationships between r c and r * were applied. The measured ETc using Bowen ratio energy balance method was applied for model validation. The statistical analysis showed that there were no significant differences between predicted ETc by proposed models and measured ETc for both maize and buckwheat crops. The model for predicting ETc at maize field showed better performance than predicting ETc at buckwheat field, the coefficients of determination were 0.92 and 0.84, respectively. The study provided an easy way for the application of Penman-Monteith equation with only general available meteorological database.

  8. Simulation skill of APCC set of global climate models for Asian summer monsoon rainfall variability

    Science.gov (United States)

    Singh, U. K.; Singh, G. P.; Singh, Vikas

    2015-04-01

    The performance of 11 Asia-Pacific Economic Cooperation Climate Center (APCC) global climate models (coupled and uncoupled both) in simulating the seasonal summer (June-August) monsoon rainfall variability over Asia (especially over India and East Asia) has been evaluated in detail using hind-cast data (3 months advance) generated from APCC which provides the regional climate information product services based on multi-model ensemble dynamical seasonal prediction systems. The skill of each global climate model over Asia was tested separately in detail for the period of 21 years (1983-2003), and simulated Asian summer monsoon rainfall (ASMR) has been verified using various statistical measures for Indian and East Asian land masses separately. The analysis found a large variation in spatial ASMR simulated with uncoupled model compared to coupled models (like Predictive Ocean Atmosphere Model for Australia, National Centers for Environmental Prediction and Japan Meteorological Agency). The simulated ASMR in coupled model was closer to Climate Prediction Centre Merged Analysis of Precipitation (CMAP) compared to uncoupled models although the amount of ASMR was underestimated in both models. Analysis also found a high spread in simulated ASMR among the ensemble members (suggesting that the model's performance is highly dependent on its initial conditions). The correlation analysis between sea surface temperature (SST) and ASMR shows that that the coupled models are strongly associated with ASMR compared to the uncoupled models (suggesting that air-sea interaction is well cared in coupled models). The analysis of rainfall using various statistical measures suggests that the multi-model ensemble (MME) performed better compared to individual model and also separate study indicate that Indian and East Asian land masses are more useful compared to Asia monsoon rainfall as a whole. The results of various statistical measures like skill of multi-model ensemble, large spread

  9. Climate variability and change

    International Nuclear Information System (INIS)

    When Australia's climate should not be definite barrier to the population reaching 30 million by 2050, it is recognised that our climate has limited the development of the nation over the past 200 years. Indeed in 1911, based on a comparison of the climate and development between the US and Australia. Griffith Taylor predicted that Australia's population would be 19 million at the end of the 20th century, which is a pretty good 90-year forecast. The climate constraint is not only due to much of the country being semi-arid with an annual rainfall below 400 millimetres, but also due to the large year-to-year variability of rainfall across the country

  10. Current Climate Variability & Change

    Science.gov (United States)

    Diem, J.; Criswell, B.; Elliott, W. C.

    2013-12-01

    Current Climate Variability & Change is the ninth among a suite of ten interconnected, sequential labs that address all 39 climate-literacy concepts in the U.S. Global Change Research Program's Climate Literacy: The Essential Principles of Climate Sciences. The labs are as follows: Solar Radiation & Seasons, Stratospheric Ozone, The Troposphere, The Carbon Cycle, Global Surface Temperature, Glacial-Interglacial Cycles, Temperature Changes over the Past Millennium, Climates & Ecosystems, Current Climate Variability & Change, and Future Climate Change. All are inquiry-based, on-line products designed in a way that enables students to construct their own knowledge of a topic. Questions representative of various levels of Webb's depth of knowledge are embedded in each lab. In addition to the embedded questions, each lab has three or four essential questions related to the driving questions for the lab suite. These essential questions are presented as statements at the beginning of the material to represent the lab objectives, and then are asked at the end as questions to function as a summative assessment. For example, the Current Climate Variability & Change is built around these essential questions: (1) What has happened to the global temperature at the Earth's surface, in the middle troposphere, and in the lower stratosphere over the past several decades?; (2) What is the most likely cause of the changes in global temperature over the past several decades and what evidence is there that this is the cause?; and (3) What have been some of the clearly defined effects of the change in global temperature on the atmosphere and other spheres of the Earth system? An introductory Prezi allows the instructor to assess students' prior knowledge in relation to these questions, while also providing 'hooks' to pique their interest related to the topic. The lab begins by presenting examples of and key differences between climate variability (e.g., Mt. Pinatubo eruption) and

  11. Changes in Southern Hemisphere circulation variability in climate change modelling experiments

    International Nuclear Information System (INIS)

    Full text: The seasonal mean of a climate variable can be considered as a statistical random variable, consisting of a signal and noise components (Madden 1976). The noise component consists of internal intraseasonal variability, and is not predictable on time-scales of a season or more ahead. The signal consists of slowly varying external and internal variability, and is potentially predictable on seasonal time-scales. The method of Zheng and Frederiksen (2004) has been applied to monthly time series of 500hPa Geopotential height from models submitted to the Coupled Model Intercomparison Project (CMIP3) experiment to obtain covariance matrices of the intraseasonal and slow components of covariability for summer and winter. The Empirical Orthogonal Functions (EOFs) of the intraseasonal and slow covariance matrices for the second half of the 20th century are compared with those observed by Frederiksen and Zheng (2007). The leading EOF in summer and winter for both the intraseasonal and slow components of covariability is the Southern Annular Mode (see, e.g. Kiladis and Mo 1998). This is generally reproduced by the CMIP3 models, although with different variance amounts. The observed secondary intraseasonal covariability modes of wave 4 patterns in summer and wave 3 or blocking in winter are also generally seen in the models, although the actual spatial pattern is different. For the slow covariabilty, the models are less successful in reproducing the two observed ENSO modes, with generally only one of them being represented among the leading EOFs. However, most models reproduce the observed South Pacific wave pattern. The intraseasonal and slow covariances matrices of 500hPa geopotential height under three climate change scenarios are also analysed and compared with those found for the second half of the 20th century. Through aggregating the results from a number of CMIP3 models, a consensus estimate of the changes in Southern Hemisphere variability, and their

  12. Climate model biases in the Indian Ocean meant state, variability and change

    Science.gov (United States)

    Xie, S. P.; Li, G.

    2015-12-01

    Long-standing biases of climate models limit the skills of climate prediction and projection. The monsoonal tropical Indian Ocean (IO) has been overlooked in bias studies because model errors compensate among seasons and do not manifest prominently in the annual means. In the phase 5 of the Coupled Model Intercomparison Project (CMIP5) multimodel ensemble, we have identified a common error pattern in climate models that resembles the IO dipole (IOD) mode of interannual variability in nature, with an excessive equatorial easterly wind bias during boreal autumn accompanied by physically consistent biases in precipitation, sea surface temperature (SST), and subsurface ocean temperature. The analyses show that such IOD-like biases can be traced back to errors in the South Asian summer monsoon. A southwest summer monsoon that is too weak over the Arabian Sea generates a warm SST bias over the western equatorial IO. In boreal autumn, Bjerknes feedback helps amplify the error into an IOD-like bias pattern in wind, precipitation, SST, and subsurface ocean temperature. Such mean state biases result in an interannual IOD variability that is too strong. Most models project an IOD-like future change for the boreal autumn mean state in the global warming scenario, which would result in more frequent occurrences of extreme positive IOD events in the future with important consequences to Indonesia and East Africa. The Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) characterizes this future IOD-like projection in the mean state as robust based on consistency among models, but the authors' results cast doubts on this conclusion since models with larger IOD amplitude biases tend to produce stronger IOD-like projected changes in the future.

  13. Changes of tropical interannual variability due to increased CO2 in a global coupled climate model

    International Nuclear Information System (INIS)

    Changes of tropical interannual variability and, in particular, variability changes associated with the south Asian summer monsoon and El Nino-Southern Oscillation (ENSO) are shown from a global coupled ocean-atmosphere general circulation climate model with increased atmospheric carbon dioxide (CO2) With a doubling of atmospheric CO2 in this model, there is a general increase of interannual variability in the tropics. The warmer land and ocean surfaces caused by increased CO2 in the model are associated with an increase in interannual variability of area-averaged south Asian monsoon rainfall. The coupled model simulates some aspects of ENSO that involve periodic warm and cold sea-surface temperature anomalies in the tropical Pacific with associated global teleconnections. In the experiment with increased CO2, the ENSO-like phenomena continue to occur, but with increased intensity of anomalously wet and dry areas in the tropics associated with ENSO events in the tropical eastern Pacific. For both the south Asian monsoon and ENSO in the model, an important process contributing to the enhanced interannual variability is the nonlinear relationship between evaporation and surface temperature

  14. Changes in atmospheric variability in a glacial climate and the impacts on proxy data: a model intercomparison

    Directory of Open Access Journals (Sweden)

    F. S. R. Pausata

    2009-03-01

    Full Text Available We investigate sea level pressure variability in the extratropical North Atlantic in the preindustrial climate (1750 A.D. and at the Last Glacial Maximum (LGM, 21 kyr before present using four climate models. In general, the models exhibit a significant reduction in interannual variance of sea level pressure during the LGM compared to pre-industrial simulations and this reduction is concentrated in winter.

    For the preindustrial climate, all the models feature a similar leading mode (EOF of sea level pressure variability that is also similar to the leading mode of variability in the instrumental record: the North Atlantic Oscillation (NAO. In contrast, the leading mode of sea level pressure variability during the LGM is model dependent, but in each model different from that in the preindustrial climate. In each model, the leading (NAO-like mode of variability explains a smaller fraction of the variance and also less absolute variance in the LGM than in the preindustrial. The leading (NAO-like mode of sea level pressure variability is shifted southward in the LGM simulations relative to the preindustrial simulations.

    Finally, we correlate the leading mode of sea level pressure variability with surface temperature and precipitation within each model and for the two time periods. In the preindustrial climate, the leading mode of sea level pressure variability is similar from model to model and the temperature and precipitation correlation patterns are also similar. In contrast, since the models find different dominant modes of sea level pressure variability for the LGM climate, they also disagree on the associated patterns of temperature and precipitation variability. Assuming stationarity of the relationship between surface climate and the leading mode of sea level pressure variability could lead to a misinterpretation of signals recorded in proxy data.

  15. Impacts of Convective Triggering on Convective Variability in a Climate Model

    Science.gov (United States)

    Wang, Y. C.

    2015-12-01

    In this study, we investigated the impacts of the triggering designs of the deep convection scheme on convective variability from diurnal rainfall cycle to intraseasonal rainfall variability by using NCAR CAM5 model. Using single-column simulations at the Southern Great Plains site, we found that the underestimated nighttime rainfall of diurnal cycle can be greatly improved when two convective triggering designs from the Simplified Arakawa-Schubert scheme (SAS) are implemented into the default Zhang-Mcfarlane (ZM) scheme. We further conducted AMIP-type climate simulations with this modified ZM scheme (ZMMOD), and found that improvements can also be seen for the diurnally propagating convection over topographical regions, such as Maritime Continent and the western coast of Columbia. We further examined the rainfall variability from synoptic to intraseasonal scales, and found that using ZMMOD scheme increases rainfall variability of 2-10-day over South America and Africa land regions. However, this improvement does not seem to transfer to the intraseasonal convective organization (20-100 days), such as the MJO. This study demonstrates the importance of convective triggering and its impacts on convective variability. This work is still on-going to understand the physical processes of such impacts and how they might affect climate systems through multiscale interactions.

  16. Modelling climate control on cropland and grassland development using phenologically tuned variables

    DEFF Research Database (Denmark)

    Horion, Stéphanie Marie Anne F; Tychon, Bernard; Cornet, Yves

    2010-01-01

    that, between 1982 and 1999, primary productivity increased by 6% globally in response to climate change. This study also stressed the need to take into account the spatial variability of climatic constraints to plant growth when analyzing the climate change impact on vegetation. Others authors......Many studies already investigated the impact of climate change and climate variability on vegetation at global and continental scales. Using time series of remote sensing and climate data, Nemani et al. (2003) analyzed trends in Net Primary Production in relation with changes in climate and showed...... described different phenomenon linked with climate change such as increases of seasonal NDVI amplitude and growing season duration in the Northern high latitude or changes in circumpolar photosynthetic activities. Understanding the interactions between climate and vegetation is also a key issue in our Ph...

  17. Recent variability of the solar spectral irradiance and its impact on climate modelling

    Directory of Open Access Journals (Sweden)

    I. Ermolli

    2012-09-01

    Full Text Available During periods of high solar activity, the Earth receives ≈ 0.1% higher total solar irradiance (TSI than during low activity periods. Variations of the solar spectral irradiance (SSI however, can be larger, with relative changes of 1 to 20% observed in the ultraviolet (UV band, and in excess of 100% in the soft X-ray range. SSI changes influence the Earth's atmosphere, both directly, through changes in shortwave (SW heating and therefore, temperature and ozone distributions in the stratosphere, and indirectly, through dynamical feedbacks. Lack of long and reliable time series of SSI measurements makes the accurate quantification of solar contributions to recent climate change difficult. In particular, the most recent SSI measurements show a larger variability in the UV spectral range and anomalous changes in the visible and near-infrared (NIR bands with respect to those from earlier observations and from models. A number of recent studies based on chemistry-climate model (CCM simulations discuss the effects and implications of these new SSI measurements on the Earth's atmosphere, which may depart from current expectations.

    This paper summarises our current knowledge of SSI variability and its impact on Earth's climate. An interdisciplinary analysis of the topic is given. New comparisons and discussions are presented on the SSI measurements and models available to date, and on the response of the Earth's atmosphere and climate to SSI changes in CCM simulations. In particular, the solar induced differences in atmospheric radiative heating, temperature, ozone, mean zonal winds, and surface signals are investigated in recent simulations using atmospheric models forced with the current lower and upper boundaries of SSI solar cycle estimated variations from the NRLSSI model data and from SORCE/SIM measurements, respectively. Additionally, the reliability of available data is discussed and additional coordinated CCM experiments are proposed.

  18. Large-basin hydrological response to climate model outputs: uncertainty caused by the internal atmospheric variability

    Directory of Open Access Journals (Sweden)

    A. Gelfan

    2015-02-01

    Full Text Available An approach is proposed to assess hydrological simulation uncertainty originating from internal atmospheric variability. The latter is one of three major factors contributing to the uncertainty of simulated climate change projections (along with so-called "forcing" and "climate model" uncertainties. Importantly, the role of the internal atmospheric variability is the most visible over the spatial–temporal scales of water management in large river basins. The internal atmospheric variability is represented by large ensemble simulations (45 members with the ECHAM5 atmospheric general circulation model. The ensemble simulations are performed using identical prescribed lower boundary conditions (observed sea surface temperature, SST, and sea ice concentration, SIC, for 1979–2012 and constant external forcing parameters but different initial conditions of the atmosphere. The ensemble of the bias-corrected ECHAM5-outputs as well as ensemble averaged ECHAM5-output are used as the distributed input for ECOMAG and SWAP hydrological models. The corresponding ensembles of runoff hydrographs are calculated for two large rivers of the Arctic basin: the Lena and the Northern Dvina rivers. A number of runoff statistics including the mean and the SD of the annual, monthly and daily runoff, as well as the annual runoff trend are assessed. The uncertainties of runoff statistics caused by the internal atmospheric variability are estimated. It is found that the uncertainty of the mean and SD of the runoff has a distinguished seasonal dependence with maximum during the periods of spring-summer snowmelt and summer-autumn rainfall floods. A noticeable non-linearity of the hydrological models' response to the ensemble ECHAM5 output is found most strongly expressed for the Northern Dvine River basin. It is shown that the averaging over ensemble members effectively filters stochastic term related to internal atmospheric variability. The simulated trends are close to

  19. Regression tree modeling of forest NPP using site conditions and climate variables across eastern USA

    Science.gov (United States)

    Kwon, Y.

    2013-12-01

    As evidence of global warming continue to increase, being able to predict forest response to climate changes, such as expected rise of temperature and precipitation, will be vital for maintaining the sustainability and productivity of forests. To map forest species redistribution by climate change scenario has been successful, however, most species redistribution maps lack mechanistic understanding to explain why trees grow under the novel conditions of chaining climate. Distributional map is only capable of predicting under the equilibrium assumption that the communities would exist following a prolonged period under the new climate. In this context, forest NPP as a surrogate for growth rate, the most important facet that determines stand dynamics, can lead to valid prediction on the transition stage to new vegetation-climate equilibrium as it represents changes in structure of forest reflecting site conditions and climate factors. The objective of this study is to develop forest growth map using regression tree analysis by extracting large-scale non-linear structures from both field-based FIA and remotely sensed MODIS data set. The major issue addressed in this approach is non-linear spatial patterns of forest attributes. Forest inventory data showed complex spatial patterns that reflect environmental states and processes that originate at different spatial scales. At broad scales, non-linear spatial trends in forest attributes and mixture of continuous and discrete types of environmental variables make traditional statistical (multivariate regression) and geostatistical (kriging) models inefficient. It calls into question some traditional underlying assumptions of spatial trends that uncritically accepted in forest data. To solve the controversy surrounding the suitability of forest data, regression tree analysis are performed using Software See5 and Cubist. Four publicly available data sets were obtained: First, field-based Forest Inventory and Analysis (USDA

  20. Climate change or variable weather

    DEFF Research Database (Denmark)

    Baron, Nina; Kjerulf Petersen, Lars

    2015-01-01

    Climate scenarios predict that an effect of climate change will be more areas at risk of extensive flooding. This article builds on a qualitative case study of homeowners in the flood-prone area of Lolland in Denmark and uses the theories of Tim Ingold and Bruno Latour to rethink the way we...... understand homeowners’ perception of climate change and local flood risk. Ingold argues that those perceptions are shaped by people’s experiences with and connections to their local landscape. People experience the local variability of the weather, and not global climate change as presented in statistical...... data and models. This influences the way they understand the future risks of climate change. Concurrently, with the theory of Latour, we can understand how those experiences with the local landscape are mediated by the existing water-managing technologies such as pumps and dikes. These technologies...

  1. Improvements made to simulated tropical variability in climate models by stochastic physics

    Science.gov (United States)

    Watson, Peter; Palmer, Tim

    2016-04-01

    Many climate models have too little variability in the tropics on daily to weekly time scales. This degrades their ability to simulate extreme events and how they will change with global warming. Stochastic parameterisations, which include a physically-based representation of the uncertainty in unresolved processes, have the potential to alleviate this problem by including variability associated with unresolved processes that is left out of deterministic models. The stochastic physics scheme used operationally by ECMWF has been shown to increase their weather forecast skill. Here we show that in an atmospheric GCM, the scheme makes the simulated tropical variability more consistent with observations by increasing daily precipitation variance, reducing its autocorrelation, and increasing the frequency of heavy-rainfall events. Stochastic physics may therefore be important for improving the model simulations and predicting how the statistics of extreme tropical events will change in the future. We also show also that even when the model's horizontal resolution is increased to that of a weather forecast model, there is still too little simulated tropical variability, so stochastic physics is likely to remain important even as computational power increases.

  2. The South American monsoon variability over the last millennium in climate models

    Science.gov (United States)

    Rojas, Maisa; Arias, Paola A.; Flores-Aqueveque, Valentina; Seth, Anji; Vuille, Mathias

    2016-08-01

    In this paper we assess South American monsoon system (SAMS) variability in the last millennium as depicted by global coupled climate model simulations. High-resolution proxy records for the South American monsoon over this period show a coherent regional picture of a weak monsoon during the Medieval Climate Anomaly and a stronger monsoon during the Little Ice Age (LIA). Due to the small external forcing during the past 1000 years, model simulations do not show very strong temperature anomalies over these two specific periods, which in turn do not translate into clear precipitation anomalies, in contrast with the rainfall reconstructions in South America. Therefore, we used an ad hoc definition of these two periods for each model simulation in order to account for model-specific signals. Thereby, several coherent large-scale atmospheric circulation anomalies are identified. The models feature a stronger monsoon during the LIA associated with (i) an enhancement of the rising motion in the SAMS domain in austral summer; (ii) a stronger monsoon-related upper-tropospheric anticyclone; (iii) activation of the South American dipole, which results in a poleward shift of the South Atlantic Convergence Zone; and (iv) a weaker upper-level subtropical jet over South America. The diagnosed changes provide important insights into the mechanisms of these climate anomalies over South America during the past millennium.

  3. Food Prices and Climate Extremes: A Model of Global Grain Price Variability with Storage

    Science.gov (United States)

    Otto, C.; Schewe, J.; Frieler, K.

    2015-12-01

    Extreme climate events such as droughts, floods, or heat waves affect agricultural production in major cropping regions and therefore impact the world market prices of staple crops. In the last decade, crop prices exhibited two very prominent price peaks in 2007-2008 and 2010-2011, threatening food security especially for poorer countries that are net importers of grain. There is evidence that these spikes in grain prices were at least partly triggered by actual supply shortages and the expectation of bad harvests. However, the response of the market to supply shocks is nonlinear and depends on complex and interlinked processes such as warehousing, speculation, and trade policies. Quantifying the contributions of such different factors to short-term price variability remains difficult, not least because many existing models ignore the role of storage which becomes important on short timescales. This in turn impedes the assessment of future climate change impacts on food prices. Here, we present a simple model of annual world grain prices that integrates grain stocks into the supply and demand functions. This firstly allows us to model explicitly the effect of storage strategies on world market price, and thus, for the first time, to quantify the potential contribution of trade policies to price variability in a simple global framework. Driven only by reported production and by long--term demand trends of the past ca. 40 years, the model reproduces observed variations in both the global storage volume and price of wheat. We demonstrate how recent price peaks can be reproduced by accounting for documented changes in storage strategies and trade policies, contrasting and complementing previous explanations based on different mechanisms such as speculation. Secondly, we show how the integration of storage allows long-term projections of grain price variability under climate change, based on existing crop yield scenarios.

  4. Uncertainty of the hydrological response to climate change conditions; 605 basins, 3 hydrological models, 5 climate models, 5 hydrological variables

    Science.gov (United States)

    Melsen, Lieke; Mizukami, Naoki; Newman, Andrew; Clark, Martyn; Teuling, Adriaan

    2016-04-01

    Many studies investigated the effect of a changing climate on the hydrological response of a catchment and uncertainty of the effect coming from hydrologic modelling (e.g., forcing, hydrologic model structures, and parameters). However, most past studies used only a single or a small number of catchments. To go beyond the case-study, and to assess the uncertainty involved in modelling the hydrological impact of climate change more comprehensively, we studied 605 basins over a wide range of climate regimes throughout the contiguous USA. We used three different widely-used hydrological models (VIC, HBV, SAC), which we forced with five distinct climate model outputs. The hydrological models have been run for a base period (1986-2008) for which observations were available, and for a future period (2070-2099). Instead of calibrating each hydrological model for each basin, the model has been run with a parameter sample (varying from 1600 to 1900 samples dependent on the number of free parameters in the model). Five hydrological states and fluxes were stored; discharge, evapotranspiration, soil moisture, SWE and snow melt, and 15 different metrics and signatures have been obtained for each model run. With the results, we conduct a sensitivity analysis over the change in signatures from the future period compared to the base period. In this way, we can identify the parameters that are responsible for certain projected changes, and identify the processes responsible for this change. By using three different models, in which VIC is most distinctive in including explicit vegetation parameters, we can compare different process representations and the effect on the projected hydrological change.

  5. A Generalized Stability Analysis of the AMOC in Earth System Models: Implication for Decadal Variability and Abrupt Climate Change

    Energy Technology Data Exchange (ETDEWEB)

    Fedorov, Alexey V. [Yale Univ., New Haven, CT (United States)

    2015-01-14

    The central goal of this research project was to understand the mechanisms of decadal and multi-decadal variability of the Atlantic Meridional Overturning Circulation (AMOC) as related to climate variability and abrupt climate change within a hierarchy of climate models ranging from realistic ocean models to comprehensive Earth system models. Generalized Stability Analysis, a method that quantifies the transient and asymptotic growth of perturbations in the system, is one of the main approaches used throughout this project. The topics we have explored range from physical mechanisms that control AMOC variability to the factors that determine AMOC predictability in the Earth system models, to the stability and variability of the AMOC in past climates.

  6. Internal variability of Earth’s energy budget simulated by CMIP5 climate models

    International Nuclear Information System (INIS)

    We analyse a large number of multi-century pre-industrial control simulations from the fifth phase of the Coupled Model Intercomparison Project (CMIP5) to investigate relationships between: net top-of-atmosphere radiation (TOA), globally averaged surface temperature (GST), and globally integrated ocean heat content (OHC) on decadal timescales. Consistent with previous studies, we find that large trends (∼0.3 K dec−1) in GST can arise from internal climate variability and that these trends are generally an unreliable indicator of TOA over the same period. In contrast, trends in total OHC explain 95% or more of the variance in TOA for two-thirds of the models analysed; emphasizing the oceans’ role as Earth’s primary energy store. Correlation of trends in total system energy (TE ≡ time integrated TOA) against trends in OHC suggests that for most models the ocean becomes the dominant term in the planetary energy budget on a timescale of about 12 months. In the context of the recent pause in global surface temperature rise, we investigate the potential importance of internal climate variability in both TOA and ocean heat rearrangement. The model simulations suggest that both factors can account for O (0.1 W m−2) on decadal timescales and may play an important role in the recently observed trends in GST and 0–700 m (and 0–1800 m) ocean heat uptake. (paper)

  7. Springtime soil moisture, natural climatic variability, and North American drought as simulated by the NCAR Community Climate Model 1

    Science.gov (United States)

    Oglesby, Robert J.

    1991-01-01

    Previous results concerning the role that summertime soil moisture reductions can play in amplifying or maintaining North American droughts are extended to include the role of springtime soil moisture reductions and the role that natural climatic variability, as expressed in soil moisture, can play. General circulation model (GCM) simulations with the NCAR Community Climate Model have been made with initial desert-like soil moisture anomalies imposed on 1 May and on 1 March. The May simulation maintained the imposed anomaly throughout the summer, while in the March simulation the anomaly was ameliorated within one month. Thus, the timing of soil moisture reductions may be crucial. A 10-year model control integration with prescribed sea surface temperatures yielded 1 year with late spring and summer soil moisture values similar to those of the 1 May anomaly simulation. This suggests that occasional widespread North American droughts may be an inherent feature of at least the GCM employed for this study. The results also demonstrate the important role played by moisture transport from the Gulf of Mexico in modulating or ameliorating drought conditions for much of the south-central United States, a topic that requires considerable further investigation.

  8. Generation and transfer of internal variability in a regional climate model

    Directory of Open Access Journals (Sweden)

    Thorsten Simon

    2013-12-01

    Full Text Available There is a strong need for tools allowing the comparison between the performance of a regional climate model (RCM and the corresponding model providing lateral boundary conditions (LBC for the RCM, which is a global general circulation model (GCM in most cases. A method is presented to investigate the temporal scales on which a RCM is able to generate internal variability on its own and on which variability is copied from the driving model. This is implemented by a cross-spectral analysis between the RCM output and a bi-linearly interpolated version of the driving model, leading to an estimate of the coherence spectrum. Applying the aforementioned technique to surface temperature and temperature and specific humidity at 850 hPa from the RCM COSMO-CLM East Asia with a horizontal resolution of 50 km and its driving model ECHAM5, it was found that features in the spatial distribution of coherence are related to atmospheric dynamics in East Asia, e.g. monsoons and inter-tropical convergence zone (ITCZ. A further application to a double-nesting approach, where COSMO-CLM East Asia is the driving model for two domains – namely the Haihe catchment and the Poyang catchment – each with a horizontal resolution of 7 km, shows that the frequencies on which internal variability is generated by the driven model are much higher compared to the first nesting step. Concluding RCMs can produce a considerable variability on the respective temporal scales. This implies that a dynamical downscaling with a re-analysis as LBC is conceptually different to a regional re-analysis, i.e. data assimilation on the regional scale.

  9. QBO Influence on Polar Stratospheric Variability in the GEOS Chemistry-Climate Model

    Science.gov (United States)

    Hurwitz, M. M.; Oman, L. D.; Li, F.; Slong, I.-S.; Newman, P. A.; Nielsen, J. E.

    2010-01-01

    The quasi-biennial oscillation modulates the strength of both the Arctic and Antarctic stratospheric vortices. Model and observational studies have found that the phase and characteristics of the quasi-biennial oscillation (QBO) contribute to the high degree of variability in the Arctic stratosphere in winter. While the Antarctic stratosphere is less variable, recent work has shown that Southern Hemisphere planetary wave driving increases in response to "warm pool" El Nino events that are coincident with the easterly phase of the QBO. These events hasten the breakup of the Antarctic polar vortex. The Goddard Earth Observing System (GEOS) chemistry-climate model (CCM) is now capable of generating a realistic QBO, due a new parameterization of gravity wave drag. In this presentation, we will use this new model capability to assess the influence of the QBO on polar stratospheric variability. Using simulations of the recent past, we will compare the modeled relationship between QBO phase and mid-winter vortex strength with the observed Holton-Tan relation, in both hemispheres. We will use simulations of the 21 St century to estimate future trends in the relationship between QBO phase and vortex strength. In addition, we will evaluate the combined influence of the QBO and El Nino/Southern Oscillation (ENSO) on the timing of the breakup of the polar stratospheric vortices in the GEOS CCM. We will compare the influence of these two natural phenomena with trends in the vortex breakup associated with ozone recovery and increasing greenhouse gas concentrations.

  10. Suitability of modelled and remotely sensed essential climate variables for monitoring Euro-Mediterranean droughts

    Science.gov (United States)

    Szczypta, C.; Calvet, J.-C.; Maignan, F.; Dorigo, W.; Baret, F.; Ciais, P.

    2014-05-01

    Two new remotely sensed leaf area index (LAI) and surface soil moisture (SSM) satellite-derived products are compared with two sets of simulations of the ORganizing Carbon and Hydrology In Dynamic EcosystEms (ORCHIDEE) and Interactions between Soil, Biosphere and Atmosphere, CO2-reactive (ISBA-A-gs) land surface models. We analyse the interannual variability over the period 1991-2008. The leaf onset and the length of the vegetation growing period (LGP) are derived from both the satellite-derived LAI and modelled LAI. The LGP values produced by the photosynthesis-driven phenology model of ISBA-A-gs are closer to the satellite-derived LAI and LGP than those produced by ORCHIDEE. In the latter, the phenology is based on a growing degree day model for leaf onset, and on both climatic conditions and leaf life span for senescence. Further, the interannual variability of LAI is better captured by ISBA-A-gs than by ORCHIDEE. In order to investigate how recent droughts affected vegetation over the Euro-Mediterranean area, a case study addressing the summer 2003 drought is presented. It shows a relatively good agreement of the modelled LAI anomalies with the observations, but the two models underestimate plant regrowth in the autumn. A better representation of the root-zone soil moisture profile could improve the simulations of both models. The satellite-derived SSM is compared with SSM simulations of ISBA-A-gs only, as ORCHIDEE has no explicit representation of SSM. Overall, the ISBA-A-gs simulations of SSM agree well with the satellite-derived SSM and are used to detect regions where the satellite-derived product could be improved. Finally, a correspondence is found between the interannual variability of detrended SSM and LAI. The predictability of LAI is less pronounced using remote sensing observations than using simulated variables. However, consistent results are found in July for the croplands of the Ukraine and southern Russia.

  11. Intraseasonal Variability of the Indian Summer Monsoon in the Regional Climate Model COSMO-CLM

    Science.gov (United States)

    Befort, Daniel J.; Leckebusch, Gregor C.; Cubasch, Ulrich

    2015-04-01

    The regional climate model COSMO-CLM driven by ERA-Interim reanalysis data with a spatial resolution of 55km is used to simulate observed features of the intraseasonal variability of the Indian summer monsoon (ISM) during the period 1979 until 2011. One of these features is the northward propagation of the monsoon intraseasonal oscillations. We find, that the temporal evolution of this oscillation between model and observation is in good agreement, but the strength is less well simulated. Additionally, the models capability to simulate observed dry and wet events on a weekly time scale is investigated using the standardized precipitation index. In general, the model is capable to simulate these events with a similar magnitude at the same time, but we find a higher ability for dry compared to wet events. We hypothesize this is related to differences in the atmospheric circulation during dry and wet events. Analyses show, that dry events are characterized by a cyclonic vortex over India as well as an anti-cyclonic vortex over Pakistan region in 500hPa, whereas wet events are characterized by an anti-cyclonic vortex over India, only. It is found that COSMO-CLM has a higher ability to simulate the observed anomalous circulation over Pakistan region compared to observed anomalous circulation patterns over India. Overall, this study shows that the current configuration of COSMO-CLM is able to simulate key features of the intraseasonal variability of the Indian summer monsoon. Thus, under consideration of its limitations, COSMO-CLM is suitable to investigate possible changes of the intraseasonal variability of ISM under changed climate conditions.

  12. Interannual Variability of Tropical Precipitation: How Well Do Climate Models Agree With Current Satellite Estimates?

    Science.gov (United States)

    Robertson, Franklin R.; Marshall, Susan; Roads, John; Oglesby, Robert J.; Fitzjarrald, Dan; Goodman, H. Michael (Technical Monitor)

    2001-01-01

    Since the beginning of the World Climate Research Program's Global Precipitation Climatology Project (GPCP) satellite remote sensing of precipitation has made dramatic improvements, particularly for tropical regions. Data from microwave and infrared sensors now form the most critical input to precipitation data sets and can be calibrated with surface gauges to so that the strengths of each data source can be maximized in some statistically optimal sense. Recent availability of the TRMM (Tropical Rainfall Measuring Mission) has further aided in narrowing uncertainties in rainfall over die tropics and subtropics. Although climate modeling efforts have long relied on space-based precipitation estimates for validation, we now are in a position to make more quantitative assessments of model performance, particularly in tropical regions. An integration of the CCM3 using observed SSTs as a lower boundary condition is used to examine how well this model responds to ENSO forcing in terms of anomalous precipitation. An integration of the NCEP spectral model used for the Reanalysis-H effort is also examined. This integration is run with specified SSTs, but with no data assimilation. Our analysis focuses on two aspects of inter-annual variability. First are the spatial anomalies that are indicative of dislocations in Hadley and Walker circulations. Second, we consider the ability of models to replicate observed increases in oceanic precipitation that are noted in satellite observations for large ENSO events. Finally, we consider a slab ocean version of the CCM3 model with prescribed ocean beat transports that mimic upwelling anomalies, but which still allows the surface energy balance to be predicted. This less restrictive experiment is used to understand why model experiments with specified SSTs seem to have noticeably less interannual variability in precipitation than do the satellite observations.

  13. How Well Do Global Climate Models Simulate the Variability of Atlantic Tropical Cyclones Associated with ENSO?

    Science.gov (United States)

    Wang, Hui; Long, Lindsey; Kumar, Arun; Wang, Wanqiu; Schemm, Jae-Kyung E.; Zhao, Ming; Vecchi, Gabriel A.; LaRow, Timorhy E.; Lim, Young-Kwon; Schubert, Siegfried D.; Shaevitz, Daniel A.; Camargo, Suzana J.; Henderson, Naomi; Kim, Daehyun; Jonas, Jeffrey A.; Walsh, Kevin J. E.

    2013-01-01

    The variability of Atlantic tropical cyclones (TCs) associated with El Nino-Southern Oscillation (ENSO) in model simulations is assessed and compared with observations. The model experiments are 28-yr simulations forced with the observed sea surface temperature from 1982 to 2009. The simulations were coordinated by the U.S. CLIVAR Hurricane Working Group and conducted with five global climate models (GCMs) with a total of 16 ensemble members. The model performance is evaluated based on both individual model ensemble means and multi-model ensemble mean. The latter has the highest anomaly correlation (0.86) for the interannual variability of TCs. Previous observational studies show a strong association between ENSO and Atlantic TC activity, as well as distinctions in the TC activities during eastern Pacific (EP) and central Pacific (CP) El Nino events. The analysis of track density and TC origin indicates that each model has different mean biases. Overall, the GCMs simulate the variability of Atlantic TCs well with weaker activity during EP El Nino and stronger activity during La Nina. For CP El Nino, there is a slight increase in the number of TCs as compared with EP El Nino. However, the spatial distribution of track density and TC origin is less consistent among the models. Particularly, there is no indication of increasing TC activity over the U.S. southeast coastal region as in observations. The difference between the models and observations is likely due to the bias of vertical wind shear in response to the shift of tropical heating associated with CP El Nino, as well as the model bias in the mean circulation.

  14. Recent variability of the solar spectral irradiance and its impact on climate modelling

    Science.gov (United States)

    Ermolli, I.; Matthes, K.; Dudok de Wit, T.; Krivova, N. A.; Tourpali, K.; Weber, M.; Unruh, Y. C.; Gray, L.; Langematz, U.; Pilewskie, P.; Rozanov, E.; Schmutz, W.; Shapiro, A.; Solanki, S. K.; Woods, T. N.

    2013-04-01

    The lack of long and reliable time series of solar spectral irradiance (SSI) measurements makes an accurate quantification of solar contributions to recent climate change difficult. Whereas earlier SSI observations and models provided a qualitatively consistent picture of the SSI variability, recent measurements by the SORCE (SOlar Radiation and Climate Experiment) satellite suggest a significantly stronger variability in the ultraviolet (UV) spectral range and changes in the visible and near-infrared (NIR) bands in anti-phase with the solar cycle. A number of recent chemistry-climate model (CCM) simulations have shown that this might have significant implications on the Earth's atmosphere. Motivated by these results, we summarize here our current knowledge of SSI variability and its impact on Earth's climate. We present a detailed overview of existing SSI measurements and provide thorough comparison of models available to date. SSI changes influence the Earth's atmosphere, both directly, through changes in shortwave (SW) heating and therefore, temperature and ozone distributions in the stratosphere, and indirectly, through dynamical feedbacks. We investigate these direct and indirect effects using several state-of-the art CCM simulations forced with measured and modelled SSI changes. A unique asset of this study is the use of a common comprehensive approach for an issue that is usually addressed separately by different communities. We show that the SORCE measurements are difficult to reconcile with earlier observations and with SSI models. Of the five SSI models discussed here, specifically NRLSSI (Naval Research Laboratory Solar Spectral Irradiance), SATIRE-S (Spectral And Total Irradiance REconstructions for the Satellite era), COSI (COde for Solar Irradiance), SRPM (Solar Radiation Physical Modelling), and OAR (Osservatorio Astronomico di Roma), only one shows a behaviour of the UV and visible irradiance qualitatively resembling that of the recent SORCE

  15. Modeling and forecasting livestock feed resources in India using climate variables.

    Science.gov (United States)

    Suresh, K P; Kiran, G Ravi; Giridhar, K; Sampath, K T

    2012-04-01

    The availability and efficient use of the feed resources in India are the primary drivers to maximize productivity of Indian livestock. Feed security is vital to the livestock management, extent of use, conservation and productivity enhancement. Assessment and forecasting of livestock feed resources are most important for effective planning and policy making. In the present study, 40 years of data on crop production, land use pattern, rainfall, its deviation from normal, area under crop and yield of crop were collected and modeled to forecast the likely production of feed resources for the next 20 years. The higher order auto-regressive (AR) models were used to develop efficient forecasting models. Use of climatic variables (actual rainfall and its deviation from normal) in combination with non-climatic factors like area under each crop, yield of crop, lag period etc., increased the efficiency of forecasting models. From the best fitting models, the current total dry matter (DM) availability in India was estimated to be 510.6 million tonnes (mt) comprising of 47.2 mt from concentrates, 319.6 mt from crop residues and 143.8 mt from greens. The availability of DM from dry fodder, green fodder and concentrates is forecasted at 409.4, 135.6 and 61.2 mt, respectively, for 2030.

  16. Constructing the reduced dynamical models of interannual climate variability from spatial-distributed time series

    Science.gov (United States)

    Mukhin, Dmitry; Gavrilov, Andrey; Loskutov, Evgeny; Feigin, Alexander

    2016-04-01

    We suggest a method for empirical forecast of climate dynamics basing on the reconstruction of reduced dynamical models in a form of random dynamical systems [1,2] derived from observational time series. The construction of proper embedding - the set of variables determining the phase space the model works in - is no doubt the most important step in such a modeling, but this task is non-trivial due to huge dimension of time series of typical climatic fields. Actually, an appropriate expansion of observational time series is needed yielding the number of principal components considered as phase variables, which are to be efficient for the construction of low-dimensional evolution operator. We emphasize two main features the reduced models should have for capturing the main dynamical properties of the system: (i) taking into account time-lagged teleconnections in the atmosphere-ocean system and (ii) reflecting the nonlinear nature of these teleconnections. In accordance to these principles, in this report we present the methodology which includes the combination of a new way for the construction of an embedding by the spatio-temporal data expansion and nonlinear model construction on the basis of artificial neural networks. The methodology is aplied to NCEP/NCAR reanalysis data including fields of sea level pressure, geopotential height, and wind speed, covering Northern Hemisphere. Its efficiency for the interannual forecast of various climate phenomena including ENSO, PDO, NAO and strong blocking event condition over the mid latitudes, is demonstrated. Also, we investigate the ability of the models to reproduce and predict the evolution of qualitative features of the dynamics, such as spectral peaks, critical transitions and statistics of extremes. This research was supported by the Government of the Russian Federation (Agreement No. 14.Z50.31.0033 with the Institute of Applied Physics RAS) [1] Y. I. Molkov, E. M. Loskutov, D. N. Mukhin, and A. M. Feigin, "Random

  17. Climate Variability and Weather Extremes: Model-Simulated and Historical Data. Chapter 9

    Science.gov (United States)

    Schubert, Siegfried D.; Lim, Young-Kwon

    2012-01-01

    basic mechanisms by which extremes vary is incomplete. As noted in IPCC (2007), Incomplete global data sets and remaining model uncertainties still restrict understanding of changes in extremes and attribution of changes to causes, although understanding of changes in the intensity, frequency and risk of extremes has improved. Separating decadal and other shorter-term variability from climate change impacts on extremes requires a better understanding of the processes responsible for the changes. In particular, the physical processes linking sea surface temperature changes to regional climate changes, and a basic understanding of the inherent variability in weather extremes and how that is impacted by atmospheric circulation changes at subseasonal to decadal and longer time scales, are still inadequately understood. Given the fundamental limitations in the time span and quality of global observations, substantial progress on these issues will rely increasingly on improvements in models, with observations continuing to play a critical role, though less as a detection tool, and more as a tool for addressing physical processes, and to insure the quality of the climate models and the verisimilitude of the simulations (CCSP SAP 1.3, 2008).

  18. Holocene glacier variability: three case studies using an intermediate-complexity climate model

    NARCIS (Netherlands)

    Weber, S.L.; Oerlemans, J.

    2003-01-01

    Synthetic glacier length records are generated for the Holocene epoch using a process-based glacier model coupled to the intermediate-complexity climate model ECBilt. The glacier model consists of a massbalance component and an ice-flow component. The climate model is forced by the insolation change

  19. Climate-Vegetation Interannual Variability in a Coupled Atmosphere-Ocean-Land Model

    Institute of Scientific and Technical Information of China (English)

    ZHI Hai; WANG Panxing; DAN Li; YU Yongqiang; XU Yongfu; ZHENG Weipeng

    2009-01-01

    The coupled models of both the Global Ocean-Atmosphere-Land System (GOALS) and the AtmosphereVegetation Interaction Model (GOALS-AVIM) are used to study the main characteristics of interannual variations. The simulated results are also used to investigate some significant interannual variability and correlation analysis of the atmospheric circulation and terrestrial ecosystem. By comparing the simulations of the climate model GOALS-AVIM and GOALS, it is known that the simulated results of the interannual variations of the spatial and temporal distributions of the surface air temperatures and precipitation are generally improved by using AVIM in GOALS-AVIM. The interannual variation displays some distinct characteristics of the geographical distribution. Both the Net Primary Production (NPP) and the Leap Area Index (LAI) have quasi 1-2-year cycles. Meanwhile, precipitation and the surface temperatures have 2-4-year cycles. Conditions when the spectrum density values of GOALS are less than those of GOALS-AVIM, tell us that the model coupled with AVIM enhances the simulative capability for interannual variability and makes the annual cycle variability more apparent. Using Singular Value Decomposition (SVD) analysis, the relationship between the ecosystem and the atmospheric circulation in East Asia is explored. The result shows that the strengthening and weakening of the East Asian monsoon, characterized by the geopotential heights at 500 hPa and the wind fields at 850 hPa, correspond to the spatiotemporal pattern of the NPP. The correlation between NPP and the air temperature, precipitation and solar radiation are different in interannual variability because of the variation in vegetation types.

  20. Tropical Intraseasonal Variability in 14 IPCC AR4 Climate Models Part I: Convective Signals

    Energy Technology Data Exchange (ETDEWEB)

    Lin, J; Kiladis, G N; Mapes, B E; Weickmann, K M; Sperber, K R; Lin, W; Wheeler, M; Schubert, S D; Genio, A D; Donner, L J; Emori, S; Gueremy, J; Hourdin, F; Rasch, P J; Roeckner, E; Scinocca, J F

    2005-05-06

    This study evaluates the tropical intraseasonal variability, especially the fidelity of Madden-Julian Oscillation (MJO) simulations, in 14 coupled general circulation models (GCMs) participating in the Inter-governmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4). Eight years of daily precipitation from each model's 20th century climate simulation are analyzed and compared with daily satellite retrieved precipitation. Space-time spectral analysis is used to obtain the variance and phase speed of dominant convectively coupled equatorial waves, including the MJO, Kelvin, equatorial Rossby (ER), mixed Rossby-gravity (MRG), and eastward inertio-gravity (EIG) and westward inertio-gravity (WIG) waves. The variance and propagation of the MJO, defined as the eastward wavenumbers 1-6, 30-70 day mode, are examined in detail. The results show that current state-of-the-art GCMs still have significant problems and display a wide range of skill in simulating the tropical intraseasonal variability. The total intraseasonal (2-128 day) variance of precipitation is too weak in most of the models. About half of the models have signals of convectively coupled equatorial waves, with Kelvin and MRG-EIG waves especially prominent. However, the variances are generally too weak for all wave modes except the EIG wave, and the phase speeds are generally too fast, being scaled to excessively deep equivalent depths. An interesting result is that this scaling is consistent within a given model across modes, in that both the symmetric and antisymmetric modes scale similarly to a certain equivalent depth. Excessively deep equivalent depths suggest that these models may not have a large enough reduction in their ''effective static stability'' due to diabatic heating. The MJO variance approaches the observed value in only two of the 14 models, but is less than half of the observed value in the other 12 models. The ratio between the eastward MJO variance

  1. Local-scale spatial modelling for interpolating climatic temperature variables to predict agricultural plant suitability

    Science.gov (United States)

    Webb, Mathew A.; Hall, Andrew; Kidd, Darren; Minansy, Budiman

    2016-05-01

    Assessment of local spatial climatic variability is important in the planning of planting locations for horticultural crops. This study investigated three regression-based calibration methods (i.e. traditional versus two optimized methods) to relate short-term 12-month data series from 170 temperature loggers and 4 weather station sites with data series from nearby long-term Australian Bureau of Meteorology climate stations. The techniques trialled to interpolate climatic temperature variables, such as frost risk, growing degree days (GDDs) and chill hours, were regression kriging (RK), regression trees (RTs) and random forests (RFs). All three calibration methods produced accurate results, with the RK-based calibration method delivering the most accurate validation measures: coefficients of determination ( R 2) of 0.92, 0.97 and 0.95 and root-mean-square errors of 1.30, 0.80 and 1.31 °C, for daily minimum, daily maximum and hourly temperatures, respectively. Compared with the traditional method of calibration using direct linear regression between short-term and long-term stations, the RK-based calibration method improved R 2 and reduced root-mean-square error (RMSE) by at least 5 % and 0.47 °C for daily minimum temperature, 1 % and 0.23 °C for daily maximum temperature and 3 % and 0.33 °C for hourly temperature. Spatial modelling indicated insignificant differences between the interpolation methods, with the RK technique tending to be the slightly better method due to the high degree of spatial autocorrelation between logger sites.

  2. Inter-model variability and biases of the global water cycle in CMIP3 coupled climate models

    International Nuclear Information System (INIS)

    Observed changes such as increasing global temperatures and the intensification of the global water cycle in the 20th century are robust results of coupled general circulation models (CGCMs). In spite of these successes, model-to-model variability and biases that are small in first order climate responses, however, have considerable implications for climate predictability especially when multi-model means are used. We show that most climate simulations of the 20th and 21st century A2 scenario performed with CMIP3 (Coupled Model Inter-comparison Project Phase 3) models have deficiencies in simulating the global atmospheric moisture balance. Large biases of only a few models (some biases reach the simulated global precipitation changes in the 20th and 21st centuries) affect the multi-model mean global moisture budget. An imbalanced flux of −0.14 Sv exists while the multi-model median imbalance is only −0.02 Sv. Moreover, for most models the detected imbalance changes over time. As a consequence, in 13 of the 18 CMIP3 models examined, global annual mean precipitation exceeds global evaporation, indicating that there should be a ‘leaking’ of moisture from the atmosphere whereas for the remaining five models a ‘flooding’ is implied. Nonetheless, in all models, the actual atmospheric moisture content and its variability correctly increases during the course of the 20th and 21st centuries. These discrepancies therefore imply an unphysical and hence ‘ghost’ sink/source of atmospheric moisture in the models whose atmospheres flood/leak. The ghost source/sink of moisture can also be regarded as atmospheric latent heating/cooling and hence as positive/negative perturbation of the atmospheric energy budget or non-radiative forcing in the range of −1 to +6 W m−2 (median +0.1 W m−2). The inter-model variability of the global atmospheric moisture transport from oceans to land areas, which impacts the terrestrial water cycle, is also quite high and ranges

  3. Assessment of inter-model variability and biases of the global water cycle in CMIP3 climate models

    CERN Document Server

    Liepert, Beate G

    2011-01-01

    Observed changes such as increasing global temperatures and the intensification of the global water cycle in the 20th century are also robust results of coupled general circulation models. In spite of this success model-to-model variability and biases that are small in first order climate responses however, have implications for climate predictability especially when multi-model means are used. We show that most climate simulations of 20th and 21st century A2 scenario performed with IPCC-AR4 models have deficiencies in simulating the global atmospheric moisture balance. Large biases of only a few models affect the multi-model mean global moisture budget and an imbalanced flux of -0.14 Sv exists whereas the multi-model median imbalance is only -0.02 Sv. For most models, the detected imbalances furthermore change over time. As a consequence, in 13 of the 18 IPCC-AR4 models examined, global annual mean precipitation exceeds global evaporation, indicating that there should be a "leaking" of moisture from the atmo...

  4. Climate models and scenarios

    Energy Technology Data Exchange (ETDEWEB)

    Fortelius, C.; Holopainen, E.; Kaurola, J.; Ruosteenoja, K.; Raeisaenen, J. [Helsinki Univ. (Finland). Dept. of Meteorology

    1996-12-31

    In recent years the modelling of interannual climate variability has been studied, the atmospheric energy and water cycles, and climate simulations with the ECHAM3 model. In addition, the climate simulations of several models have been compared with special emphasis in the area of northern Europe

  5. Climate Impact of Solar Variability

    Science.gov (United States)

    Schatten, Kenneth H. (Editor); Arking, Albert (Editor)

    1990-01-01

    The conference on The Climate Impact of Solar Variability, was held at Goddard Space Flight Center from April 24 to 27, 1990. In recent years they developed a renewed interest in the potential effects of increasing greenhouse gases on climate. Carbon dioxide, methane, nitrous oxide, and the chlorofluorocarbons have been increasing at rates that could significantly change climate. There is considerable uncertainty over the magnitude of this anthropogenic change. The climate system is very complex, with feedback processes that are not fully understood. Moreover, there are two sources of natural climate variability (volcanic aerosols and solar variability) added to the anthropogenic changes which may confuse our interpretation of the observed temperature record. Thus, if we could understand the climatic impact of the natural variability, it would aid our interpretation and understanding of man-made climate changes.

  6. A modelling framework to project future climate change impacts on streamflow variability and extremes in the West River, China

    Science.gov (United States)

    Fei, Y.; Yeou-Koung, T.; Liliang, R.

    2014-09-01

    In this study, a hydrological modelling framework was introduced to assess the climate change impacts on future river flow in the West River basin, China, especially on streamflow variability and extremes. The modelling framework includes a delta-change method with the quantile-mapping technique to construct future climate forcings on the basis of observed meteorological data and the downscaled climate model outputs. This method is able to retain the signals of extreme weather events, as projected by climate models, in the constructed future forcing scenarios. Fed with the historical and future forcing data, a large-scale hydrologic model (the Variable Infiltration Capacity model, VIC) was executed for streamflow simulations and projections at daily time scales. A bootstrapping resample approach was used as an indirect alternative to test the equality of means, standard deviations and the coefficients of variation for the baseline and future streamflow time series, and to assess the future changes in flood return levels. The West River basin case study confirms that the introduced modelling framework is an efficient effective tool to quantify streamflow variability and extremes in response to future climate change.

  7. Remote Sensing-Driven Climatic/Environmental Variables for Modelling Malaria Transmission in Sub-Saharan Africa.

    Science.gov (United States)

    Ebhuoma, Osadolor; Gebreslasie, Michael

    2016-01-01

    Malaria is a serious public health threat in Sub-Saharan Africa (SSA), and its transmission risk varies geographically. Modelling its geographic characteristics is essential for identifying the spatial and temporal risk of malaria transmission. Remote sensing (RS) has been serving as an important tool in providing and assessing a variety of potential climatic/environmental malaria transmission variables in diverse areas. This review focuses on the utilization of RS-driven climatic/environmental variables in determining malaria transmission in SSA. A systematic search on Google Scholar and the Institute for Scientific Information (ISI) Web of Knowledge(SM) databases (PubMed, Web of Science and ScienceDirect) was carried out. We identified thirty-five peer-reviewed articles that studied the relationship between remotely-sensed climatic variable(s) and malaria epidemiological data in the SSA sub-regions. The relationship between malaria disease and different climatic/environmental proxies was examined using different statistical methods. Across the SSA sub-region, the normalized difference vegetation index (NDVI) derived from either the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) or Moderate-resolution Imaging Spectrometer (MODIS) satellite sensors was most frequently returned as a statistically-significant variable to model both spatial and temporal malaria transmission. Furthermore, generalized linear models (linear regression, logistic regression and Poisson regression) were the most frequently-employed methods of statistical analysis in determining malaria transmission predictors in East, Southern and West Africa. By contrast, multivariate analysis was used in Central Africa. We stress that the utilization of RS in determining reliable malaria transmission predictors and climatic/environmental monitoring variables would require a tailored approach that will have cognizance of the geographical/climatic

  8. Remote Sensing-Driven Climatic/Environmental Variables for Modelling Malaria Transmission in Sub-Saharan Africa.

    Science.gov (United States)

    Ebhuoma, Osadolor; Gebreslasie, Michael

    2016-01-01

    Malaria is a serious public health threat in Sub-Saharan Africa (SSA), and its transmission risk varies geographically. Modelling its geographic characteristics is essential for identifying the spatial and temporal risk of malaria transmission. Remote sensing (RS) has been serving as an important tool in providing and assessing a variety of potential climatic/environmental malaria transmission variables in diverse areas. This review focuses on the utilization of RS-driven climatic/environmental variables in determining malaria transmission in SSA. A systematic search on Google Scholar and the Institute for Scientific Information (ISI) Web of Knowledge(SM) databases (PubMed, Web of Science and ScienceDirect) was carried out. We identified thirty-five peer-reviewed articles that studied the relationship between remotely-sensed climatic variable(s) and malaria epidemiological data in the SSA sub-regions. The relationship between malaria disease and different climatic/environmental proxies was examined using different statistical methods. Across the SSA sub-region, the normalized difference vegetation index (NDVI) derived from either the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) or Moderate-resolution Imaging Spectrometer (MODIS) satellite sensors was most frequently returned as a statistically-significant variable to model both spatial and temporal malaria transmission. Furthermore, generalized linear models (linear regression, logistic regression and Poisson regression) were the most frequently-employed methods of statistical analysis in determining malaria transmission predictors in East, Southern and West Africa. By contrast, multivariate analysis was used in Central Africa. We stress that the utilization of RS in determining reliable malaria transmission predictors and climatic/environmental monitoring variables would require a tailored approach that will have cognizance of the geographical/climatic

  9. Remote Sensing-Driven Climatic/Environmental Variables for Modelling Malaria Transmission in Sub-Saharan Africa

    Science.gov (United States)

    Ebhuoma, Osadolor; Gebreslasie, Michael

    2016-01-01

    Malaria is a serious public health threat in Sub-Saharan Africa (SSA), and its transmission risk varies geographically. Modelling its geographic characteristics is essential for identifying the spatial and temporal risk of malaria transmission. Remote sensing (RS) has been serving as an important tool in providing and assessing a variety of potential climatic/environmental malaria transmission variables in diverse areas. This review focuses on the utilization of RS-driven climatic/environmental variables in determining malaria transmission in SSA. A systematic search on Google Scholar and the Institute for Scientific Information (ISI) Web of KnowledgeSM databases (PubMed, Web of Science and ScienceDirect) was carried out. We identified thirty-five peer-reviewed articles that studied the relationship between remotely-sensed climatic variable(s) and malaria epidemiological data in the SSA sub-regions. The relationship between malaria disease and different climatic/environmental proxies was examined using different statistical methods. Across the SSA sub-region, the normalized difference vegetation index (NDVI) derived from either the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) or Moderate-resolution Imaging Spectrometer (MODIS) satellite sensors was most frequently returned as a statistically-significant variable to model both spatial and temporal malaria transmission. Furthermore, generalized linear models (linear regression, logistic regression and Poisson regression) were the most frequently-employed methods of statistical analysis in determining malaria transmission predictors in East, Southern and West Africa. By contrast, multivariate analysis was used in Central Africa. We stress that the utilization of RS in determining reliable malaria transmission predictors and climatic/environmental monitoring variables would require a tailored approach that will have cognizance of the geographical/climatic

  10. Remote Sensing-Driven Climatic/Environmental Variables for Modelling Malaria Transmission in Sub-Saharan Africa

    OpenAIRE

    Ebhuoma, Osadolor; Gebreslasie, Michael

    2016-01-01

    Malaria is a serious public health threat in Sub-Saharan Africa (SSA), and its transmission risk varies geographically. Modelling its geographic characteristics is essential for identifying the spatial and temporal risk of malaria transmission. Remote sensing (RS) has been serving as an important tool in providing and assessing a variety of potential climatic/environmental malaria transmission variables in diverse areas. This review focuses on the utilization of RS-driven climatic/environment...

  11. Tropical deforestation and climate variability

    Energy Technology Data Exchange (ETDEWEB)

    Voldoire, A.; Royer, J.F. [CNRM/GMGEC/UDC, Meteo-France, 42 Avenue G. Coriolis, 31057, Toulouse Cedex 1 (France)

    2004-07-01

    A new tropical deforestation experiment has been performed, with the ARPEGE-Climat atmospheric global circulation model associated with the ISBA land surface scheme. Simulations are forced with observed monthly mean sea surface temperatures and thus inter-annual variability of the ocean system is taken into account. The local mean response to deforestation over Amazonia and Africa is relatively weak compared with most published studies and compensation effects are particularly important. However, a large increase in daily maximum temperatures is obtained during the dry season when soil water stress dominates. The analysis of daily variability shows that the distributions of daily minimum and maximum temperatures are noticeably modified with an increase in extreme temperatures. Daily precipitation amounts also indicate a weakening of the convective activity. Conditions for the onset of convection are less frequently gathered, particularly over southern Amazonia and western equatorial Africa. At the same time, the intensity of convective events is reduced, especially over equatorial deforested regions. The inter-annual variability is also enhanced. For instance, El Nino events generally induce a large drying over northern Amazonia, which is well reproduced in the control simulation. In the deforested experiment, a positive feedback effect leads to a strong intensification of this drying and a subsequent increase in surface temperature. The change in variability as a response to deforestation can be more crucial than the change of the mean climate since more intense extremes could be more detrimental for agriculture than an increase in mean temperatures. (orig.)

  12. Attributing runoff changes to climate variability and human activities: Uncertainty analysis using four monthly water balance models

    Energy Technology Data Exchange (ETDEWEB)

    Li, Shuai; Xiong, Lihua; Li, Hongyi; Leung, Lai-Yung R.; Demissie, Yonas

    2016-01-08

    Hydrological simulations to delineate the impacts of climate variability and human activities are subjected to uncertainties related to both parameter and structure of the hydrological models. To analyze the impact of these uncertainties on the model performance and to yield more reliable simulation results, a global calibration and multimodel combination method that integrates the Shuffled Complex Evolution Metropolis (SCEM) and Bayesian Model Averaging (BMA) of four monthly water balance models was proposed. The method was applied to the Weihe River Basin (WRB), the largest tributary of the Yellow River, to determine the contribution of climate variability and human activities to runoff changes. The change point, which was used to determine the baseline period (1956-1990) and human-impacted period (1991-2009), was derived using both cumulative curve and Pettitt’s test. Results show that the combination method from SCEM provides more skillful deterministic predictions than the best calibrated individual model, resulting in the smallest uncertainty interval of runoff changes attributed to climate variability and human activities. This combination methodology provides a practical and flexible tool for attribution of runoff changes to climate variability and human activities by hydrological models.

  13. Climate variability and change

    CERN Document Server

    Grassl, H

    1998-01-01

    Many factors influence climate. The present knowledge concerning the climate relevance of earth orbital parameters, solar luminosity, volcanoes, internal interactions, and human activities will be reported as well as the vulnerability of emission scenarios for given stabilization goals for greenhouse gas concentrations and the main points of the Kyoto Protocol

  14. Correcting North Atlantic sea surface salinity biases in the Kiel Climate Model: influences on ocean circulation and Atlantic Multidecadal Variability

    Science.gov (United States)

    Park, T.; Park, W.; Latif, M.

    2016-10-01

    A long-standing problem in climate models is the large sea surface salinity (SSS) biases in the North Atlantic. In this study, we describe the influences of correcting these SSS biases on the circulation of the North Atlantic as well as on North Atlantic sector mean climate and decadal to multidecadal variability. We performed integrations of the Kiel Climate Model (KCM) with and without applying a freshwater flux correction over the North Atlantic. The quality of simulating the mean circulation of the North Atlantic Ocean, North Atlantic sector mean climate and decadal variability is greatly enhanced in the freshwater flux-corrected integration which, by definition, depicts relatively small North Atlantic SSS biases. In particular, a large reduction in the North Atlantic cold sea surface temperature bias is observed and a more realistic Atlantic Multidecadal Variability simulated. Improvements relative to the non-flux corrected integration also comprise a more realistic representation of deep convection sites, sea ice, gyre circulation and Atlantic Meridional Overturning Circulation. The results suggest that simulations of North Atlantic sector mean climate and decadal variability could strongly benefit from alleviating sea surface salinity biases in the North Atlantic, which may enhance the skill of decadal predictions in that region.

  15. Measurement and structural relations of an authoritative school climate model: A multi-level latent variable investigation.

    Science.gov (United States)

    Konold, Timothy R; Cornell, Dewey

    2015-12-01

    This study tested a conceptual model of school climate in which two key elements of an authoritative school, structure and support variables, are associated with student engagement in school and lower levels of peer aggression. Multilevel multivariate structural modeling was conducted in a statewide sample of 48,027 students in 323 public high schools who completed the Authoritative School Climate Survey. As hypothesized, two measures of structure (Disciplinary Structure and Academic Expectations) and two measures of support (Respect for Students and Willingness to Seek Help) were associated with higher student engagement (Affective Engagement and Cognitive Engagement) and lower peer aggression (Prevalence of Teasing and Bullying) on both student and school levels of analysis, controlling for the effects of school demographics (school size, percentage of minority students, and percentage of low income students). These results support the extension of authoritative school climate model to high school and guide further research on the conditions for a positive school climate. PMID:26563598

  16. Mirador - Climate Variability and Change

    Data.gov (United States)

    National Aeronautics and Space Administration — Earth Science data access made simple. NASA's role in climate variability study is centered around providing the global scale observational data sets on oceans and...

  17. Solar Variability and Planetary Climates

    CERN Document Server

    Calisesi, Y; Gray, L; Langen, J; Lockwood, M

    2007-01-01

    Variations in solar activity, as revealed by variations in the number of sunspots, have been observed since ancient times. To what extent changes in the solar output may affect planetary climates, though, remains today more than ever a subject of controversy. In 2000, the SSSI volume on Solar Variability and Climate reviewed the to-date understanding of the physics of solar variability and of the associated climate response. The present volume on Solar Variability and Planetary Climates provides an overview of recent advances in this field, with particular focus at the Earth's middle and lower atmosphere. The book structure mirrors that of the ISSI workshop held in Bern in June 2005, the collection of invited workshop contributions and of complementary introductory papers synthesizing the current understanding in key research areas such as middle atmospheric processes, stratosphere-troposphere dynamical coupling, tropospheric aerosols chemistry, solar storm influences, solar variability physics, and terrestri...

  18. Singular vector decomposition of the internal variability of the Canadian Regional Climate Model

    Energy Technology Data Exchange (ETDEWEB)

    Diaconescu, Emilia Paula; Laprise, Rene [University of Quebec at Montreal (UQAM), Department of Earth and Atmospheric Sciences, Canadian Network for Regional Climate Modelling and Diagnostics, P.O. Box 8888, Montreal, QC (Canada); Centre ESCER (Etude et Simulation du Climat a l' Echelle Regionale), Montreal, QC (Canada); Zadra, Ayrton [University of Quebec at Montreal (UQAM), Department of Earth and Atmospheric Sciences, Canadian Network for Regional Climate Modelling and Diagnostics, P.O. Box 8888, Montreal, QC (Canada); Environment Canada, Meteorological Research Division, Montreal, QC (Canada); Centre ESCER (Etude et Simulation du Climat a l' Echelle Regionale), Montreal, QC (Canada)

    2012-03-15

    Previous studies have shown that Regional Climate Models (RCM) internal variability (IV) fluctuates in time depending on synoptic events. This study focuses on the physical understanding of episodes with rapid growth of IV. An ensemble of 21 simulations, differing only in their initial conditions, was run over North America using version 5 of the Canadian RCM (CRCM). The IV is quantified in terms of energy of CRCM perturbations with respect to a reference simulation. The working hypothesis is that IV is arising through rapidly growing perturbations developed in dynamically unstable regions. If indeed IV is triggered by the growth of unstable perturbations, a large proportion of the CRCM perturbations must project onto the most unstable singular vectors (SVs). A set of ten SVs was computed to identify the orthogonal set of perturbations that provide the maximum growth with respect to the dry total-energy norm during the course of the CRCM ensemble of simulations. CRCM perturbations were then projected onto the subspace of SVs. The analysis of one episode of rapid growth of IV is presented in detail. It is shown that a large part of the IV growth is explained by initially small-amplitude unstable perturbations represented by the ten leading SVs, the SV subspace accounting for over 70% of the CRCM IV growth in 36 h. The projection on the leading SV at final time is greater than the projection on the remaining SVs and there is a high similarity between the CRCM perturbations and the leading SV after 24-36 h tangent-linear model integration. The vertical structure of perturbations revealed that the baroclinic conversion is the dominant process in IV growth for this particular episode. (orig.)

  19. Mechanistic variables can enhance predictive models of endotherm distributions: The American pika under current, past, and future climates

    Science.gov (United States)

    Mathewson, Paul; Moyer-Horner, Lucas; Beever, Erik; Briscoe, Natalie; Kearney, Michael T; Yahn, Jeremiah; Porter, Warren P.

    2016-01-01

    How climate constrains species’ distributions through time and space is an important question in the context of conservation planning for climate change. Despite increasing awareness of the need to incorporate mechanism into species distribution models (SDMs), mechanistic modeling of endotherm distributions remains limited in this literature. Using the American pika (Ochotona princeps) as an example, we present a framework whereby mechanism can be incorporated into endotherm SDMs. Pika distribution has repeatedly been found to be constrained by warm temperatures, so we used Niche Mapper, a mechanistic heat-balance model, to convert macroclimate data to pika-specific surface activity time in summer across the western United States. We then explored the difference between using a macroclimate predictor (summer temperature) and using a mechanistic predictor (predicted surface activity time) in SDMs. Both approaches accurately predicted pika presences in current and past climate regimes. However, the activity models predicted 8–19% less habitat loss in response to annual temperature increases of ~3–5 °C predicted in the region by 2070, suggesting that pikas may be able to buffer some climate change effects through behavioral thermoregulation that can be captured by mechanistic modeling. Incorporating mechanism added value to the modeling by providing increased confidence in areas where different modeling approaches agreed and providing a range of outcomes in areas of disagreement. It also provided a more proximate variable relating animal distribution to climate, allowing investigations into how unique habitat characteristics and intraspecific phenotypic variation may allow pikas to exist in areas outside those predicted by generic SDMs. Only a small number of easily obtainable data are required to parameterize this mechanistic model for any endotherm, and its use can improve SDM predictions by explicitly modeling a widely applicable direct physiological effect

  20. Modelling regional variability of irrigation requirements due to climate change in Northern Germany.

    Science.gov (United States)

    Riediger, Jan; Breckling, Broder; Svoboda, Nikolai; Schröder, Winfried

    2016-01-15

    The question whether global climate change invalidates the efficiency of established land use practice cannot be answered without systemic considerations on a region specific basis. In this context plant water availability and irrigation requirements, respectively, were investigated in Northern Germany. The regions under investigation--Diepholz, Uelzen, Fläming and Oder-Spree--represent a climatic gradient with increasing continentality from West to East. Besides regional climatic variation and climate change, soil conditions and crop management differ on the regional scale. In the model regions, temporal seasonal droughts influence crop success already today, but on different levels of intensity depending mainly on climate conditions. By linking soil water holding capacities, crop management data and calculations of evapotranspiration and precipitation from the climate change scenario RCP 8.5 irrigation requirements for maintaining crop productivity were estimated for the years 1991 to 2070. Results suggest that water requirement for crop irrigation is likely to increase with considerable regional variation. For some of the regions, irrigation requirements might increase to such an extent that the established regional agricultural practice might be hard to retain. Where water availability is limited, agricultural practice, like management and cultivated crop spectrum, has to be changed to deal with the new challenges.

  1. Processes Understanding of Decadal Climate Variability

    Science.gov (United States)

    Prömmel, Kerstin; Cubasch, Ulrich

    2016-04-01

    The realistic representation of decadal climate variability in the models is essential for the quality of decadal climate predictions. Therefore, the understanding of those processes leading to decadal climate variability needs to be improved. Several of these processes are already included in climate models but their importance has not yet completely been clarified. The simulation of other processes requires sometimes a higher resolution of the model or an extension by additional subsystems. This is addressed within one module of the German research program "MiKlip II - Decadal Climate Predictions" (http://www.fona-miklip.de/en/) with a focus on the following processes. Stratospheric processes and their impact on the troposphere are analysed regarding the climate response to aerosol perturbations caused by volcanic eruptions and the stratospheric decadal variability due to solar forcing, climate change and ozone recovery. To account for the interaction between changing ozone concentrations and climate a computationally efficient ozone chemistry module is developed and implemented in the MiKlip prediction system. The ocean variability and air-sea interaction are analysed with a special focus on the reduction of the North Atlantic cold bias. In addition, the predictability of the oceanic carbon uptake with a special emphasis on the underlying mechanism is investigated. This addresses a combination of physical, biological and chemical processes.

  2. High Resolution Simulations of Future Climate in West Africa Using a Variable-Resolution Atmospheric Model

    Science.gov (United States)

    Adegoke, J. O.; Engelbrecht, F.; Vezhapparambu, S.

    2013-12-01

    In previous work demonstrated the application of a var¬iable-resolution global atmospheric model, the conformal-cubic atmospheric model (CCAM), across a wide range of spatial and time scales to investigate the ability of the model to provide realistic simulations of present-day climate and plausible projections of future climate change over sub-Saharan Africa. By applying the model in stretched-grid mode the versatility of the model dynamics, numerical formulation and physical parameterizations to function across a range of length scales over the region of interest, was also explored. We primarily used CCAM to illustrate the capability of the model to function as a flexible downscaling tool at the climate-change time scale. Here we report on additional long term climate projection studies performed by downscaling at much higher resolutions (8 Km) over an area that stretches from just south of Sahara desert to the southern coast of the Niger Delta and into the Gulf of Guinea. To perform these simulations, CCAM was provided with synoptic-scale forcing of atmospheric circulation from 2.5 deg resolution NCEP reanalysis at 6-hourly interval and SSTs from NCEP reanalysis data uses as lower boundary forcing. CCAM 60 Km resolution downscaled to 8 Km (Schmidt factor 24.75) then 8 Km resolution simulation downscaled to 1 Km (Schmidt factor 200) over an area approximately 50 Km x 50 Km in the southern Lake Chad Basin (LCB). Our intent in conducting these high resolution model runs was to obtain a deeper understanding of linkages between the projected future climate and the hydrological processes that control the surface water regime in this part of sub-Saharan Africa.

  3. NPOESS, Essential Climates Variables and Climate Change

    Science.gov (United States)

    Forsythe-Newell, S. P.; Bates, J. J.; Barkstrom, B. R.; Privette, J. L.; Kearns, E. J.

    2008-12-01

    Advancement in understanding, predicting and mitigating against climate change implies collaboration, close monitoring of Essential Climate Variable (ECV)s through development of Climate Data Record (CDR)s and effective action with specific thematic focus on human and environmental impacts. Towards this end, NCDC's Scientific Data Stewardship (SDS) Program Office developed Climate Long-term Information and Observation system (CLIO) for satellite data identification, characterization and use interrogation. This "proof-of-concept" online tool provides the ability to visualize global CDR information gaps and overlaps with options to temporally zoom-in from satellite instruments to climate products, data sets, data set versions and files. CLIO provides an intuitive one-stop web site that displays past, current and planned launches of environmental satellites in conjunction with associated imagery and detailed information. This tool is also capable of accepting and displaying Web-based input from Subject Matter Expert (SME)s providing a global to sub-regional scale perspective of all ECV's and their impacts upon climate studies. SME's can access and interact with temporal data from the past and present, or for future planning of products, datasets/dataset versions, instruments, platforms and networks. CLIO offers quantifiable prioritization of ECV/CDR impacts that effectively deal with climate change issues, their associated impacts upon climate, and this offers an intuitively objective collaboration and consensus building tool. NCDC's latest tool empowers decision makers and the scientific community to rapidly identify weaknesses and strengths in climate change monitoring strategies and significantly enhances climate change collaboration and awareness.

  4. Simulation of the Intraseasonal Variability over the Eastern Pacific ITCZ in Climate Models

    Science.gov (United States)

    Jiang, Xianan; Waliser, Duane E.; Kim, Daehyun; Zhao, Ming; Sperber, Kenneth R.; Stern, W. F.; Schubert, Siegfried D.; Zhang, Guang J.; Wang, Wanqiu; Khairoutdinov, Marat; Neale, Richard B.; Lee, Myong-In

    2012-01-01

    During boreal summer, convective activity over the eastern Pacific (EPAC) inter-tropical convergence zone (ITCZ) exhibits vigorous intraseasonal variability (ISV). Previous observational studies identified two dominant ISV modes over the EPAC, i.e., a 40-day mode and a quasi-biweekly mode (QBM). The 40-day ISV mode is generally considered a local expression of the Madden-Julian Oscillation. However, in addition to the eastward propagation, northward propagation of the 40-day mode is also evident. The QBM mode bears a smaller spatial scale than the 40-day mode, and is largely characterized by northward propagation. While the ISV over the EPAC exerts significant influences on regional climate/weather systems, investigation of contemporary model capabilities in representing these ISV modes over the EPAC is limited. In this study, the model fidelity in representing these two dominant ISV modes over the EPAC is assessed by analyzing six atmospheric and three coupled general circulation models (GCMs), including one super-parameterized GCM (SPCAM) and one recently developed high-resolution GCM (GFDL HIRAM) with horizontal resolution of about 50 km. While it remains challenging for GCMs to faithfully represent these two ISV modes including their amplitude, evolution patterns, and periodicities, encouraging simulations are also noted. In general, SPCAM and HIRAM exhibit relatively superior skill in representing the two ISV modes over the EPAC. While the advantage of SPCAM is achieved through explicit representation of the cumulus process by the embedded 2-D cloud resolving models, the improved representation in HIRAM could be ascribed to the employment of a strongly entraining plume cumulus scheme, which inhibits the deep convection, and thus effectively enhances the stratiform rainfall. The sensitivity tests based on HIRAM also suggest that fine horizontal resolution could also be conducive to realistically capture the ISV over the EPAC, particularly for the QBM mode

  5. Simulation of the intraseasonal variability over the Eastern Pacific ITCZ in climate models

    Energy Technology Data Exchange (ETDEWEB)

    Jiang, Xianan [Univ. of California, Los Angeles, CA (United States); Waliser, Duane E. [California Inst. of Technology (CalTech), La Canada Flintridge, CA (United States). Jet Propulsion Lab.; Kim, Daehyun [Columbia Univ., New York, NY (United States); Zhao, Ming [Princeton Univ., NJ (United States); Sperber, Kenneth R. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Stern, William F. [Princeton Univ., NJ (United States); Schubert, Siegfried D. [NASA Goddard Space Flight Center (GSFC), Greenbelt, MD (United States); Zhang, Guang J. [Scripps Institute of Oceanography. La Jolla, California (United States); Wang, Wanqiu [National Oceanic and Atmospheric Administration (NOAA), National Centers for Environmental Protection. Camp Springs, MD (United States); Khairoutdinov, Marat [Institute for Terrestrial and Planetary Atmospheres. Stony Brook Univ., NY (United States); Neale, Richard B. [National Center for Atmospheric Research. Boulder, CO (United States); Lee, Myong-In [Ulsan National Institute for Science and Technology. Seoul (Korea)

    2012-08-01

    During boreal summer, convective activity over the eastern Pacific (EPAC) inter-tropical convergence zone (ITCZ) exhibits vigorous intraseasonal variability (ISV). Previous observational studies identified two dominant ISV modes over the EPAC, i.e., a 40-day mode and a quasi-biweekly mode (QBM). The 40-day ISV mode is generally considered a local expression of the Madden-Julian Oscillation. However, in addition to the eastward propagation, northward propagation of the 40-day mode is also evident. The QBM mode bears a smaller spatial scale than the 40-day mode, and is largely characterized by northward propagation. While the ISV over the EPAC exerts significant influences on regional climate/weather systems, investigation of contemporary model capabilities in representing these ISV modes over the EPAC is limited. In this study, the model fidelity in representing these two dominant ISV modes over the EPAC is assessed by analyzing six atmospheric and three coupled general circulation models (GCMs), including one super-parameterized GCM (SPCAM) and one recently developed high-resolution GCM (GFDL HIRAM) with horizontal resolution of about 50 km. While it remains challenging for GCMs to faithfully represent these two ISV modes including their amplitude, evolution patterns, and periodicities, encouraging simulations are also noted. In general, SPCAM and HIRAM exhibit relatively superior skill in representing the two ISV modes over the EPAC. While the advantage of SPCAM is achieved through explicit representation of the cumulus process by the embedded 2-D cloud resolving models, the improved representation in HIRAM could be ascribed to the employment of a strongly entraining plume cumulus scheme, which inhibits the deep convection, and thus effectively enhances the stratiform rainfall. The sensitivity tests based on HIRAM also suggest that fine horizontal resolution could also be conducive to realistically capture the ISV over the EPAC, particularly for the QBM mode

  6. Variability of 500-mb geopotential heights in a general circulation model and the projection of regional greenhouse effect climate change

    International Nuclear Information System (INIS)

    Many researchers have utilized general circulation models (GCMs) in establishing climate change scenarios for specific regions or locations, despite the mismatch of spatial scales involved. A major underlying assumption involved in utilizing model output in this manner is that the GCM contains mid-tropospheric dynamics that are internally consistent with those of the real climate system. The main purpose of this study is examine the forms and processes of mid-tropospheric variability in the Goddard Institute for Space Studies (GISS) GCM, with the hope of shedding light on this model-analog strategy. The response of mean 500 mb and surface air temperature fields in the GISS GCM to a doubling of CO2 indicates a substantial relationship between the two. Unfortunately, the GISS GCM demonstrates systematic flaws in its simulation of mid-tropospheric dynamics. These are revealed in an examination of high-frequency and low-frequency 500-mb teleconnections in the model. The shapes and amplitudes of known teleconnection patterns are not well simulated. This is likely due to the weak stationary wave structure found in the control run of the model. More importantly, several model teleconnections appear to coincide geographically with the patterns of mean climate change. This may indicate a direct relationship between the modeled mid-tropospheric dynamics and the spatial patterns of mean climate change. This finding has two important implications. First, it is necessary to further study the influence of GCM mid-tropospheric dynamics on the spatial distribution of climate changes being modeled. Second, and more fundamentally, spatially specific climate system feedbacks may be substantially affected by variations in teleconnection strength and frequency, potentially impacting the global climate far beyond the regional scale

  7. Studies of regional-scale climate variability and change. Hidden Markov models and coupled ocean-atmosphere modes

    Energy Technology Data Exchange (ETDEWEB)

    Ghil, M. [Univ. of California, Los Angeles, CA (United States); Kravtsov, S. [Univ. of Wisconsin, Madison, WI (United States); Robertson, A. W. [IRI, Palisades, NY (United States); Smyth, P. [Univ. of California, Irvine, CA (United States)

    2008-10-14

    This project was a continuation of previous work under DOE CCPP funding, in which we had developed a twin approach of probabilistic network (PN) models (sometimes called dynamic Bayesian networks) and intermediate-complexity coupled ocean-atmosphere models (ICMs) to identify the predictable modes of climate variability and to investigate their impacts on the regional scale. We had developed a family of PNs (similar to Hidden Markov Models) to simulate historical records of daily rainfall, and used them to downscale GCM seasonal predictions. Using an idealized atmospheric model, we had established a novel mechanism through which ocean-induced sea-surface temperature (SST) anomalies might influence large-scale atmospheric circulation patterns on interannual and longer time scales; we had found similar patterns in a hybrid coupled ocean-atmosphere-sea-ice model. The goal of the this continuation project was to build on these ICM results and PN model development to address prediction of rainfall and temperature statistics at the local scale, associated with global climate variability and change, and to investigate the impact of the latter on coupled ocean-atmosphere modes. Our main results from the grant consist of extensive further development of the hidden Markov models for rainfall simulation and downscaling together with the development of associated software; new intermediate coupled models; a new methodology of inverse modeling for linking ICMs with observations and GCM results; and, observational studies of decadal and multi-decadal natural climate results, informed by ICM results.

  8. The amplitude of decadal to multidecadal variability in precipitation simulated by state-of-the-art climate models

    Science.gov (United States)

    Ault, T. R.; Cole, J. E.; St. George, S.

    2012-11-01

    We assess the magnitude of decadal to multidecadal (D2M) variability in Climate Model Intercomparison Project 5 (CMIP5) simulations that will be used to understand, and plan for, climate change as part of the Intergovernmental Panel on Climate Change's 5th Assessment Report. Model performance on D2M timescales is evaluated using metrics designed to characterize the relative and absolute magnitude of variability at these frequencies. In observational data, we find that between 10% and 35% of the total variance occurs on D2M timescales. Regions characterized by the high end of this range include Africa, Australia, western North America, and the Amazon region of South America. In these areas D2M fluctuations are especially prominent and linked to prolonged drought. D2M fluctuations account for considerably less of the total variance (between 5% and 15%) in the CMIP5 archive of historical (1850-2005) simulations. The discrepancy between observation and model based estimates of D2M prominence reflects two features of the CMIP5 archive. First, interannual components of variability are generally too energetic. Second, decadal components are too weak in several key regions. Our findings imply that projections of the future lack sufficient decadal variability, presenting a limited view of prolonged drought and pluvial risk.

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

    Directory of Open Access Journals (Sweden)

    Ravi Chandra Pavan Kumar Srimath-Tirumula-Peddinti

    Full Text Available 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

  10. Mexican drought: an observational modeling and tree ring study of variability and climate change

    Energy Technology Data Exchange (ETDEWEB)

    Seager, R.; Ting, M. [Lamont-Doherty Earth Observatory of Columbia University, Palisades, NY (United States)]. E-mail: seager@ldeo.columbia.edu; Davis, M. [Department of History, University of California at Irvine, CA (United States); Cane, M.; Naik, N.; Nakamura, J.; Li, C.; Cook, E. [Lamont-Doherty Earth Observatory of Columbia University, Palisades, NY (United States); Stahle, D.W. [Tree Ring Laboratory, University of Arkansas, Fayetteville, Arkansas (United States)

    2009-01-15

    Variability of Mexican hydroclimate, with special attention to persistent drought, is examined using observations, model simulations forced by historical sea surface temperature (SST), tree ring reconstructions of past climate and model simulations and projections of naturally and anthropogenically forced climate change. During the winter half year, hydroclimate across Mexico is influenced by the state of the tropical Pacific Ocean with the Atlantic playing little role. Mexican winters tend to be wetter during El Nino conditions. In the summer half year northern Mexico is also wetter when El Nino conditions prevail, but southern Mexico is drier. A warm tropical North Atlantic Ocean makes northern Mexico dry and southern Mexico wet. These relationships are reasonably well reproduced in ensembles of atmosphere model simulations forced by historical SST for the period from 1856 to 2002. Large ensembles of 100 day long integrations are used to examine the day to day evolution of the atmospheric circulation and precipitation in response to a sudden imposition of a El Nino SST anomaly in the summer half year. Kelvin waves propagate east and immediately cause increased column-integrated moisture divergence and reduced precipitation over the tropical Americas and Intra-America Seas. Within a few days a low level high pressure anomaly develops over the Gulf of Mexico. A forced nonlinear model is used to demonstrate that this low is forced by the reduced atmospheric heating over the tropical Atlantic-Intra-America Seas area. Tree ring reconstructions that extend back before the period of instrumental precipitation data coverage are used to verify long model simulations forced by historical SST. The early to mid 1950s drought in northern Mexico appears to have been the most severe since the mid nineteenth century and likely arose as a response to both a multiyear La Nina and a warm tropical North Atlantic. A drought in the 1890s was also severe and appears driven by a

  11. Validation of EURO-CORDEX regional climate models in reproducing the variability of precipitation extremes in Romania

    Science.gov (United States)

    Dumitrescu, Alexandru; Busuioc, Aristita

    2016-04-01

    EURO-CORDEX is the European branch of the international CORDEX initiative that aims to provide improved regional climate change projections for Europe. The main objective of this paper is to document the performance of the individual models in reproducing the variability of precipitation extremes in Romania. Here three EURO-CORDEX regional climate models (RCMs) ensemble (scenario RCP4.5) are analysed and inter-compared: DMI-HIRHAM5, KNMI-RACMO2.2 and MPI-REMO. Compared to previous studies, when the RCM validation regarding the Romanian climate has mainly been made on mean state and at station scale, a more quantitative approach of precipitation extremes is proposed. In this respect, to have a more reliable comparison with observation, a high resolution daily precipitation gridded data set was used as observational reference (CLIMHYDEX project). The comparison between the RCM outputs and observed grid point values has been made by calculating three extremes precipitation indices, recommended by the Expert Team on Climate Change Detection Indices (ETCCDI), for the 1976-2005 period: R10MM, annual count of days when precipitation ≥10mm; RX5DAY, annual maximum 5-day precipitation and R95P%, precipitation fraction of annual total precipitation due to daily precipitation > 95th percentile. The RCMs capability to reproduce the mean state for these variables, as well as the main modes of their spatial variability (given by the first three EOF patterns), are analysed. The investigation confirms the ability of RCMs to simulate the main features of the precipitation extreme variability over Romania, but some deficiencies in reproducing of their regional characteristics were found (for example, overestimation of the mea state, especially over the extra Carpathian regions). This work has been realised within the research project "Changes in climate extremes and associated impact in hydrological events in Romania" (CLIMHYDEX), code PN II-ID-2011-2-0073, financed by the Romanian

  12. GEOLAND2 global LAI, FAPAR Essential Climate Variables for terrestrial carbon modeling: principles and validation

    Science.gov (United States)

    Baret, F.; Weiss, M.; Lacaze, R.; Camacho, F.; Smets, B.; Pacholczyk, P.; Makhmara, H.

    2010-12-01

    LAI and fAPAR are recognized as Essential Climate Variables providing key information for the understanding and modeling of canopy functioning. Global remote sensing observations at medium resolution are routinely acquired since the 80’s mainly with AVHRR, SEAWIFS, VEGETATION, MODIS and MERIS sensors. Several operational products have been derived and provide global maps of LAI and fAPAR at daily to monthly time steps. Inter-comparison between MODIS, CYCLOPES, GLOBCARBON and JRC-FAPAR products showed generally consistent seasonality, while large differences in magnitude and smoothness may be observed. One of the objectives of the GEOLAND2 European project is to develop such core products to be used in a range of application services including the carbon monitoring. Rather than generating an additional product from scratch, the version 1 of GEOLAND2 products was capitalizing on the existing products by combining them to retain their pros and limit their cons. For these reasons, MODIS and CYCLOPES products were selected since they both include LAI and fAPAR while having relatively close temporal sampling intervals (8 to 10 days). GLOBCARBON products were not used here because of the too long monthly time step inducing large uncertainties in the seasonality description. JRC-FAPAR was not selected as well to preserve better consistency between LAI and fAPAR products. MODIS and CYCLOPES products were then linearly combined to take advantage of the good performances of CYCLOPES products for low to medium values of LAI and fAPAR while benefiting from the better MODIS performances for the highest LAI values. A training database representative of the global variability of vegetation type and conditions was thus built. A back-propagation neural network was then calibrated to estimate the new LAI and fAPAR products from VEGETATION preprocessed observations. Similarly, the vegetation cover fraction (fCover) was also derived by scaling the original CYCLOPES fCover products

  13. Climate variability and predictability associated with the Indo-Pacific Oceanic Channel Dynamics in the CCSM4 Coupled System Model

    Science.gov (United States)

    Yuan, Dongliang; Xu, Peng; Xu, Tengfei

    2016-03-01

    An experiment using the Community Climate System Model (CCSM4), a participant of the Coupled Model Intercomparison Project phase-5 (CMIP5), is analyzed to assess the skills of this model in simulating and predicting the climate variabilities associated with the oceanic channel dynamics across the Indo-Pacific Oceans. The results of these analyses suggest that the model is able to reproduce the observed lag correlation between the oceanic anomalies in the southeastern tropical Indian Ocean and those in the cold tongue in the eastern equatorial Pacific Ocean at a time lag of 1 year. This success may be largely attributed to the successful simulation of the interannual variations of the Indonesian Throughflow, which carries the anomalies of the Indian Ocean Dipole (IOD) into the western equatorial Pacific Ocean to produce subsurface temperature anomalies, which in turn propagate to the eastern equatorial Pacific to generate ENSO. This connection is termed the "oceanic channel dynamics" and is shown to be consistent with the observational analyses. However, the model simulates a weaker connection between the IOD and the interannual variability of the Indonesian Throughflow transport than found in the observations. In addition, the model overestimates the westerly wind anomalies in the western-central equatorial Pacific in the year following the IOD, which forces unrealistic upwelling Rossby waves in the western equatorial Pacific and downwelling Kelvin waves in the east. This assessment suggests that the CCSM4 coupled climate system has underestimated the oceanic channel dynamics and overestimated the atmospheric bridge processes.

  14. Modeling Low-Flow Sensitivity to Climate Variability and Forest Harvesting in the Willamette Basin: A Multi-scale Approach.

    Science.gov (United States)

    Choate, J.; Tague, C.; Grant, G.

    2002-12-01

    In the mountainous region of the Pacific Northwest, underlying geologic and vegetation patterns, forest management practices and climate regimes at different elevations mediate the response of low flows occurring in late summer. Low-stream flow conditions, occurring during the warm, dry summers are critical to river ecosystem function and crucial to many aquatic and riparian species life cycles as well as human uses of streams. Understanding the different controls on low flow variability in this region requires a multi-scale perspective. This particular study is part of a larger strategy designed to use both empirical analysis and physically based, hydro-ecological modeling to disentangle the role that climate, geology and forest harvesting play in controlling low flows in 1st to 5th order watersheds within the Willamette basin. Our empirical analysis of summer low flow for a range of streams has shown that summer, unit-area discharge volumes are significantly lower for streams in the geologically distinct and low elevation Western Cascade versus High Cascade areas. This empirical analysis outlines large-scale regional variability. To assess and compare this with smaller scale variability, we use the RHESSys model (Regional Hydro-Ecologic Simulation System) to assess low flow behavior for small 1st order streams within the Western Cascade region. The goal is to examine low flow variability due to both climate and forest harvesting and recovery and place this in the context of regional scale analysis. We use multiple simulations to predict low flow volumes under cut and uncut conditions for wet/dry and warm/cool climate scenarios. Future work will replicate this study to examine 1st order watershed sensitivity within the contrasting High Cascade geologic region. The combined multi-scale empirical and modeling approach will then be used to provide a more comprehensive assessment of low flow patterns and sensitivity within this region.

  15. The Importance of Considering the Temporal Distribution of Climate Variables for Ecological-Economic Modeling to Calculate the Consequences of Climate Change for Agriculture

    Science.gov (United States)

    Plegnière, Sabrina; Casper, Markus; Hecker, Benjamin; Müller-Fürstenberger, Georg

    2014-05-01

    The basis of many models to calculate and assess climate change and its consequences are annual means of temperature and precipitation. This method leads to many uncertainties especially at the regional or local level: the results are not realistic or too coarse. Particularly in agriculture, single events and the distribution of precipitation and temperature during the growing season have enormous influences on plant growth. Therefore, the temporal distribution of climate variables should not be ignored. To reach this goal, a high-resolution ecological-economic model was developed which combines a complex plant growth model (STICS) and an economic model. In this context, input data of the plant growth model are daily climate values for a specific climate station calculated by the statistical climate model (WETTREG). The economic model is deduced from the results of the plant growth model STICS. The chosen plant is corn because corn is often cultivated and used in many different ways. First of all, a sensitivity analysis showed that the plant growth model STICS is suitable to calculate the influences of different cultivation methods and climate on plant growth or yield as well as on soil fertility, e.g. by nitrate leaching, in a realistic way. Additional simulations helped to assess a production function that is the key element of the economic model. Thereby the problems when using mean values of temperature and precipitation in order to compute a production function by linear regression are pointed out. Several examples show why a linear regression to assess a production function based on mean climate values or smoothed natural distribution leads to imperfect results and why it is not possible to deduce a unique climate factor in the production function. One solution for this problem is the additional consideration of stress indices that show the impairment of plants by water or nitrate shortage. Thus, the resulting model takes into account not only the ecological

  16. Solar variability, weather, and climate

    Science.gov (United States)

    1982-01-01

    Advances in the understanding of possible effects of solar variations on weather and climate are most likely to emerge by addressing the subject in terms of fundamental physical principles of atmospheric sciences and solar-terrestrial physis. The limits of variability of solar inputs to the atmosphere and the depth in the atmosphere to which these variations have significant effects are determined.

  17. Natural variability of CO2 and O2 fluxes: What can we learn from centuries-long climate models simulations?

    Science.gov (United States)

    Resplandy, L.; Séférian, R.; Bopp, L.

    2015-01-01

    carbon uptake and oxygen content estimates over the past decades suggest that the anthropogenic carbon sink has changed and that the oxygen concentration in the ocean interior has decreased. Although these detected changes appear consistent with those expected from anthropogenic forced climate change, large uncertainties remain in the contribution of natural variability. Using century-long simulations (500-1000 years) of unforced natural variability from six Earth System Models (ESMs), we examine the internally driven natural variability of carbon and oxygen fluxes from interannual to multidecadal time scales. The intensity of natural variability differs between the ESMs, in particular, decadal variability locally accounts for 10-50% of the total variance. Although the variability is higher in all regions with strong climate modes (North Atlantic, North Pacific, etc.), we find that only the Southern Ocean and the tropical Pacific significantly modulate the global fluxes. On (multi)decadal time scales, deep convective events along the Antarctic shelf drive the global fluxes variability by transporting deep carbon-rich/oxygen-depleted waters to the surface and by reducing the sea-ice coverage. On interannual time scales, the global flux is modulated by (1) variations of the upwelling of circumpolar deep waters associated with the southern annular mode in the subpolar Southern Ocean and (2) variations of the equatorial/costal upwelling combined with changes in the solubility-driven fluxes in response to El Niño Southern Oscillation (ENSO) in the tropical Pacific. We discuss the challenges of measuring and detecting long-term trends from a few decade-long records influenced by internal variability.

  18. Generalised Linear Spatial Model for Tree Species Richness in Eastern US Forest using FIA plot data and Climate variables.

    Science.gov (United States)

    Kwon, Y.

    2015-12-01

    Large-scale patterns of woody plant species diversity have long been studied yet it is still one of the most controversial issues in biogeography. At continental to global scale, energy availability measured by potential evapotranspiration (PET) (i.e. PET-only model) and related water-energy dynamics model (i.e. Wang's model based on China's woody plant richness) has been two primary determinants for species richness. We identified several issues in existing modeling approaches that 1) species richness are derived from species range map not a plot data, 2) they over-predicted richness in Florida peninsular at the cost of R square values for better overall model fit and 3) they lack thorough examination for spatial autocorrelation of residuals. The plot-level forest inventory and analysis (FIA) program data set (total 2,745,363 tally trees from 79,145 ground plots in the eastern US forest) used for species richness showed different pattern to range-map based richness. We applied Elastic-Net regularization for variable selections then used spatial Poisson Generalized Linear Model (GLM) and to handle spatial autocorrelations. Elastic-Net approach produced Frost frequency days (FRS), PET, AET, and seasonality of precipitation (PSN, defined as the coefficient of variation of monthly mean precipitation) as best explanatory variables and produced good model fit (R2 of 0.67) without over-prediction for Florida peninsular. Partial regression revealed that PSN successfully accounted for very low species richness in Florida. The seasonality of precipitation as climatic variability explained climatic stability permitted species specialization than greater seasonality. Also, we compared our best model with two other richness models (i.e. PET-only and Wang's model) and demonstrated that spatial autocorrelation was highest for the use of just PET-only, intermediate for Wang's model, and lowest for ours.

  19. The Arctic Sea ice in the CMIP3 climate model ensemble – variability and anthropogenic change

    Directory of Open Access Journals (Sweden)

    L. K. Behrens

    2012-12-01

    Full Text Available The strongest manifestation of global warming is observed in the Arctic. The warming in the Arctic during the recent decades is about twice as strong as in the global average and has been accompanied by a summer sea ice decline that is very likely unprecedented during the last millennium. Here, Arctic sea ice variability is analyzed in the ensemble of CMIP3 models. Complementary to several previous studies, we focus on regional aspects, in particular on the Barents Sea. We also investigate the changes in the seasonal cycle and interannual variability. In all regions, the models predict a reduction in sea ice area and sea ice volume during 1900–2100. Toward the end of the 21st century, the models simulate higher sea ice area variability in September than in March, whereas the variability in the preindustrial control runs is higher in March. Furthermore, the amplitude and phase of the sea ice seasonal cycle change in response to enhanced greenhouse warming. The amplitude of the sea ice area seasonal cycle increases due to the very strong sea ice area decline in September. The seasonal cycle amplitude of the sea ice volume decreases due to the stronger reduction of sea ice volume in March.

    Multi-model mean estimates for the late 20th century are comparable with observational data only for the entire Arctic and the Central Arctic. In the Barents Sea, differences between the multi-model mean and the observational data are more pronounced. Regional sea ice sensitivity to Northern Hemisphere average surface warming has been investigated.

  20. Climate data, analysis and models for the study of natural variability and anthropogenic change

    Energy Technology Data Exchange (ETDEWEB)

    Jones, Philip D. [Univ. of East Anglia, Norwich (United Kingdom)

    2014-07-31

    Gridded Temperature Under prior/current support, we completed and published (Jones et al., 2012) the fourth major update to our global land dataset of near-surface air temperatures, CRUTEM4. This is one of the most widely used records of the climate system, having been updated, maintained and further developed with DoE support since the 1980s. We have continued to update the CRUTEM4 (Jones et al., 2012) database that is combined with marine data to produce HadCRUT4 (Morice et al., 2012). The emphasis in our use of station temperature data is to access as many land series that have been homogenized by National Meteorological Services (NMSs, including NCDC/NOAA, Asheville, NC). Unlike the three US groups monitoring surface temperatures in a similar way, we do not infill areas that have no or missing data. We can only infill such regions in CRUTEM4 by accessing more station temperature series. During early 2014, we have begun the extensive task of updating as many of these series as possible using data provided by some NMSs and also through a number of research projects and programs around the world. All the station data used in CRUTEM4 have been available since 2009, but in Osborn and Jones (2014) we have made this more usable using a Google Earth interface (http://www.cru.uea.ac.uk/cru/data/crutem/ge/ ). We have recently completed the update of our infilled land multi-variable dataset (CRU TS 3.10, Harris et al., 2014). This additionally produces complete land fields (except for the Antarctic) for temperature, precipitation, diurnal temperature range, vapour pressure and sunshine/cloud. Using this dataset we have calculated sc-PDSI (self-calibrating Palmer Drought Severity Index) data and compared with other PDSI datasets (Trenberth et al., 2014). Also using CRU TS 3.10 and Reanalysis datasets, we showed no overall increase in global temperature variability despite changing regional patterns (Huntingford et al., 2013). Harris et al. (2014) is an update of an earlier

  1. Variability in projected elevation dependent warming in boreal midlatitude winter in CMIP5 climate models and its potential drivers

    Science.gov (United States)

    Rangwala, Imtiaz; Sinsky, Eric; Miller, James R.

    2016-04-01

    The future rate of climate change in mountains has many potential human impacts, including those related to water resources, ecosystem services, and recreation. Analysis of the ensemble mean response of CMIP5 global climate models (GCMs) shows amplified warming in high elevation regions during the cold season in boreal midlatitudes. We examine how the twenty-first century elevation-dependent response in the daily minimum surface air temperature [d(ΔTmin)/dz] varies among 27 different GCMs during winter for the RCP 8.5 emissions scenario. The focus is on regions within the northern hemisphere mid-latitude band between 27.5°N and 40°N, which includes both the Rocky Mountains and the Tibetan Plateau/Himalayas. We find significant variability in d(ΔTmin)/dz among the individual models ranging from 0.16 °C/km (10th percentile) to 0.97 °C/km (90th percentile), although nearly all of the GCMs (24 out of 27) show a significant positive value for d(ΔTmin)/dz. To identify some of the important drivers associated with the variability in d(ΔTmin)/dz during winter, we evaluate the co-variance between d(ΔTmin)/dz and the differential response of elevation-based anomalies in different climate variables as well as the GCMs' spatial resolution, their global climate sensitivity, and their elevation-dependent free air temperature response. We find that d(ΔTmin)/dz has the strongest correlation with elevation-dependent increases in surface water vapor, followed by elevation-dependent decreases in surface albedo, and a weak positive correlation with the GCMs' free air temperature response.

  2. Direct and semi-direct aerosol radiative effect on the Mediterranean climate variability using a coupled regional climate system model

    Science.gov (United States)

    Nabat, Pierre; Somot, Samuel; Mallet, Marc; Sevault, Florence; Chiacchio, Marc; Wild, Martin

    2015-02-01

    A fully coupled regional climate system model (CNRM-RCSM4) has been used over the Mediterranean region to investigate the direct and semi-direct effects of aerosols, but also their role in the radiation-atmosphere-ocean interactions through multi-annual ensemble simulations (2003-2009) with and without aerosols and ocean-atmosphere coupling. Aerosols have been taken into account in CNRM-RCSM4 through realistic interannual monthly AOD climatologies. An evaluation of the model has been achieved, against various observations for meteorological parameters, and has shown the ability of CNRM-RCSM4 to reproduce the main patterns of the Mediterranean climate despite some biases in sea surface temperature (SST), radiation and cloud cover. The results concerning the aerosol radiative effects show a negative surface forcing on average because of the absorption and scattering of the incident radiation. The SW surface direct effect is on average -20.9 Wm-2 over the Mediterranean Sea, -14.7 Wm-2 over Europe and -19.7 Wm-2 over northern Africa. The LW surface direct effect is weaker as only dust aerosols contribute (+4.8 Wm-2 over northern Africa). This direct effect is partly counterbalanced by a positive semi-direct radiative effect over the Mediterranean Sea (+5.7 Wm-2 on average) and Europe (+5.0 Wm-2) due to changes in cloud cover and atmospheric circulation. The total aerosol effect is consequently negative at the surface and responsible for a decrease in land (on average -0.4 °C over Europe, and -0.5 °C over northern Africa) and sea surface temperature (on average -0.5 °C for the Mediterranean SST). In addition, the latent heat loss is shown to be weaker (-11.0 Wm-2) in the presence of aerosols, resulting in a decrease in specific humidity in the lower troposphere, and a reduction in cloud cover and precipitation. Simulations also indicate that dust aerosols warm the troposphere by absorbing solar radiation, and prevent radiation from reaching the surface, thus

  3. Hydrological Responses of Andean Lakes and Tropical Floodplains to Climate Variability and Human Intervention: an Integrative Modelling Framework

    Science.gov (United States)

    Hoyos, I. C.; González Morales, C.; Serna López, J. P.; Duque, C. L.; Canon Barriga, J. E.; Dominguez, F.

    2013-12-01

    Andean water bodies in tropical regions are significantly influenced by fluctuations associated with climatic and anthropogenic drivers, which implies long term changes in mountain snow peaks, land covers and ecosystems, among others. Our work aims at providing an integrative framework to realistically assess the possible future of natural water bodies with different degrees of human intervention. We are studying in particular the evolution of three water bodies in Colombia: two Andean lakes and a floodplain wetland. These natural reservoirs represent the accumulated effect of hydrological processes in their respective basins, which exhibit different patterns of climate variability and distinct human intervention and environmental histories. Modelling the hydrological responses of these local water bodies to climate variability and human intervention require an understanding of the strong linkage between geophysical and social factors. From the geophysical perspective, the challenge is how to downscale global climate projections in the local context: complex orography and relative lack of data. To overcome this challenge we combine the correlational and physically based analysis of several sources of spatially distributed biophysical and meteorological information to accurately determine aspects such as moisture sources and sinks and past, present and future local precipitation and temperature regimes. From the social perspective, the challenge is how to adequately represent and incorporate into the models the likely response of social agents whose water-related interests are diverse and usually conflictive. To deal with the complexity of these systems we develop interaction matrices, which are useful tools to holistically discuss and represent each environment as a complex system. Our goal is to assess partially the uncertainties of the hydrological balances in these intervened water bodies we establish climate/social scenarios, using hybrid models that combine

  4. Assessing the response of the Australian carbon balance to climate variability by assimilating satellite observations in a distributed ecosystem model

    Science.gov (United States)

    Exbrayat, Jean-François; Bloom, A. Anthony; Smallman, T. Luke; Williams, Mathew

    2016-04-01

    Terrestrial ecosystems offset about 25% of anthropogenic emissions of fossil fuel responsible for the current global warming. This long-term carbon sink exhibits a large inter-annual variability that recent studies have associated to the response of semi-arid ecosystems to variations in climate conditions and especially the occurrence of extreme events. For example, wet conditions during the 2010-2011 La Niña episode led to the strongest annual terrestrial carbon sink ever observed. Satellite observations of plant productivity and modelling experiments indicate that this anomalous sink was mostly located in the southern hemisphere where Australia experienced record-breaking rainfall. However, the durability of this extra-sink has yet to be assessed as dry conditions returned in northern Australia at the end of 2011, causing large-scale fires. In this paper we investigate the influence of climate variability on Australian ecosystems and we particularly focus on the resilience of the La Niña driven 2010-2011 sink to subsequent dry years. Therefore, we use the CARbon Data MOdel fraMework (CARDAMOM) data-assimilation system to retrieve the 21st century Australian terrestrial carbon cycle simulated by an ecosystem model in agreement with climate data and Earth Observations relevant to the biosphere: burned area, leaf area index and biomass. Accordingly with previous studies results indicate a strong influence of the El Niño/Southern Oscillation on the inter-annual variability of the Australian carbon balance at the continent-scale. More precisely, in 2010-2011 the La Niña-driven wet conditions led the continent to become a strong sink of atmospheric carbon. Then, dry conditions accompanied by intense fires returned at the end of 2011 and our analyses indicate that the totality of the northern Australian sink (north of 30°S) was re-emitted by late 2011 as fires immediately burnt the extra-fuel produced during the record wet seasons. These results raise concerns on

  5. CERES model application for increasing preparedness to climate variability in agricultural planning - calibration and validation test

    Science.gov (United States)

    Popova, Zornitsa; Kercheva, Milena

    The procedure of stepwise calibration and validation of CERES-maize and CERES-wheat models was used for models adjustment in two fields under contrastive soil conditions (Chromic Luvisol and Vertisol) in Sofia region. Both models reflected well the phenomenon of water/nitrogen extraction and retention in the root zone after the calibration. Adjusted CERES wheat was validated over a range of soils, varieties, climate, and management conditions and proved acceptable reliability of model predictions in most of the tested situations. The highest degrees of association ( R2 > 0.85%) was established between observed and simulated series of potentially extractable soil water (PESW) on fertilised Vertisol and Chromic Luvisol when the error (RMSE) ranged from 8.9 to13.3 mm in moderately wet, moderate and dry wheat vegetation seasons. Detailed observations of the vulnerable “Chromic Luvisol-maize” agroecosystem in lysimeters, including water and nitrogen fluxes at the bottom boundary, enabled to validate integrally the prediction capacity of calibrated CERES-maize model. Simulation outputs after calibration about PESW, water uptake, drainage, soil nitrogen storage, nitrogen leaching and crop dry weights, proved to be accurate enough over a comparatively long period (1.05.1997-1.10.1999). Graphical and statistical test of CERES-maize output characterised the performance of the model as acceptable over the specific conditions of validation lysimeter trial.

  6. Time series analysis of dengue incidence in Guadeloupe, French West Indies: Forecasting models using climate variables as predictors

    Directory of Open Access Journals (Sweden)

    Ruche Guy

    2011-06-01

    Full Text Available Abstract Background During the last decades, dengue viruses have spread throughout the Americas region, with an increase in the number of severe forms of dengue. The surveillance system in Guadeloupe (French West Indies is currently operational for the detection of early outbreaks of dengue. The goal of the study was to improve this surveillance system by assessing a modelling tool to predict the occurrence of dengue epidemics few months ahead and thus to help an efficient dengue control. Methods The Box-Jenkins approach allowed us to fit a Seasonal Autoregressive Integrated Moving Average (SARIMA model of dengue incidence from 2000 to 2006 using clinical suspected cases. Then, this model was used for calculating dengue incidence for the year 2007 compared with observed data, using three different approaches: 1 year-ahead, 3 months-ahead and 1 month-ahead. Finally, we assessed the impact of meteorological variables (rainfall, temperature and relative humidity on the prediction of dengue incidence and outbreaks, incorporating them in the model fitting the best. Results The 3 months-ahead approach was the most appropriate for an effective and operational public health response, and the most accurate (Root Mean Square Error, RMSE = 0.85. Relative humidity at lag-7 weeks, minimum temperature at lag-5 weeks and average temperature at lag-11 weeks were variables the most positively correlated to dengue incidence in Guadeloupe, meanwhile rainfall was not. The predictive power of SARIMA models was enhanced by the inclusion of climatic variables as external regressors to forecast the year 2007. Temperature significantly affected the model for better dengue incidence forecasting (p-value = 0.03 for minimum temperature lag-5, p-value = 0.02 for average temperature lag-11 but not humidity. Minimum temperature at lag-5 weeks was the best climatic variable for predicting dengue outbreaks (RMSE = 0.72. Conclusion Temperature improves dengue outbreaks forecasts

  7. Orbital modulation of millennial-scale climate variability in an earth system model of intermediate complexity

    Directory of Open Access Journals (Sweden)

    T. Friedrich

    2009-07-01

    Full Text Available The effect of orbital variations on simulated millennial-scale variability of the Atlantic Meridional Overturning Circulation (AMOC is studied using the earth system model of intermediate complexity LOVECLIM. It is found that for present-day topographic boundary conditions low obliquity values (~22.1° favor the triggering of internally generated millennial-scale variability in the North Atlantic region. Reducing the obliquity leads to changes of the pause-pulse ratio of the corresponding AMOC oscillations. Stochastic excitations of the density-driven overturning circulation in the Nordic Seas can create regional sea-ice anomalies and a subsequent reorganization of the atmospheric circulation. The resulting remote atmospheric anomalies over the Hudson Bay can release freshwater pulses into the Labrador Sea leading to a subsequent reduction of convective activity. The millennial-scale AMOC oscillations disappear if LGM bathymetry (with closed Hudson Bay is prescribed. Furthermore, our study documents the marine and terrestrial carbon cycle response to millennial-scale AMOC variability. Our model results support the notion that stadial regimes in the North Atlantic are accompanied by relatively high levels of oxygen in thermocline and intermediate waters off California – in agreement with paleo-proxy data.

  8. Uncertainty in Model Predictions of Vibrio Vulnificus Response to Climate Variability and Change: A Chesapeake Bay Case Study

    Science.gov (United States)

    Urquhart, Erin A.; Zaitchik, Benjamin F.; Waugh, Darryn W.; Guikema, Seth D.; Del Castillo, Carlos E.

    2014-01-01

    The effect that climate change and variability will have on waterborne bacteria is a topic of increasing concern for coastal ecosystems, including the Chesapeake Bay. Surface water temperature trends in the Bay indicate a warming pattern of roughly 0.3-0.4 C per decade over the past 30 years. It is unclear what impact future warming will have on pathogens currently found in the Bay, including Vibrio spp. Using historical environmental data, combined with three different statistical models of Vibrio vulnificus probability, we explore the relationship between environmental change and predicted Vibrio vulnificus presence in the upper Chesapeake Bay. We find that the predicted response of V. vulnificus probability to high temperatures in the Bay differs systematically between models of differing structure. As existing publicly available datasets are inadequate to determine which model structure is most appropriate, the impact of climatic change on the probability of V. vulnificus presence in the Chesapeake Bay remains uncertain. This result points to the challenge of characterizing climate sensitivity of ecological systems in which data are sparse and only statistical models of ecological sensitivity exist.

  9. Uncertainty in model predictions of Vibrio vulnificus response to climate variability and change: a Chesapeake Bay case study.

    Directory of Open Access Journals (Sweden)

    Erin A Urquhart

    Full Text Available The effect that climate change and variability will have on waterborne bacteria is a topic of increasing concern for coastal ecosystems, including the Chesapeake Bay. Surface water temperature trends in the Bay indicate a warming pattern of roughly 0.3-0.4°C per decade over the past 30 years. It is unclear what impact future warming will have on pathogens currently found in the Bay, including Vibrio spp. Using historical environmental data, combined with three different statistical models of Vibrio vulnificus probability, we explore the relationship between environmental change and predicted Vibrio vulnificus presence in the upper Chesapeake Bay. We find that the predicted response of V. vulnificus probability to high temperatures in the Bay differs systematically between models of differing structure. As existing publicly available datasets are inadequate to determine which model structure is most appropriate, the impact of climatic change on the probability of V. vulnificus presence in the Chesapeake Bay remains uncertain. This result points to the challenge of characterizing climate sensitivity of ecological systems in which data are sparse and only statistical models of ecological sensitivity exist.

  10. Abrupt millennial variability and interdecadal-interstadial oscillations in a global coupled model: sensitivity to the background climate state

    Energy Technology Data Exchange (ETDEWEB)

    Arzel, Olivier [The University of New South Wales, Climate Change Research Centre (CCRC), Sydney (Australia); Universite de Bretagne Occidentale, Laboratoire de Physique des Oceans (LPO), Brest (France); England, Matthew H. [The University of New South Wales, Climate Change Research Centre (CCRC), Sydney (Australia); Verdiere, Alain Colin de; Huck, Thierry [Universite de Bretagne Occidentale, Laboratoire de Physique des Oceans (LPO), Brest (France)

    2012-07-15

    The origin and bifurcation structure of abrupt millennial-scale climate transitions under steady external solar forcing and in the absence of atmospheric synoptic variability is studied by means of a global coupled model of intermediate complexity. We show that the origin of Dansgaard-Oeschger type oscillations in the model is caused by the weaker northward oceanic heat transport in the Atlantic basin. This is in agreement with previous studies realized with much simpler models, based on highly idealized geometries and simplified physics. The existence of abrupt millennial-scale climate transitions during glacial times can therefore be interpreted as a consequence of the weakening of the negative temperature-advection feedback. This is confirmed through a series of numerical experiments designed to explore the sensitivity of the bifurcation structure of the Atlantic meridional overturning circulation to increased atmospheric CO{sub 2} levels under glacial boundary conditions. Contrasting with the cold, stadial, phases of millennial oscillations, we also show the emergence of strong interdecadal variability in the North Atlantic sector during warm interstadials. The instability driving these interdecadal-interstadial oscillations is shown to be identical to that found in ocean-only models forced by fixed surface buoyancy fluxes, that is, a large-scale baroclinic instability developing in the vicinity of the western boundary current in the North Atlantic. Comparisons with modern observations further suggest a physical mechanism similar to that driving the 30-40 years time scale associated with the Atlantic multidecadal oscillation. (orig.)

  11. Arctic sea ice area in CMIP3 and CMIP5 climate model ensembles – variability and change

    OpenAIRE

    V. A. Semenov; Martin, T.; Behrens, L. K.; M Latif

    2015-01-01

    The shrinking Arctic sea ice cover observed during the last decades is probably the clearest manifestation of ongoing climate change. While climate models in general reproduce the sea ice retreat in the Arctic during the 20th century and simulate further sea ice area loss during the 21st century in response to anthropogenic forcing, the models suffer from large biases and the model results exhibit considerable spread. The last generation of climate models from World Climate Research ...

  12. How reliable are climate models?

    OpenAIRE

    Räisänen, Jouni

    2007-01-01

    How much can we trust model-based projections of future anthropogenic climate change? This review attempts to give an overview of this important but difficult topic by using three main lines of evidence: the skill of models in simulating present-day climate, intermodel agreement on future climate changes, and the ability of models to simulate climate changes that have already occurred. A comparison of simulated and observed present-day climates shows good agreement for many basic variables, p...

  13. Comparing the effect of modeled climatic variables on the distribution of African horse sickness in South Africa and Namibia.

    Science.gov (United States)

    Liebenberg, Danica; van Hamburg, Huib; Piketh, Stuart; Burger, Roelof

    2015-12-01

    Africa horse sickness (AHS) is a lethal disease of horses with a seasonal occurrence that is influenced by environmental conditions that favor the development of Culicoides midges (Diptera: Ceratopogonidae). This study compared and evaluated the relationship of various modeled climatic variables with the distribution and abundance of AHS in South Africa and Namibia. A comprehensive literature review of the historical AHS reported data collected from the Windhoek archives as well as annual reports from the Directorate of Veterinary services in Namibia were conducted. South African AHS reported data were collected from the South African Department of Agriculture, Forestry, and Fisheries. Daily climatic data were extracted for the time period 1993-2011 from the ERA-interim re-analysis dataset. The principal component analysis of the complete dataset indicated a significant statistical difference between Namibia and South Africa for the various climate variables and the outbreaks of AHS. The most influential parameters in the distribution of AHS included humidity, precipitation, evaporation, and minimum temperature. In South Africa, temperature had the most significant effect on the outbreaks of AHS, whereas in Namibia, humidity and precipitation were the main drivers. The maximum AHS cases in South Africa occurred at temperatures of 20-22° C and relative humidity between 50-70%. Furthermore, anthropogenic effects must be taken into account when trying to understand the distribution of AHS. PMID:26611969

  14. Diagnosing turnover times of carbon in terrestrial ecosystems to address global climate co-variability and for model evaluation

    Science.gov (United States)

    Carvalhais, Nuno; Thurner, Martin; Forkel, Matthias; Beer, Christian; Reichstein, Markus

    2016-04-01

    The response of the global terrestrial carbon cycle to climate change and the associated climate-carbon feedback has been shown to be highly uncertain. Ultimately this response depends on how carbon assimilation by vegetation changes relatively to the effective mean turnover time of carbon in vegetation and soils. Consequently, these turnover times of carbon are expected to depend on vegetation longevity and relative allocation to woody and non-woody biomass, and to litter and soil organic matter decomposition rates, which depend on climate variables, but also soil properties, biological activity and chemical composition of the litter. Data oriented estimates of whole ecosystem carbon turnover rates (τ) are based on global datasets of carbon stocks and fluxes and used to diagnose the co-variability of τ with climate. The overall mean global carbon turnover time estimated is 23 years (with 95% confidence intervals between 19 and 30 years), showing a strong spatial variability ranging from 15 years in equatorial regions to 255 years at latitudes north of 75°N. This latitudinal pattern reflects the expected dependencies of metabolic activity and ecosystem dynamics to temperature. However, a strong local correlation of τ with mean annual precipitation patterns is at least as prevalent as the expected effect of temperature on the global patterns of τ. The comparing between observation-based estimates of τ with current state-of-the-art Earth system models shows a consistent latitudinal pattern but a significant underestimation bias of ˜36% globally. Models consistently show a stronger association of τ to temperature and do not reproduce the observed association to mean annual precipitation in different latitudinal bands. A further breakdown of τ focusing on forest background mortality also shows contrasting regional patterns to those of global vegetation models, suggesting that the treatment of plant mortality may be overly simplistic in different model

  15. Simulation of the spatiotemporal variability of the World Ocean sea surface hight by the INM climate models

    Science.gov (United States)

    Iakovlev, N. G.; Volodin, E. M.; Gritsun, A. S.

    2016-07-01

    The results of simulations of the World Ocean sea surface hight (SSH) in by various versions of the Climate Model of the Institute of Numerical Mathematics, Russian Academy of Sciences, are compared with the CNES-CLS09 fields of the mean dynamic topography (deviation of the ocean level from the geoid). Three models with different ocean blocks are considered which slightly differ in numerical schemes and have various horizontal spatial resolution, i.e., the INMCM4 model, which participated in the Climate Model Intercomparison Project (CMIP Phase 5, resolution of 1° × 1/2°); the INMCM5 model, which participates in the next project, CMIP6 (resolution of 1/2° × 1/4°); and the advanced INMCM-ER eddy-resolving model (resolution of 1/6° × 1/8°). It is shown that an increase in the spatial resolution improves the reproduction of ocean currents (with Agulhas and Kuroshio currents as examples) and their variability. A probable cause of relatively high errors in the reproduction of the SSH of Southern and Indian oceans is discussed.

  16. Adaptive management in crop pest control in the face of climate variability: an agent-based modeling approach

    Directory of Open Access Journals (Sweden)

    François Rebaudo

    2015-06-01

    Full Text Available Climate changes are occurring rapidly at both regional and global scales. Farmers are faced with the challenge of developing new agricultural practices to help them to cope with unpredictable changes in environmental, social, and economic conditions. Under these conditions, adaptive management requires a farmer to learn by monitoring provisional strategies and changing conditions, and then incrementally adjust management practices in light of new information. Exploring adaptive management will increase our understanding of the underlying processes that link farmer societies with their environment across space and time, while accounting for the impacts of an unpredictable climate. Here, we assessed the impacts of temperature and crop price, as surrogates for climate and economic changes, on farmers' adaptive management in crop pest control using an agent-based modeling approach. Our model simulated an artificial society of farmers that relied on field data obtained in the Ecuadorian Andes. Farmers were represented as heterogeneous autonomous agents who interact with and influence each other, and who are capable of adapting to changing environmental conditions. The results of our simulation suggest that variable temperatures led to less effective pest control strategies than those used under stable temperatures. Moreover, farmers used information gained through their own past experience or through interactions with other farmers to initiate an adaptive management approach. At a broader scale, this study generates more than an increased understanding of adaptive management; it highlights how people depend on one another to manage common problems.

  17. An Assessment of the Potential Predictability of Interannual and Decadal Variability Based on Climate Model Simulations with Specified SST

    Science.gov (United States)

    Schubert, Siegfried; Wang, Hailan; Suarez, Max; Koster, Randal

    2009-01-01

    The USCLIVAR working group on drought recently initiated a series of global climate model simulations forced with idealized SST anomaly patterns, designed to address a number of uncertainties regarding the impact of SST forcing and the role of land-atmosphere feedbacks on regional drought. The runs were done with several global atmospheric models including NASA/NSIPP-1, NCEP/GFS, GFDL/AM2, and NCAR CCM3 and CAM3.5. Here we focus on the potential predictability associated with the leading patterns of inter-annual and decadal Pacific SST variability. Specific issues addressed include the nature of the seasonality and regionality of the signal, the noise, and the signal-to-noise ratios, as well as the dependence of the results on the models.

  18. Investigation of North American vegetation variability under recent climate: A study using the SSiB4/TRIFFID biophysical/dynamic vegetation model

    OpenAIRE

    Zhang, Z.; Xue, Y.; Macdonald, G.; Cox, PM; Collatz, GJ

    2015-01-01

    ©2015. American Geophysical Union. All Rights Reserved. Recent studies have shown that current dynamic vegetation models have serious weaknesses in reproducing the observed vegetation dynamics and contribute to bias in climate simulations. This study intends to identify the major factors that underlie the connections between vegetation dynamics and climate variability and investigates vegetation spatial distribution and temporal variability at seasonal to decadal scales over North America (NA...

  19. Understanding climate variability and change in the Altiplano

    OpenAIRE

    Seth, Anji

    2007-01-01

    This presentation addresses climate variability in the climate change models for 20th and 21st centuries for the Altiplano Region. The models appear to simulate this mechanism in the present, but respond quite differently in 21st century climate. This poses a question: Is this related to LTRA-4 (Practices and Strategies for Vulnerable Agro-Ecosystems)

  20. Achieving stringent climate targets. An analysis of the role of transport and variable renewable energies using energy-economy-climate models

    International Nuclear Information System (INIS)

    technologies photovoltaics (PV) and concentrating solar power (CSP) in REMIND confirms the dominant role of these variable renewable energies for the decarbonization of the power sector. Recent cost reductions have brought PV to cost-competitiveness in regions with high midday electricity demand and high solar irradiance. The representation of system integration costs in REMIND is found to have significant impact on the competition between PV and CSP in the model: the low integration requirements of CSP equipped with thermal storage and hydrogen co-firing make CSP competitive at high shares of variable renewable energies, which leads to substantial deployment of both PV and CSP in low stabilization scenarios. A cross-model study of transport sector decarbonization confirms the earlier finding that the transport sector is not very reactive to intermediate carbon price levels: Until 2050, transport decarbonization lags 10-30 years behind the decarbonization of other sectors, and liquid fuels dominate the transport sector. In the long term, however, transportation does not seem to be an insurmountable barrier to stringent climate targets: As the price signals on CO2 increase further, transport emissions can be reduced substantially - if either hydrogen fuel cells or electromobility open a route to low-carbon energy carriers, or second generation biofuels (possibly in combination with CCS) allow the use of liquid-based transport modes with low emissions. The last study takes up the fundamental question of this thesis and analyses the trade-off between the stringency of a climate target and the resulting techno-economic requirements and costs. We find that transforming the global energy-economy system to keep a two-thirds likelihood of limiting global warming to below 2 C is achievable at moderate economic implications. This result is contingent on the near-term implementation of stringent global climate policies and full availability of several technologies that are still in the

  1. Agricultural production and groundwater depletion under climate variability in India - Results from a regional scale crop modeling approach

    Science.gov (United States)

    Siegfried, T. U.; Sobolowski, S.; Fishman, R.; Vasquez, V.; Raj, P.; Narula, K. K.; Modi, V.; Lall, U.

    2009-12-01

    In India, recent declines in national food security may point to systemic deficiencies of agricultural production. Over the past decade and in the face of declining public investments in irrigation projects, the growth of production has increasingly become reliant on the allocation of large volumes of groundwater in an unsustainable manner. As a result, shallow as well as deep fossil groundwater resources are increasingly depleted and the buffer that mitigates negative impacts on production in case of Monsoonal dry-spells / drought conditions is lost. In the face of future climate and food supply uncertainty, it is vital that the connections between climate variability, unsustainable irrigation practices and their impacts on regional scale agricultural production be quantified and better understood. In our analysis, we focus on rice production in the Telengana region in Andhra Pradesh, which is characterized by a semi-arid tropical climate that is driven by the bimodal seasonality of the south-western monsoon. Traditionally, agricultural production of rice was constrained by precipitation variations during the wet season (Kharif). However, the advent of inexpensive pump technology in the 1970's, coupled with governmentally subsidized electricity has allowed year-round rice production. Thus, the Monsoon rains must not only drive wet season production but must also sufficiently recharge groundwater in order to support dry season production. Observed Production time series are characterized by non-stationarity and heteroscedasticity. Using a subset of eight districts, a non-linear Gaussian Process regression model is developed and yearly crop production is modeled at the district level over 48 years. We show that interannual climate variations, in the form of the monsoon rains, play a significant role in determining the area of land set aside for dry season planting and thus affect total yearly production. The results suggest that a non-linear Bayesian regression

  2. Climate data, analysis and models for the study of natural variability and anthropogenic change

    Energy Technology Data Exchange (ETDEWEB)

    Jones, Philip D. [Univ. of East Anglia, Norwich (United Kingdom)

    2014-07-31

    Gridded Temperature Under prior/current support, we completed and published (Jones et al., 2012) the fourth major update to our global land dataset of near-surface air temperatures, CRUTEM4. This is one of the most widely used records of the climate system, having been updated, maintained and further developed with DoE support since the 1980s. We have continued to update the CRUTEM4 (Jones et al., 2012) database that is combined with marine data to produce HadCRUT4 (Morice et al., 2012). The emphasis in our use of station temperature data is to access as many land series that have been homogenized by National Meteorological Services (NMSs, including NCDC/NOAA, Asheville, NC). Unlike the three US groups monitoring surface temperatures in a similar way, we do not infill areas that have no or missing data. We can only infill such regions in CRUTEM4 by accessing more station temperature series. During early 2014, we have begun the extensive task of updating as many of these series as possible using data provided by some NMSs and also through a number of research projects and programs around the world. All the station data used in CRUTEM4 have been available since 2009, but in Osborn and Jones (2014) we have made this more usable using a Google Earth interface (http://www.cru.uea.ac.uk/cru/data/crutem/ge/ ). We have recently completed the update of our infilled land multi-variable dataset (CRU TS 3.10, Harris et al., 2014). This additionally produces complete land fields (except for the Antarctic) for temperature, precipitation, diurnal temperature range, vapour pressure and sunshine/cloud. Using this dataset we have calculated sc-PDSI (self-calibrating Palmer Drought Severity Index) data and compared with other PDSI datasets (Trenberth et al., 2014). Also using CRU TS 3.10 and Reanalysis datasets, we showed no overall increase in global temperature variability despite changing regional patterns (Huntingford et al., 2013). Harris et al. (2014) is an update of an earlier

  3. Climate Variability-Observations, Reconstructions, and Model Simulations for the Atlantic-European and Alpine Region from 1500-2100 AD

    Energy Technology Data Exchange (ETDEWEB)

    Raible, Christoph C. [Climate and Environmental Physics, Physics Institute, University of Bern, Sidlerstrasse 5, CH-3012 Bern (Switzerland); Casty, C.; Luterbacher, J.; Pauling, A.; Wanner, H. [Institute of Geography, University of Bern, Hallerstrasse 12, CH-3012 Bern (Switzerland); Esper, J.; Frank, D.C.; Buentgen, U. [Swiss Federal Research Institute WSL, Zuercherstrasse 111, CH-8903 Birmensdorf (Switzerland); Roesch, A.C.; Tschuck, P.; Wild, M.; Vidale, P.L.; Schaer, C. [Institute for Atmospheric and Climate Science ETH, Winterthurerstrasse 190, CH-8057 Zuerich (Switzerland)

    2006-11-15

    A detailed analysis is undertaken of the Atlantic-European climate using data from 500-year-long proxy-based climate reconstructions, a long climate simulation with perpetual 1990 forcing, as well as two global and one regional climate change scenarios. The observed and simulated interannual variability and teleconnectivity are compared and interpreted in order to improve the understanding of natural climate variability on interannual to decadal time scales for the late Holocene. The focus is set on the Atlantic-European and Alpine regions during the winter and summer seasons, using temperature, precipitation, and 500 hPa geopotential height fields. The climate reconstruction shows pronounced interdecadal variations that appear to 'lock' the atmospheric circulation in quasi-steady long-term patterns over multi-decadal periods controlling at least part of the temperature and precipitation variability. Different circulation patterns are persistent over several decades for the period 1500 to 1900. The 500-year-long simulation with perpetual 1990 forcing shows some substantial differences, with a more unsteady teleconnectivity behaviour. Two global scenario simulations indicate a transition towards more stable teleconnectivity for the next 100 years. Time series of reconstructed and simulated temperature and precipitation over the Alpine region show comparatively small changes in interannual variability within the time frame considered, with the exception of the summer season, where a substantial increase in interannual variability is simulated by regional climate models.

  4. Modelling spatial and temporal vegetation variability with the Climate Constrained Vegetation Index: evidence of CO2 fertilisation and of water stress in continental interiors

    Directory of Open Access Journals (Sweden)

    S. O. Los

    2015-06-01

    Full Text Available A model was developed to simulate spatial, seasonal and interannual variations in vegetation in response to temperature, precipitation and atmospheric CO2 concentrations; the model addresses shortcomings in current implementations. The model uses the minimum of 12 temperature and precipitation constraint functions to simulate NDVI. Functions vary based on the Köppen–Trewartha climate classification to take adaptations of vegetation to climate into account. The simulated NDVI, referred to as the climate constrained vegetation index (CCVI, captured the spatial variability (0.82 r r = 0.83 and interannual variability (median global r = 0.24 in NDVI. The CCVI simulated the effects of adverse climate on vegetation during the 1984 drought in the Sahel and during dust bowls of the 1930s and 1950s in the Great Plains in North America. A global CO2 fertilisation effect was found in NDVI data, similar in magnitude to that of earlier estimates (8 % for the 20th century. This effect increased linearly with simple ratio, a transformation of the NDVI. Three CCVI scenarios, based on climate simulations using the representative concentration pathway RCP4.5, showed a greater sensitivity of vegetation towards precipitation in Northern Hemisphere mid latitudes than is currently implemented in climate models. This higher sensitivity is of importance to assess the impact of climate variability on vegetation, in particular on agricultural productivity.

  5. Characterizing, modelling and understanding the climate variability of the deep water formation in the North-Western Mediterranean Sea

    Science.gov (United States)

    Somot, Samuel; Houpert, Loic; Sevault, Florence; Testor, Pierre; Bosse, Anthony; Taupier-Letage, Isabelle; Bouin, Marie-Noelle; Waldman, Robin; Cassou, Christophe; Sanchez-Gomez, Emilia; Durrieu de Madron, Xavier; Adloff, Fanny; Nabat, Pierre; Herrmann, Marine

    2016-08-01

    Observing, modelling and understanding the climate-scale variability of the deep water formation (DWF) in the North-Western Mediterranean Sea remains today very challenging. In this study, we first characterize the interannual variability of this phenomenon by a thorough reanalysis of observations in order to establish reference time series. These quantitative indicators include 31 observed years for the yearly maximum mixed layer depth over the period 1980-2013 and a detailed multi-indicator description of the period 2007-2013. Then a 1980-2013 hindcast simulation is performed with a fully-coupled regional climate system model including the high-resolution representation of the regional atmosphere, ocean, land-surface and rivers. The simulation reproduces quantitatively well the mean behaviour and the large interannual variability of the DWF phenomenon. The model shows convection deeper than 1000 m in 2/3 of the modelled winters, a mean DWF rate equal to 0.35 Sv with maximum values of 1.7 (resp. 1.6) Sv in 2013 (resp. 2005). Using the model results, the winter-integrated buoyancy loss over the Gulf of Lions is identified as the primary driving factor of the DWF interannual variability and explains, alone, around 50 % of its variance. It is itself explained by the occurrence of few stormy days during winter. At daily scale, the Atlantic ridge weather regime is identified as favourable to strong buoyancy losses and therefore DWF, whereas the positive phase of the North Atlantic oscillation is unfavourable. The driving role of the vertical stratification in autumn, a measure of the water column inhibition to mixing, has also been analyzed. Combining both driving factors allows to explain more than 70 % of the interannual variance of the phenomenon and in particular the occurrence of the five strongest convective years of the model (1981, 1999, 2005, 2009, 2013). The model simulates qualitatively well the trends in the deep waters (warming, saltening, increase in the

  6. Achieving stringent climate targets. An analysis of the role of transport and variable renewable energies using energy-economy-climate models

    Energy Technology Data Exchange (ETDEWEB)

    Pietzcker, Robert Carl

    2014-07-01

    technologies photovoltaics (PV) and concentrating solar power (CSP) in REMIND confirms the dominant role of these variable renewable energies for the decarbonization of the power sector. Recent cost reductions have brought PV to cost-competitiveness in regions with high midday electricity demand and high solar irradiance. The representation of system integration costs in REMIND is found to have significant impact on the competition between PV and CSP in the model: the low integration requirements of CSP equipped with thermal storage and hydrogen co-firing make CSP competitive at high shares of variable renewable energies, which leads to substantial deployment of both PV and CSP in low stabilization scenarios. A cross-model study of transport sector decarbonization confirms the earlier finding that the transport sector is not very reactive to intermediate carbon price levels: Until 2050, transport decarbonization lags 10-30 years behind the decarbonization of other sectors, and liquid fuels dominate the transport sector. In the long term, however, transportation does not seem to be an insurmountable barrier to stringent climate targets: As the price signals on CO{sub 2} increase further, transport emissions can be reduced substantially - if either hydrogen fuel cells or electromobility open a route to low-carbon energy carriers, or second generation biofuels (possibly in combination with CCS) allow the use of liquid-based transport modes with low emissions. The last study takes up the fundamental question of this thesis and analyses the trade-off between the stringency of a climate target and the resulting techno-economic requirements and costs. We find that transforming the global energy-economy system to keep a two-thirds likelihood of limiting global warming to below 2 C is achievable at moderate economic implications. This result is contingent on the near-term implementation of stringent global climate policies and full availability of several technologies that are still in

  7. Capability of a regional climate model to simulate climate variables requested for water balance computation: a case study over northeastern France

    Science.gov (United States)

    Boulard, Damien; Castel, Thierry; Camberlin, Pierre; Sergent, Anne-Sophie; Bréda, Nathalie; Badeau, Vincent; Rossi, Aurélien; Pohl, Benjamin

    2016-05-01

    This paper documents the capability of the ARW/WRF regional climate model to regionalize near-surface atmospheric variables at high resolution (8 km) over Burgundy (northeastern France) from daily to interannual timescales. To that purpose, a 20-year continuous simulation (1989-2008) was carried out. The WRF model driven by ERA-Interim reanalyses was compared to in situ observations and a mesoscale atmospheric analyses system (SAFRAN) for five near-surface variables: precipitation, air temperature, wind speed, relative humidity and solar radiation, the last four variables being used for the calculation of potential evapotranspiration (ET0). Results show a significant improvement upon ERA-Interim. This is due to a good skill of the model to reproduce the spatial distribution for all weather variables, in spite of a slight over-estimation of precipitation amounts mostly during the summer convective season, and wind speed during winter. As compared to the Météo-France observations, WRF also improves upon SAFRAN analyses, which partly fail at showing realistic spatial distributions for wind speed, relative humidity and solar radiation—the latter being strongly underestimated. The SAFRAN ET0 is thus highly under-estimated too. WRF ET0 is in better agreement with observations. In order to evaluate WRF's capability to simulate a reliable ET0, the water balance of thirty Douglas-fir stands was computed using a process-based model. Three soil water deficit indexes corresponding to the sum of the daily deviations between the relative extractible water and a critical value of 40 % below which the low soil water content affects tree growth, were calculated using the nearest weather station, SAFRAN analyses weather data, or by merging observation and WRF weather variables. Correlations between Douglas-fir growth and the three estimated soil water deficit indexes show similar results. These results showed through the ET0 estimation and the relation between mean annual SWDI

  8. Timing of climate variability and grassland productivity

    OpenAIRE

    Craine, Joseph M.; Nippert, Jesse B.; Andrew J Elmore; Skibbe, Adam M.; Hutchinson, Stacy L.; Brunsell, Nathaniel A.

    2012-01-01

    Future climates are forecast to include greater precipitation variability and more frequent heat waves, but the degree to which the timing of climate variability impacts ecosystems is uncertain. In a temperate, humid grassland, we examined the seasonal impacts of climate variability on 27 y of grass productivity. Drought and high-intensity precipitation reduced grass productivity only during a 110-d period, whereas high temperatures reduced productivity only during 25 d in July. The effects o...

  9. Advances in Understanding Decadal Climate Variability

    Science.gov (United States)

    Busalacchi, Antonio J.

    1999-01-01

    Recently, a joint Brazil-France-U.S. program, known as PIRATA (Pilot Research moored Array in the Tropical Atlantic), was proposed to begin the deployment of moored measurement platforms in the tropical Atlantic in order to enhance the existing observational data base and subsequent understanding of the processes by which the ocean and atmosphere couple in key regions of the tropical Atlantic Ocean. Empirical studies have suggested that there are strong relationships between tropical Atlantic upper ocean variability, SST, ocean-atmosphere coupling and regional climate variability. During the early 1980's a coordinated set of surface wind, subsurface thermal structure, and subsurface current observations were obtained as part of the U.S.-France SEQUAL-FOCAL process experiment designed to observe the seasonal response of the tropical Atlantic Ocean to surface forcing. Since that time, however, the observational data base for the tropical Atlantic Ocean has disintegrated to a few ship-tracks measuring ocean temperatures and a small collection of tide gauge stations measuring sea level. A more comprehensive set of observations, modeling and empirical studies is now in order to make progress on understanding the regional climate variability. The proposed PIRATA program will use mooring platforms similar to the tropical Pacific Ocean TAO array to measure surface fluxes of momentum and heat and the corresponding changes in the upper ocean thermal structure. It is anticipated that the oceanic data from this monitoring array will also be used in a predictive mode for initialization studies of regional coupled climate models. Of particular interest are zonal and meridional modes of ocean-atmosphere variability within the tropical Atlantic basin that have significant impacts on the regional climate of the bordering continents.

  10. Space-time structure of climate variability

    Science.gov (United States)

    Laepple, Thomas; Reschke, Maria; Huybers, Peter; Rehfeld, Kira

    2016-04-01

    The spatial scale of climate variability is closely linked to the temporal scale. Whereas fast variations such as weather are regional, glacial-interglacial cycles appear to be globally coherent. Quantifying the relationship between local and large-scale climate variations is essential for mapping the extent of past climate changes. Larger spatial scales of climate variations on longer time scales are expected if one views the atmosphere and oceans as primarily diffusive with respect to heat. On the other hand, the interaction of a dynamical system with spatially variable boundary conditions --- for example: topography, gradients in insolation, and variations in rotational effects --- will lead to spatially heterogeneous structures that are largely independent of time scale. It has been argued that the increase in spatial scales continues across all time scales [Mitchell, 1976], but up to now, the space-time structure of variations beyond the decadal scale is basically unexplored. Here, we attempt to estimate the spatial extent of temperature changes up to millennial time-scales using instrumental observations, paleo-observations and climate model simulations. Although instrumental and climate model data show an increase in spatial scale towards slower variations, paleo-proxy data, if interpreted as temperature signals, lead to ambiguous results. An analysis of a global Holocene stack [Marcott et al., 2013], for example, suggests a jump towards more localized patterns when leaving the instrumental time scale. Localization contradicts physical expectations and may instead reflect the presence of various types of noise. Turning the problem around, and imposing a consistent space-time structure across instruments and proxy records allows us to constrain the interpretation of the climate signal in proxy records. In the case of the Holocene stack, preliminary results suggest that the time-uncertainty on the Holocene records would have to be much larger than published in

  11. Present and Future Modes of Low Frequency Climate Variability

    Energy Technology Data Exchange (ETDEWEB)

    Cane, Mark A.

    2014-02-20

    This project addressed area (1) of the FOA, “Interaction of Climate Change and Low Frequency Modes of Natural Climate Variability”. Our overarching objective is to detect, describe and understand the changes in low frequency variability between model simulations of the preindustrial climate and simulations of a doubled CO2 climate. The deliverables are a set of papers providing a dynamical characterization of interannual, decadal, and multidecadal variability in coupled models with attention to the changes in this low frequency variability between pre-industrial concentrations of greenhouse gases and a doubling of atmospheric concentrations of CO2. The principle mode of analysis, singular vector decomposition, is designed to advance our physical, mechanistic understanding. This study will include external natural variability due to solar and volcanic aerosol variations as well as variability internal to the climate system. An important byproduct is a set of analysis tools for estimating global singular vector structures from the archived output of model simulations.

  12. Climate Variability and Phytoplankton in the Pacific Ocean

    Science.gov (United States)

    Rousseaux, Cecile

    2012-01-01

    The effect of climate variability on phytoplankton communities was assessed for the tropical and sub-tropical Pacific Ocean between 1998 and 2005 using an established biogeochemical assimilation model. The phytoplankton communities exhibited wide range of responses to climate variability, from radical shifts in the Equatorial Pacific, to changes of only a couple of phytoplankton groups in the North Central Pacific, to no significant changes in the South Pacific. In the Equatorial Pacific, climate variability dominated the variability of phytoplankton. Here, nitrate, chlorophyll and all but one of the 4 phytoplankton types (diatoms, cyanobacteria and coccolithophores) were strongly correlated (pclimate variability can play in ocean biology.

  13. Prioritizing Global Observations Along Essential Climate Variables

    Science.gov (United States)

    Bojinski, Stephan; Richter, Carolin

    2010-12-01

    The Global Climate Observing System (GCOS) Secretariat, housed within the World Meteorological Organization, released in August 2010 updated guidance for priority actions worldwide in support of observations of GCOS Essential Climate Variables (ECVs). This guidance states that full achievement of the recommendations in the 2010 Implementation Plan for the Global Observing System for Climate in Support of the UNFCCC (http://www.wmo.int/pages/prog/gcos/Publications/gcos­138.pdf) is required to ensure that countries are able to understand and predict climate change and its impacts and manage their response throughout the 21st century and beyond. GCOS is sponsored by the United Nations and the International Council for Science (ICSU) and is an internationally coordinated network of observing systems and a program of activities that support and improve the network, which is designed to meet evolving national and international requirements for climate observations. One of the main objectives of GCOS is to sustain observations into the future to allow evaluation of how climate is changing, so that informed decisions can be made on prevention, mitigation, and adaptation strategies. GCOS priorities are based on the belief that observations are crucial to supporting the research needed to refine understanding of the climate system and its changes, to initialize predictions on time scales out to decades, and to develop the models used to make these predictions and longer­term scenario-based projections. Observations are also needed to assess social and economic vulnerabilities and to support related actions needed across a broad range of societal sectors by underpinning emerging climate services.

  14. Models of simulation and prediction of the behavior of dengue in four Colombian cities, including climate like modulating variable of the disease

    International Nuclear Information System (INIS)

    ARIMA-type models are proposed to simulate the behavior of dengue and to make apparent the relations with the climatic variability in four localities of Colombia. The climatic variable was introduced into the models as an index that modulates the behavior of the disease. It was obtained by means of a multivariate analysis of principal components. The investigation was carried out with information corresponding to the epidemiological weeks from January 1997 to December 2000, for both the number of disease cases and the data corresponding to the meteorological variables. The study shows that the variations of the climate between the previous 9 to 14 weeks have influence on the appearance of new cases of dengue. In particular, the precipitation in these weeks was seen to be greater when in later periods the disease presented epidemic characteristics than the precipitation in those weeks preceded the disease within endemic limits

  15. Use of the HadGEM2 climate-chemistry model to investigate interannual variability in methane sources

    Science.gov (United States)

    Hayman, Garry; O'Connor, Fiona; Clark, Douglas; Huntingford, Chris; Gedney, Nicola

    2013-04-01

    The global mean atmospheric concentration of methane (CH4) has more than doubled during the industrial era [1] and now constitutes ? 20% of the anthropogenic climate forcing by greenhouse gases [2]. The globally-averaged CH4 growth rate, derived from surface measurements, has fallen significantly from a high of 16 ppb yr-1 in the late 1970s/early 1980s and was close to zero between 1999 and 2006 [1]. This overall period of declining or low growth was however interspersed with years of positive growth-rate anomalies (e.g., in 1991-1992, 1998-1999 and 2002-2003). Since 2007, renewed growth has been evident [1, 3], with the largest increases observed over polar northern latitudes and the Southern Hemisphere in 2007 and in the tropics in 2008. The observed inter-annual variability in atmospheric methane concentrations and the associated changes in growth rates have variously been attributed to changes in different methane sources and sinks [1, 4]. In this paper, we report results from runs of the HadGEM2 climate-chemistry model [5] using year- and month-specific emission datasets. The HadGEM2 model includes the comprehensive atmospheric chemistry and aerosol package, the UK Chemistry Aerosol community model (UKCA, http://www.ukca.ac.uk/wiki/index.php). The Standard Tropospheric Chemistry scheme was selected for this work. This chemistry scheme simulates the Ox, HOx and NOx chemical cycles and the oxidation of CO, methane, ethane and propane. Year- and month-specific emission datasets were generated for the period from 1997 to 2009 for the emitted species in the chemistry scheme (CH4, CO, NOx, HCHO, C2H6, C3H8, CH3CHO, CH3CHOCH3). The approach adopted varied depending on the source sector: Anthropogenic: The emissions from anthropogenic sources were based on decadal-averaged emission inventories compiled by [6] for the Coupled Carbon Cycle Climate Model Intercomparison Project (C4MIP). These were then used to derive year-specific emission datasets by scaling the

  16. Simulation of climate variability and anthropogenic climate change

    International Nuclear Information System (INIS)

    The climatic changes in the last century were discussed and focus was on the questions: 1) What are the causes of the rapid climate fluctuations and 2) Is the global warming, which is observed during the last century, caused by natural or anthropogenic effects. It is concluded that an understanding of climate based on the interpretation of observational data only is not feasible, unless supported by an adequate theoretical interpretation. The capabilities of climatic models were discussed and the importance of incorporating 1) calculations of the internal variability of the atmosphere when forced from an ocean with prescribed sea surface temperature as well as for a system consisting of an atmosphere and a mixed ocean of limited depth, 2) a fully coupled atmospheric and ocean model and finally, 3) a fully coupled system including transiently changing greenhouse gases and aerosols. A short summation of the results is presented. The pronounced warming during the last century is not reproduced under the assumption of constant forcing and pollution emissions have to be incorporated into the models in order to bring the simulated data in agreement with observations

  17. Variability of the earth's climate

    International Nuclear Information System (INIS)

    In this paper, the global evolution of the Earth's climate since the Precambrian is described and the reconstruction of the last major oscillations generally referred to as the last climatic cycles which occurred during the Quarternary is presented: isotope geochemistry, micropaleontological transfer functions; ice volume and sea level, temperatures, deep water circulation of the last climatic cycle

  18. Interannual-to-decadal variability of the stratosphere during the 20th century: ensemble simulations with a chemistry-climate model

    Directory of Open Access Journals (Sweden)

    A. M. Fischer

    2008-07-01

    Full Text Available Interannual-to-decadal variability in stratospheric ozone and climate have a number of common sources, such as variations in solar irradiance, stratospheric aerosol loading due to volcanic eruptions, El Niño Southern Oscillation variability and the quasi-biennial oscillation (QBO. Currently available data records as well as model simulations addressing stratospheric chemical climate variability mostly cover only the past few decades, which is often insufficient to address natural interannual-to-decadal variability. Here we make use of recently reconstructed and re-evaluated data products to force and validate transient ensemble model simulations (nine members across the twentieth century computed by means of the chemistry-climate model SOCOL. The forcings included sea surface temperatures, sea ice, solar irradiance, stratospheric aerosols, QBO, changes in land properties, greenhouse gases, ozone depleting substances, and emissions of carbon monoxides, and nitrogen oxides. The transient simulations are in good agreement with observations, reconstructions and reanalyses and allow quantification of interannual-to-decadal variability during the 20th century. All ensemble members are able to capture the low-frequency variability in tropical and mid-latitudinal total ozone as well as in the strength of the subtropical jet, suggesting a realistic response to external forcings in this area. The region of the northern polar vortex exhibits a large internal model variability that is found in the frequency, seasonality, and strength of major warmings as well as in the strength of the modeled polar vortex. Results from process-oriented analysis, such as correlation between the vertical Eliassen Palm flux (EP flux component and polar variables as well as stratospheric ozone trends, are of comparable magnitude to those observed and are consistent in all analysed ensemble members. Yet, trend estimates of the vertical EP flux component vary greatly among

  19. Tropical North Africa hydro climate variability

    International Nuclear Information System (INIS)

    NCEP/NCAR data are used to study the modulating circulations of the hydro climate of tropical North Africa. Wavelet analysis is used to identify modes of variability of stream flows within the region. Ocean-atmosphere circulation composites are considered to unravel the mechanisms for swing of stream flows. The one of the main finding of the study reveals that hydro climate variability swings within ENSO and decadal timescale. Pacific and Atlantic sea surface temperatures control the hydro climate mode of variability. Associated to Pacific sea surface temperature, the Atlantic Walker Circulation modulates the hydro climate swing of tropical North Africa. The detail result will be discussed.(Author)

  20. TRACKING CLIMATE MODELS

    Data.gov (United States)

    National Aeronautics and Space Administration — CLAIRE MONTELEONI*, GAVIN SCHMIDT, AND SHAILESH SAROHA* Climate models are complex mathematical models designed by meteorologists, geophysicists, and climate...

  1. On the ability of statistical wind-wave models to capture the variability and long-term trends of the North Atlantic winter wave climate

    OpenAIRE

    Martínez-Asensio, Adrián; Marcos, Marta; Tsimplis, Michael N.; Jordà, Gabriel; Feng, Xiangbo; Gomis, Damià

    2016-01-01

    A dynamical wind-wave climate simulation covering the North Atlantic Ocean and spanning the whole 21st century under the A1B scenario has been compared with a set of statistical projections using atmospheric variables or large scale climate indices as predictors. As a first step, the performance of all statistical models has been evaluated for the present-day climate; namely they have been compared with a dynamical wind-wave hindcast in terms of winter Significant Wave Height (SWH) trends and...

  2. Climate variability and climate change in Mexico: A review

    OpenAIRE

    E. Jáuregui

    1997-01-01

    A review of research on climate variability, fluctuations and climate change in Mexico is presented. Earlier approaches include different time scales from paleoclimatic to historical and instrumental. The nature and causes of variability in Mexico have been attributed to large-scale southward/northward shifts of the mid-latitude major circulation and more recently to the ENSO cycle. Global greenhouse warming has become a major environmental issue and has spawned a large number of climate-chan...

  3. Natural climate variability and teleconnections to precipitation over the Pacific-North American region in CMIP3 and CMIP5 models

    Science.gov (United States)

    Polade, Suraj D.; Gershunov, Alexander; Cayan, Daniel R.; Dettinger, Michael D.; Pierce, David W.

    2013-01-01

    Natural climate variability will continue to be an important aspect of future regional climate even in the midst of long-term secular changes. Consequently, the ability of climate models to simulate major natural modes of variability and their teleconnections provides important context for the interpretation and use of climate change projections. Comparisons reported here indicate that the CMIP5 generation of global climate models shows significant improvements in simulations of key Pacific climate mode and their teleconnections to North America compared to earlier CMIP3 simulations. The performance of 14 models with simulations in both the CMIP3 and CMIP5 archives are assessed using singular value decomposition analysis of simulated and observed winter Pacific sea surface temperatures (SSTs) and concurrent precipitation over the contiguous United States and northwestern Mexico. Most of the models reproduce basic features of the key natural mode and their teleconnections, albeit with notable regional deviations from observations in both SST and precipitation. Increasing horizontal resolution in the CMIP5 simulations is an important, but not a necessary, factor in the improvement from CMIP3 to CMIP5.

  4. Long-term changes and variability in a transient simulation with a chemistry-climate model employing realistic forcing

    Directory of Open Access Journals (Sweden)

    M. Dameris

    2005-04-01

    Full Text Available A transient simulation with the interactively coupled chemistry-climate model (CCM E39/C has been carried out which covers the 40-year period between 1960 and 1999. Forcing of natural and anthropogenic origin is prescribed where the characteristics are sufficiently well known and the typical timescales are slow compared to synoptic timescale so that the simulated atmospheric chemistry and climate evolves under a ''slowly'' varying external forcing. Based on observations, sea surface temperature (SST and ice cover are prescribed. The increase of greenhouse gas and chloroflurocarbon concentrations, as well as nitrogen oxide emissions is taken into account. The 11-year solar cycle is considered in the calculation of heating rates and photolysis of chemical species. The three major volcanic eruptions during that time (Agung, 1963; El Chichon, 1982; Pinatubo, 1991 are considered. The quasi-biennial oscillation (QBO is forced by linear relaxation, also known as nudging, of the equatorial zonal wind in the lower stratosphere towards observed zonal wind profiles. Beyond a reasonable reproduction of mean parameters and long-term variability characteristics there are many apparent features of episodic similarities between simulation and observation: In the years 1986 and 1988 the Antarctic ozone holes are smaller than in the other years of the respective decade. In mid-latitudes of the Southern Hemisphere ozone anomalies, especially in 1985, 1989, 1991/1992, and 1996, resemble the corresponding observations. In the Northern Hemisphere, the first half of the 1990s is dynamically quiet, no stratospheric warming is found for a period of at least 6 years. As observed, volcanic eruptions strongly influence dynamics and chemistry, though only for few years. Obviously, planetary wave activity is strongly driven by the prescribed SST and modulated by the QBO. Preliminary evidence of realistic cause and effect relationships strongly suggest

  5. Interactions of Mean Climate Change and Climate Variability on Food Security Extremes

    Science.gov (United States)

    Ruane, Alexander C.; McDermid, Sonali; Mavromatis, Theodoros; Hudson, Nicholas; Morales, Monica; Simmons, John; Prabodha, Agalawatte; Ahmad, Ashfaq; Ahmad, Shakeel; Ahuja, Laj R.

    2015-01-01

    Recognizing that climate change will affect agricultural systems both through mean changes and through shifts in climate variability and associated extreme events, we present preliminary analyses of climate impacts from a network of 1137 crop modeling sites contributed to the AgMIP Coordinated Climate-Crop Modeling Project (C3MP). At each site sensitivity tests were run according to a common protocol, which enables the fitting of crop model emulators across a range of carbon dioxide, temperature, and water (CTW) changes. C3MP can elucidate several aspects of these changes and quantify crop responses across a wide diversity of farming systems. Here we test the hypothesis that climate change and variability interact in three main ways. First, mean climate changes can affect yields across an entire time period. Second, extreme events (when they do occur) may be more sensitive to climate changes than a year with normal climate. Third, mean climate changes can alter the likelihood of climate extremes, leading to more frequent seasons with anomalies outside of the expected conditions for which management was designed. In this way, shifts in climate variability can result in an increase or reduction of mean yield, as extreme climate events tend to have lower yield than years with normal climate.C3MP maize simulations across 126 farms reveal a clear indication and quantification (as response functions) of mean climate impacts on mean yield and clearly show that mean climate changes will directly affect the variability of yield. Yield reductions from increased climate variability are not as clear as crop models tend to be less sensitive to dangers on the cool and wet extremes of climate variability, likely underestimating losses from water-logging, floods, and frosts.

  6. On the ability of statistical wind-wave models to capture the variability and long-term trends of the North Atlantic winter wave climate

    Science.gov (United States)

    Martínez-Asensio, Adrián; Marcos, Marta; Tsimplis, Michael N.; Jordà, Gabriel; Feng, Xiangbo; Gomis, Damià

    2016-07-01

    A dynamical wind-wave climate simulation covering the North Atlantic Ocean and spanning the whole 21st century under the A1B scenario has been compared with a set of statistical projections using atmospheric variables or large scale climate indices as predictors. As a first step, the performance of all statistical models has been evaluated for the present-day climate; namely they have been compared with a dynamical wind-wave hindcast in terms of winter Significant Wave Height (SWH) trends and variance as well as with altimetry data. For the projections, it has been found that statistical models that use wind speed as independent variable predictor are able to capture a larger fraction of the winter SWH inter-annual variability (68% on average) and of the long term changes projected by the dynamical simulation. Conversely, regression models using climate indices, sea level pressure and/or pressure gradient as predictors, account for a smaller SWH variance (from 2.8% to 33%) and do not reproduce the dynamically projected long term trends over the North Atlantic. Investigating the wind-sea and swell components separately, we have found that the combination of two regression models, one for wind-sea waves and another one for the swell component, can improve significantly the wave field projections obtained from single regression models over the North Atlantic.

  7. Understanding the contributions of aerosol properties and parameterization discrepancies to droplet number variability in a global climate model

    Science.gov (United States)

    Morales Betancourt, R.; Nenes, A.

    2014-05-01

    Aerosol indirect effects in climate models strongly depend on the representation of the aerosol activation process. In this study, we assess the process-level differences across activation parameterizations that contribute to droplet number uncertainty by using the adjoints of the Abdul-Razzak and Ghan (2000) and Fountoukis and Nenes (2005) droplet activation parameterizations in the framework of the Community Atmospheric Model version 5.1 (CAM5.1). The adjoint sensitivities of Nd to relevant input parameters are used to (i) unravel the spatially resolved contribution of aerosol number, mass, and chemical composition to changes in Nd between present-day and pre-industrial simulations and (ii) identify the key variables responsible for the differences in Nd fields and aerosol indirect effect estimates when different activation schemes are used within the same modeling framework. The sensitivities are computed online at minimal computational cost. Changes in aerosol number and aerosol mass concentrations were found to contribute to Nd differences much more strongly than chemical composition effects. The main sources of discrepancy between the activation parameterizations considered were the treatment of the water uptake by coarse mode particles, and the sensitivity of the parameterized Nd accumulation mode aerosol geometric mean diameter. These two factors explain the different predictions of Nd over land and over oceans when these parameterizations are employed. Discrepancies in the sensitivity to aerosol size are responsible for an exaggerated response to aerosol volume changes over heavily polluted regions. Because these regions are collocated with areas of deep clouds, their impact on shortwave cloud forcing is amplified through liquid water path changes. The same framework is also utilized to efficiently explore droplet number uncertainty attributable to hygroscopicity parameter of organic aerosol (primary and secondary). Comparisons between the parameterization

  8. Empirical-Statistical Methodology and Methods for Modeling and Forecasting of Climate Variability of Different Temporal Scales

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    Main problem of modern climatology is to assess the present as well as future climate change. For this aim two approaches are used: physic-mathematic modeling on the basis of GCMs and palaeoclimatic analogues. The third approach is based on the empirical-statistical methodology and is developed in this paper. This ap proach allows to decide two main problems: to give a real assessment of climate changes by observed data for climate monitoring and extrapolation of obtained climate tendencies to the nearest future (10-15 years) and give the empiricai basis for further development of physic-mathematicai models. The basic theory and methodology of empirical-statistic approach have been developed as well as a common model for description of space-time climate variatiom taking into account the processes of different time scales. The way of decreasing of the present and future uncertainty is suggested as the extraction of long-term climate changes components in the particular time series and spatial generalization of the same climate tendencies in the obtained homogeneous regions. Algorithm and methods for realization of empirical-statistic methodology have been developed along with methods for generalization of intraannual fluctuations, methods for extraction of homogeneous components of different time scales (interannual, decadal, century), methodology and methods for spatial generalization and modeling, methods for extrapolation on the basis of two main kinds of time models: stochastic and deterministic--stochastic. Some applications of developed methodology and methods are given for the longest time series of temperature and precipitation over the world and for spatial generalization over the European area.

  9. Toward hydro-social modeling: Merging human variables and the social sciences with climate-glacier runoff models (Santa River, Peru)

    Science.gov (United States)

    Carey, Mark; Baraer, Michel; Mark, Bryan G.; French, Adam; Bury, Jeffrey; Young, Kenneth R.; McKenzie, Jeffrey M.

    2014-10-01

    Glacier shrinkage caused by climate change is likely to trigger diminished and less consistent stream flow in glacier-fed watersheds worldwide. To understand, model, and adapt to these climate-glacier-water changes, it is vital to integrate the analysis of both water availability (the domain of hydrologists) and water use (the focus for social scientists). Drawn from a case study of the Santa River watershed below Peru’s glaciated Cordillera Blanca mountain range, this paper provides a holistic hydro-social framework that identifies five major human variables critical to hydrological modeling because these forces have profoundly influenced water use over the last 60 years: (1) political agendas and economic development; (2) governance: laws and institutions; (3) technology and engineering; (4) land and resource use; and (5) societal responses. Notable shifts in Santa River water use-including major expansions in hydroelectricity generation, large-scale irrigation projects, and other land and resource-use practices-did not necessarily stem from changing glacier runoff or hydrologic shifts, but rather from these human variables. Ultimately, then, water usage is not predictable based on water availability alone. Glacier runoff conforms to certain expected trends predicted by models of progressively reduced glacier storage. However, societal forces establish the legal, economic, political, cultural, and social drivers that actually shape water usage patterns via human modification of watershed dynamics. This hydro-social framework has widespread implications for hydrological modeling in glaciated watersheds from the Andes and Alps to the Himalaya and Tien Shan, as well as for the development of climate change adaptation plans.

  10. Glacial-interglacial variability in Tropical Pangaean Precipitation during the Late Paleozoic Ice Age: simulations with the Community Climate System Model

    Directory of Open Access Journals (Sweden)

    N. G. Heavens

    2012-05-01

    Full Text Available The Late Paleozoic Ice Age (LPIA, the Earth's penultimate "icehouse climate", was a critical time in the history of biological and ecological evolution. Many questions remain about the connections between high-latitude glaciation in Gondwanaland and low-latitude precipitation variability in Pangaea. We have simulated the Earth's climate during Asselian-Sakmarian time (299–284 Ma with the Community Climate System Model version 3 (CCSM3, a coupled dynamic atmosphere-ocean-land-sea-ice model. Our simulations test the sensitivity of the model climate to direct and indirect effects of glaciation as well as variability in the Earth's orbit. Our focus is on precipitation variability in tropical (30° S–30° N Pangaea, where there has been the most interpretation of glacial-interglacial climate change during the LPIA. The results of these simulations suggest that glacials generally were drier than interglacials in tropical Pangaea, though exceptional areas may have been wetter, depending on location and the mode of glaciation. Lower sea level, an indirect effect of changes in glacial extent, appears to reduce tropical Pangaean precipitation more than the direct radiative/topographic effects of high-latitude glaciation. Glaciation of the Central Pangaean Mountains would have greatly reduced equatorial Pangaean precipitation, while perhaps enhancing precipitation at higher tropical latitudes and in equatorial rain shadows. Variability evident in strata with 5th order stratigraphic cycles may have resulted from precipitation changes owing to precession forcing of monsoon circulations and would have differed in character between greenhouse and icehouse climates.

  11. Gamma generalized linear model to investigate the effects of climate variables on the area burned by forest fire in northeast China

    Institute of Scientific and Technical Information of China (English)

    Futao Guo; Guangyu Wang; John L Innes; Xiangqing Ma; Long Sun; Haiqing Hu

    2015-01-01

    The purpose of this study was to determine a suitable model for investigating the effects of climate factors on the area burned by forest fire in the Tahe forest region, Daxing’an Mountains, in northeast China. The response variables were the area burned by lightning-caused fire, human-caused fire, and total burned area. The predictor variables were nine climate variables collected from the local weather station. Three regression models were utilized, including multiple linear regression, log-linear model (log-transformation on both response and predictor variables), and gamma-generalized linear model. The goodness-of-fit of the models were compared based on model fitting statistics such as R2, AIC, and RMSE. The results revealed that the gamma-generalized linear model was generally superior to both multiple linear regression model and log-linear model for fitting the fire data. Further, the best models were selected based on the criteria that the climate variables were statistically significant at a=0.05. The gamma best models indicated that maximum wind speed, precipitation, and days that rainfall greater than 0.1 mm had significant impacts on the area burned by the lightning-caused fire, while the mean temperature and minimum relative humidity were the main drivers of the burned area caused by human activities. Overall, the total burned area by forest fire was significantly influenced by days that rainfall greater than 0.1 mm and minimum rela-tive humidity, indicating that the moisture condition of forest stands determine the burned area by forest fire.

  12. Climate and hydrological variability: the catchment filtering role

    Science.gov (United States)

    Andrés-Doménech, I.; García-Bartual, R.; Montanari, A.; Marco, J. B.

    2015-01-01

    Measuring the impact of climate change on flood frequency is a complex and controversial task. Identifying hydrological changes is difficult given the factors, other than climate variability, which lead to significant variations in runoff series. The catchment filtering role is often overlooked and thus may hinder the correct identification of climate variability signatures on hydrological processes. Does climate variability necessarily imply hydrological variability? This research aims to analytically derive the flood frequency distribution based on realistic hypotheses about the rainfall process and the rainfall-runoff transformation. The annual maximum peak flow probability distribution is analytically derived to quantify the filtering effect of the rainfall-runoff process on climate change. A sensitivity analysis is performed according to typical semi-arid Mediterranean climatic and hydrological conditions, assuming a simple but common scheme for the rainfall-runoff transformation in small-size ungauged catchments, i.e. the CN-SCS model. Variability in annual maximum peak flows and its statistical significance are analysed when changes in the climatic input are introduced. Results show that depending on changes in the annual number of rainfall events, the catchment filtering role is particularly significant, especially when the event rainfall volume distribution is not strongly skewed. Results largely depend on the return period: for large return periods, peak flow variability is significantly affected by the climatic input, while for lower return periods, infiltration processes smooth out the impact of climate change.

  13. Climate and hydrological variability: the catchment filtering role

    Directory of Open Access Journals (Sweden)

    I. Andrés-Doménech

    2014-09-01

    Full Text Available Measuring the impact of climate change on flood frequency is a complex and controversial task. Identifying hydrological changes is difficult given the factors, other than climate variability, which lead to significant variations in runoff series. The catchment filtering role is often overlooked and in fact, this may hinder the correct identification of climate variability signatures on hydrological processes. Does climate variability necessarily imply hydrological variability? The research herein presented aims to analytically derive the flood frequency distribution basing on realistic hypotheses about the rainfall process and the rainfall–runoff transformation. The peak flow probability distribution is analytically derived to quantify the filtering effect operated by the rainfall–runoff process on climate change. A sensitivity analysis is performed according to typical semi-arid Mediterranean climatic and hydrological conditions, assuming a simple but common scheme for the rainfall–runoff transformation in small-size ungauged catchments, i.e. the CN-SCS model. Variability in peak flows and its statistical significance are analysed when changes in the climatic input are introduced. Results show that in regard to changes in the annual number of rainfall events, the catchment filtering role is particularly significant when the event rainfall volume distribution is not strongly skewed. Results largely depend on the return period: for large return periods, peak flow variability is significantly impacted by the climatic input, while for lower return periods, infiltration processes smooth out the effects of climate change.

  14. Spatial distribution analysis on climatic variables in northeast China

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    Information ecology is a new research area of modern ecology.Here describes spatial distribution analysis methods of four sorts of climatic variables, i.e. temperature, precipitation, relative humidity and sunshine fraction on Northeast China. First,Digital terrain models was built with large-scale maps and vector data. Then trend surface analysis and interpolation method were used to analyze the spatial distribution of these four kinds of climatic variables at three temporal scale: (1) monthly data; (2)mean monthly data of thirty years, and (3) mean annual data of thirty years. Ecological information system were used for graphics analysis on the spatial distribution of these climate variables.

  15. Short-Term Effects of Climatic Variables on Hand, Foot, and Mouth Disease in Mainland China, 2008–2013: A Multilevel Spatial Poisson Regression Model Accounting for Overdispersion

    Science.gov (United States)

    Yang, Fang; Yang, Min; Hu, Yuehua; Zhang, Juying

    2016-01-01

    Background Hand, Foot, and Mouth Disease (HFMD) is a worldwide infectious disease. In China, many provinces have reported HFMD cases, especially the south and southwest provinces. Many studies have found a strong association between the incidence of HFMD and climatic factors such as temperature, rainfall, and relative humidity. However, few studies have analyzed cluster effects between various geographical units. Methods The nonlinear relationships and lag effects between weekly HFMD cases and climatic variables were estimated for the period of 2008–2013 using a polynomial distributed lag model. The extra-Poisson multilevel spatial polynomial model was used to model the exact relationship between weekly HFMD incidence and climatic variables after considering cluster effects, provincial correlated structure of HFMD incidence and overdispersion. The smoothing spline methods were used to detect threshold effects between climatic factors and HFMD incidence. Results The HFMD incidence spatial heterogeneity distributed among provinces, and the scale measurement of overdispersion was 548.077. After controlling for long-term trends, spatial heterogeneity and overdispersion, temperature was highly associated with HFMD incidence. Weekly average temperature and weekly temperature difference approximate inverse “V” shape and “V” shape relationships associated with HFMD incidence. The lag effects for weekly average temperature and weekly temperature difference were 3 weeks and 2 weeks. High spatial correlated HFMD incidence were detected in northern, central and southern province. Temperature can be used to explain most of variation of HFMD incidence in southern and northeastern provinces. After adjustment for temperature, eastern and Northern provinces still had high variation HFMD incidence. Conclusion We found a relatively strong association between weekly HFMD incidence and weekly average temperature. The association between the HFMD incidence and climatic

  16. Capturing Crop Response to Climate and Management Variability in Models: Evaluation Using FLUXNET Data with Applications at the Regional Scale

    Science.gov (United States)

    Twine, T. E.; Kucharik, C. J.

    2009-12-01

    Dynamic global vegetation models (DGVMs) simulate the response of ecosystems to environmental drivers at multiple time scales (e.g., a fast response to diurnal variations in radiation and a slower response to interannual variations in climate). Until recently, these models only represented natural ecosystems, which neglected the approximately 30% of Earth’s land surface covered by managed ecosystems. We have incorporated the representation of four major crops of the United States (i.e., maize, soybean, spring wheat, and winter wheat) into the Agro-IBIS DGVM and have tested the model at the site level and regional scale. Here we present results of an evaluation of carbon, energy, and water fluxes from a multi-year simulation of maize and soybean at FLUXNET sites in Minnesota and Illinois. These two sites are not only located along a climate gradient, allowing evaluation of model sensitivity to climate variation, they also have different land use histories and are currently under different tillage management. As an application of the model at the regional scale, we examined the relationship of temperature and precipitation trends to net primary productivity (NPP) changes from 1982-2002 over both natural and managed ecosystems across the central and eastern U.S. In order to isolate the vegetation growth response to climate trends, we minimized the representation of management for agroecosystems and forested ecosystems by removing nitrogen stress and irrigation from the model. Maize had the largest NPP trend of 6.43 g C m-2 yr-2, followed by soybean, spring wheat, deciduous forest, then grassland. Winter wheat had a trend of -0.64 g C m-2 yr-2 and evergreen needleleaf forest had a negligible NPP trend. We found that 19% of maize and 11% of soybean NPP trends could be explained by temperature trends while 23% of corn and 44% of soybean trends could be explained by precipitation trends. Our results provide further evidence supporting observational results that suggest

  17. How much of the NAO monthly variability is from ocean-atmospheric coupling: results from an interactive ensemble climate model

    Science.gov (United States)

    Xin, Xiaoge; Xue, Wei; Zhang, Minghua; Li, Huimin; Zhang, Tao; Zhang, Jie

    2015-02-01

    The chaotic atmospheric circulations and the ocean-atmosphere coupling may both cause variations in the North Atlantic Oscillation (NAO). This study uses an interactive ensemble (IE) coupled model to study the contribution of the atmospheric noise and coupling to the monthly variability of the NAO. In the IE model, seven atmospheric general circulation model (AGCM) realizations with different initial states are coupled with a single realization of the land, ocean and ice component models. The chaotic noise from the atmosphere at the air-sea interface is therefore reduced. The time variances of monthly NAO index in the ensemble AGCM mean of the IE model is found to be about 20.1 % of that in the SC model. Therefore, more than 79.9 % of the simulated monthly variability of NAO is caused by atmospheric noise. The coupling between sea surface temperature (SST) and NAO is only found in regions south of about 40°N in the North Atlantic Ocean. The IE strategy highlighted the interaction between the NAO and the SST in the region (28°-38°N, 20°W-50°W) to the southeast of the Gulf Stream extension. While the ocean-atmosphere coupling explains <1/5th of the NAO variability in the IE model, it shows slightly larger persistence than the SC model, consistent with the hypothesis of a slower mode of variability from ocean-atmosphere coupling that has larger predictability than the variability driven by the atmosphere.

  18. Food Price Volatility and Decadal Climate Variability

    Science.gov (United States)

    Brown, M. E.

    2013-12-01

    The agriculture system is under pressure to increase production every year as global population expands and more people move from a diet mostly made up of grains, to one with more meat, dairy and processed foods. Weather shocks and large changes in international commodity prices in the last decade have increased pressure on local food prices. This paper will review several studies that link climate variability as measured with satellite remote sensing to food price dynamics in 36 developing countries where local monthly food price data is available. The focus of the research is to understand how weather and climate, as measured by variations in the growing season using satellite remote sensing, has affected agricultural production, food prices and access to food in agricultural societies. Economies are vulnerable to extreme weather at multiple levels. Subsistence small holders who hold livestock and consume much of the food they produce are vulnerable to food production variability. The broader society, however, is also vulnerable to extreme weather because of the secondary effects on market functioning, resource availability, and large-scale impacts on employment in trading, trucking and wage labor that are caused by weather-related shocks. Food price variability captures many of these broad impacts and can be used to diagnose weather-related vulnerability across multiple sectors. The paper will trace these connections using market-level data and analysis. The context of the analysis is the humanitarian aid community, using the guidance of the USAID Famine Early Warning Systems Network and the United Nation's World Food Program in their response to food security crises. These organizations have worked over the past three decades to provide baseline information on food production through satellite remote sensing data and agricultural yield models, as well as assessments of food access through a food price database. Econometric models and spatial analysis are used

  19. Impact of climate variability on tropospheric ozone

    International Nuclear Information System (INIS)

    A simulation with the climate-chemistry model (CCM) E39/C is presented, which covers both the troposphere and stratosphere dynamics and chemistry during the period 1960 to 1999. Although the CCM, by its nature, is not exactly representing observed day-by-day meteorology, there is an overall model's tendency to correctly reproduce the variability pattern due to an inclusion of realistic external forcings, like observed sea surface temperatures (e.g. El Nino), major volcanic eruption, solar cycle, concentrations of greenhouse gases, and Quasi-Biennial Oscillation. Additionally, climate-chemistry interactions are included, like the impact of ozone, methane, and other species on radiation and dynamics, and the impact of dynamics on emissions (lightning). However, a number of important feedbacks are not yet included (e.g. feedbacks related to biogenic emissions and emissions due to biomass burning). The results show a good representation of the evolution of the stratospheric ozone layer, including the ozone hole, which plays an important role for the simulation of natural variability of tropospheric ozone. Anthropogenic NOx emissions are included with a step-wise linear trend for each sector, but no interannual variability is included. The application of a number of diagnostics (e.g. marked ozone tracers) allows the separation of the impact of various processes/emissions on tropospheric ozone and shows that the simulated Northern Hemisphere tropospheric ozone budget is not only dominated by nitrogen oxide emissions and other ozone pre-cursors, but also by changes of the stratospheric ozone budget and its flux into the troposphere, which tends to reduce the simulated positive trend in tropospheric ozone due to emissions from industry and traffic during the late 80s and early 90s. For tropical regions the variability in ozone is dominated by variability in lightning (related to ENSO) and stratosphere-troposphere exchange (related to Northern Hemisphere Stratospheric

  20. Impact of climate variability on tropospheric ozone

    International Nuclear Information System (INIS)

    A simulation with the climate-chemistry model (CCM) E39/C is presented, which covers both the troposphere and stratosphere dynamics and chemistry during the period 1960 to 1999. Although the CCM, by its nature, is not exactly representing observed day-by-day meteorology, there is an overall model's tendency to correctly reproduce the variability pattern due to an inclusion of realistic external forcings, like observed sea surface temperatures (e.g. El Nino), major volcanic eruption, solar cycle, concentrations of greenhouse gases, and Quasi-Biennial Oscillation. Additionally, climate-chemistry interactions are included, like the impact of ozone, methane, and other species on radiation and dynamics, and the impact of dynamics on emissions (lightning). However, a number of important feedbacks are not yet included (e.g. feedbacks related to biogenic emissions and emissions due to biomass burning). The results show a good representation of the evolution of the stratospheric ozone layer, including the ozone hole, which plays an important role for the simulation of natural variability of tropospheric ozone. Anthropogenic NO x emissions are included with a step-wise linear trend for each sector, but no interannual variability is included. The application of a number of diagnostics (e.g. marked ozone tracers) allows the separation of the impact of various processes/emissions on tropospheric ozone and shows that the simulated Northern Hemisphere tropospheric ozone budget is not only dominated by nitrogen oxide emissions and other ozone pre-cursors, but also by changes of the stratospheric ozone budget and its flux into the troposphere, which tends to reduce the simulated positive trend in tropospheric ozone due to emissions from industry and traffic during the late 80s and early 90s. For tropical regions the variability in ozone is dominated by variability in lightning (related to ENSO) and stratosphere-troposphere exchange (related to Northern Hemisphere Stratospheric

  1. Impact of subgrid-scale radiative heating variability on the stratocumulus-to-trade cumulus transition in climate models

    Energy Technology Data Exchange (ETDEWEB)

    Xiao, Heng; Gustafson, William I.; Wang, Hailong

    2014-04-29

    Subgrid-scale interactions between turbulence and radiation are potentially important for accurately reproducing marine low clouds in climate models. To better understand the impact of these interactions, the Weather Research and Forecasting (WRF) model is configured for large eddy simulation (LES) to study the stratocumulus-to-trade cumulus (Sc-to-Cu) transition. Using the GEWEX Atmospheric System Studies (GASS) composite Lagrangian transition case and the Atlantic Trade Wind Experiment (ATEX) case, it is shown that the lack of subgrid-scale turbulence-radiation interaction, as is the case in current generation climate models, accelerates the Sc-to-Cu transition. Our analysis suggests that in cloud-topped boundary layers subgrid-scale turbulence-radiation interactions contribute to stronger production of temperature variance, which in turn leads to stronger buoyancy production of turbulent kinetic energy and helps to maintain the Sc cover.

  2. Evaluation of the inter-annual variability of stratospheric chemical composition in chemistry-climate models using ground-based multi species time series

    Science.gov (United States)

    Poulain, V.; Bekki, S.; Marchand, M.; Chipperfield, M. P.; Khodri, M.; Lefèvre, F.; Dhomse, S.; Bodeker, G. E.; Toumi, R.; De Maziere, M.; Pommereau, J.-P.; Pazmino, A.; Goutail, F.; Plummer, D.; Rozanov, E.; Mancini, E.; Akiyoshi, H.; Lamarque, J.-F.; Austin, J.

    2016-07-01

    The variability of stratospheric chemical composition occurs on a broad spectrum of timescales, ranging from day to decades. A large part of the variability appears to be driven by external forcings such as volcanic aerosols, solar activity, halogen loading, levels of greenhouse gases (GHG), and modes of climate variability (quasi-biennial oscillation (QBO), El Niño-Southern Oscillation (ENSO)). We estimate the contributions of different external forcings to the interannual variability of stratospheric chemical composition and evaluate how well 3-D chemistry-climate models (CCMs) can reproduce the observed response-forcing relationships. We carry out multivariate regression analyses on long time series of observed and simulated time series of several traces gases in order to estimate the contributions of individual forcings and unforced variability to their internannual variability. The observations are typically decadal time series of ground-based data from the international Network for the Detection of Atmospheric Composition Change (NDACC) and the CCM simulations are taken from the CCMVal-2 REF-B1 simulations database. The chemical species considered are column O3, HCl, NO2, and N2O. We check the consistency between observations and model simulations in terms of the forced and internal components of the total interannual variability (externally forced variability and internal variability) and identify the driving factors in the interannual variations of stratospheric chemical composition over NDACC measurement sites. Overall, there is a reasonably good agreement between regression results from models and observations regarding the externally forced interannual variability. A much larger fraction of the observed and modelled interannual variability is explained by external forcings in the tropics than in the extratropics, notably in polar regions. CCMs are able to reproduce the amplitudes of responses in chemical composition to specific external forcings

  3. Climate Variability over India and Bangladesh from the Perturbed UK Met Office Hadley Model: Impacts on Flow and Nutrient Fluxes in the Ganges Delta System

    Science.gov (United States)

    Whitehead, P. G.; Caesar, J.; Crossman, J.; Barbour, E.; Ledesma, J.; Futter, M. N.

    2015-12-01

    A semi-distributed flow and water quality model (INCA- Integrated Catchments Model) has been set up for the whole of the Ganges- Brahmaputra- Meghna (GBM) River system in India and Bangladesh. These massive rivers transport large fluxes of water and nutrients into the Bay of Bengal via the GBM Delta system in Bangladesh. Future climate change will impact these fluxes with changing rainfall, temperature, evapotranspiration and soil moisture deficits being altered in the catchment systems. In this study the INCA model has been used to assess potential impacts of climate change using the UK Met Office Hadley Centre GCM model linked to a regionally coupled model of South East Asia, covering India and Bangladesh. The Hadley Centre model has been pururbed by varying the parameters in the model to generate 17 realisations of future climates. Some of these reflect expected change but others capture the more extreme potential behaviour of future climate conditions. The 17 realisations have been used to drive the INCA Flow and Nitrogen model inorder to generate downstream times series of hydrology and nitrate- nitrogen. The variability of the climates on these fluxes are investigated and and their likley impact on the Bay of Begal Delta considered. Results indicate a slight shift in the monsoon season with increased wet season flows and increased temperatures which alter nutrient fluxes. Societal Importance to Stakeholders The GBM Delta supports one of the most densely populated regions of people living in poverty, who rely on ecosystem services provided by the Delta for survival. These ecosystem services are dependent upon fluxes of water and nutrients. Freshwater for urban, agriculture, and aquaculture requirements are essential to livelihoods. Nutrient loads stimulate estuarine ecosystems, supporting fishing stocks, which contribute significantly the economy of Bangladesh. Thus the societal importance of upstream climate driven change change in Bangladesh are very

  4. Future Warming Patterns Linked to Today's Climate Variability.

    Science.gov (United States)

    Dai, Aiguo

    2016-01-11

    The reliability of model projections of greenhouse gas (GHG)-induced future climate change is often assessed based on models' ability to simulate the current climate, but there has been little evidence that connects the two. In fact, this practice has been questioned because the GHG-induced future climate change may involve additional physical processes that are not important for the current climate. Here I show that the spatial patterns of the GHG-induced future warming in the 21(st) century is highly correlated with the patterns of the year-to-year variations of surface air temperature for today's climate, with areas of larger variations during 1950-1979 having more GHG-induced warming in the 21(st) century in all climate models. Such a relationship also exists in other climate fields such as atmospheric water vapor, and it is evident in observed temperatures from 1950-2010. The results suggest that many physical processes may work similarly in producing the year-to-year climate variations in the current climate and the GHG-induced long-term changes in the 21(st) century in models and in the real world. They support the notion that models that simulate present-day climate variability better are likely to make more reliable predictions of future climate change.

  5. Future Warming Patterns Linked to Today's Climate Variability.

    Science.gov (United States)

    Dai, Aiguo

    2016-01-01

    The reliability of model projections of greenhouse gas (GHG)-induced future climate change is often assessed based on models' ability to simulate the current climate, but there has been little evidence that connects the two. In fact, this practice has been questioned because the GHG-induced future climate change may involve additional physical processes that are not important for the current climate. Here I show that the spatial patterns of the GHG-induced future warming in the 21(st) century is highly correlated with the patterns of the year-to-year variations of surface air temperature for today's climate, with areas of larger variations during 1950-1979 having more GHG-induced warming in the 21(st) century in all climate models. Such a relationship also exists in other climate fields such as atmospheric water vapor, and it is evident in observed temperatures from 1950-2010. The results suggest that many physical processes may work similarly in producing the year-to-year climate variations in the current climate and the GHG-induced long-term changes in the 21(st) century in models and in the real world. They support the notion that models that simulate present-day climate variability better are likely to make more reliable predictions of future climate change. PMID:26750759

  6. Human Responses to Climate Variability: The Case of South Africa

    Science.gov (United States)

    Oppenheimer, M.; Licker, R.; Mastrorillo, M.; Bohra-Mishra, P.; Estes, L. D.; Cai, R.

    2014-12-01

    Climate variability has been associated with a range of societal and individual outcomes including migration, violent conflict, changes in labor productivity, and health impacts. Some of these may be direct responses to changes in mean temperature or precipitation or extreme events, such as displacement of human populations by tropical cyclones. Others may be mediated by a variety of biological, social, or ecological factors such as migration in response to long-term changes in crops yields. Research is beginning to elucidate and distinguish the many channels through which climate variability may influence human behavior (ranging from the individual to the collective, societal level) in order to better understand how to improve resilience in the face of current variability as well as future climate change. Using a variety of data sets from South Africa, we show how climate variability has influenced internal (within country) migration in recent history. We focus on South Africa as it is a country with high levels of internal migration and dramatic temperature and precipitation changes projected for the 21st century. High poverty rates and significant levels of rain-fed, smallholder agriculture leave large portions of South Africa's population base vulnerable to future climate change. In this study, we utilize two complementary statistical models - one micro-level model, driven by individual and household level survey data, and one macro-level model, driven by national census statistics. In both models, we consider the effect of climate on migration both directly (with gridded climate reanalysis data) and indirectly (with agricultural production statistics). With our historical analyses of climate variability, we gain insights into how the migration decisions of South Africans may be influenced by future climate change. We also offer perspective on the utility of micro and macro level approaches in the study of climate change and human migration.

  7. Study of seasonal climatology and interannual variability over India and its subregions using a regional climate model (RegCM3)

    Indian Academy of Sciences (India)

    P Maharana; A P Dimri

    2014-07-01

    The temporal and spatial variability of the various meteorological parameters over India and its different subregions is high. The Indian subcontinent is surrounded by the complex Himalayan topography in north and the vast oceans in the east, west and south. Such distributions have dominant influence over its climate and thus make the study more complex and challenging. In the present study, the climatology and interannual variability of basic meteorological fields over India and its six homogeneous monsoon subregions (as defined by Indian Institute of Tropical Meteorology (IITM) for all the four meteorological seasons) are analysed using the Regional Climate Model Version 3 (RegCM3). A 22-year (1980–2001) simulation with RegCM3 is carried out to develop such understanding. The National Centre for Environmental Prediction/National Centre for Atmospheric Research, US (NCEP-NCAR) reanalysis 2 (NNRP2) is used as the initial and lateral boundary conditions. The main seasonal features and their variability are represented in model simulation. The temporal variation of precipitation, i.e., the mean annual cycle, is captured over complete India and its homogenous monsoon subregions. The model captured the contribution of seasonal precipitation to the total annual precipitation over India. The model showed variation in the precipitation contribution for some subregions to the total and seasonal precipitation over India. The correlation coefficient (CC) and difference between the coefficient of variation between model fields and the corresponding observations in percentage (COV) is calculated and compared. In most of the cases, the model could represent the magnitude but not the variability. The model processes are found to be more important than in the corresponding observations defining the variability. The model performs quite well over India in capturing the climatology and the meteorological process. The model shows good skills over the relevant subregions during a

  8. Future Warming Patterns Linked to Today’s Climate Variability

    Science.gov (United States)

    Dai, Aiguo

    2016-01-01

    The reliability of model projections of greenhouse gas (GHG)-induced future climate change is often assessed based on models’ ability to simulate the current climate, but there has been little evidence that connects the two. In fact, this practice has been questioned because the GHG-induced future climate change may involve additional physical processes that are not important for the current climate. Here I show that the spatial patterns of the GHG-induced future warming in the 21st century is highly correlated with the patterns of the year-to-year variations of surface air temperature for today’s climate, with areas of larger variations during 1950–1979 having more GHG-induced warming in the 21st century in all climate models. Such a relationship also exists in other climate fields such as atmospheric water vapor, and it is evident in observed temperatures from 1950–2010. The results suggest that many physical processes may work similarly in producing the year-to-year climate variations in the current climate and the GHG-induced long-term changes in the 21st century in models and in the real world. They support the notion that models that simulate present-day climate variability better are likely to make more reliable predictions of future climate change.

  9. Randomness and Earth climate variability

    CERN Document Server

    Levinshtein, Michael E; Dmitriev, Alexander P; Shmakov, Pavel M

    2015-01-01

    Paleo-Sciences including palaeoclimatology and palaeoecology have accumulated numerous records related to climatic changes. The researchers have usually tried to identify periodic and quasi-periodic processes in these paleoscientific records. In this paper, we show that this analysis is incomplete. As follows from our results, random processes, namely processes with a single-time-constant (noise with a Lorentzian noise spectrum), play a very important and, perhaps, a decisive role in numerous natural phenomena. For several of very important natural phenomena the characteristic time constants are very similar and equal to (5-8)x10^3 years. However, this value is not universal. For example, the spectral density fluctuations of the atmospheric radiocarbon 14C are characterized by a Lorentzian with time constant 300 years. The frequency dependence of spectral density fluctuations for benthic 18O records contains two Lorentzians with time constans 8000 years and > 105 years.

  10. Evaluating and Quantifying the Climate-Driven Interannual Variability in Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) at Global Scales

    Science.gov (United States)

    Zeng, Fanwei; Collatz, George James; Pinzon, Jorge E.; Ivanoff, Alvaro

    2013-01-01

    Satellite observations of surface reflected solar radiation contain informationabout variability in the absorption of solar radiation by vegetation. Understanding thecauses of variability is important for models that use these data to drive land surface fluxesor for benchmarking prognostic vegetation models. Here we evaluated the interannualvariability in the new 30.5-year long global satellite-derived surface reflectance index data,Global Inventory Modeling and Mapping Studies normalized difference vegetation index(GIMMS NDVI3g). Pearsons correlation and multiple linear stepwise regression analyseswere applied to quantify the NDVI interannual variability driven by climate anomalies, andto evaluate the effects of potential interference (snow, aerosols and clouds) on the NDVIsignal. We found ecologically plausible strong controls on NDVI variability by antecedent precipitation and current monthly temperature with distinct spatial patterns. Precipitation correlations were strongest for temperate to tropical water limited herbaceous systemswhere in some regions and seasons 40 of the NDVI variance could be explained byprecipitation anomalies. Temperature correlations were strongest in northern mid- to-high-latitudes in the spring and early summer where up to 70 of the NDVI variance was explained by temperature anomalies. We find that, in western and central North America,winter-spring precipitation determines early summer growth while more recent precipitation controls NDVI variability in late summer. In contrast, current or prior wetseason precipitation anomalies were correlated with all months of NDVI in sub-tropical herbaceous vegetation. Snow, aerosols and clouds as well as unexplained phenomena still account for part of the NDVI variance despite corrections. Nevertheless, this study demonstrates that GIMMS NDVI3g represents real responses of vegetation to climate variability that are useful for global models.

  11. The effects of solar variability on climate

    International Nuclear Information System (INIS)

    It has been hypothesized for at least a century that some of the observed variance in global temperature records arises from variations in solar output. Theories of solar-variability effects on climate could not be tested directly prior to satellite measurements because uncertainties in ground-based measurements of solar irradiance were larger than the solar variations themselves. Measurements by the Active Cavity Radiometer (ACRIM) onboard the Solar Max satellite and by the Earth Radiation Budget (ERB) instrument onboard Nimbus 6 are now available which indicate solar-constant variations are positively correlated with solar activity over an 11-yr solar cycle, and are of order ± 1.0 W m-2 relative to a mean solar constant of S0 = 1,367 W m-2, ΔS/S0 ∼ ± 0.07%. For a typical climate sensitivity parameter of β = S0 ∂T/∂S ∼ 100 C, the corresponding variations in radiative equilibrium temperature at the Earth's surface are ΔTe ∼ ± 0.07 C. The realized temperature variations from solar forcing, ΔT, can be significantly smaller because of thermal damping by the ocean. The author considers effects of solar variability on the observed and projected history of the global temperature record in light of this data using an upwelling-diffusion ocean model to assess the effect of ocean thermal inertia on the thermal response. The response to harmonic variations of the 11-yr sunspot cycle is of order ΔT ∼ ± 0.02 C, though the coupling between response and forcing is stronger for long-term variations in the envelope of the solar cycle which more nearly match the thermal response time of the deep ocean

  12. An analysis of the daily precipitation variability in the Himalayan orogen using a statistical parameterisation and its potential in driving landscape evolution models with stochastic climatic forcing

    Science.gov (United States)

    Deal, Eric; Braun, Jean

    2015-04-01

    A current challenge in landscape evolution modelling is to integrate realistic precipitation patterns and behaviour into longterm fluvial erosion models. The effect of precipitation on fluvial erosion can be subtle as well as nonlinear, implying that changes in climate (e.g. precipitation magnitude or storminess) may have unexpected outcomes in terms of erosion rates. For example Tucker and Bras (2000) show theoretically that changes in the variability of precipitation (storminess) alone can influence erosion rate across a landscape. To complicate the situation further, topography, ultimately driven by tectonic uplift but shaped by erosion, has a major influence on the distribution and style of precipitation. Therefore, in order to untangle the coupling between climate, erosion and tectonics in an actively uplifting orogen where fluvial erosion is dominant it is important to understand how the 'rain dial' used in a landscape evolution model (LEM) corresponds to real precipitation patterns. One issue with the parameterisation of rainfall for use in an LEM is the difference between the timescales for precipitation (≤ 1 year) and landscape evolution (> 103 years). As a result, precipitation patterns must be upscaled before being integrated into a model. The relevant question then becomes: What is the most appropriate measure of precipitation on a millennial timescale? Previous work (Tucker and Bras, 2000; Lague, 2005) has shown that precipitation can be properly upscaled by taking into account its variable nature, along with its average magnitude. This captures the relative size and frequency of extreme events, ensuring a more accurate characterisation of the integrated effects of precipitation on erosion over long periods of time. In light of this work, we present a statistical parameterisation that accurately models the mean and daily variability of ground based (APHRODITE) and remotely sensed (TRMM) precipitation data in the Himalayan orogen with only a few

  13. The Effects of Climate Variability on Phytoplankton Composition in the Equatorial Pacific Ocean using a Model and a Satellite-Derived Approach

    Science.gov (United States)

    Rousseaux, C. S.; Gregg, W. W.

    2012-01-01

    Compared the interannual variation in diatoms, cyanobacteria, coccolithophores and chlorophytes from the NASA Ocean Biogeochemical Model with those derived from satellite data (Hirata et al. 2011) between 1998 and 2006 in the Equatorial Pacific. Using NOBM, La Ni a events were characterized by an increase in diatoms (correlation with MEI, r=-0.81, Pclimate variability. However, satellite-derived phytoplankton groups were all negatively correlated with climate variability (r ranged from -0.39 for diatoms to -0.64 for coccolithophores, P<0.05). Spatially, the satellite-derived approach was closer to an independent in situ dataset for all phytoplankton groups except diatoms than NOBM. However, the different responses of phytoplankton to intense interannual events in the Equatorial Pacific raises questions about the representation of phytoplankton dynamics in models and algorithms: is a phytoplankton community shift as in the model or an across-the-board change in abundances of all phytoplankton as in the satellite-derived approach.

  14. Variability of the thermospheric temperatures of Mars during 9 Martian Years as given by a ground-to-exosphere Global Climate Model

    Science.gov (United States)

    Gonzalez-Galindo, Francisco; Forget, Francois; Garcia-Comas, Maya; Millour, Ehouarn; Lopez-Valverde, Miguel; Montabone, Luca

    2016-07-01

    The temperature of the Martian upper thermosphere is one of the main factors affecting the rate of the different escape to space processes which shape the Martian atmosphere and its long-term evolution. A good knowledge of the variability of this parameter is thus very important in order to gain a deeper understanding of the present-day escape rate and of the evolutive history of Mars. We have used a ground-to-exosphere Global Climate Model, the LMD-MGCM, to simulate the variability of the temperatures at the Martian exobase during the last 9 Martian Years (MY24-MY32, approximately 17 terrestrial years). The simulations include for the first time a realistic day-to-day variability of the UV solar flux. The simulated temperatures are in good agreement with the exospheric temperatures derived from Precise Orbit Determination of Mars Global Surveyor. A significant inter-annual variability of the temperatures, due to both the 11 year solar cycle and the variability of the dust load in the lower atmosphere, is predicted by the model. The variation in the solar output produced by the 27 day solar rotation cycle is seen in the simulated exobase temperatures. We also find that the global dust storms in MY25 and MY28 significantly impact the temperatures at the exobase. These results underline the importance of properly taking into account the dust and solar variabilities to simulate the upper atmosphere of Mars.

  15. Climatic Variability In Tropical Countries

    Science.gov (United States)

    Seneviratne, L. W.

    2003-04-01

    atmospheric condition and hence reduces rainfall for about 1.5 years in tropical countries. This was proved in 2001. This forecast was presented as a paper in 1998 Stockholm Water Symposium. The results were true for Brazil as well. The danger is now over when the episode is relaxed. Second half of 2002 was heavily wet and all the tanks in Sri Lanka except Kirindioya complex in Hambanthoa area got filled. This condition was seen in 1997 where all tanks got filled. El Nino analysts declared 1997 as a drought year as the previous year had experienced warming in Pacific Ocean. Southern Oscillation events are now dissociating to conformity. Discussion Hambanthoa District remained in the dry zone of Sri Lanka for 2000 years as the soil forms expressed as reddish brown earths. Original kingdoms had its base in Anuradhapura in Northcentral Province and Magama in Hambanthota district. Tools used by contemporary farmers were not powerful to use enormous water resources in wet zone. A system of diversion dams and use of run of the river irrigation has proved as the main criteria of that era. Diversion dams and canal projects were in existence. The diversion dams with special shape was mistaken by british surveyors and marked as broken dams in plans. DLOMendis later identified these as effective deflecting dams. The purpose was to wet the area to do cultivation. This system of wetting the land was suitable for dry climates with low rainfall. High technology was introduced by Irrigation Department to construct several reservoirs in Hambanthota. This was planned after the insufficient water use of Ellagala anicut from Kirindi Oya. Next step was to plan a reservoir project at Lunugamvehera dam site. Precipitation data available for 50 years were studied and a reservoir was designed for 20 000acres of paddy. It was planned to cultivate rice for Maha season and other field crops for Yala season. Cultivation commenced in 1985 and the farmers had enough water for 20000acres including

  16. Current climate variability and future climate change: Estimated growth and poverty impacts for Zambia

    OpenAIRE

    Thurlow, James; Zhu, Tingju; Diao, Xinshen

    2011-01-01

    Economy-wide and hydrological-crop models are combined to estimate and compare the economic impacts of current climate variability and future anthropogenic climate change in Zambia. Accounting for uncertainty, simulation results indicate that, on average, current variability reduces gross domestic product by four percent over a ten-year period and pulls over two percent of the population below the poverty line. Socio-economic impacts are much larger during major drought years, thus underscori...

  17. Climate Change and Variability in Ghana: Stocktaking

    Directory of Open Access Journals (Sweden)

    Felix A. Asante

    2014-12-01

    Full Text Available This paper provides a holistic literature review of climate change and variability in Ghana by examining the impact and projections of climate change and variability in various sectors (agricultural, health and energy and its implication on ecology, land use, poverty and welfare. The findings suggest that there is a projected high temperature and low rainfall in the years 2020, 2050 and 2080, and desertification is estimated to be proceeding at a rate of 20,000 hectares per annum. Sea-surface temperatures will increase in Ghana’s waters and this will have drastic effects on fishery. There will be a reduction in the suitability of weather within the current cocoa-growing areas in Ghana by 2050 and an increase evapotranspiration of the cocoa trees. Furthermore, rice and rooted crops (especially cassava production are expected to be low. Hydropower generation is also at risk and there will be an increase in the incidence rate of measles, diarrheal cases, guinea worm infestation, malaria, cholera, cerebro-spinal meningitis and other water related diseases due to the current climate projections and variability. These negative impacts of climate change and variability worsens the plight of the poor, who are mostly women and children.

  18. Generalized Instrumental Variable Models

    OpenAIRE

    Chesher, Andrew; Rosen, Adam

    2013-01-01

    The ability to allow for flexible forms of unobserved heterogeneity is an essential ingredient in modern microeconometrics. In this paper we extend the application of instrumental variable (IV) methods to a wide class of problems in which multiple values of unobservable variables can be associated with particular combinations of observed endogenous and exogenous variables. In our Generalized Instrumental Variable (GIV) models, in contrast to traditional IV models, the mapping from unobserved ...

  19. Coupling Land Use Change Modeling with Climate Projections to Estimate Seasonal Variability in Runoff from an Urbanizing Catchment Near Cincinnati, Ohio

    Directory of Open Access Journals (Sweden)

    Diana Mitsova

    2014-12-01

    Full Text Available This research examines the impact of climate and land use change on watershed hydrology. Seasonal variability in mean streamflow discharge, 100-year flood, and 7Q10 low-flow of the East Fork Little Miami River watershed, Ohio was analyzed using simulated land cover change and climate projections for 2030. Future urban growth in the Greater Cincinnati area, Ohio, by the year 2030 was projected using cellular automata. Projected land cover was incorporated into a calibrated BASINS-HSPF model. Downscaled climate projections of seven GCMs based on the assumptions of two IPCC greenhouse gas emissions scenarios were integrated through the BASINS Climate Assessment Tool (CAT. The discrete CAT output was used to specify a seed for a Monte Carlo simulation and derive probability density functions of anticipated seasonal hydrologic responses to account for uncertainty. Sensitivity analysis was conducted for a small catchment in the watershed using the Storm Water Management Model (SWMM developed U.S. Environmental Protection Agency. The results indicated higher probability of exceeding the 100-year flood over the fall and winter months, and a likelihood of decreasing summer low flows.

  20. Regionalizing global climate models

    NARCIS (Netherlands)

    Pitman, A.J.; Arneth, A.; Ganzeveld, L.N.

    2012-01-01

    Global climate models simulate the Earth's climate impressively at scales of continents and greater. At these scales, large-scale dynamics and physics largely define the climate. At spatial scales relevant to policy makers, and to impacts and adaptation, many other processes may affect regional and

  1. Internal variability in a 1000-yr control simulation with the coupled climate model ECHO-G - II. El Nino Southern Oscillation and North Atlantic Oscillation

    Energy Technology Data Exchange (ETDEWEB)

    Min, Seung-Ki; Hense, Andreas [Univ. of Bonn (Germany). Meteorological Inst.; Legutke, Stephanie [Max Planck Inst. for Meteorology, Hamburg (Germany); Kwon, Won-Tae [Meteorological Research Inst., Seoul (Korea, Republic of)

    2005-08-01

    A 1000-yr control simulation (CTL) performed with the atmosphere-ocean global climate model ECHO-G is analysed with regard to the El Nino Southern Oscillation (ENSO) and North Atlantic Oscillation (NAO), the two major natural climatic variabilities, in comparison with observations and other model simulations. The ENSO-related sea surface temperature climate and its seasonal cycle in the tropical Pacific and a single Intertropical Convergence Zone in the eastern tropical Pacific are simulated reasonably, and the ENSO phase-locking to the annual cycle and the subsurface ocean behaviour related to equatorial wave dynamics are also reproduced well. The simulated amplitude of the ENSO signal is however too large and its occurrence is too regular and frequent. Also, the observed westward propagation of zonal wind stress over the equatorial Pacific is not captured by the model. Nevertheless, the ENSO-related teleconnection patterns of near-surface temperature (T2m), precipitation (PCP) and mean sea level pressure (MSLP) are reproduced realistically. The NAO index, defined as the MSLP difference between Gibraltar and Iceland, has a 'white' noise spectrum similar to that of the detrended index obtained from observed data. The correlation and regression patterns of T2m, PCP and MSLP with the NAO index are also successfully simulated. However, the model overestimates the warming over the North Pacific in the high index phase of the NAO, a feature it shares with other coupled models. This might be associated with an enhanced Atlantic/Pacific teleconnection, which is hardly seen in the observations. A detection analysis of the NAO index shows that the observed recent 4060 yr trend cannot be explained by the model's internal variability while the recent 2030 yr trend occurs with a more than 1% chance in ECHO-G CTL.

  2. An ice-ocean model study to explore climate change mechanisms in comparison with interannual-to-decadal variability of geochemical tracers

    Institute of Scientific and Technical Information of China (English)

    Motoyoshi Ikeda

    2014-01-01

    One way to identify the mechanisms that are crucial to Arctic climate change is to use existing data that exhibit interannual-to-decadal variability in the sea ice and ocean interior due to atmospheric forcing. Since around 1960s, valuable geochemical data of the ocean interior, together with atmospheric and sea ice data, have been analyzed and examined in a coupled ice–ocean model with an idealized configuration of the Arctic Basin. This is fundamentally driven by negative salt flux, in addition to atmospheric circulation and cooling. This strategy has a clear advantage over more sophisticated models with higher resolution that require extensive data collections for veriifcation. Around 1990, the dominant atmospheric mode shifted from the Northern Annular Mode (NAM) to the Arctic Dipole Mode (ADM). The variability of sea ice cover was explained by these two modes sequentially and reproduced in the model. In particular, the geochemical ifelds indicated a movement of the Transpolar Drift Stream due to the NAM and an oscillation of the Paciifc water between the Atlantic and Paciifc sides due to the ADM. Both these features were reproduced reasonably well by the oceanic tracers in the model, including the time lags of about one third of the oscillation periods. Thus, this strategy can suggest methods and locations for monitoring oceanographic responses to Arctic climate change.

  3. Quantifying the sources of uncertainty in upper air climate variables

    Science.gov (United States)

    Eghdamirad, Sajjad; Johnson, Fiona; Woldemeskel, Fitsum; Sharma, Ashish

    2016-04-01

    Future estimates of precipitation and streamflow are of utmost interest in hydrological climate change impact assessments. Just as important as the estimate itself, is the variance around the ensemble mean of the projections, this variance being defined as uncertainty in the context of this study. This uncertainty in the hydrological variables of interest is affected by uncertainty in upper air climate variables which are used in statistical downscaling of precipitation or streamflow. Here the extent of uncertainty in upper air climate variables has been assessed for a selection of commonly used atmospheric variables for downscaling, namely, geopotential height and its difference in the north-south direction, specific humidity, and eastward and northward wind speeds. Generally, in statistical downscaling, no consideration is usually given to the uncertainty of different individual variables, which can result in biases in future climate simulations. The approach of quantifying uncertainty presented here has the potential to enable modelers to better formulate downscaling approaches, leading to more accurate characterization of future precipitation and its associated uncertainty. Based on the spread of multiple-model outputs, an uncertainty measure called square root of error variance has been used to quantify the contribution of different sources of uncertainty (i.e., models, scenarios, and ensembles) in monthly future climate projections in the 21st century at the 500 hPa and 850 hPa pressure levels. It has been shown that the different climate variables and levels of the atmosphere have distinct patterns in terms of their total future uncertainty and the contributions from the three sources. Scenario and model uncertainties in general contribute reasonably evenly to total uncertainty, with smaller contributions from the initial condition ensembles.

  4. Multi-scale enhancement of climate prediction over land by increasing the model sensitivity to vegetation variability in EC-Earth

    Science.gov (United States)

    Alessandri, Andrea; Catalano, Franco; De Felice, Matteo; Van Den Hurk, Bart; Doblas Reyes, Francisco; Boussetta, Souhail; Balsamo, Gianpaolo; Miller, Paul

    2016-04-01

    The EC-Earth earth system model has been recently developed to include the dynamics of vegetation. In its original formulation, vegetation variability is simply operated by the Leaf Area Index (LAI), which affects climate basically by changing the vegetation physiological resistance to evapotranspiration. This coupling has been found to have only a weak effect on the surface climate modeled by EC-Earth. In reality, the effective sub-grid vegetation fractional coverage will vary seasonally and at interannual time-scales in response to leaf-canopy growth, phenology and senescence. Therefore it affects biophysical parameters such as the albedo, surface roughness and soil field capacity. To adequately represent this effect in EC-Earth, we included an exponential dependence of the vegetation cover on the LAI. By comparing two sets of simulations performed with and without the new variable fractional-coverage parameterization, spanning retrospective predictions at the decadal (5-years), seasonal and sub-seasonal 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 tends to correct the winter warm biases, improves the climate change sensitivity, the decadal potential predictability as well as the skill of forecasts at seasonal and sub-seasonal 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

  5. Investigation of North American vegetation variability under recent climate: A study using the SSiB4/TRIFFID biophysical/dynamic vegetation model

    Science.gov (United States)

    Zhang, Zhengqiu; Xue, Yongkang; MacDonald, Glen; Cox, Peter M.; Collatz, G. James

    2015-02-01

    Recent studies have shown that current dynamic vegetation models have serious weaknesses in reproducing the observed vegetation dynamics and contribute to bias in climate simulations. This study intends to identify the major factors that underlie the connections between vegetation dynamics and climate variability and investigates vegetation spatial distribution and temporal variability at seasonal to decadal scales over North America (NA) to assess a 2-D biophysical model/dynamic vegetation model's (Simplified Simple Biosphere Model version 4, coupled with the Top-down Representation of Interactive Foliage and Flora Including Dynamics Model (SSiB4/TRIFFID)) ability to simulate these characteristics for the past 60 years (1948 through 2008). Satellite data are employed as constraints for the study and to compare the relationships between vegetation and climate from the observational and the simulation data sets. Trends in NA vegetation over this period are examined. The optimum temperature for photosynthesis, leaf drop threshold temperatures, and competition coefficients in the Lotka-Volterra equation, which describes the population dynamics of species competing for some common resource, have been identified as having major impacts on vegetation spatial distribution and obtaining proper initial vegetation conditions in SSiB4/TRIFFID. The finding that vegetation competition coefficients significantly affect vegetation distribution suggests the importance of including biotic effects in dynamical vegetation modeling. The improved SSiB4/TRIFFID can reproduce the main features of the NA distributions of dominant vegetation types, the vegetation fraction, and leaf area index (LAI), including its seasonal, interannual, and decadal variabilities. The simulated NA LAI also shows a general increasing trend after the 1970s in responding to warming. Both simulation and satellite observations reveal that LAI increased substantially in the southeastern U.S. starting from the 1980

  6. Polar forcing of natural variability of the atmospheric climate

    International Nuclear Information System (INIS)

    A comparative analysis of the latitudinal distribution of the climatic variability of different characteristics of the upper atmospheric state and heat balance constituents from data of climatic archives reveals the features of the variability forcing in the direction of the North Pole. The polar forcing of the variability of thermodynamic atmospheric parameters (temperature, pressure, geopotential) is shown to be formed by the fluctuations of meridional air exchange. Intercorrelated analytical expressions are obtained for root-mean-square deviations of average zonal values of the parameters, indicating the effect of polar forcing in the observed variability. The effect of polar forcing of air temperature variability is simulated and its dependence on albedo changes and the Greenhouse effect is considered by means of the energy-balance zonal atmospheric model

  7. Enabling Philippine Farmers to Adapt to Climate Variability Using Seasonal Climate and Weather Forecast with a Crop Simulation Model in an SMS-based Farmer Decision Support System

    Science.gov (United States)

    Ebardaloza, J. B. R.; Trogo, R.; Sabido, D. J.; Tongson, E.; Bagtasa, G.; Balderama, O. F.

    2015-12-01

    Corn farms in the Philippines are rainfed farms, hence, it is of utmost importance to choose the start of planting date so that the critical growth stages that are in need of water will fall on dates when there is rain. Most farmers in the Philippines use superstitions and traditions as basis for farming decisions such as when to start planting [1]. Before climate change, superstitions like planting after a feast day of a saint has worked for them but with the recent progression of climate change, farmers now recognize that there is a need for technological intervention [1]. The application discussed in this paper presents a solution that makes use of meteorological station sensors, localized seasonal climate forecast, localized weather forecast and a crop simulation model to provide recommendations to farmers based on the crop cultivar, soil type and fertilizer type used by farmers. It is critical that the recommendations given to farmers are not generic as each farmer would have different needs based on their cultivar, soil, fertilizer, planting schedule and even location [2]. This application allows the farmer to inquire about whether it will rain in the next seven days, the best date to start planting based on the potential yield upon harvest, when to apply fertilizer and by how much, when to water and by how much. Short messaging service (SMS) is the medium chosen for this application because while mobile penetration in the Philippines is as high as 101%, the smart phone penetration is only at 15% [3]. SMS has been selected as it has been identified as the most effective way of reaching farmers with timely agricultural information and knowledge [4,5]. The recommendations while derived from making use of Automated Weather Station (AWS) sensor data, Weather Research Forecasting (WRF) models and DSSAT 4.5 [9], are translated into the local language of the farmers and in a format that is easily understood as recommended in [6,7,8]. A pilot study has been started

  8. Climate variability according to triple saros gravity cycles

    CERN Document Server

    Livingston, William R

    2013-01-01

    I describe a climate model which corresponds directly to eclipse cycles. The theory is based upon a similarity between the 54 year triple saros eclipse period and the periodicity of drought. I argue that eclipse shadows are an indication of gravity cycles, and that variable lunar gravitation is the most significant aspect of the eclipse process. I reinforce the idea that lunar gravitational forcing has a profound effect on the water vapor in Earth's atmosphere, and can affect the density and location of clouds. I explore the possibility that decadal variability of ocean surface levels may be explained by triple saros gravity cycles. I point out that lunar gravitation was excluded from the most significant climate report of 2007, and that climate data contradictions have been overlooked by researchers. I focus on the value of data that has not been aggregated into global averages. I touch upon the history of global warming, and I offer predictions based upon 54 year climate periodicity.

  9. The ocean's role in climate variability

    Institute of Scientific and Technical Information of China (English)

    CHEN Dake

    2008-01-01

    Because of its vast volume and heat capacity, the ocean contains most of the memory of the earth's ocean - atmosphere coupled system. It has been suggested that the ocean may delay global warming by absorbing large amounts of heat, that it may cause ab- rupt climate change due to its disrupted thermohaline circulation, and that it may set the time-scales for various climate oscilla- tions. Although the slow pace and persistence of oceanic variations give hope to long-range prediction, there still exist large uncer- tainties in climate predictability. Presently available observations and models are generally inadequate for studying and predicting long-term climate changes. However, some short-term fluctuations such as ENSO have been well studied and shown to be highly predictable even with simplified models.

  10. Vegetation Interaction Enhances Interdecadal Climate Variability in the Sahel

    Science.gov (United States)

    Zeng, Ning; Neelin, J. David; Lau, William K.-M.

    1999-01-01

    The role of naturally varying vegetation in influencing the climate variability in the Sahel is explored in a coupled atmosphere-land-vegetation model. The Sahel rainfall variability is influenced by sea surface temperature (SST) variations in the oceans. Land-surface feedback is found to increase this variability both on interannual and interdecadal time scales. Interactive vegetation enhances the interdecadal variation significantly, but can reduce year to year variability due to a phase lag introduced by the relatively slow vegetation adjustment time. Variations in vegetation accompany the changes in rainfall, in particular, the multi-decadal drying trend from the 1950s to the 80s.

  11. Investigation of North American Vegetation Variability under Recent Climate: A Study Using the SSiB4/TRIFFID Biophysical/Dynamic Vegetation Model

    Science.gov (United States)

    Zhang, Zhengqiu; Xue, Yongkang; MacDonald, Glen; Cox, Peter M.; Collatz, George J.

    2015-01-01

    Recent studies have shown that current dynamic vegetation models have serious weaknesses in reproducing the observed vegetation dynamics and contribute to bias in climate simulations. This study intends to identify the major factors that underlie the connections between vegetation dynamics and climate variability and investigates vegetation spatial distribution and temporal variability at seasonal to decadal scales over North America (NA) to assess a 2-D biophysical model/dynamic vegetation model's (Simplified Simple Biosphere Model version 4, coupled with the Top-down Representation of Interactive Foliage and Flora Including Dynamics Model (SSiB4/TRIFFID)) ability to simulate these characteristics for the past 60 years (1948 through 2008). Satellite data are employed as constraints for the study and to compare the relationships between vegetation and climate from the observational and the simulation data sets. Trends in NA vegetation over this period are examined. The optimum temperature for photosynthesis, leaf drop threshold temperatures, and competition coefficients in the Lotka-Volterra equation, which describes the population dynamics of species competing for some common resource, have been identified as having major impacts on vegetation spatial distribution and obtaining proper initial vegetation conditions in SSiB4/TRIFFID. The finding that vegetation competition coefficients significantly affect vegetation distribution suggests the importance of including biotic effects in dynamical vegetation modeling. The improved SSiB4/TRIFFID can reproduce the main features of the NA distributions of dominant vegetation types, the vegetation fraction, and leaf area index (LAI), including its seasonal, interannual, and decadal variabilities. The simulated NA LAI also shows a general increasing trend after the 1970s in responding to warming. Both simulation and satellite observations reveal that LAI increased substantially in the southeastern U.S. starting from the 1980

  12. Tufted puffin reproduction reveals ocean climate variability

    OpenAIRE

    Gjerdrum, Carina; Vallée, Anne M. J.; St. Clair, Colleen Cassady; Bertram, Douglas F.; John L. Ryder; Blackburn, Gwylim S.

    2003-01-01

    Anomalously warm sea-surface temperatures (SSTs) are associated with interannual and decadal variability as well as with long-term climate changes indicative of global warming. Such oscillations could precipitate changes in a variety of oceanic processes to affect marine species worldwide. As global temperatures continue to rise, it will be critically important to be able to predict the effects of such changes on species' abundance, distribution, and ecological relatio...

  13. Climate variability and change in Ethiopia : exploring impacts and adaptation options for cereal production

    NARCIS (Netherlands)

    Kassie, B.T.

    2014-01-01

    Key words: Climate change, Adaptation, Crop modelling, Uncertainty, Maize (Zea mays), Central Rift Valley. Smallholder farmers in Ethiopia have been facing severe climate related hazards, in particular highly variable rainfall and severe droughts that negativelyaffect their livelihoods.Anticipated

  14. Evaluation of the inter-annual variability of stratospheric chemical composition in chemistry-climate models using ground-based multi species time series

    OpenAIRE

    Poulain, Virginie; Bekki, Slimane; Marchand, Marion; Chipperfield, Martyn P.; Khodri, Myriam; Lefèvre, Franck; Dhomse, Sandip; Bodeker, Greg E.; Toumi, Ralf; De Mazière, Martine; Pommereau, Jean-Pierre; Pazmino, Andrea; Goutail, Florence; Plummer, David; Rozanov, E.

    2016-01-01

    The variability of stratospheric chemical composition occurs on a broad spectrum of timescales, ranging from day to decades. A large part of the variability appears to be driven by external forcings such as volcanic aerosols, solar activity, halogen loading, levels of greenhouse gases (GHG), and modes of climate variability (quasi-biennial oscillation (QBO), El Niño-Southern Oscillation (ENSO)). We estimate the contributions of different external forcings to the interannual variability of str...

  15. Relationship between ozon changes and solar variability through climate observation

    International Nuclear Information System (INIS)

    A number of photo model studies of the earth's atmosphere have raised the possibility that atmospheric O3 concentrations may be altered significantly by antropogenic sources of trace chemical species and by solar variability. The possible climatic effect of such O3, perturbations, as they are understood currently, are reviewed. A change in tropospheric O3 can influence the tropospheric climate directly through its effect on tropospheric radiative heating, where as stratospheric O3 change exerts its influence on the tropospheric climate through radiative and dynamical coupling mechanics. Changes in both tropospheric and stratospheric O3 are considered and model result are described for their effects on: surface temperature, surface tropospheric radiative heating, and vertical and latitudinal temperature gradients within the stratosphere. The solar variability effects on stratospheric temperature gradients and on radiative dissipation rates are discussed

  16. Human activity and climate variability project: annual report 2001

    International Nuclear Information System (INIS)

    Knowledge of the state of the Australian environment, including natural climate variability, prior to colonial settlement is vital if we are to define and understand the impact of over two hundred years of post-industrial human activity on our landscape. ANSTO, in conjunction with university partners, is leading a major research effort to provide natural archives of human activity and climate variability over the last 500 years in Australia, utilising a variety of techniques, including lead-210 and radiocarbon dating and analyses of proxy indicators (such as microfossils) as well as direct evidence (such as trace elements) of human activity and climate variability. The other major project objectives were to contribute to the understanding of the impact of human induced and natural aerosols in the East Asian region on climate through analysis and sourcing of fine particles and characterisation of air samples using radon concentrations and to contribute to the improvement of land surface parameterisation schemes and investigate the potential to use stable isotopes to improve global climate models and thus improve our understanding of future climate

  17. Sensitivity of Climate Change Detection and Attribution to the Characterization of Internal Climate Variability

    KAUST Repository

    Imbers, Jara

    2014-05-01

    The Intergovernmental Panel on Climate Change\\'s (IPCC) "very likely" statement that anthropogenic emissions are affecting climate is based on a statistical detection and attribution methodology that strongly depends on the characterization of internal climate variability. In this paper, the authors test the robustness of this statement in the case of global mean surface air temperature, under different representations of such variability. The contributions of the different natural and anthropogenic forcings to the global mean surface air temperature response are computed using a box diffusion model. Representations of internal climate variability are explored using simple stochastic models that nevertheless span a representative range of plausible temporal autocorrelation structures, including the short-memory first-order autoregressive [AR(1)] process and the long-memory fractionally differencing process. The authors find that, independently of the representation chosen, the greenhouse gas signal remains statistically significant under the detection model employed in this paper. The results support the robustness of the IPCC detection and attribution statement for global mean temperature change under different characterizations of internal variability, but they also suggest that a wider variety of robustness tests, other than simple comparisons of residual variance, should be performed when dealing with other climate variables and/or different spatial scales. © 2014 American Meteorological Society.

  18. Analysis of the Relationship Between Climate and NDVI Variability at Global Scales

    Science.gov (United States)

    Zeng, Fan-Wei; Collatz, G. James; Pinzon, Jorge; Ivanoff, Alvaro

    2011-01-01

    interannual variability in modeled (CASA) C flux is in part caused by interannual variability in Normalized Difference Vegetation Index (NDVI) Fraction of Photosynthetically Active Radiation (FPAR). This study confirms a mechanism producing variability in modeled NPP: -- NDVI (FPAR) interannual variability is strongly driven by climate; -- The climate driven variability in NDVI (FPAR) can lead to much larger fluctuation in NPP vs. the NPP computed from FPAR climatology

  19. Solar Variability in the Context of Other Climate Forcing Mechanisms

    Science.gov (United States)

    Hansen, James E.

    1999-01-01

    I compare and contrast climate forcings due to solar variability with climate forcings due to other mechanisms of climate change, interpretation of the role of the sun in climate change depends upon climate sensitivity and upon the net forcing by other climate change mechanisms. Among the potential indirect climate forcings due to solar variability, only that due to solar cycle induced ozone changes has been well quantified. There is evidence that the sun has been a significant player in past climate change on decadal to century time scales, and that it has the potential to contribute to climate change in the 21st century.

  20. Holocene climate variability in Texas, USA: An integration of existing paleoclimate data and modeling with a new, high-resolution speleothem record

    Science.gov (United States)

    Wong, Corinne I.; Banner, Jay L.; Musgrove, Marylynn

    2015-01-01

    Delineating the climate processes governing precipitation variability in drought-prone Texas is critical for predicting and mitigating climate change effects, and requires the reconstruction of past climate beyond the instrumental record. We synthesize existing paleoclimate proxy data and climate simulations to provide an overview of climate variability in Texas during the Holocene. Conditions became progressively warmer and drier transitioning from the early to mid Holocene, culminating between 7 and 3 ka (thousand years ago), and were more variable during the late Holocene. The timing and relative magnitude of Holocene climate variability, however, is poorly constrained owing to considerable variability among the different records. To help address this, we present a new speleothem (NBJ) reconstruction from a central Texas cave that comprises the highest resolution proxy record to date, spanning the mid to late Holocene. NBJ trace-element concentrations indicate variable moisture conditions with no clear temporal trend. There is a decoupling between NBJ growth rate, trace-element concentrations, and δ18O values, which indicate that (i) the often direct relation between speleothem growth rate and moisture availability is likely complicated by changes in the overlying ecosystem that affect subsurface CO2 production, and (ii) speleothem δ18O variations likely reflect changes in moisture source (i.e., proportion of Pacific-vs. Gulf of Mexico-derived moisture) that appear not to be linked to moisture amount.

  1. Impacts of climate change and variability on European agriculture

    DEFF Research Database (Denmark)

    Orlandini, Simone; Nejedlik, Pavol; Eitzinger, Josef;

    2008-01-01

    and simulation models, including review and assessment of tools used to relate climate and agricultural processes; evaluation of the current trends of agroclimatic indices and model outputs, including remote sensing; developing and assessing future regional and local scenarios of agroclimatic conditions......Climate plays a fundamental role in agriculture because of to its influence on production. All processes are regulated by specific climatic requirements. Furthermore, European agriculture, based on highly developed farming techniques, is mainly oriented to high quality food production that is more...... susceptible to meteorological hazards. These hazards can modify environment-genotype interactions, which can affect the quality of production. The COST 734 Action (Impacts of Climate Change and Variability on European Agriculture), launched in 2006, is composed of 28 signature countries and is funded...

  2. Sensitivity of global terrestrial ecosystems to climate variability.

    Science.gov (United States)

    Seddon, Alistair W R; Macias-Fauria, Marc; Long, Peter R; Benz, David; Willis, Kathy J

    2016-03-10

    The identification of properties that contribute to the persistence and resilience of ecosystems despite climate change constitutes a research priority of global relevance. Here we present a novel, empirical approach to assess the relative sensitivity of ecosystems to climate variability, one property of resilience that builds on theoretical modelling work recognizing that systems closer to critical thresholds respond more sensitively to external perturbations. We develop a new metric, the vegetation sensitivity index, that identifies areas sensitive to climate variability over the past 14 years. The metric uses time series data derived from the moderate-resolution imaging spectroradiometer (MODIS) enhanced vegetation index, and three climatic variables that drive vegetation productivity (air temperature, water availability and cloud cover). Underlying the analysis is an autoregressive modelling approach used to identify climate drivers of vegetation productivity on monthly timescales, in addition to regions with memory effects and reduced response rates to external forcing. We find ecologically sensitive regions with amplified responses to climate variability in the Arctic tundra, parts of the boreal forest belt, the tropical rainforest, alpine regions worldwide, steppe and prairie regions of central Asia and North and South America, the Caatinga deciduous forest in eastern South America, and eastern areas of Australia. Our study provides a quantitative methodology for assessing the relative response rate of ecosystems--be they natural or with a strong anthropogenic signature--to environmental variability, which is the first step towards addressing why some regions appear to be more sensitive than others, and what impact this has on the resilience of ecosystem service provision and human well-being.

  3. Sensitivity of global terrestrial ecosystems to climate variability

    Science.gov (United States)

    Seddon, Alistair W. R.; Macias-Fauria, Marc; Long, Peter R.; Benz, David; Willis, Kathy J.

    2016-03-01

    The identification of properties that contribute to the persistence and resilience of ecosystems despite climate change constitutes a research priority of global relevance. Here we present a novel, empirical approach to assess the relative sensitivity of ecosystems to climate variability, one property of resilience that builds on theoretical modelling work recognizing that systems closer to critical thresholds respond more sensitively to external perturbations. We develop a new metric, the vegetation sensitivity index, that identifies areas sensitive to climate variability over the past 14 years. The metric uses time series data derived from the moderate-resolution imaging spectroradiometer (MODIS) enhanced vegetation index, and three climatic variables that drive vegetation productivity (air temperature, water availability and cloud cover). Underlying the analysis is an autoregressive modelling approach used to identify climate drivers of vegetation productivity on monthly timescales, in addition to regions with memory effects and reduced response rates to external forcing. We find ecologically sensitive regions with amplified responses to climate variability in the Arctic tundra, parts of the boreal forest belt, the tropical rainforest, alpine regions worldwide, steppe and prairie regions of central Asia and North and South America, the Caatinga deciduous forest in eastern South America, and eastern areas of Australia. Our study provides a quantitative methodology for assessing the relative response rate of ecosystems—be they natural or with a strong anthropogenic signature—to environmental variability, which is the first step towards addressing why some regions appear to be more sensitive than others, and what impact this has on the resilience of ecosystem service provision and human well-being.

  4. Sensitivity of global terrestrial ecosystems to climate variability.

    Science.gov (United States)

    Seddon, Alistair W R; Macias-Fauria, Marc; Long, Peter R; Benz, David; Willis, Kathy J

    2016-03-10

    The identification of properties that contribute to the persistence and resilience of ecosystems despite climate change constitutes a research priority of global relevance. Here we present a novel, empirical approach to assess the relative sensitivity of ecosystems to climate variability, one property of resilience that builds on theoretical modelling work recognizing that systems closer to critical thresholds respond more sensitively to external perturbations. We develop a new metric, the vegetation sensitivity index, that identifies areas sensitive to climate variability over the past 14 years. The metric uses time series data derived from the moderate-resolution imaging spectroradiometer (MODIS) enhanced vegetation index, and three climatic variables that drive vegetation productivity (air temperature, water availability and cloud cover). Underlying the analysis is an autoregressive modelling approach used to identify climate drivers of vegetation productivity on monthly timescales, in addition to regions with memory effects and reduced response rates to external forcing. We find ecologically sensitive regions with amplified responses to climate variability in the Arctic tundra, parts of the boreal forest belt, the tropical rainforest, alpine regions worldwide, steppe and prairie regions of central Asia and North and South America, the Caatinga deciduous forest in eastern South America, and eastern areas of Australia. Our study provides a quantitative methodology for assessing the relative response rate of ecosystems--be they natural or with a strong anthropogenic signature--to environmental variability, which is the first step towards addressing why some regions appear to be more sensitive than others, and what impact this has on the resilience of ecosystem service provision and human well-being. PMID:26886790

  5. Variable temperature seat climate control system

    Science.gov (United States)

    Karunasiri, Tissa R.; Gallup, David F.; Noles, David R.; Gregory, Christian T.

    1997-05-06

    A temperature climate control system comprises a variable temperature seat, at least one heat pump, at least one heat pump temperature sensor, and a controller. Each heat pump comprises a number of Peltier thermoelectric modules for temperature conditioning the air in a main heat exchanger and a main exchanger fan for passing the conditioned air from the main exchanger to the variable temperature seat. The Peltier modules and each main fan may be manually adjusted via a control switch or a control signal. Additionally, the temperature climate control system may comprise a number of additional temperature sensors to monitor the temperature of the ambient air surrounding the occupant as well as the temperature of the conditioned air directed to the occupant. The controller is configured to automatically regulate the operation of the Peltier modules and/or each main fan according to a temperature climate control logic designed both to maximize occupant comfort during normal operation, and minimize possible equipment damage, occupant discomfort, or occupant injury in the event of a heat pump malfunction.

  6. Conceptual model for millennial climate variability: a possible combined solar-thermohaline circulation origin for the {proportional_to}1,500-year cycle

    Energy Technology Data Exchange (ETDEWEB)

    Dima, Mihai [Alfred Wegener Institute for Polar and Marine Research, Bremerhaven (Germany); University of Bucharest, Department of Atmospheric Physics, Faculty of Physics, P.O. Box 11440, Magurele, Bucharest (Romania); Lohmann, Gerrit [Alfred Wegener Institute for Polar and Marine Research, Bremerhaven (Germany)

    2009-02-15

    Dansgaard-Oeschger and Heinrich events are the most pronounced climatic changes over the last 120,000 years. Although many of their properties were derived from climate reconstructions, the associated physical mechanisms are not yet fully understood. These events are paced by a {proportional_to}1,500-year periodicity whose origin remains unclear. In a conceptual model approach, we show that this millennial variability can originate from rectification of an external (solar) forcing, and suggest that the thermohaline circulation, through a threshold response, could be the rectifier. We argue that internal threshold response of the thermohaline circulation (THC) to solar forcing is more likely to produce the observed DO cycles than amplification of weak direct {proportional_to}1,500-year forcing of unknown origin, by THC. One consequence of our concept is that the millennial variability is viewed as a derived mode without physical processes on its characteristic time scale. Rather, the mode results from the linear representation in the Fourier space of nonlinearly transformed fundamental modes. (orig.)

  7. Farmers' Perceptions of Climate Variability and Factors Influencing Adaptation: Evidence from Anhui and Jiangsu, China

    Science.gov (United States)

    Kibue, Grace Wanjiru; Liu, Xiaoyu; Zheng, Jufeng; zhang, Xuhui; Pan, Genxing; Li, Lianqing; Han, Xiaojun

    2016-05-01

    Impacts of climate variability and climate change are on the rise in China posing great threat to agriculture and rural livelihoods. Consequently, China is undertaking research to find solutions of confronting climate change and variability. However, most studies of climate change and variability in China largely fail to address farmers' perceptions of climate variability and adaptation. Yet, without an understanding of farmers' perceptions, strategies are unlikely to be effective. We conducted questionnaire surveys of farmers in two farming regions, Yifeng, Jiangsu and Qinxi, Anhui achieving 280 and 293 responses, respectively. Additionally, we used climatological data to corroborate the farmers' perceptions of climate variability. We found that farmers' were aware of climate variability such that were consistent with climate records. However, perceived impacts of climate variability differed between the two regions and were influenced by farmers' characteristics. In addition, the vast majorities of farmers were yet to make adjustments in their farming practices as a result of numerous challenges. These challenges included socioeconomic and socio-cultural barriers. Results of logit modeling showed that farmers are more likely to adapt to climate variability if contact with extension services, frequency of seeking information, household heads' education, and climate variability perceptions are improved. These results suggest the need for policy makers to understand farmers' perceptions of climate variability and change in order to formulate policies that foster adaptation, and ultimately protect China's agricultural assets.

  8. Climate variability and Great Plains agriculture

    International Nuclear Information System (INIS)

    The ways in which inhabitants of the Great Plains, including Indians, early settlers, and 20th century farmers, have adapted to climate changes on the Great Plains are explored. The climate of the Great Plains, because of its variability and extremes, can be very stressful to plants, animals and people. It is suggested that agriculture and society on the Great Plains have, during the last century, become less vulnerable to the stresses imposed by climate. Opinions as to the sustainability of agriculture on the Great Plains vary substantially. Lockeretz (1981) suggests that large scale, high cost technologies have stressed farmers by creating surpluses and by requiring large investments. Opie (1989) sees irrigation as a climate substitute, however he stresses that the Ogallala aquifer must inevitably become depleted. Deborah and Frank Popper (1987) believe that farming on the Plains is unsustainable, and destruction of shelterbelts, out-migration of the rural population and environmental problems will lead to total collapse. With global warming, water in the Great Plains is expected to become scarcer, and although improvements in irrigation efficiency may slow depletion of the Ogallala aquifer, ultimately the acreage under irrigation must decrease to levels that can be sustained by natural recharge and reliable surface flows. 23 refs., 2 figs

  9. Modelling Interglacial Climate

    DEFF Research Database (Denmark)

    Pedersen, Rasmus Anker

    Past warm climate states could potentially provide information on future global warming. The past warming was driven by changed insolation rather than an increased greenhouse effect, and thus the warm climate states are expected to be different. Nonetheless, the response of the climate system...... involves some of the same mechanisms in the two climate states. This thesis aims to investigate these mechanisms through climate model experiments. This two-part study has a special focus on the Arctic region, and the main paleoclimate experiments are supplemented by idealized experiments detailing...... the impact of a changing sea ice cover. The first part focusses on the last interglacial climate (125,000 years before present) which was characterized by substantial warming at high northern latitudes due to an increased insolation during summer. The simulations reveal that the oceanic changes dominate...

  10. Do regional climate models represent regional climate?

    Science.gov (United States)

    Maraun, Douglas; Widmann, Martin

    2014-05-01

    When using climate change scenarios - either from global climate models or further downscaled - to assess localised real world impacts, one has to ensure that the local simulation indeed correctly represents the real world local climate. Representativeness has so far mainly been discussed as a scale issue: simulated meteorological variables in general represent grid box averages, whereas real weather is often expressed by means of point values. As a result, in particular simulated extreme values are not directly comparable with observed local extreme values. Here we argue that the issue of representativeness is more general. To illustrate this point, assume the following situations: first, the (GCM or RCM) simulated large scale weather, e.g., the mid-latitude storm track, might be systematically distorted compared to observed weather. If such a distortion at the synoptic scale is strong, the simulated local climate might be completely different from the observed. Second, the orography even of high resolution RCMs is only a coarse model of true orography. In particular in mountain ranges the simulated mesoscale flow might therefore considerably deviate from the observed flow, leading to systematically displaced local weather. In both cases, the simulated local climate does not represent observed local climate. Thus, representativeness also encompasses representing a particular location. We propose to measure this aspect of representativeness for RCMs driven with perfect boundary conditions as the correlation between observations and simulations at the inter-annual scale. In doing so, random variability generated by the RCMs is largely averaged out. As an example, we assess how well KNMIs RACMO2 RCM at 25km horizontal resolution represents winter precipitation in the gridded E-OBS data set over the European domain. At a chosen grid box, RCM precipitation might not be representative of observed precipitation, in particular in the rain shadow of major moutain ranges

  11. Geomorphic Parameters for Developing a Hydrologic Model to Infer Holocene Climate Variability, Middle Snake River near Bliss, Idaho

    Science.gov (United States)

    Bullard, T. F.; Bacon, S. N.; Kimball, V. R.

    2015-12-01

    The geomorphology and stratigraphy preserved in a canyon reach of the Middle Snake River provide model parameter constraints for estimating Holocene paleohydrology. Channel constrictions, which acted as hydraulic weirs throughout the Holocene, were created in this reach by the Bonneville Flood (~17.5 ka) that left very large (>10 m) slabs of basalt and 2-3 m diameter boulder deposits near the canyon floor. Post-Bonneville Flood landforms and deposits that formed during the Holocene are situated less than ~30 m above river level (arl) in this reach and include fluvial and boulder terraces, alluvial fans, and incised tributary alluvial units. Relative topographic position of these geomorphic features, cross-cutting relations, multiple buried soils, depositional and erosional contacts, and radiocarbon dates from terraces (Qt) and alluvial fans provide a geomorphic and stratigraphic framework and a Holocene chronology for this area. The relative stratigraphic position of a massive silty sand that overlies Bonneville Flood gravel in Qt5 (~20 m arl) and Qt4 (~10 m arl) deposits and comprises all of Qt3 (~5 m arl) deposits indicates changes in Holocene discharge; longitudinal profiles of fluvial terraces graded to hydraulic constrictions provide reasonable estimates of paleo-stage. Fifteen radiocarbon dates yielded ages of ~8670 and ~3500 cal yr BP for Qt4 deposits and ~1100 and ~100 cal yr BP for Qt3 deposits and help define periods of episodic cutting and filling. Timing of Qt4 and Qt3 cut-and-fill episodes and alluvial fan formation correlates well with Holocene global and regional paleoclimate events inferred from Great Basin lake histories including wet periods from ~9.0 to 8.0 ka and ~4.2 to 2.5 ka, the Medieval Climatic Anomaly (~1.2 to 0.8 ka), and the Little Ice Age (~0.3 to 0.6 ka). The fluvial geomorphology documented in this study will be used to develop a watershed-scale hydrologic model to infer paleoprecipitation in the region during the Holocene.

  12. Global climate models’ bias in surface temperature trends and variability

    International Nuclear Information System (INIS)

    The Earth has warmed in the last century with the most rapid warming occurring near the surface in the Arctic. This Arctic amplification occurs partly because the extra heat is trapped in a thin layer of air near the surface due to the persistent stable-stratification found in this region. The amount of warming depends upon the extent of turbulent mixing in the atmosphere, which is described by the depth of the atmospheric boundary layer (ABL). Global climate models (GCMs) tend to over-estimate the depth of stably-stratified ABLs, and here we show that GCM biases in the ABL depth are strongly correlated with biases in the surface temperature variability. This highlights the need for a better description of the stably-stratified ABL in GCMs in order to constrain the current uncertainty in climate variability and projections of climate change in the surface layer. (letter)

  13. Revealing Relationships among Relevant Climate Variables with Information Theory

    CERN Document Server

    Knuth, Kevin H; Curry, Charles T; Huyser, Karen A; Wheeler, Kevin R; Rossow, William B

    2013-01-01

    A primary objective of the NASA Earth-Sun Exploration Technology Office is to understand the observed Earth climate variability, thus enabling the determination and prediction of the climate's response to both natural and human-induced forcing. We are currently developing a suite of computational tools that will allow researchers to calculate, from data, a variety of information-theoretic quantities such as mutual information, which can be used to identify relationships among climate variables, and transfer entropy, which indicates the possibility of causal interactions. Our tools estimate these quantities along with their associated error bars, the latter of which is critical for describing the degree of uncertainty in the estimates. This work is based upon optimal binning techniques that we have developed for piecewise-constant, histogram-style models of the underlying density functions. Two useful side benefits have already been discovered. The first allows a researcher to determine whether there exist suf...

  14. Climate variability and sedimentation of a hydropower reservoir

    International Nuclear Information System (INIS)

    As part of the relicensing of a large Hydroelectric Project in the central Appalachians, large scale watershed and reservoir sedimentation models were developed to forecast potential sedimentation scenarios. The GIS based watershed model was spatially explicit and calibrated to long term observed data. Potential socio/economic development scenarios were used to construct future watershed land cover scenarios. Climatic variability and potential change analysis were used to identify future climate regimes and shifts in precipitation and temperature patterns. Permutations of these development and climate changes were forecasted over 50 years and used to develop sediment yield regimes to the project reservoir. Extensive field work and reservoir survey, including current and wave instrumentation, were used to characterize the project watershed, rivers and reservoir hydrodynamics. A fully 3 dimensional hydrodynamic reservoir sedimentation model was developed for the project and calibrated to observed data. Hydrologic and sedimentation results from watershed forecasting provided boundary conditions for reservoir inputs. The calibrated reservoir model was then used to forecast changes in reservoir sedimentation and storage capacity under different future climate scenarios. Results indicated unique zones of advancing sediment deltas and temporary storage areas. Forecasted changes in reservoir bathymetry and sedimentation patterns were also developed for the various climate change scenarios. The warmer and wetter scenario produced sedimentation impacts similar to extensive development under no climate change. The results of these analyses are being used to develop collaborative watershed and soil conservation partnerships to reduce future soil losses and reservoir sedimentation from projected development. (author)

  15. Tropical cloud feedbacks and natural variability of climate

    Science.gov (United States)

    Miller, R. L.; Del Genio, A. D.

    1994-01-01

    Simulations of natural variability by two general circulation models (GCMs) are examined. One GCM is a sector model, allowing relatively rapid integration without simplification of the model physics, which would potentially exclude mechanisms of variability. Two mechanisms are found in which tropical surface temperature and sea surface temperature (SST) vary on interannual and longer timescales. Both are related to changes in cloud cover that modulate SST through the surface radiative flux. Over the equatorial ocean, SST and surface temperature vary on an interannual timescale, which is determined by the magnitude of the associated cloud cover anomalies. Over the subtropical ocean, variations in low cloud cover drive SST variations. In the sector model, the variability has no preferred timescale, but instead is characterized by a 'red' spectrum with increasing power at longer periods. In the terrestrial GCM, SST variability associated with low cloud anomalies has a decadal timescale and is the dominant form of global temperature variability. Both GCMs are coupled to a mixed layer ocean model, where dynamical heat transports are prescribed, thus filtering out El Nino-Southern Oscillation (ENSO) and thermohaline circulation variability. The occurrence of variability in the absence of dynamical ocean feedbacks suggests that climatic variability on long timescales can arise from atmospheric processes alone.

  16. A Study of Spatio-Temporal Variability in Future Wind Energy over the Korean Peninsula Using Regional Climate Model Ensemble Projections

    Science.gov (United States)

    KIM, Y.; Lim, Y. J.; Kim, Y. H.; Kim, B. J.

    2015-12-01

    The impacts of climate change on wind speed, wind energy density (WED), and potential electronic production (PEP) over the Korean peninsula have been investigated by using five regional climate models (HadGEM3-RA, RegCM, WRF, GRIMs and MM5) ensemble projection data. HadGEM2-AO based two RCP scenarios (RCP4.5/8.5) data have been used for initial and boundary condition to all RCMs. Wind energy density and its annual and seasonal variability have been estimated based on monthly near-surface wind speeds, and the potential electronic production and its change have been also analyzed. As a result of comparison ensemble models based annual mean wind speed for 25-yr historical period (1981-2005) to the ERA-interim, it is shown that all RCMs overestimate near-surface wind speed compared to the reanalysis data but the results of HadGEM3-RA are most comparable. The changes annual and seasonal mean of WED and PEP for the historical period and comparison results to future projection (2021-2050) will be presented in this poster session. We also scrutinize the changes in mean sea level pressure and mean sea level pressure gradient in driving GCM/RCM as a factor inducing the variations. Our results can be used as a background data for devising a plan to develop and operate wind farm over the Korean Peninsula.

  17. Using short-term climate variability to infer equilibrium climate sensitivity

    Science.gov (United States)

    Dessler, A. E.; Zhou, C.

    2015-12-01

    We provide a constraint on the magnitude of the Earth's equilibrium climate sensitivity (ECS) using observations short-term climate variability between 2000 and 2014 along with short- and long-term climate model simulations. Our best estimate of the ECS from this analysis 2.5°C, with a likely range of 1.5-3.4°C, which falls in the bottom half of the canonical IPCC ECS range of 1.5-4.5°C.

  18. Investigating arctic cloud and radiative properties associated with the large-scale climate variability through observations, reanalysis, and mesoscale modeling

    Science.gov (United States)

    Barton, Neil P.

    This dissertation examines two decades of Arctic cloud cover data and the variability in Arctic clouds with relation to changes in sea ice using observational and reanalysis data, as well as a state-of-the-art mesoscale model. Decadal length Arctic cloud cover data are examined because of the inherent differences within these measurements that have not been explored in previous research. Cloud cover data are analyzed from regions poleward of 60°N from several sources of visual surface observations including surface remotely sensed measurements at two locations, two spaced-based passive remotely sensed datasets (Advanced Very High Resolution Radiometer Polar Pathfinder extended (APPx) and Television Infrared Observation Satellite Operational Vertical Sounder (TOVS) Polar Pathfinder (TPP)), and one reanalysis dataset (European Center for Medium-Range Weather Forecasting Reanalysis (ERA-40)) are compared. The passive remotely sensed data are sensitive to surface type. Cloud amounts from the APPx and TPP decrease with increases in sea ice concentrations. In comparison to the surface remotely sensed measurements over sea ice, the APPx and TPP cloud amounts are consistently low. The ERA-40 output cloud cover not contain a sharp decrease from water to ice surfaces, and compares reasonably with the remotely sensed surface measurements over sea ice. During the northern hemisphere winter at land stations, the TPP and ERA-40 cloud amounts are similar. This is most likely a result of the ERA-40 model using TOVS irradiances as input data. The APPx and surface cloud amounts are similar during all seasons, but they are not in precise agreement with the TPP/ERA-40 values. Cloud amounts from the ERA-40 are also most similar to surface measurements in regions where radiosonde data are used as input. Cloud radiative forcing calculated from the ERA-40 output is examined with relation to sea ice concentrations using 20 years of data. The radiative effect of clouds varies linearly with

  19. Does internal climate variability overwhelm climate change signals in streamflow? The upper Po and Rhone basin case studies

    Energy Technology Data Exchange (ETDEWEB)

    Fatichi, S., E-mail: simone.fatichi@ifu.baug.ethz.ch; Rimkus, S.; Burlando, P.; Bordoy, R.

    2014-09-15

    Projections of climate change effects in streamflow are increasingly required to plan water management strategies. These projections are however largely uncertain due to the spread among climate model realizations, internal climate variability, and difficulties in transferring climate model results at the spatial and temporal scales required by catchment hydrology. A combination of a stochastic downscaling methodology and distributed hydrological modeling was used in the ACQWA project to provide projections of future streamflow (up to year 2050) for the upper Po and Rhone basins, respectively located in northern Italy and south-western Switzerland. Results suggest that internal (stochastic) climate variability is a fundamental source of uncertainty, typically comparable or larger than the projected climate change signal. Therefore, climate change effects in streamflow mean, frequency, and seasonality can be masked by natural climatic fluctuations in large parts of the analyzed regions. An exception to the overwhelming role of stochastic variability is represented by high elevation catchments fed by glaciers where streamflow is expected to be considerably reduced due to glacier retreat, with consequences appreciable in the main downstream rivers in August and September. Simulations also identify regions (west upper Rhone and Toce, Ticino river basins) where a strong precipitation increase in the February to April period projects streamflow beyond the range of natural climate variability during the melting season. This study emphasizes the importance of including internal climate variability in climate change analyses, especially when compared to the limited uncertainty that would be accounted for by few deterministic projections. The presented results could be useful in guiding more specific impact studies, although design or management decisions should be better based on reliability and vulnerability criteria as suggested by recent literature. - Highlights:

  20. Does internal climate variability overwhelm climate change signals in streamflow? The upper Po and Rhone basin case studies

    International Nuclear Information System (INIS)

    Projections of climate change effects in streamflow are increasingly required to plan water management strategies. These projections are however largely uncertain due to the spread among climate model realizations, internal climate variability, and difficulties in transferring climate model results at the spatial and temporal scales required by catchment hydrology. A combination of a stochastic downscaling methodology and distributed hydrological modeling was used in the ACQWA project to provide projections of future streamflow (up to year 2050) for the upper Po and Rhone basins, respectively located in northern Italy and south-western Switzerland. Results suggest that internal (stochastic) climate variability is a fundamental source of uncertainty, typically comparable or larger than the projected climate change signal. Therefore, climate change effects in streamflow mean, frequency, and seasonality can be masked by natural climatic fluctuations in large parts of the analyzed regions. An exception to the overwhelming role of stochastic variability is represented by high elevation catchments fed by glaciers where streamflow is expected to be considerably reduced due to glacier retreat, with consequences appreciable in the main downstream rivers in August and September. Simulations also identify regions (west upper Rhone and Toce, Ticino river basins) where a strong precipitation increase in the February to April period projects streamflow beyond the range of natural climate variability during the melting season. This study emphasizes the importance of including internal climate variability in climate change analyses, especially when compared to the limited uncertainty that would be accounted for by few deterministic projections. The presented results could be useful in guiding more specific impact studies, although design or management decisions should be better based on reliability and vulnerability criteria as suggested by recent literature. - Highlights:

  1. The influence of climate variables on dengue in Singapore.

    Science.gov (United States)

    Pinto, Edna; Coelho, Micheline; Oliver, Leuda; Massad, Eduardo

    2011-12-01

    In this work we correlated dengue cases with climatic variables for the city of Singapore. This was done through a Poisson Regression Model (PRM) that considers dengue cases as the dependent variable and the climatic variables (rainfall, maximum and minimum temperature and relative humidity) as independent variables. We also used Principal Components Analysis (PCA) to choose the variables that influence in the increase of the number of dengue cases in Singapore, where PC₁ (Principal component 1) is represented by temperature and rainfall and PC₂ (Principal component 2) is represented by relative humidity. We calculated the probability of occurrence of new cases of dengue and the relative risk of occurrence of dengue cases influenced by climatic variable. The months from July to September showed the highest probabilities of the occurrence of new cases of the disease throughout the year. This was based on an analysis of time series of maximum and minimum temperature. An interesting result was that for every 2-10°C of variation of the maximum temperature, there was an average increase of 22.2-184.6% in the number of dengue cases. For the minimum temperature, we observed that for the same variation, there was an average increase of 26.1-230.3% in the number of the dengue cases from April to August. The precipitation and the relative humidity, after analysis of correlation, were discarded in the use of Poisson Regression Model because they did not present good correlation with the dengue cases. Additionally, the relative risk of the occurrence of the cases of the disease under the influence of the variation of temperature was from 1.2-2.8 for maximum temperature and increased from 1.3-3.3 for minimum temperature. Therefore, the variable temperature (maximum and minimum) was the best predictor for the increased number of dengue cases in Singapore.

  2. Climate dynamics and fluid mechanics: Natural variability and related uncertainties

    CERN Document Server

    Ghil, Michael; Simonnet, Eric; 10.1016/j.physd.2008.03.036

    2010-01-01

    The purpose of this review-and-research paper is twofold: (i) to review the role played in climate dynamics by fluid-dynamical models; and (ii) to contribute to the understanding and reduction of the uncertainties in future climate-change projections. To illustrate the first point, we focus on the large-scale, wind-driven flow of the mid-latitude oceans which contribute in a crucial way to Earth's climate, and to changes therein. We study the low-frequency variability (LFV) of the wind-driven, double-gyre circulation in mid-latitude ocean basins, via the bifurcation sequence that leads from steady states through periodic solutions and on to the chaotic, irregular flows documented in the observations. This sequence involves local, pitchfork and Hopf bifurcations, as well as global, homoclinic ones. The natural climate variability induced by the LFV of the ocean circulation is but one of the causes of uncertainties in climate projections. Another major cause of such uncertainties could reside in the structural ...

  3. Climate Model Intercomparisons: Preparing for the Next Phase

    Science.gov (United States)

    Meehl, Gerald A.; Moss, Richard; Taylor, Karl E.; Eyring, Veronika; Stouffer, Ronald J.; Bony, Sandrine; Stevens, Bjorn

    2014-03-01

    Since 1995, the Coupled Model Intercomparison Project (CMIP) has coordinated climate model experiments involving multiple international modeling teams. Through CMIP, climate modelers and scientists from around the world have analyzed and compared state-of-the-art climate model simulations to gain insights into the processes, mechanisms, and consequences of climate variability and climate change. This has led to a better understanding of past, present, and future climate, and CMIP model experiments have routinely been the basis for future climate change assessments made by the Intergovernmental Panel on Climate Change (IPCC) [e.g., IPCC, 2013, and references therein].

  4. Societal Vulnerability to Climate Change and Variability

    International Nuclear Information System (INIS)

    Institutions in many wealthy industrialised countries are robust and their societies appear to be relatively well insulated against the impacts of climate variability, economic problems elsewhere and so on. However, many countries are not in this position, and there is a growing group of humanity which is not benefiting from the apparent global adaptive trends. Worst case scenarios reinforce the impact of this uneven distribution of adaptive capacity, both between and within countries. Nevertheless, at the broad global scale human societies are strongly adaptive and not threatened by climate change for many decades. At the local level the picture is quite different and the survival of some populations at their present locations is in doubt. In the absence of abatement, the longer term outlook is highly uncertain. Adaptation research needs to begin with an understanding of social and economic vulnerability. It requires a different approach to the traditional IPCC impacts assessment, as human behaviour, institutional capacity and culture are more important than biophysical impacts. This is consistent with the intellectual history of the IPCC which has gradually embraced an increasing range of disciplines. 32 refs

  5. Effects of interannual climate variability on tropical tree cover

    NARCIS (Netherlands)

    Holmgren, M.; Hirota, M.; Nes, van E.H.; Scheffer, M.

    2013-01-01

    Climatic warming is substantially intensifying the global water cycle1 and is projected to increase rainfall variability2. Using satellite data, we show that higher climatic variability is associated with reduced tree cover in the wet tropics globally. In contrast, interannual variability in rainfal

  6. Investigation of North American vegetation variability under recent climate - A study using the SSiB4/TRIFFID biophysical/dynamic vegetation model

    Science.gov (United States)

    Zhang, Z.; Xue, Y.; MacDonald, G. M.; Cox, P. M.; Collatz, G. J.

    2014-12-01

    This study applies a 2-D biophysical model/dynamic vegetation model (SSiB4/TRIFFID) to investigate the dominant factors affecting vegetation equilibrium conditions, to assess the model's ability to simulate seasonal to decadal variability for the past 60 years (from 1948 through 2008), to analyze vegetation spatiotemporal characteristics over North America (NA), and to identify the relationships between vegetation and climate. Satellite data are employed as constraints for this study. The optimum temperature for photosynthesis, leaf drop threshold temperatures, and competition coefficients in the Lotka-Volterra equation have major impact on the vegetation spatial distribution and reach to equilibrium status in SSiB4/TRIFFID. The phenomenon that vegetation competition coefficients affect equilibrium suggests the importance of including biotic effects in dynamical vegetation modeling. SSiB4/TRIFFID can reproduce the features of NA distributions of dominant vegetation types, the vegetation fraction, and LAI, including its seasonal, interannual, and decadal variability, well compared with satellite-derived products. The NA LAI shows an increasing trend after the 1970s in responding to warming. Meanwhile, both simulation and satellite observations reveal LAI increased in the southeastern U.S. starting from the 1980s. The effects of the severe drought during 1987-1992 and the last decade in the southwestern U.S.on vegetation are also evident from the simulated and satellite-derived LAIs.Both simulated and satellite-derived LAIs have the strongest correlations with air temperature at northern middle to high latitudes in spring through their effect on photosynthesis and phenological processes. During the summer, the areas with positive correlations retreat northward. Meanwhile, in southwestern dry lands, the negative correlations appear due to the heat stress there during the summer. Furthermore, there are also positive correlations between soil wetness and LAI, which

  7. Methods for assessment of climate variability and climate changes in different time-space scales

    International Nuclear Information System (INIS)

    Main problem of hydrology and design support for water projects connects with modern climate change and its impact on hydrological characteristics as observed as well as designed. There are three main stages of this problem: - how to extract a climate variability and climate change from complex hydrological records; - how to assess the contribution of climate change and its significance for the point and area; - how to use the detected climate change for computation of design hydrological characteristics. Design hydrological characteristic is the main generalized information, which is used for water management and design support. First step of a research is a choice of hydrological characteristic, which can be as a traditional one (annual runoff for assessment of water resources, maxima, minima runoff, etc) as well as a new one, which characterizes an intra-annual function or intra-annual runoff distribution. For this aim a linear model has been developed which has two coefficients connected with an amplitude and level (initial conditions) of seasonal function and one parameter, which characterizes an intensity of synoptic and macro-synoptic fluctuations inside a year. Effective statistical methods have been developed for a separation of climate variability and climate change and extraction of homogeneous components of three time scales from observed long-term time series: intra annual, decadal and centural. The first two are connected with climate variability and the last (centural) with climate change. Efficiency of new methods of decomposition and smoothing has been estimated by stochastic modeling and well as on the synthetic examples. For an assessment of contribution and statistical significance of modern climate change components statistical criteria and methods have been used. Next step has been connected with a generalization of the results of detected climate changes over the area and spatial modeling. For determination of homogeneous region with the same

  8. The Variable Climate Impact of Volcanic Eruptions

    Science.gov (United States)

    Graf, H.

    2011-12-01

    The main effect of big volcanic eruptions in the climate system is due to their efficient transport of condensable gases and their precursors into the stratosphere. There the formation of aerosols leads to effects on atmospheric radiation transfer inducing a reduction of incoming solar radiation by reflection (i.e. cooling of the Earth surface) and absorption of near infrared radiation (i.e. heating) in the aerosol laden layers. In the talk processes determining the climate effect of an eruption will be illustrated by examples, mainly from numerical modelling. The amount of gases released from a magma during an eruption and the efficiency of their transport into very high altitudes depends on the geological setting (magma type) and eruption style. While mid-sized eruption plumes of Plinian style quickly can develop buoyancy by entrainment of ambient air, very large eruptions with high magma flux rates often tend to collapsing plumes and co-ignimbrite style. These cover much bigger areas and are less efficient in entraining ambient air. Vertical transport in these plumes is chaotic and less efficient, leading to lower neutral buoyancy height and less gas and particles reaching high stratospheric altitudes. Explosive energy and amount of released condensable gases are not the only determinants for the climatic effect of an eruption. The effect on shortwave radiation is not linear with the amount of aerosols formed since according to the Lambert-Beer Law atmospheric optical depth reaches a saturation limit with increased absorber concentration. In addition, if more condensable gas is available for aerosol growth, particles become larger and this affects their optical properties to less reflection and more absorption. Larger particles settle out faster, thus reducing the life time of the aerosol disturbance. Especially for big tropical eruptions the strong heating of the stratosphere in low latitudes leads to changes in atmospheric wave propagation by strengthened

  9. Climate variation explains a third of global crop yield variability

    OpenAIRE

    Ray, Deepak K.; James S Gerber; MacDonald, Graham K.; West, Paul C.

    2015-01-01

    Many studies have examined the role of mean climate change in agriculture, but an understanding of the influence of inter-annual climate variations on crop yields in different regions remains elusive. We use detailed crop statistics time series for ~13,500 political units to examine how recent climate variability led to variations in maize, rice, wheat and soybean crop yields worldwide. While some areas show no significant influence of climate variability, in substantial areas of the global b...

  10. Variable climatic conditions dominate recent phytoplankton dynamics in Chesapeake Bay

    Science.gov (United States)

    Harding, Lawrence W., Jr.; Mallonee, Michael E.; Perry, Elgin S.; Miller, W. David; Adolf, Jason E.; Gallegos, Charles L.; Paerl, Hans W.

    2016-03-01

    Variable climatic conditions strongly influence phytoplankton dynamics in estuaries globally. Our study area is Chesapeake Bay, a highly productive ecosystem providing natural resources, transportation, and recreation for nearly 16 million people inhabiting a 165,000-km2 watershed. Since World War II, nutrient over-enrichment has led to multiple ecosystem impairments caused by increased phytoplankton biomass as chlorophyll-a (chl-a). Doubled nitrogen (N) loadings from 1945–1980 led to increased chl-a, reduced water clarity, and low dissolved oxygen (DO), while decreased N loadings from 1981–2012 suggest modest improvement. The recent 30+ years are characterized by high inter-annual variability of chl-a, coinciding with irregular dry and wet periods, complicating the detection of long-term trends. Here, we synthesize time-series data for historical and recent N loadings (TN, NO2 + NO3), chl-a, floral composition, and net primary productivity (NPP) to distinguish secular changes caused by nutrient over-enrichment from spatio-temporal variability imposed by climatic conditions. Wet years showed higher chl-a, higher diatom abundance, and increased NPP, while dry years showed lower chl-a, lower diatom abundance, and decreased NPP. Our findings support a conceptual model wherein variable climatic conditions dominate recent phytoplankton dynamics against a backdrop of nutrient over-enrichment, emphasizing the need to separate these effects to gauge progress toward improving water quality in estuaries.

  11. Modulation of extremes in the Atlantic region by modes of climate variability/change: A mechanistic coupled regional model study

    Energy Technology Data Exchange (ETDEWEB)

    Saravanan, Ramalingam

    2015-01-09

    During the course of this project, we have accomplished the following: 1) Explored the parameter space of component models to minimize regional model bias 2) Assessed the impact of air-sea interaction on hurricanes, focusing in particular on the role of the oceanic barrier layer 3) Contributed to the activities of the U.S. CLIVAR Hurricane Working Group 4) Assessed the impact of lateral and lower boundary conditions on extreme flooding events in the U.S. Midwest in regional model simulations 5) Analyzed the concurrent impact of El Niño-Southern Oscillation and Atlantic Meridional Mode on Atlantic Hurricane activity using observations and regional model simulations

  12. Abrupt change in climate and climate models

    Directory of Open Access Journals (Sweden)

    A. J. Pitman

    2006-01-01

    Full Text Available First, we review the evidence that abrupt climate changes have occurred in the past and then demonstrate that climate models have developing capacity to simulate many of these changes. In particular, the processes by which changes in the ocean circulation drive abrupt changes appear to be captured by climate models to a degree that is encouraging. The evidence that past changes in the ocean have driven abrupt change in terrestrial systems is also convincing, but these processes are only just beginning to be included in climate models. Second, we explore the likelihood that climate models can capture those abrupt changes in climate that may occur in the future due to the enhanced greenhouse effect. We note that existing evidence indicates that a major collapse of the thermohaline circulation seems unlikely in the 21st century, although very recent evidence suggests that a weakening may already be underway. We have confidence that current climate models can capture a weakening, but a collapse in the 21st century of the thermohaline circulation is not projected by climate models. Worrying evidence of instability in terrestrial carbon, from observations and modelling studies, is beginning to accumulate. Current climate models used by the Intergovernmental Panel on Climate Change for the 4th Assessment Report do not include these terrestrial carbon processes. We therefore can not make statements with any confidence regarding these changes. At present, the scale of the terrestrial carbon feedback is believed to be small enough that it does not significantly affect projections of warming during the first half of the 21st century. However, the uncertainties in how biological systems will respond to warming are sufficiently large to undermine confidence in this belief and point us to areas requiring significant additional work.

  13. Abrupt change in climate and climate models

    Directory of Open Access Journals (Sweden)

    A. J. Pitman

    2006-07-01

    Full Text Available First, we review the evidence that abrupt climate changes have occurred in the past and then demonstrate that climate models have developing capacity to simulate many of these changes. In particular, the processes by which changes in the ocean circulation drive abrupt changes appear to be captured by climate models to a degree that is encouraging. The evidence that past changes in the ocean have driven abrupt change in terrestrial systems is also convincing, but these processes are only just beginning to be included in climate models. Second, we explore the likelihood that climate models can capture those abrupt changes in climate that may occur in the future due to the enhanced greenhouse effect. We note that existing evidence indicates that a major collapse of the thermohaline circulate seems unlikely in the 21st century, although very recent evidence suggests that a weakening may already be underway. We have confidence that current climate models can capture a weakening, but a collapse of the thermohaline circulation in the 21st century is not projected by climate models. Worrying evidence of instability in terrestrial carbon, from observations and modelling studies, is beginning to accumulate. Current climate models used by the Intergovernmental Panel on Climate Change for the 4th Assessment Report do not include these terrestrial carbon processes. We therefore can not make statements with any confidence regarding these changes. At present, the scale of the terrestrial carbon feedback is believed to be small enough that it does not significantly affect projections of warming during the first half of the 21st century. However, the uncertainties in how biological systems will respond to warming are sufficiently large to undermine confidence in this belief and point us to areas requiring significant additional work.

  14. The spatial and temporal variability of the surface mass balance in Antarctica: results from a regional climate model

    NARCIS (Netherlands)

    Lipzig, N.P.M. van; Meijgaard, E. van; Oerlemans, J.

    2002-01-01

    A 14 year integration with a regional atmospheric model (RACMO) is used to obtain detailed information on the Antarctic surface mass balance and to understand the mechanisms that are responsible for the spatial and temporal distribution of the surface mass balance. The model (Δx = 55 km) uses the pa

  15. Energy balance climate models

    Science.gov (United States)

    North, G. R.; Cahalan, R. F.; Coakley, J. A., Jr.

    1981-01-01

    An introductory survey of the global energy balance climate models is presented with an emphasis on analytical results. A sequence of increasingly complicated models involving ice cap and radiative feedback processes are solved, and the solutions and parameter sensitivities are studied. The model parameterizations are examined critically in light of many current uncertainties. A simple seasonal model is used to study the effects of changes in orbital elements on the temperature field. A linear stability theorem and a complete nonlinear stability analysis for the models are developed. Analytical solutions are also obtained for the linearized models driven by stochastic forcing elements. In this context the relation between natural fluctuation statistics and climate sensitivity is stressed.

  16. Impacts of forced and unforced climate variability on extreme floods using a large climate ensemble

    Science.gov (United States)

    Martel, Jean-Luc; Brissette, François; Chen, Jie

    2016-04-01

    Frequency analysis has been widely used for the inference of flood magnitude and rainfall intensity required in engineering design. However, this inference is based on the concept of stationarity. How accurate is it when taking into account climate variability (i.e. both internal- and externally-forced variabilities)? Even in the absence of human-induced climate change, the short temporal horizon of the historical records renders this task extremely difficult to accomplish. To overcome this situation, large ensembles of simulations from a single climate model can be used to assess the impact of climate variability on precipitation and streamflow extremes. Thus, the objective of this project is to determine the reliability of return period estimates using the CanESM2 large ensemble. The spring flood annual maxima metric over snowmelt-dominated watersheds was selected to take into account the limits of global circulation models to properly simulate convective precipitation. The GR4J hydrological model coupled with the CemaNeige snow model was selected and calibrated using gridded observation datasets on snowmelt-dominated watersheds in Quebec, Canada. Using the hydrological model, streamflows were simulated using bias corrected precipitation and temperature data from the 50 members of CanESM2. Flood frequency analyses on the spring flood annual maxima were then computed using the Gumbel distribution with a 90% confidence interval. The 20-year return period estimates were then compared to assess the impact of natural climate variability over the 1971-2000 return period. To assess the impact of global warming, this methodology was then repeated for three time slices: reference period (1971-2000), near future (2036-2065) and far future (2071-2100). Over the reference period results indicate that the relative error between the return period estimates of two members can be up to 25%. Regarding the near future and far future periods, natural climate variability of extreme

  17. Smallholder agriculture in India and adaptation to current and future climate variability and climate change

    Science.gov (United States)

    Murari, K. K.; Jayaraman, T.

    2014-12-01

    Modeling studies have indicated that global warming, in many regions, will increase the exposure of major crops to rainfall and temperature stress, leading to lower crop yields. Climate variability alone has a potential to decrease yield to an extent comparable to or greater than yield reductions expected due to rising temperature. For India, where agriculture is important, both in terms of food security as well as a source of livelihoods to a majority of its population, climate variability and climate change are subjects of serious concern. There is however a need to distinguish the impact of current climate variability and climate change on Indian agriculture, especially in relation to their socioeconomic impact. This differentiation is difficult to determine due to the secular trend of increasing production and yield of the past several decades. The current research in this aspect is in an initial stage and requires a multi-disciplinary effort. In this study, we assess the potential differential impacts of environmental stress and shock across different socioeconomic strata of the rural population, using village level survey data. The survey data from eight selected villages, based on the Project on Agrarian Relations in India conducted by the Foundation for Agrarian Studies, indicated that income from crop production of the top 20 households (based on the extent of operational land holding, employment of hired labour and asset holdings) is a multiple of the mean income of the village. In sharp contrast, the income of the bottom 20 households is a fraction of the mean and sometimes negative, indicating a net loss from crop production. The considerable differentials in output and incomes suggest that small and marginal farmers are far more susceptible to climate variability and climate change than the other sections. Climate change is effectively an immediate threat to small and marginal farmers, which is driven essentially by socioeconomic conditions. The impact

  18. Climate variability and sedimentation of a hydropower reservoir

    International Nuclear Information System (INIS)

    This presentation discussed a large-scale watershed and reservoir sedimentation model developed to predict potential sedimentation scenarios for a large hydroelectric power project located in the central Appalachians. The geographic information system (GIS) watershed model was calibrated using observed long-term meteorological and hydrological data. Potential development scenarios were then used to construct future watershed land cover scenarios. Future climate change regimes and precipitation and temperature pattern shifts were identified using climatic variability and potential change analyses. Results of the study were then forecast for a period of 50 years and used to develop sediment yield regimes for the project's reservoir. The model was validated using reservoir and fields studies for watershed, river, and reservoir hydrodynamics. The resulting 3-D hydrological sedimentation model was then used to forecast changes in river sedimentation and storage capacity under various future climate scenarios. Results of the study showed the development of unique zones of advancing sediment deltas and temporary storage areas. Warmer and wetter scenarios produced sedimentation impacts similar to scenarios without climatic change. It was concluded that results of the analyses will be used to help reduce future soil losses in the reservoir. tabs., figs

  19. A hybrid approach to incorporating climate change and variability into climate scenario for impact assessments

    OpenAIRE

    Gebretsadik, Yohannes; Strzepek, Kenneth; Schlosser, C. Adam

    2014-01-01

    Traditional 'delta-change' approach of scenario generation for climate change impact assessment to water resources strongly depends on the selected base-case observed historical climate conditions that the climate shocks are to be super-imposed. This method disregards the combined effect of climate change and the inherent hydro-climatological variability in the system. Here we demonstrated a hybrid uncertainty approach in which uncertainties in historical climate variability are combined with...

  20. Impacts of Austrian Climate Variability on Honey Bee Mortality

    Science.gov (United States)

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

    2015-04-01

    Global food production, as it is today, is not possible without pollinators such as the honey bee. It is therefore alarming that honey bee populations across the world have seen increased mortality rates in the last few decades. The challenges facing the honey bee calls into question the future of our food supply. Beside various infectious diseases, Varroa destructor is one of the main culprits leading to increased rates of honey bee mortality. Varroa destructor is a parasitic mite which strongly depends on honey bee brood for reproduction and can wipe out entire colonies. However, climate variability may also importantly influence honey bee breeding cycles and bee mortality rates. Persistent weather events affects vegetation and hence foraging possibilities for honey bees. This study first defines critical statistical relationships between key climate indicators (e.g., precipitation and temperature) and bee mortality rates across Austria, using 6 consecutive years of data. Next, these leading indicators, as they vary in space and time, are used to build a statistical model to predict bee mortality rates and the respective number of colonies affected. Using leave-one-out cross validation, the model reduces the Root Mean Square Error (RMSE) by 21% with respect to predictions made with the mean mortality rate and the number of colonies. Furthermore, a Monte Carlo test is used to establish that the model's predictions are statistically significant at the 99.9% confidence level. These results highlight the influence of climate variables on honey bee populations, although variability in climate, by itself, cannot fully explain colony losses. This study was funded by the Austrian project 'Zukunft Biene'.

  1. Climate variability impacts on rice crop production in pakistan

    International Nuclear Information System (INIS)

    The climate variability has affected the agriculture production all over the globe. This concern has motivated important changes in the field of research during the last decade. Climate changes are believed to have declining effects towards crop production in Pakistan. This study carries an empirical investigation of the effects of climate change on rice crop of Pakistan by employing Vector Auto Regression (VAR) model. Annual seasonal data of the climatic variables from 1980 to 2013 has been used. Results confirmed that rising mean maximum temperature would lead to reduction in rice production while increase in mean minimum temperature would be advantageous towards rice production. Variation in mean minimum temperature brought about seven percent increase in rice productivity as shown by Variance Decomposition. Mean precipitation and mean temperature would increase rice production but simulations scenarios for 2030 confirmed that much increase in rainfall and mean temperature in long run will negatively affect rice production in future. It is therefore important to follow adequate policy action to safeguard crop productions from disastrous effects. Development of varieties resistant to high temperatures as well as droughts will definitely enhance resilience of rice crop in Pakistan. (author)

  2. Assessing effects of variation in global climate data sets on spatial predictions from climate envelope models

    Science.gov (United States)

    Romanach, Stephanie; Watling, James I.; Fletcher, Robert J., Jr.; Speroterra, Carolina; Bucklin, David N.; Brandt, Laura A.; Pearlstine, Leonard G.; Escribano, Yesenia; Mazzotti, Frank J.

    2014-01-01

    Climate change poses new challenges for natural resource managers. Predictive modeling of species–environment relationships using climate envelope models can enhance our understanding of climate change effects on biodiversity, assist in assessment of invasion risk by exotic organisms, and inform life-history understanding of individual species. While increasing interest has focused on the role of uncertainty in future conditions on model predictions, models also may be sensitive to the initial conditions on which they are trained. Although climate envelope models are usually trained using data on contemporary climate, we lack systematic comparisons of model performance and predictions across alternative climate data sets available for model training. Here, we seek to fill that gap by comparing variability in predictions between two contemporary climate data sets to variability in spatial predictions among three alternative projections of future climate. Overall, correlations between monthly temperature and precipitation variables were very high for both contemporary and future data. Model performance varied across algorithms, but not between two alternative contemporary climate data sets. Spatial predictions varied more among alternative general-circulation models describing future climate conditions than between contemporary climate data sets. However, we did find that climate envelope models with low Cohen's kappa scores made more discrepant spatial predictions between climate data sets for the contemporary period than did models with high Cohen's kappa scores. We suggest conservation planners evaluate multiple performance metrics and be aware of the importance of differences in initial conditions for spatial predictions from climate envelope models.

  3. Climatic history - answers on the variability of weather and climate?

    International Nuclear Information System (INIS)

    The paper is concerned with various aspects of climatic history. Emphasis is on the spectrum of data and methods used in historical climatology. The following section is devoted to an outline of the short- and long-range climatic changes since 1500 A.D. that show how much the climate has varied in space and time. It is pointed out that climatic extremes have been an ever-recurrent phenomenon throughout history. (orig.)

  4. Permafrozen temperature regime affected by climate variability

    International Nuclear Information System (INIS)

    The paper reports on the numerical-analytical solution for the problem of periodically constant heat exchange in permafrost. There are no initial conditions and the task at issue is based upon the soil conductive heat exchange simulation. In addition, at thawing or freezing, the parameters of water/ice transition, geothermal temperature gradient and the snow cover impact upon the soil heat transition to outer ground have also been taken into account. This solution is governed by the following characteristics: annual air temperature change; winter precipitation accumulation; thermo-physical soil properties either in thawed or in frozen state. Considering the adduced solution the following parameters can be determined: the soil temperature at zero year amplitude level; the frost penetration lower boundary depth; and others. The calculated data are presented and compared with the results of previous field tests. The influence of the quantitative characteristics, such as variable climate and winter precipitation accumulation, upon the soil temperature pattern will be shown; in particular, the frost penetration lower boundary depth is varied by yearly average temperature increase or decrease. The regions where one-two degree yearly average temperature increases result in total permafrost disappearance have been located

  5. Future climate variability impacts on potential erosion and soil organic carbon in European croplands

    OpenAIRE

    Van der Velde, M.; Balkovič, J.; Beer, C.; Khabarov, N.; M. Kuhnert; Obersteiner, M.; Skalský, R.; Xiong, W; Smith, P

    2014-01-01

    We investigate the impact of future climate variability on the potential vulnerability of soils to erosion and the consequences for soil organic carbon (SOC) in European croplands. Soil erosion is an important carbon flux not characterized in Earth System Models. We use a~European implementation of EPIC, driven by reference climate data (CNTRL), and climate data with reduced variability (REDVAR). Whether erosion regimes will change across European cropland d...

  6. Climate variability: Possible changes with climate change and impacts on crop yields

    International Nuclear Information System (INIS)

    A pilot study was carried out of the sensitivity of the CERES wheat model, a deterministic crop-climate model, to changes in the interannual variability of temperature and precipitation. The study was designed to determine the effect of changed temperature variance on the mean and variance of the simulated yields, to compare the effect with the effect of mean temperature changes, and to determine the interacting effects of changes in mean and variance of temperature. The CERES model was applied to 29 cropping years (1952-1980), using three different soil types and two different management practices (fully irrigated and dryland). The coefficients of variation of the yields for irrigated and dryland conditions are plotted against variance change. It was found that in both management systems, the yield response is usually greater to increases rather than decreases in variance. The combined effect of mean and variance temperature changes are most striking under irrigated conditions, with a dramatic decrease in yield variability in the high mean climate change scenario with decreased temperature variance. This suggests that the variability decrease might mitigate the effect of a mean increase in temperature. This result is not found with the dryland case, where decreased temperature variability has little impact on yield variability. 12 refs., 4 figs

  7. Intraseasonal and Interannual Variability of Mars Present Climate

    Science.gov (United States)

    Hollingsworth, Jeffery L.; Bridger, Alison F. C.; Haberle, Robert M.

    1996-01-01

    This is a Final Report for a Joint Research Interchange (JRI) between NASA Ames Research Center and San Jose State University, Department of Meteorology. The focus of this JRI has been to investigate the nature of intraseasonal and interannual variability of Mars'present climate. We have applied a three-dimensional climate model based on the full hydrostatic primitive equations to determine the spatial, but primarily, the temporal structures of the planet's large-scale circulation as it evolves during a given seasonal advance, and, over multi-annual cycles. The particular climate model applies simplified physical parameterizations and is computationally efficient. It could thus easily be integrated in a perpetual season or advancing season configuration, as well as over many Mars years. We have assessed both high and low-frequency components of the circulation (i.e., motions having periods of Omicron(2-10 days) or greater than Omicron(10 days), respectively). Results from this investigation have explored the basic issue whether Mars' climate system is naturally 'chaotic' associated with nonlinear interactions of the large-scale circulation-regardless of any allowance for year-to-year variations in external forcing mechanisms. Titles of papers presented at scientific conferences and a manuscript to be submitted to the scientific literature are provided. An overview of a areas for further investigation is also presented.

  8. Climate change and climate variability: personal motivation for adaptation and mitigation

    Directory of Open Access Journals (Sweden)

    Ploubidis George B

    2011-05-01

    Full Text Available Abstract Background Global climate change impacts on human and natural systems are predicted to be severe, far reaching, and to affect the most physically and economically vulnerable disproportionately. Society can respond to these threats through two strategies: mitigation and adaptation. Industry, commerce, and government play indispensable roles in these actions but so do individuals, if they are receptive to behavior change. We explored whether the health frame can be used as a context to motivate behavioral reductions of greenhouse gas emissions and adaptation measures. Methods In 2008, we conducted a cross-sectional survey in the United States using random digit dialing. Personal relevance of climate change from health threats was explored with the Health Belief Model (HBM as a conceptual frame and analyzed through logistic regressions and path analysis. Results Of 771 individuals surveyed, 81% (n = 622 acknowledged that climate change was occurring, and were aware of the associated ecologic and human health risks. Respondents reported reduced energy consumption if they believed climate change could affect their way of life (perceived susceptibility, Odds Ratio (OR = 2.4 (95% Confidence Interval (CI: 1.4 - 4.0, endanger their life (perceived severity, OR = 1.9 (95% CI: 1.1 - 3.1, or saw serious barriers to protecting themselves from climate change, OR = 2.1 (95% CI: 1.2 - 3.5. Perceived susceptibility had the strongest effect on reduced energy consumption, either directly or indirectly via perceived severity. Those that reported having the necessary information to prepare for climate change impacts were more likely to have an emergency kit OR = 2.1 (95% CI: 1.4 - 3.1 or plan, OR = 2.2 (95% CI: 1.5 -3.2 for their household, but also saw serious barriers to protecting themselves from climate change or climate variability, either by having an emergency kit OR = 1.6 (95% CI: 1.1 - 2.4 or an emergency plan OR = 1.5 (95%CI: 1.0 - 2

  9. Estimating maritime snow density from seasonal climate variables

    Science.gov (United States)

    Bormann, K. J.; Evans, J. P.; Westra, S.; McCabe, M. F.; Painter, T. H.

    2013-12-01

    Snow density is a complex parameter that influences thermal, optical and mechanical snow properties and processes. Depth-integrated properties of snowpacks, including snow density, remain very difficult to obtain remotely. Observations of snow density are therefore limited to in-situ point locations. In maritime snowfields such as those in Australia and in parts of the western US, snow densification rates are enhanced and inter-annual variability is high compared to continental snow regions. In-situ snow observation networks in maritime climates often cannot characterise the variability in snowpack properties at spatial and temporal resolutions required for many modelling and observations-based applications. Regionalised density-time curves are commonly used to approximate snow densities over broad areas. However, these relationships have limited spatial applicability and do not allow for interannual variability in densification rates, which are important in maritime environments. Physically-based density models are relatively complex and rely on empirical algorithms derived from limited observations, which may not represent the variability observed in maritime snow. In this study, seasonal climate factors were used to estimate late season snow densities using multiple linear regressions. Daily snow density estimates were then obtained by projecting linearly to fresh snow densities at the start of the season. When applied spatially, the daily snow density fields compare well to in-situ observations across multiple sites in Australia, and provide a new method for extrapolating existing snow density datasets in maritime snow environments. While the relatively simple algorithm for estimating snow densities has been used in this study to constrain snowmelt rates in a temperature-index model, the estimates may also be used to incorporate variability in snow depth to snow water equivalent conversion.

  10. Disease in a more variable and unpredictable climate

    Science.gov (United States)

    McMahon, T. A.; Raffel, T.; Rohr, J. R.; Halstead, N.; Venesky, M.; Romansic, J.

    2014-12-01

    Global climate change is shifting the dynamics of infectious diseases of humans and wildlife with potential adverse consequences for disease control. Despite this, the role of global climate change in the decline of biodiversity and the emergence of infectious diseases remains controversial. Climate change is expected to increase climate variability in addition to increasing mean temperatures, making climate less predictable. However, few empirical or theoretical studies have considered the effects of climate variability or predictability on disease, despite it being likely that hosts and parasites will have differential responses to climatic shifts. Here we present a theoretical framework for how temperature variation and its predictability influence disease risk by affecting host and parasite acclimation responses. Laboratory experiments and field data on disease-associated frog declines in Latin America support this framework and provide evidence that unpredictable temperature fluctuations, on both monthly and diurnal timescales, decrease frog resistance to the pathogenic chytrid fungus Batrachochytrium dendrobatidis (Bd). Furthermore, the pattern of temperature-dependent growth of the fungus on frogs was inconsistent with the pattern of Bd growth in culture, emphasizing the importance of accounting for the host-parasite interaction when predicting climate-dependent disease dynamics. Consistent with our laboratory experiments, increased regional temperature variability associated with global El Niño climatic events was the best predictor of widespread amphibian losses in the genus Atelopus. Thus, incorporating the effects of small-scale temporal variability in climate can greatly improve our ability to predict the effects of climate change on disease.

  11. Incorporating climate change trends to near future variability of crop yields in Iberia Peninsula

    Science.gov (United States)

    Capa-Morocho, Mirian; Baethgen, Walter E.; Fernandes, Kátia; Rodríguez-Fonseca, Belén; Ruiz-Ramos, Margarita

    2016-04-01

    In this study, we analyze the effects of near future climate variability on cropping systems in Iberian Peninsula (IP). For this purpose, we generated climate sequences that simulate realistic variability on annual to decadal time scales. The sequences incorporate nonlinear climate change trends, using statistical methods and and an ensemble of global climate models from the Coupled Model Intercomparison Project (CMIP5). Then, the climate sequences are temporal downscaled into daily weather data and used as inputs to crop models. As case study, we evaluate the impacts of plausible future climate scenarios on rain-fed wheat yield two agricultural locations in IP. We adapted the method by Greene et al., (2012 and 2015) for informing climate projections for the coming decades with a combination of seasonal to interannual and anthropogenically forced climate change information for accounting the Near-term Climate Change. Long-term data containing solar radiation, maximum and minimum temperature and rainfall are needed to apply this method. The climate variability observed was decomposed into long-range trend, decadal and interannual variability to understand the relative importance of each time scale. The interannual variability was modeled based on the observational records. The results of this study may have important implications on public and private sectors to analyze the probabilistic projections of impacts and agronomic adaptations of near future climate variability in Iberian Peninsula. This study has been funded by MACSUR project from FACCE-JPI. References Greene, A.M., Goddard, L., Gonzalez, P.L., Ines, A.V. and Chryssanthacopoulos, J., 2015.A climate generator for agricultural planning in southeastern South America.Agricultural and Forest Meteorology, 203: 217-228. Greene, A.M., Hellmuth, M. and Lumsden, T., 2012. Stochastic decadal climate simulations for the Berg and Breede water management areas, western Cape province, South Africa. Water Resources

  12. Adaptation to Climate Change and Climate Variability: Do It Now or Wait and See?

    OpenAIRE

    Narita, Daiju; Quaas, Martin F.

    2012-01-01

    As growing attention is paid to climate change adaptation as an actual policy issue, the significant meaning of climate variability in adaptation decisions is beginning to be recognized. By using a real option framework for adaptation in agricultural production, we shed light on how climate change and climate variability affect individuals' (farmers') investment decisions with regard to adaptation (switching from rainfed to irrigated farming). The option value delays adaptation easily for sev...

  13. Linking the uncertainty of low frequency variability in tropical forcing in regional climate change

    Energy Technology Data Exchange (ETDEWEB)

    Forest, Chris E. [The Pennsylvania State University; Barsugli, Joseph J. [Cooperative Institute for Research in Environmental Sciences (CIRES) at the University of Colorado at Boulder and NOAA Earth Systems Research Laboratory; Li, Wei [The Pennsylvania State University

    2015-02-20

    The project utilizes multiple atmospheric general circulation models (AGCMs) to examine the regional climate sensitivity to tropical sea surface temperature forcing through a series of ensemble experiments. The overall goal for this work is to use the global teleconnection operator (GTO) as a metric to assess the impact of model structural differences on the uncertainties in regional climate variability.

  14. Climate Variability and Trends in Bolivia

    NARCIS (Netherlands)

    Seiler, C.; Hutjes, R.W.A.; Kabat, P.

    2013-01-01

    Climate-related disasters in Bolivia are frequent, severe, and manifold and affect large parts of the population, economy, and ecosystems. Potentially amplified through climate change, natural hazards are of growing concern. To better understand these events, homogenized daily observations of temper

  15. Comparison of two soya bean simulation models under climate change : II Application of climate change scenarios

    NARCIS (Netherlands)

    Wolf, J.

    2002-01-01

    The effects of climate change (for 2050 compared to ambient climate) and change in climatic variability on soya bean growth and production at 3 sites in the EU have been calculated. These calculations have been done with both a simple growth model, SOYBEANW, and a comprehensive model, CROPGRO. Compa

  16. Climate variability and climate change vulnerability and adaptation. Workshop summary

    International Nuclear Information System (INIS)

    Representatives from fifteen countries met in Prague, Czech Republic, on September 11-15, 1995, to share results from the analysis of vulnerability and adaptation to global climate change. The workshop focused on the issues of global climate change and its impacts on various sectors of a national economy. The U.N. Framework Convention on Climate Change (FCCC), which has been signed by more than 150 governments worldwide, calls on signatory parties to develop and communicate measures they are implementing to respond to global climate change. An analysis of a country's vulnerability to changes in the climate helps it identify suitable adaptation measures. These analyses are designed to determine the extent of the impacts of global climate change on sensitive sectors such as agricultural crops, forests, grasslands and livestock, water resources, and coastal areas. Once it is determined how vulnerable a country may be to climate change, it is possible to identify adaptation measures for ameliorating some or all of the effects.The objectives of the vulnerability and adaptation workshop were to: The objectives of the vulnerability and adaptation workshop were to: Provide an opportunity for countries to describe their study results; Encourage countries to learn from the experience of the more complete assessments and adjust their studies accordingly; Identify issues and analyses that require further investigation; and Summarize results and experiences for governmental and intergovernmental organizations

  17. Philosophy of climate science part II: modelling climate change

    OpenAIRE

    Frigg, Roman; Thompson, Erica; Werndl, Charlotte

    2015-01-01

    This is the second of three parts of an introduction to the philosophy of climate science. In this second part about modelling climate change, the topics of climate modelling, confirmation of climate models, the limits of climate projections, uncertainty and finally model ensembles will be discussed.

  18. Intraseasonal Variability in the Atmosphere-Ocean Climate System. Second Edition

    Science.gov (United States)

    Lau, William K. M.; Waliser, Duane E.

    2011-01-01

    Understanding and predicting the intraseasonal variability (ISV) of the ocean and atmosphere is crucial to improving long-range environmental forecasts and the reliability of climate change projections through climate models. This updated, comprehensive and authoritative second edition has a balance of observation, theory and modeling and provides a single source of reference for all those interested in this important multi-faceted natural phenomenon and its relation to major short-term climatic variations.

  19. Effects of climate change on productivity of cereals and legumes; model evaluation of observed year-to-year variability of the CO2 response

    NARCIS (Netherlands)

    Grashoff, Cees; Dijkstra, Paul; Nonhebel, Sanderine; Schapendonk, Ad H.C.M.; Geijn, Siebe C. van de

    1995-01-01

    The effect of elevated [CO2] on the productivity of spring wheat, winter wheat and faba bean was studied in experiments in climatized crop enclosures in the Wageningen Rhizolab in 1991–93. Simulation models for crop growth were used to explore possible causes for the observed differences in the CO2

  20. Solar irradiance forcing of centennial climate variability during the Holocene

    Energy Technology Data Exchange (ETDEWEB)

    Weber, S.L.; Schrier, G. van der [Royal Netherlands Meteorological Institute (KNMI), PO Box 201, 3730 AE De Bilt (Netherlands); Crowley, T.J. [Duke University, Durham, North Carolina (United States)

    2004-05-01

    Centennial climate variability during the Holocene has been simulated in two 10,000 year experiments using the intermediate-complexity ECBilt model. ECBilt contains a dynamic atmosphere, a global 3-D ocean model and a thermodynamic sea-ice model. One experiment uses orbital forcing and solar irradiance forcing, which is based on the Stuiver et al. residual {sup 14}C record spliced into the Lean et al. reconstruction. The other experiment uses orbital forcing alone. A glacier model is coupled off-line to the climate model. A time scale analysis shows that the response in atmospheric parameters to the irradiance forcing can be characterised as the direct response of a system with a large thermal inertia. This is evident in parameters like surface air temperature, monsoon precipitation and glacier length, which show a stronger response for longer time scales. The oceanic response, on the other hand, is strongly modified by internal feedback processes. The solar irradiance forcing excites a (damped) mode of the thermohaline circulation (THC) in the North Atlantic Ocean, similar to the loop-oscillator modes associated with random-noise freshwater forcing. This results in a significant peak (at time scales 200-250 year) in the THC spectrum which is absent in the reference run. The THC response diminishes the sea surface temperature response at high latitudes, while it gives rise to a signal in the sea surface salinity. A comparison of the model results with observations shows a number of encouraging similarities. (orig.)

  1. The Dynamics of Ocean Climate Variability.

    Science.gov (United States)

    White, Warren B.; Haney, Robert L.

    1978-01-01

    Halfway through a five-year experimental program designed to test classical concepts of ocean/atmosphere climate dynamics, researchers are finding that the theories may conflict with new data on disturbances in the ocean thermal structure. (Author BB)

  2. Investigating the effects of climatic variables and reservoir on the incidence of hemorrhagic fever with renal syndrome in Huludao City, China: a 17-year data analysis based on structure equation model

    Directory of Open Access Journals (Sweden)

    Shen Tiefeng

    2009-07-01

    Full Text Available Abstract Background HFRS is a serious public health problem in China and the study on HFRS is important in China for its large population. The present study aimed to explore the impact of climatic variables and reservoir on the incidence of HFRS in Huludao City, an epidemic focus of the disease in northeastern China. Methods Structure Equation Model (SEM, a statistical technique for testing and estimating causal relationships, was conducted based on climatic variables, virus-carrying index among rodents, and incidence of HFRS in the city during the period 1990 to 2006. The linear structural relationships (LISREL software (Scientific Software International, Lincolnwood, IL was used to fit SEMs. Results Temperature, precipitation, relative humidity and virus-carrying index among rodents have shown positive correlations with the monthly incidence of HFRS, while air pressure had a negative correlation with the incidence. The best-fit SEM model fitted well with the data-based correlation matrix, P value was more than 0.56, root mean square error of approximation (RMSEA equaled to 0, goodness-of-fit index (GFI was more than 0.99. Conclusion Climate and reservoirs have affected the incidence of HFRS in Huludao City, located in northeastern China. Climate affects HFRS incidence mainly through the effect on reservoir in the study area. HFRS prevention and control should give more consideration to rodent control and climate variations.

  3. Investigating the effects of climatic variables and reservoir on the incidence of hemorrhagic fever with renal syndrome in Huludao City, China: a 17-year data analysis based on structure equation model

    Science.gov (United States)

    2009-01-01

    Background HFRS is a serious public health problem in China and the study on HFRS is important in China for its large population. The present study aimed to explore the impact of climatic variables and reservoir on the incidence of HFRS in Huludao City, an epidemic focus of the disease in northeastern China. Methods Structure Equation Model (SEM), a statistical technique for testing and estimating causal relationships, was conducted based on climatic variables, virus-carrying index among rodents, and incidence of HFRS in the city during the period 1990 to 2006. The linear structural relationships (LISREL) software (Scientific Software International, Lincolnwood, IL) was used to fit SEMs. Results Temperature, precipitation, relative humidity and virus-carrying index among rodents have shown positive correlations with the monthly incidence of HFRS, while air pressure had a negative correlation with the incidence. The best-fit SEM model fitted well with the data-based correlation matrix, P value was more than 0.56, root mean square error of approximation (RMSEA) equaled to 0, goodness-of-fit index (GFI) was more than 0.99. Conclusion Climate and reservoirs have affected the incidence of HFRS in Huludao City, located in northeastern China. Climate affects HFRS incidence mainly through the effect on reservoir in the study area. HFRS prevention and control should give more consideration to rodent control and climate variations. PMID:19583875

  4. Detection of trends in surface ozone in the presence of climate variability

    Science.gov (United States)

    Barnes, Elizabeth A.; Fiore, Arlene M.; Horowitz, Larry W.

    2016-05-01

    Trends in trace atmospheric constituents can be driven not also by trends in their (precursor) emissions but also by trends in meteorology. Here we use ground-level ozone as an example to highlight the extent to which unforced, low-frequency climate variability can drive multidecadal trends. Using output from six experiments of the Geophysical Fluid Dynamics Laboratory chemistry-climate model (CM3), we demonstrate that 20 year trends in surface ozone driven by climate variability alone can be as large as those forced by changes in ozone precursor emissions or by anthropogenic climate change. We highlight regions and seasons where surface ozone is strongly influenced by climate variability and thus where a given forced trend may be more difficult to detect. A corollary is that this approach identifies regions and seasons of low variability, where measurement sites may be most effectively deployed to detect a particular trend driven by changing precursor emissions. We find that the representative concentration pathways 4.5 (RCP4.5) and RCP8.5 forced surface ozone trends in most locations emerge over background variability during the first half of the 21st century. Ozone trends are found to respond mostly to changes in emissions of ozone precursors and unforced climate variability, with a comparatively small impact from anthropogenic climate change. Thus, attempts to attribute observed trends to regional emissions changes require consideration of internal climate variability, particularly for short record lengths and small forced trends.

  5. CLIMATE CHANGE, VARIABILITY AND SUSTAINABLE AGRICULTURE IN ZIMBABWE'S RURAL COMMUNITIES

    Directory of Open Access Journals (Sweden)

    Gukurume Simbarashe

    2013-02-01

    Full Text Available This article explores the impact of climate change and variability on agricultural productivity in the communal area of Bikita. The article further examines the adaptation and mitigation strategies devised by farmers to deal with the vagaries of climate change and variability. The sustainability of these is also interrogated in this article. This study juxtaposed qualitative and quantitative methodologies albeit with more bias on the former. A total of 40 farmers were sampled for unstructured interviews and focus group discussions. This article argues that the adverse impacts of climate change and variability are felt heavily by the poor communal farmers who are directly dependent on agriculture for livelihood. From the study, some of the widely reported signs of climate variability in Bikita included late and unpredictable rains, high temperatures (heat waves, successive drought, shortening rainfall seasons and seasonal changes in the timing of rainfall. The paper argues that climate change has compounded the vulnerability of peasant farmers in the drought - prone district of Bikita plunging them into food insecurity and abject poverty. It emerged in the study that some of effects of climate variability felt by communal farmers in Bikita included failure of crops, death of livestock and low crop yields, all of which have led to declining agricultural productivity. Findings in this study however established that communal farmers have not been passive victims of the vagaries of climate change and variability. They have rationally responded to it through various adaptation and mitigation strategies both individually and collectively.

  6. Climate Model Diagnostic Analyzer Web Service System

    Science.gov (United States)

    Lee, S.; Pan, L.; Zhai, C.; Tang, B.; Kubar, T. L.; Li, J.; Zhang, J.; Wang, W.

    2015-12-01

    Both the National Research Council Decadal Survey and the latest Intergovernmental Panel on Climate Change Assessment Report stressed the need for the comprehensive and innovative evaluation of climate models with the synergistic use of global satellite observations in order to improve our weather and climate simulation and prediction capabilities. The abundance of satellite observations for fundamental climate parameters and the availability of coordinated model outputs from CMIP5 for the same parameters offer a great opportunity to understand and diagnose model biases in climate models. In addition, the Obs4MIPs efforts have created several key global observational datasets that are readily usable for model evaluations. However, a model diagnostic evaluation process requires physics-based multi-variable comparisons that typically involve large-volume and heterogeneous datasets, making them both computationally- and data-intensive. In response, we have developed a novel methodology to diagnose model biases in contemporary climate models and implementing the methodology as a web-service based, cloud-enabled, provenance-supported climate-model evaluation system. The evaluation system is named Climate Model Diagnostic Analyzer (CMDA), which is the product of the research and technology development investments of several current and past NASA ROSES programs. The current technologies and infrastructure of CMDA are designed and selected to address several technical challenges that the Earth science modeling and model analysis community faces in evaluating and diagnosing climate models. In particular, we have three key technology components: (1) diagnostic analysis methodology; (2) web-service based, cloud-enabled technology; (3) provenance-supported technology. The diagnostic analysis methodology includes random forest feature importance ranking, conditional probability distribution function, conditional sampling, and time-lagged correlation map. We have implemented the

  7. Vulnerability and adaptation to climate variability and change in smallholder farming systems in Zimbabwe

    NARCIS (Netherlands)

    Rurinda, J.

    2014-01-01

        Keywords: Climate change; Increased climate variability; Vulnerability; Smallholder farmers; Adaptation   Climate change and increased climate variability are currently seen as the major constraints to the already stressed smallholder farming livelihood syst

  8. Impacts of Interannual Climate Variability on Agricultural and Marine Ecosystems

    Science.gov (United States)

    Cane, M. A.; Zebiak, S.; Kaplan, A.; Chen, D.

    2001-01-01

    The El Nino - Southern Oscillation (ENSO) is the dominant mode of global interannual climate variability, and seems to be the only mode for which current prediction methods are more skillful than climatology or persistence. The Zebiak and Cane intermediate coupled ocean-atmosphere model has been in use for ENSO prediction for more than a decade, with notable success. However, the sole dependence of its original initialization scheme and the improved initialization on wind fields derived from merchant ship observations proved to be a liability during 1997/1998 El Nino event: the deficiencies of wind observations prevented the oceanic component of the model from reaching the realistic state during the year prior to the event, and the forecast failed. Our work on the project was concentrated on the use of satellite data for improving various stages of ENSO prediction technology: model initialization, bias correction, and data assimilation. Close collaboration with other teams of the IDS project was maintained throughout.

  9. Modeling lakes and reservoirs in the climate system

    NARCIS (Netherlands)

    MacKay, M.D.; Neale, P.J.; Arp, C.D.; De Senerpont Domis, L.N.; Fang, X.; Gal, G.; Jöhnk, K.D.; Kirillin, G.; Lenters, J.D.; Litchman, E.; MacIntyre, S.; Marsh, P.; Melack, J.; Mooij, W.M.; Peeters, F.; Quesada, A.; Schladow, S.G.; Schmid, M.; Spence, C.; Stokes, S.L.

    2009-01-01

    Modeling studies examining the effect of lakes on regional and global climate, as well as studies on the influence of climate variability and change on aquatic ecosystems, are surveyed. Fully coupled atmosphere–land surface–lake climate models that could be used for both of these types of study simu

  10. Adaptation to Climate Variability and Change. Methodological Issues

    International Nuclear Information System (INIS)

    The Intergovernmental Panel on Climate Change (IPCC) convened a Workshop on Adaptation to Climate Variability and Change in Costa Rica in 1998 that involved more than 200 expects and incorporated views from many research communities. This paper summarizes the recommendations from the Workshop and profiles the contributions to the advancement of methodologies for adaptation science. 25 refs

  11. Climate change variability and Andean agriculture: The context

    OpenAIRE

    Valdivia, Corinne

    2008-01-01

    A presentation by Valdivia from lessons learned in the SANREM CRSP and past research to frame the two day workshop. First session of the workshop: I. Climate Change Variability and Andean Agriculture: The Context Lessons learned from SANREM CRSP on adapting to climate change. LTRA-4 (Practices and Strategies for Vulnerable Agro-Ecosystems)

  12. Effects of climate variability and climate change on crop production in southern Mali

    NARCIS (Netherlands)

    Traore, B.; Corbeels, M.; Wijk, van M.T.; Rufino, M.C.; Giller, K.E.

    2013-01-01

    In West Africa predictions of future changes in climate and especially rainfall are highly uncertain, and up to now no long-term analyses are available of the effects of climate on crop production. This study analyses long-term trends in climate variability at N'Tarla and Sikasso in southern Mali us

  13. Climate Reconstructions of the Younger Dryas: An ELA Model Investigating Variability in ELA Depressions, Temperature, and Precipitation Changes for the Graubϋnden Alps

    Science.gov (United States)

    Keeler, D. G.; Rupper, S.; Schaefer, J. M.; Finkel, R. C.

    2015-12-01

    The high sensitivity of mountain glaciers to even small perturbations in climate, combined with a near global distribution, make alpine glaciers an important target for terrestrial paleoclimate reconstructions. The geomorphic remnant of past glaciers can yield important insights into past climate, particularly in regions where other methods of reconstruction are not possible. The quantitative conversion of these changes in geomorphology to a climate signal, however, presents a significant challenge. A particular need exists for a versatile climate reconstruction method applicable to diverse glacierized regions around the globe. Because the glacier equilibrium line altitude (ELA) provides a more explicit comparison of climate than properties such as glacier length or area, ELA methods lend themselves well to such a need, and allow for a more direct investigation of the primary drivers of mountain glaciations during specific events. Here, we present an ELA model for quantifying changes in climate based on changes in glacier extent, while accounting for differences in glacier width, glacier shape, bed topography, ice thickness, and glacier length. The model furthermore provides bounds on the ΔELA using Monte Carlo simulations. These methods are validated using published mass balances and ELA measurements from 4 modern glaciers in the European Alps. We then use this ELA model, combined with a surface mass and energy balance model, to estimate the changes in temperature/precipitation between the Younger Dryas (constrained by 10Be surface exposure ages) and the present day for three glacier systems in the Graubϋnden Alps. Our results indicate an ELA depression in this area of 257 m ±45 m during the Younger Dryas (YD) relative to today. This corresponds to a 1.3 °C ±0.36 °C decrease in temperature or a 156% ±30% increase in precipitation relative to today. These results indicate the likelihood of a predominantly temperature-driven change rather than a strong

  14. Future climate variability impacts on potential erosion and soil organic carbon in European croplands

    Directory of Open Access Journals (Sweden)

    M. van der Velde

    2014-01-01

    Full Text Available We investigate the impact of future climate variability on the potential vulnerability of soils to erosion and the consequences for soil organic carbon (SOC in European croplands. Soil erosion is an important carbon flux not characterized in Earth System Models. We use a~European implementation of EPIC, driven by reference climate data (CNTRL, and climate data with reduced variability (REDVAR. Whether erosion regimes will change across European cropland depends on the spatial conjunction of expected changes in climate variability and physiographic conditions conducive to erosion. We isolated the effect of erosion by performing simulations with and without erosion. Median CNTRL and REDVAR erosion rates equalled 14.4 and 9.1 ton ha−1, and 19.1 and 9.7, for 1981–2010 and 2071–2100, respectively. The total amount of carbon lost from European cropland due to erosion was estimated at 769 Tg C for 1981–2010 (from a total storage of 6197 Tg C without erosion under CNTRL climate. Climate trend impacts reduce the European cropland SOC stock by 578 Tg C without – and by 683 Tg C with erosion, from 1981 to 2100. Climate variability compounds these impacts and decreases the stock by an estimated 170 Tg without erosion and by 314 Tg C with erosion, by the end of the century. Future climate variability and erosion will thus compound impacts on SOC stocks arising from gradual climate change alone.

  15. Last Millennium Climate and Its Variability in CCSM4

    Science.gov (United States)

    Otto-Bliesner, B. L.; Landrum, L.; Conley, A.; Lawrence, P.; Rosenbloom, N. A.; Teng, H.

    2011-12-01

    The Last Millennium simulation of the Community Climate System Model version 4 (CCSM4) reproduces many large-scaled climate patterns suggested by historical and proxy-data records including cooling from the Medieval Climate Anomaly to the Little Ice Age, a "hockey-stick" pattern of surface temperature changes from 850-2005, and a broad cooling with a delayed La Niña-type of pattern in the tropical Pacific response to large volcanic events. Atmospheric modes, one oceanic mode (the Pacific Decadal Oscillation), and one ocean-atmosphere coupled mode (the El Niño-Southern Oscillation) of variability show little or no change in their variances, teleconnection patterns and spectra between the Last Millennium simulation and the 1850 non-transient control run. Two oceanic modes, the Atlantic Multidecadal Oscillation and the Atlantic Meridional Overturning Circulation have higher variances and increased power at low frequencies in the Last Millennium simulation compared with the control run, suggesting long-term oceanic response to natural solar and volcanic forcings.

  16. On the role of climate variability on tropospheric ozone

    Science.gov (United States)

    Lin, M.

    2014-12-01

    The response of tropospheric ozone to changing atmospheric circulation is poorly understood owing to a lack of reliable long-term observations. There is great current interest in quantifying the extent to which observed ozone trends over recent decades at northern mid-latitude sites are driven by changes in precursor emissions versus shifts in atmospheric circulation patterns. In this talk, I present a detailed analysis of the impact of interannual to decadal climate variability on tropospheric ozone, based on observations and a suite of chemistry-climate model hindcast simulations. Decadal shifts in circulation regimes modulate long-range transport of Asian pollution, leading to very different seasonal ozone trends at Mauna Loa Observatory in the subtropical Pacific Ocean. During autumn, the flow of ozone-rich air from Eurasia towards Hawaii strengthened in the mid-1990s onwards, as a result of the positive phase of the Pacific North American pattern, increasing ozone at Mauna Loa. During spring, weakening airflow from Asia in the 2000s, tied to La-Niña-like decadal cooling in the equatorial Pacific Ocean, offsets ozone increases at Mauna Loa that otherwise would have occurred due to rising Asian emissions. The circulation-driven variability in Asian pollution over the subtropical North Pacific regions manifests mainly as changes in the mean as opposed to in transport events. At high-elevation Western U.S. sites, intrusions of stratospheric ozone deep into the troposphere during spring exert a greater influence than Asian pollution, particularly on the high tail of observed surface ozone distribution. We show that year-to-year variability in springtime high-ozone episodes measured in Western U.S. surface air is tied to known modes of climate variability, which modulate meanders in the polar frontal jet conducive to deep stratospheric ozone intrusions. Specifically, the La Niña-related increase in the frequency of deep stratospheric intrusion events plays a

  17. Climate variability in a coupled GCM. Pt. 2

    International Nuclear Information System (INIS)

    The seasonal cycle and the interannual variability of the tropical Indian Ocean circulation are investigated and the Indian Summer Monsoon is simulated by a coupled ocean-atmosphere general circulation model in a 26 year integration. Although the model exhibits significant climate drift, it simulates realistically the seasonal changes in the tropical Indian Ocean and the onset and evolution of the Indian Summer Monsoon. The amplitudes of the seasonal changes, however, are somewhat underestimated. The coupled GCM also simulates considerable interannual variability in the tropical Indian Ocean circulation which is partly related to the El Nino/Southern Oscillation (ENSO) phenomenon and the associated changes in the Walker Circulation. Changes in the surface wind stress appear to be crucial in forcing interannual variations in the Indian Ocean SST. As in the Pacific Ocean, the net surface heat flux acts as a negative feedback on the SST anomalies. The interannual variability in Monsoon rainfall is simulated by the coupled GCM only about half as strongly as observed. (orig.)

  18. Quality Assurance for Essential Climate Variables

    Science.gov (United States)

    Folkert Boersma, K.; Muller, Jan-Peter

    2015-04-01

    Satellite data are of central interest to the QA4ECV project. Satellites have revolutionized the Earth's observation system of climate change and air quality over the past three decades, providing continuous data for the entire Earth. However, many users of these data are lost in the fog as to the quality of these satellite data. Because of this, the European Union expressed in its 2013 FP7 Space Research Call a need for reliable, traceable, and understandable quality information on satellite data records that could serve as a blueprint contribution to a future Copernicus Climate Change Service. The potential of satellite data to benefit climate change and air quality services is too great to be ignored. QA4ECV therefore bridges the gap between end-users of satellite data and the satellite data products. We are developing an internationally acceptable Quality Assurance (QA) framework that provides understandable and traceable quality information for satellite data used in climate and air quality services. Such a framework should deliver the historically linked long-term data sets that users need, in a format that they can readily use. QA4ECV has approached more than 150 users and suppliers of satellite data to collect their needs and expectations. The project will use their response as a guideline for developing user-friendly tools to obtain information on the completeness, accuracy, and fitness-for-purpose of the satellite datasets. QA4ECV collaborates with 4 joint FP7 Space projects in reaching out to scientists, policy makers, and other end-users of satellite data to improve understanding of the special challenges -and also opportunities- of working with satellite data for climate and air quality purposes. As a demonstration of its capacity, QA4ECV will generate multi-decadal climate data records for 3 atmospheric ECV precursors (nitrogen dioxide, formaldehyde, and carbon monoxide) and 3 land ECVs (albedo, leaf area index and absorbed photosynthetically active

  19. Modeling Shared Variables in VHDL

    DEFF Research Database (Denmark)

    Madsen, Jan; Brage, Jens P.

    1994-01-01

    A set of concurrent processes communicating through shared variables is an often used model for hardware systems. This paper presents three modeling techniques for representing such shared variables in VHDL, depending on the acceptable constraints on accesses to the variables. Also a set of guide...... of guidelines for handling atomic updates of multiple shared variables is given. 1 Introduction It is often desirable to partition a computational system into discrete functional units which cooperates to.......A set of concurrent processes communicating through shared variables is an often used model for hardware systems. This paper presents three modeling techniques for representing such shared variables in VHDL, depending on the acceptable constraints on accesses to the variables. Also a set...

  20. Stochastic Climate Theory and Modelling

    CERN Document Server

    Franzke, Christian L E; Berner, Judith; Williams, Paul D; Lucarini, Valerio

    2014-01-01

    Stochastic methods are a crucial area in contemporary climate research and are increasingly being used in comprehensive weather and climate prediction models as well as reduced order climate models. Stochastic methods are used as subgrid-scale parameterizations as well as for model error representation, uncertainty quantification, data assimilation and ensemble prediction. The need to use stochastic approaches in weather and climate models arises because we still cannot resolve all necessary processes and scales in comprehensive numerical weather and climate prediction models. In many practical applications one is mainly interested in the largest and potentially predictable scales and not necessarily in the small and fast scales. For instance, reduced order models can simulate and predict large scale modes. Statistical mechanics and dynamical systems theory suggest that in reduced order models the impact of unresolved degrees of freedom can be represented by suitable combinations of deterministic and stochast...

  1. Climate variability and change and related drought on Balkan Peninsula

    International Nuclear Information System (INIS)

    In this paper, results on climate variability including variations of air temperature and precipitation in Bulgaria during the 20th century are presented. There has been an increase of air temperature during the last two decades. The years 1994 and 2000 were the warmest years on record in the country. Annual precipitation in Bulgaria varied considerably from year to year during the 20th century. In some years, very low annual precipitation caused droughts of different intensities. The country has experienced severe drought episodes in the 1940s, 1980s and 1990s. There was a decreasing trend in precipitation during the period April-September from the end of 1970s. Precipitation was below the 1961-1990 average for 14 of the last 20 years of investigation. A winter precipitation deficit was observed during the last decade. Both spring and summer as well as autumn precipitation had a tendency to decrease at the end of the 20th century. The anomalies of annual air temperature and precipitation as well as related drought occurrence on the Balkan Peninsula were also analyzed. For this purpose, different weather sources (such as the CRU climate dataset, ATEAM weather dataset for Europe, etc.) were used. Several climate change scenarios for the Balkan Peninsula were developed and analyzed. These scenarios were based on GCM (global circulation model) weather outputs. Both GCM outputs with coarse spatial resolution (e.g. MAGICC/SCENGEN scenarios: 500 km x 500 km) as well as with high resolution (e.g. HadCM3 scenarios: 10'x 10' (less than 20 km x 20 km)) were used. The GCM climate change scenarios created by the Tyndall Centre (UK) for the Balkan countries were also considered and discussed. (Author)

  2. Southern Ocean deep convection in global climate models: A driver for variability of subpolar gyres and Drake Passage transport on decadal timescales

    Science.gov (United States)

    Behrens, Erik; Rickard, Graham; Morgenstern, Olaf; Martin, Torge; Osprey, Annette; Joshi, Manoj

    2016-06-01

    We investigate the individual and joint decadal variability of Southern Ocean state quantities, such as the strength of the Ross and Weddell Gyres, Drake Passage transport, and sea ice area, using the National Institute of Water and Atmospheric Research UK Chemistry and Aerosols (NIWA-UKCA) model and CMIP5 models. Variability in these quantities is stimulated by strong deep reaching convective events in the Southern Ocean, which produce an Antarctic Bottom Water-like water mass and affect the large-scale meridional density structure in the Southern Ocean. An increase in the (near) surface stratification, due to freshwater forcing, can be a precondition for subsequent strong convection activity. The combination of enhanced-gyre driven sea ice and freshwater export, as well as ongoing subsurface heat accumulation, lead to a time lag between changes in oceanic freshwater and heat content. This causes an ongoing weakening of the stratification until sudden strong mixing events emerge and the heat is released to the atmosphere. We find that strong convection reduces sea ice cover, weakens the subpolar gyres, increases the meridional density gradient and subsequently results in a positive Drake Passage transport anomaly. Results of available CMIP5 models confirm that variability in sea ice, Drake Passage transport, and the Weddell Gyre strength is enhanced if models show strong open ocean convective events. Consistent relationships between convection, sea ice, Drake Passage transport, and Ross Gyre strength variability are evident in most models, whether or not they host open ocean convection.

  3. Holocene Caribbean climate variability reconstructed from speleothems from western Cuba

    OpenAIRE

    Fensterer, Claudia

    2011-01-01

    Proxy records o ffer a high potential tool to investigate past climate variability. Stalagmites as a natural archive have the advantage that they are absolutely datable and past changes in precipitation or temperature can be highly resolved by the use of stable isotopes such as d18O and d13C. This study uses three stalagmites from north-western Cuba to investigate past precipitation variability in the Northern Caribbean. The records cover the whole Holocene and reveal variability on several t...

  4. Role of climate variability in the heatstroke death rates of Kanto region in Japan.

    Science.gov (United States)

    Akihiko, Takaya; Morioka, Yushi; Behera, Swadhin K

    2014-07-10

    The death toll by heatstroke in Japan, especially in Kanto region, has sharply increased since 1994 together with large interannual variability. The surface air temperature and humidity observed during boreal summers of 1980-2010 were examined to understand the role of climate in the death toll. The extremely hot days, when the daily maximum temperature exceeds 35 °C, are more strongly associated with the death toll than the conventional Wet Bulb Globe Temperature index. The extremely hot days tend to be associated with El Niño/Southern Oscillation or the Indian Ocean Dipole, suggesting a potential link with tropical climate variability to the heatstroke related deaths. Also, the influence of these climate modes on the death toll has strengthened since 1994 probably related to global warming. It is possible to develop early warning systems based on seasonal climate predictions since recent climate models show excellent predictability skills for those climate modes.

  5. Role of climate variability in the heatstroke death rates of Kanto region in Japan

    Science.gov (United States)

    Akihiko, Takaya; Morioka, Yushi; Behera, Swadhin K.

    2014-07-01

    The death toll by heatstroke in Japan, especially in Kanto region, has sharply increased since 1994 together with large interannual variability. The surface air temperature and humidity observed during boreal summers of 1980-2010 were examined to understand the role of climate in the death toll. The extremely hot days, when the daily maximum temperature exceeds 35°C, are more strongly associated with the death toll than the conventional Wet Bulb Globe Temperature index. The extremely hot days tend to be associated with El Niño/Southern Oscillation or the Indian Ocean Dipole, suggesting a potential link with tropical climate variability to the heatstroke related deaths. Also, the influence of these climate modes on the death toll has strengthened since 1994 probably related to global warming. It is possible to develop early warning systems based on seasonal climate predictions since recent climate models show excellent predictability skills for those climate modes.

  6. Climate Change and Climate Variability in the Latin American Region

    Science.gov (United States)

    Magrin, G. O.; Gay Garcia, C.; Cruz Choque, D.; Gimenez-Sal, J. C.; Moreno, A. R.; Nagy, G. J.; Nobre, C.; Villamizar, A.

    2007-05-01

    Over the past three decades LA was subjected to several climate-related impacts due to increased El Niño occurrences. Two extremely intense episodes of El Niño and other increased climate extremes happened during this period contributing greatly to augment the vulnerability of human systems to natural disasters. In addition to weather and climate, the main drivers of the increased vulnerability are demographic pressure, unregulated urban growth, poverty and rural migration, low investment in infrastructure and services, and problems in inter-sector coordination. As well, increases in temperature and increases/decreases in precipitation observed during the last part of 20th century have yet led to intensification of glaciers melting, increases in floods/droughts and forest fires frequency, increases in morbidity and mortality, increases in plant diseases incidence; lost of biodiversity, reduction in dairy cattle production, and problems with hydropower generation, highly affecting LA human system. For the end of the 21st century, the projected mean warming for LA ranges from 1 to 7.5ºC and the frequency of weather and climate extremes could increase. Additionally, deforestation is projected to continue leading to a reduction of 25 percent in Amazonia forest in 2020 and 40 percent in 2050. Soybeans planted area in South America could increase by 55 percent by 2020 enhancing aridity/desertification in many of the already water- stressed regions. By 2050 LA population is likely to be 50 percent larger than in 2000, and migration from the country sides to the cities will continue. In the near future, these predicted changes are very likely to severely affect a number of ecosystems and sectors distribution; b) Disappearing most tropical glaciers; c) Reducing water availability and hydropower generation; d) Increasing desertification and aridity; e) Severely affecting people, resources and economic activities in coastal areas; f) Increasing crop's pests and diseases

  7. Deglacial climate variability in central Florida, USA

    Science.gov (United States)

    Willard, D.A.; Bernhardt, C.E.; Brooks, G.R.; Cronin, T. M.; Edgar, T.; Larson, R.

    2007-01-01

    Pollen and ostracode evidence from lacustrine sediments underlying modern Tampa Bay, Florida, document frequent and abrupt climatic and hydrological events superimposed on deglacial warming in the subtropics. Radiocarbon chronology on well-preserved mollusk shells and pollen residue from core MD02-2579 documents continuous sedimentation in a variety of non-marine habitats in a karst-controlled basin from 20 ka to 11.5 ka. During the last glacial maximum (LGM), much drier and cooler-than-modern conditions are indicated by pollen assemblages enriched in Chenopodiaceae and Carya, with rare Pinus (Pinus pollen increased to 20–40% during the warming of the initial deglaciation (∼ 17.2 ka), reaching near modern abundance (60–80%) during warmer, moister climates of the Bølling/Allerød interval (14.7–12.9 ka). Within the Bølling/Allerød, centennial-scale dry events corresponding to the Older Dryas and Intra-Allerød Cold Period indicate rapid vegetation response (

  8. Rainfall variability modelling in Rwanda

    Science.gov (United States)

    Nduwayezu, E.; Kanevski, M.; Jaboyedoff, M.

    2012-04-01

    Support to climate change adaptation is a priority in many International Organisations meetings. But is the international approach for adaptation appropriate with field reality in developing countries? In Rwanda, the main problems will be heavy rain and/or long dry season. Four rainfall seasons have been identified, corresponding to the four thermal Earth ones in the south hemisphere: the normal season (summer), the rainy season (autumn), the dry season (winter) and the normo-rainy season (spring). The spatial rainfall decreasing from West to East, especially in October (spring) and February (summer) suggests an «Atlantic monsoon influence» while the homogeneous spatial rainfall distribution suggests an «Inter-tropical front » mechanism. The torrential rainfall that occurs every year in Rwanda disturbs the circulation for many days, damages the houses and, more seriously, causes heavy losses of people. All districts are affected by bad weather (heavy rain) but the costs of such events are the highest in mountains districts. The objective of the current research is to proceed to an evaluation of the potential rainfall risk by applying advanced geospatial modelling tools in Rwanda: geostatistical predictions and simulations, machine learning algorithm (different types of neural networks) and GIS. The research will include rainfalls variability mapping and probabilistic analyses of extreme events.

  9. Examine Climate Models by Using Infrared Spectrum

    OpenAIRE

    Yi Huang; Ramaswamy, V.

    2008-01-01

    We examine global climate models by comparing the satellite-observed high resolution global infrared spectra with the model-simulated counterpart. Because the topof-the-atmosphere outgoing Earth thermal emission at different frequencies is sensitive to different geophysical variables (temperature, water vapor and other greenhouse gas concentrations, clouds, etc.) at various levels, a comparison of observed and simulated spectra is as challenging as examining a variety of model-simulated geoph...

  10. Exploring the climate response to the 1815 Tambora eruption with respect to natural climate variability

    Science.gov (United States)

    Lorenz, Stephan J.; Timmreck, Claudia; Jungclaus, Johann H.

    2010-05-01

    The largest historic volcanic eruption with known origin was the explosion of Mount Tambora in Indonesia in April 1815. In the aftermath of this devastating eruption, the following year 1816 came to be known as the "year without a summer", in particular in USA, Canada, and Europe, where the worst famine over a century as well as typhus epidemics accompanied by enhanced emigration from Europe were recorded. The stratospheric aerosol mass load was estimated to be about three times that of the Pinatubo eruption in 1991, leading to strong impact on the Earth's climate system. In a series of ensemble simulations of the last Millennium we applied our Earth system model, based on the ECHAM5/MPIOM model family, to investigate the climate signal of the Tambora eruption with respect to natural and forced variability. This event contributed to one of the strongest cooling periods during the last Millennium in the ensemble of simulations. However, this period is associated with a large ensemble spread in simulated air temperature on a hemispheric and global as well as on a regional scale, with limited to very strong atmospheric response. The unique path of the climate evolution through the Earth's history yielding the extreme summer in 1816 in North America and Europe is compared with the simulations. A special focus of our analysis is Tambora's impact on climate and its relationship with the status of the climate system, e.g. the ENSO state, at the time of the eruption. Additionally, the contribution of the large volcanic eruption with tropical but unknown location about six years prior to the Tambora in 1809 will be discussed.

  11. Data Requirements for Developing Adaptations to Climate Variability and Change

    International Nuclear Information System (INIS)

    An extensive foundation of high quality data and information on the climate and on the biological, environmental and social systems affected by climate is required in order to understand the climate impact processes involved, to develop new adaptation practices, and to subsequently implement these practices. Experience of the impacts of current and past variability of climate and sea level is a prime source of information. Many practices are in use to reduce climate impacts, for example in engineering design, agricultural risk management and climate prediction services, though their roles as adaptations to climate change are not widely appreciated. While there are good data sets on some factors and in some regions, in many cases the databases are inadequate and there are few data sets on adaptation-specific quantities such as vulnerability, resilience and adaptation effectiveness. Current international action under the United Nations Framework Convention on Climate Change (UNFCCC) pays little attention to adaptation and its information requirements. Furthermore there are trends toward reduced data gathering and to restrictions on access to data sets, especially arising from cost and commercialisation pressures. To effectively respond to the changes in climate that are now inevitable, governments will need to more clearly identify adaptation as a central feature of climate change policy and make a renewed shared commitment to collecting and freely exchanging the necessary data. 12 refs

  12. THE EFFECTS OF CLIMATIC VARIABLES AND CROP AREA ON MAIZE YIELD AND VARIABILITY IN GHANA

    Directory of Open Access Journals (Sweden)

    Henry De-Graft Acquah

    2012-10-01

    Full Text Available Climate change tends to have negative effects on crop yield through its influence on crop production. Understanding the relationship between climatic variables and crop area on the mean and variance of crop yield will facilitate development of appropriate policies to cope with climate change. This paper examines the effects of climatic variables and crop area on the mean and variance of maize yield in Ghana. The Just and Pope stochastic production function using the Cobb-Douglas functional form was employed. The results show that average maize yield is positively related to crop area and negatively related to rainfall and temperature. Furthermore, increase in crop area and temperature will enlarge maize yield variability while rainfall increase will decrease the variability in maize yield.

  13. Incorporating Climate Variability into Precipitation Isoscapes for Interpreting Animal Migration

    Science.gov (United States)

    Vander Zanden, H.; Hobson, K. A.; Wassenaar, L. I.; Wunder, M. B.; Welker, J. M.; Bowen, G. J.

    2013-12-01

    Large-scale continental gradients in δ2H and δ18O values of precipitation lead to predictable isotopic patterns across the landscape. These light isotopes are thus useful endogenous markers in tracing long-distance movements of animals. Hydrogen in water is assimilated into tissues that are inert after synthesis, such as chitin or keratin, that are not altered when the animal moves so that the tissue reflects the environment or region from which it originated at the time when the tissue was synthesized. Models to predict the patters of δ2H in precipitation with the Global Network of Isotopes in Precipitation (GNIP) use long-term averages because models allowing estimation of isotopic values in more specific time periods have often not been available. Yet, inter-annual variation in precipitation and other climate variables may lead to large deviations from the mean values modeled over four decades, and particular regions may be more susceptible to higher departures from long-term average δ2H values. We examine whether incorporating such variation offers an improvement over static isoscapes to understand patterns of animal movement and geographic origin. Here we investigate the accuracy of Bayesian geographic assignments to predict the origin of two migratory species (monarch butterflies in the eastern United States and reed warblers in western Europe) using time-specific isoscapes. We use known-origin data from these organisms to provide calibration and validation datasets to compare the sensitivity of predictions from both year-specific and long-term isoscapes developed in IsoMAP, a freely available online workspace for modeling and predicting isotope ratio variation in precipitation. Determining how to incorporate climate and inter-annual variation into models that predict isotopic values of animal tissues can aid in improving geospatial assignments across a wide range of taxa.

  14. Vegetation response to climate variability in India from 2001 to 2010

    Science.gov (United States)

    Hashimoto, H.; Milesi, C.; Wang, W.; Ganguly, S.; Michaelis, A.; Nemani, R.

    2011-12-01

    Food supply in India is a critical issue in sustaining a large population, and more accurate predictability of agricultural productivity is necessary for policy makers. After the Green revolution, the productivity in India has increased dramatically, but the leveling-off of the productivity was expected in the near future. Decreasing of ground water was already observed and some climate models predict a higher frequency of drought in the 21st century. For a better understanding of vegetation response to climate change, we analyzed the satellite images of India from 2001 to 2010. MODIS satellite imagery shows high spatial variability in vegetation indices in response to climate variability. In this study we scrutinize the cause and mechanism of the spatial variability in vegetation growth in India. First, we tried to find the corresponding climate variability from re-analysis data (MERRA and NCEP-NCAR reanalysis data) and satellite imagery such as TRMM, GIMMS, and MODIS, as well as interpolated climate observation data (CRU). Although the precipitation variability due to ENSO has the strongest impact on vegetation growth, the other climate variability, such as shortwave radiation, also perturbed the vegetation response to climate changes. Second, we proved our hypothesis explaining the vegetation growth trend by running the Terrestrial Observation and Prediction System (TOPS) model. The model results were compared with satellite images and showed reasonable spatial pattern of net primary production to explain the observed vegetation growth variability to climate change. Those results can contribute to a more profound understanding of the mechanism of vegetation growth in India toward future prediction in food supply.

  15. Climate model uncertainty vs. conceptual geological uncertainty in hydrological modeling

    Directory of Open Access Journals (Sweden)

    T. O. Sonnenborg

    2015-04-01

    Full Text Available Projections of climate change impact are associated with a cascade of uncertainties including CO2 emission scenario, climate model, downscaling and impact model. The relative importance of the individual uncertainty sources is expected to depend on several factors including the quantity that is projected. In the present study the impacts of climate model uncertainty and geological model uncertainty on hydraulic head, stream flow, travel time and capture zones are evaluated. Six versions of a physically based and distributed hydrological model, each containing a unique interpretation of the geological structure of the model area, are forced by 11 climate model projections. Each projection of future climate is a result of a GCM-RCM model combination (from the ENSEMBLES project forced by the same CO2 scenario (A1B. The changes from the reference period (1991–2010 to the future period (2081–2100 in projected hydrological variables are evaluated and the effects of geological model and climate model uncertainties are quantified. The results show that uncertainty propagation is context dependent. While the geological conceptualization is the dominating uncertainty source for projection of travel time and capture zones, the uncertainty on the climate models is more important for groundwater hydraulic heads and stream flow.

  16. Effects of climate change on productivity of cereals and legumes; model evaluation of observed year-to-year variability of the CO2 response

    OpenAIRE

    Grashoff, Cees; Dijkstra, Paul; Nonhebel, Sanderine; Schapendonk, Ad H.C.M.; Geijn, Siebe C. van de

    1995-01-01

    The effect of elevated [CO2] on the productivity of spring wheat, winter wheat and faba bean was studied in experiments in climatized crop enclosures in the Wageningen Rhizolab in 1991–93. Simulation models for crop growth were used to explore possible causes for the observed differences in the CO2 response. Measurements of the canopy gas exchange (CO2 and water vapour) were made continuously from emergence until harvest. At an external [CO2] of 700 μmol mol−1 Maximum Canopy CO2 Exchange Rate...

  17. MODELING SUPPLY CHAIN PERFORMANCE VARIABLES

    Directory of Open Access Journals (Sweden)

    Ashish Agarwal

    2005-01-01

    Full Text Available In order to understand the dynamic behavior of the variables that can play a major role in the performance improvement in a supply chain, a System Dynamics-based model is proposed. The model provides an effective framework for analyzing different variables affecting supply chain performance. Among different variables, a causal relationship among different variables has been identified. Variables emanating from performance measures such as gaps in customer satisfaction, cost minimization, lead-time reduction, service level improvement and quality improvement have been identified as goal-seeking loops. The proposed System Dynamics-based model analyzes the affect of dynamic behavior of variables for a period of 10 years on performance of case supply chain in auto business.

  18. Causes of decadal climate variability over the North Pacific and North America

    International Nuclear Information System (INIS)

    The cause of decadal climate variability over the North Pacific and North America is investigated by analyzing data from a multi-decadal integration with a state of the art coupled ocean-atmosphere model and observations. About one third of the low-frequency climate variability in the region of interest can be attributed to a cycle involving unstable air-sea interactions between the subtropical gyre circulation in the North Pacific and the Aleutian low pressure system. The existence of this cycle provides a basis for long-range climate forecasting over the western United States at decadal time scales. (orig.)

  19. Pacific Decadal Variability and Central Pacific Warming El Niño in a Changing Climate

    Energy Technology Data Exchange (ETDEWEB)

    Di Lorenzo, Emanuele

    2015-02-27

    This research aimed at understanding the dynamics controlling decadal variability in the Pacific Ocean and its interactions with global-scale climate change. The first goal was to assess how the dynamics and statistics of the El Niño Southern Oscillation and the modes of Pacific decadal variability are represented in global climate models used in the IPCC. The second goal was to quantify how decadal dynamics are projected to change under continued greenhouse forcing, and determine their significance in the context of paleo-proxy reconstruction of long-term climate.

  20. Long-range variability and predictability of the Ozark Highlands climate elements

    Science.gov (United States)

    Lee, Jae-Won

    Interannual variations and intraannual variation of regional-scale and global-scale climate variables are characterized by principal component analysis (PCA). Climate consistency is detected among the entire United States, the North Central states, and the Ozark Highlands (OZHI). The regional-scale modes of the OZHI climate are classified as the predictands of the statistical climate model. Characteristic patterns and time coefficients are examined in global-scale climate variables as the predictor of the models. Relationships between regional-scale and global-scale climate variables are identified by the month lead cross- correlation analysis. The OZHI temperatures in January and July are highly correlated to lead time global-scale climate variables in the tropical and subtropical Pacific and Atlantic and those of lead time in the eastern subtropical and midlatitude Pacific, respectively. The OZHI precipitation levels in January and May are highly correlated to lead time global-scale climate variables in the western tropical Pacific and in the western tropical Indian, and South Pacific Convergence Zone (SPCZ), respectively. From multiple linear regression (MLR) and principal components regression (PCR) analysis, the predictability of OZHI regional temperature and precipitation are discussed with model diagnostics and measurements of forecasting performance. This study suggests that PCR can clearly eliminate the multicollinearity among predictors. For the purpose of building the statistical climate model, the sensitivities of the main predictors (i.e., temperature and precipitation) are investigated, and relatively long-memory and short-memory predictors are uncovered. The sea surface temperatures have a relatively long-memory effect.

  1. Ecology and the ratchet of events: Climate variability, niche dimensions, and species distributions

    Science.gov (United States)

    Jackson, S.T.; Betancourt, J.L.; Booth, R.K.; Gray, S.T.

    2009-01-01

    Climate change in the coming centuries will be characterized by interannual, decadal, and multidecadal fluctuations superimposed on anthropogenic trends. Predicting ecological and biogeographic responses to these changes constitutes an immense challenge for ecologists. Perspectives from climatic and ecological history indicate that responses will be laden with contingencies, resulting from episodic climatic events interacting with demographic and colonization events. This effect is compounded by the dependency of environmental sensitivity upon life-stage for many species. Climate variables often used in empirical niche models may become decoupled from the proximal variables that directly influence individuals and populations. Greater predictive capacity, and morefundamental ecological and biogeographic understanding, will come from integration of correlational niche modeling with mechanistic niche modeling, dynamic ecological modeling, targeted experiments, and systematic observations of past and present patterns and dynamics.

  2. The relationship between the thermohaline circulation and climate variability

    Institute of Scientific and Technical Information of China (English)

    2000-01-01

    The long-term integration with the Global Ocean-Atmosphere-Land System model of the State Key Laboratory of Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics(IAP), Chinese Academy of Sciences has been used in the investigations on the relationship between the thermohaline circulation and climate variability. The results show that the strength of the North Atlantic Thermohaline circulation (THC) is negatively correlated with the North Atlantic Oscillation (NAO). Based on this kind of relationship, and also the instrument-measured climate record such as air pressure and sea surface temperature, the activity of the thermohaline circulation during the 20th century has been evaluated. The inferred variations of the strength of the THC is that, during two multi-decadal periods of 1867-1903 and 1934-1972, the THC is estimated to have been running stronger, whereas during the two periods of 1904-1933 and 1973-1994, it appears to have been weaker.

  3. Climate variability, climate changes and their impact on water cycles

    International Nuclear Information System (INIS)

    Water availability in Pakistan particularly depends upon both summer and winter rainfall in plains and snowfall over the mountains. Climatically being located in subtropical region, the major amount of rainfall is in monsoon season, which extends from July, to September. Incidentally the deficient or surplus rainfall years are dependent upon intensity of Monsoon current. The same Monsoon current is also responsible for rainfall over the catchment area of eastern rivers i.e. Sutlej, Ravi, Chenab. These catchments are located across the border of eastern rivers. Westerly wave component is another aspect, responsible for rainfall in Jhelum and Indus River though some times Monsoon depressions penetrate up to Jhelum and give heavy rainfall along the route over the Eastern rivers causing the net surplus water availability. The rainfall pattern determines the agriculture output and the crops to be sown along with the area determination. This is particularly dictated by the different regimes of the Monsoon rainfall to mitigate both the surplus and deficient water availability, comprehensive study of statistical data indicates future reservoir/dam location, its construction, and a shift in crops pattern and water utility in commensurate with Climatological dictates in this region of south Asia. (Author)

  4. Adaptation to climate change and variability in Canadian water resources

    International Nuclear Information System (INIS)

    A survey is presented of topics and issues related to the adaptation to climate change in Canadian water resources. These resources are seen as especially sensitive to changes in variability in climate and hydrology. Based on current knowledge of global warming, significant changes in climate and hydrology are plausible within a time period that is significant for water resource management. Global warming will tend to exacerbate existing water resources problems in the southern Prairies and the Great Lakes. The Prairies can expect increased drought during summer, and the Great Lakes can expect a decline in mean lake levels to historic lows. Measures for adapting to climate change include traditional practices (supply management), which stress system reliability. They provide some adaptation to climate change but are limited in their ability to respond to rapid change. Nontraditional and non-management measures stress flexibility and resilience. These measures also address other concerns and can be implemented immediately, before the effects of climate change are evident. Water resources managers require methods of assessing the vulnerability of water resources systems to climate change to help identify when and where adaptive measures should be applied. Adaptation to climate change requires ongoing observation and interpretation of climate, hydrology, and related environmental processes. 29 refs., 1 fig., 3 tabs

  5. Mediterranean climate variability during the Holocene

    Directory of Open Access Journals (Sweden)

    J.S.L. CASFORD

    2012-12-01

    Full Text Available We present a study on four high sedimentation-rate marine cores with suppressed bioturbation effects, recovered along the northern margin of the eastern Mediterranean. We demonstrate that this region, central to the development of modern civilisation, was substantially affected throughout the Holocene by a distinct cycle of cooling events on the order of 2o C. In the best-preserved cases the onset of these events appears particularly abrupt, within less than a century. The cooling events typically lasted several centuries, and there are compelling indications that they were associated with increased aridity in the Levantine/NE African sector (Rossignol-Strick, 1995; 1998; Alley et al., 1997; Hassan, 1986; 1996; 1997a,b; McKim Malville et al., 1998. Several of these episodes appear coincident with cultural reorganisations, with indigenous developments (eg. cattle domestication, new technologies and population migrations and fusion of peoples and ideas (Hassan, 1986; 1996; 1997a,b; McKim Malville, 1998. We infer that climatic events of a likely high-latitude origin (O’Brien et al., 1995; Bond et al., 1997; Mayewski et al., 1997; Alley et al., 1997 caused cooling and aridity in and around the eastern Mediterranean via a direct atmospheric link, and therefore played an important role in the development of modern civilisation.

  6. Climate Model Diagnostic Analyzer Web Service System

    Science.gov (United States)

    Lee, S.; Pan, L.; Zhai, C.; Tang, B.; Jiang, J. H.

    2013-12-01

    The latest Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report stressed the need for the comprehensive and innovative evaluation of climate models with newly available global observations. The traditional approach to climate model evaluation, which compares a single parameter at a time, identifies symptomatic model biases and errors but fails to diagnose the model problems. The model diagnosis process requires physics-based multi-variable comparisons that typically involve large-volume and heterogeneous datasets, making them both computationally- and data-intensive. To address these challenges, we are developing a parallel, distributed web-service system that enables the physics-based multi-variable model performance evaluations and diagnoses through the comprehensive and synergistic use of multiple observational data, reanalysis data, and model outputs. We have developed a methodology to transform an existing science application code into a web service using a Python wrapper interface and Python web service frameworks (i.e., Flask, Gunicorn, and Tornado). The web-service system, called Climate Model Diagnostic Analyzer (CMDA), currently supports (1) all the datasets from Obs4MIPs and a few ocean datasets from NOAA and Argo, which can serve as observation-based reference data for model evaluation and (2) many of CMIP5 model outputs covering a broad range of atmosphere, ocean, and land variables from the CMIP5 specific historical runs and AMIP runs. Analysis capabilities currently supported by CMDA are (1) the calculation of annual and seasonal means of physical variables, (2) the calculation of time evolution of the means in any specified geographical region, (3) the calculation of correlation between two variables, and (4) the calculation of difference between two variables. A web user interface is chosen for CMDA because it not only lowers the learning curve and removes the adoption barrier of the tool but also enables instantaneous use

  7. Climatic variables and malaria incidence in Dehradun, Uttaranchal, India

    Directory of Open Access Journals (Sweden)

    N. Pemola Devi ; R.K. Jauhari

    2006-03-01

    Full Text Available Background & objectives: Mosquito-borne diseases particularly malaria and Japanese encephalitis(JE are becoming most dreaded health problems in Dehradun district. Keeping in view that theclimatic factors particularly temperature and rainfall may alter the distribution of vector species–increasing or decreasing the ranges, depending on weather conditions that are favourable orunfavourable for mosquito breeding, it is aimed to find out the effect of climatic factors on malariaincidence with particular emphasis to capture the essential events as a result of climatic variability.Methods: Mosquito sampling and identification was done using WHO entomological methods andfollow-up of recognised keys and catalogues. Data on malaria incidence and meteorologicalinformation were gathered in a collaborative study with the District Malaria Office, and the ForestResearch Institute, Dehradun respectively. Pearson’s correlation analysis was applied for establishingrelationship between climate variables and malaria transmission.Results: Higher positive correlation of association was found between monthly parasite incidenceand climatic variables (temperature, rainfall and humidity. However, highest significant correlationwas found between rainfall and malaria incidence (r = 0.718, p < 0.0001 when the data were staggeredto allow a lag of one-month.Interpretation & conclusion: Climatic variables that predict the presence or absence of malaria arelikely to be the best suited for forecasting the distribution of this disease at the edges of its range

  8. Experiences on climate variability education from an empirical perspective

    Science.gov (United States)

    Rodriguez-Puebla, Concepcion

    2015-04-01

    Education materials based on investigations are prepared for teaching climate matters using graphics representation, data analysis and GrADS software. An example of how climate teleconnection are included in the teaching activities would be presented. The goal is for students to learn about how climate variability and extreme events over a region are connected to large-scale atmospheric and oceanic circulation from an empirical perspective. Exercises and questions are prepared for collaborative and interactive learning considering the visualization and workshop activities included in the Moodle learning platform.

  9. Final Report. Evaluating the Climate Sensitivity of Dissipative Subgrid-Scale Mixing Processes and Variable Resolution in NCAR's Community Earth System Model

    Energy Technology Data Exchange (ETDEWEB)

    Jablonowski, Christiane [Univ. of Michigan, Ann Arbor, MI (United States)

    2015-12-14

    The goals of this project were to (1) assess and quantify the sensitivity and scale-dependency of unresolved subgrid-scale mixing processes in NCAR’s Community Earth System Model (CESM), and (2) to improve the accuracy and skill of forthcoming CESM configurations on modern cubed-sphere and variable-resolution computational grids. The research thereby contributed to the description and quantification of uncertainties in CESM’s dynamical cores and their physics-dynamics interactions.

  10. Cropping frequency and area response to climate variability can exceed yield response

    Science.gov (United States)

    Cohn, Avery S.; Vanwey, Leah K.; Spera, Stephanie A.; Mustard, John F.

    2016-06-01

    The sensitivity of agricultural output to climate change has often been estimated by modelling crop yields under climate change scenarios or with statistical analysis of the impacts of year-to-year climatic variability on crop yields. However, the area of cropland and the number of crops harvested per growing season (cropping frequency) both also affect agricultural output and both also show sensitivity to climate variability and change. We model the change in agricultural output associated with the response of crop yield, crop frequency and crop area to year-to-year climate variability in Mato Grosso (MT), Brazil, a key agricultural region. Roughly 70% of the change in agricultural output caused by climate was determined by changes in frequency and/or changes in area. Hot and wet conditions were associated with the largest losses and cool and dry conditions with the largest gains. All frequency and area effects had the same sign as total effects, but this was not always the case for yield effects. A focus on yields alone may therefore bias assessments of the vulnerability of agriculture to climate change. Efforts to reduce climate impacts to agriculture should seek to limit production losses not only from crop yield, but also from changes in cropland area and cropping frequency.

  11. Evaluating historical simulations of CMIP5 GCMs for key climatic variables in Zhejiang Province, China

    Science.gov (United States)

    Xuan, Weidong; Ma, Chong; Kang, Lili; Gu, Haiting; Pan, Suli; Xu, Yue-Ping

    2015-12-01

    Assessing the regional impact of climate change on agriculture, hydrology, and forests is vital for sustainable management. Trustworthy projections of climate change are needed to support these assessments. In this paper, 18 global climate models (GCMs) from the fifth phase of the Coupled Model Intercomparison Project (CMIP5) are evaluated for their ability to simulate regional climate change in Zhejiang Province, Southeast China. Simple graphical approaches and three indices are used to evaluate the performance of six key climatic variables during simulations from 1971 to 2000. These variables include maximum and minimum air temperature, precipitation, wind speed, solar radiation, and relative humidity. These variables are of great importance to researchers and decision makers in climate change impact studies and developing adaptation strategies. This study found that most GCMs failed to reproduce the observed spatial patterns, due to insufficient resolution. However, the seasonal variations of the six variables are simulated well by most GCMs. Maximum and minimum air temperatures are simulated well on monthly, seasonal, and yearly scales. Solar radiation is reasonably simulated on monthly, seasonal, and yearly scales. Compared to air temperature and solar radiation, it was found that precipitation, wind speed, and relative humidity can only be simulated well at seasonal and yearly scales. Wind speed was the variable with the poorest simulation results across all GCMs.

  12. Capturing subregional variability in regional-scale climate change vulnerability assessments of natural resources.

    Science.gov (United States)

    Buotte, Polly C; Peterson, David L; McKelvey, Kevin S; Hicke, Jeffrey A

    2016-03-15

    Natural resource vulnerability to climate change can depend on the climatology and ecological conditions at a particular site. Here we present a conceptual framework for incorporating spatial variability in natural resource vulnerability to climate change in a regional-scale assessment. The framework was implemented in the first regional-scale vulnerability assessment conducted by the US Forest Service. During this assessment, five subregional workshops were held to capture variability in vulnerability and to develop adaptation tactics. At each workshop, participants answered a questionnaire to: 1) identify species, resources, or other information missing from the regional assessment, and 2) describe subregional vulnerability to climate change. Workshop participants divided into six resource groups; here we focus on wildlife resources. Participants identified information missing from the regional assessment and multiple instances of subregional variability in climate change vulnerability. We provide recommendations for improving the process of capturing subregional variability in a regional vulnerability assessment. We propose a revised conceptual framework structured around pathways of climate influence, each with separate rankings for exposure, sensitivity, and adaptive capacity. These revisions allow for a quantitative ranking of species, pathways, exposure, sensitivity, and adaptive capacity across subregions. Rankings can be used to direct the development and implementation of future regional research and monitoring programs. The revised conceptual framework is equally applicable as a stand-alone model for assessing climate change vulnerability and as a nested model within a regional assessment for capturing subregional variability in vulnerability.

  13. The impact of climate variability and change on economic growth and poverty in Zambia:

    OpenAIRE

    Thurlow, James; Zhu, Tingju; DIAO, Xinshen

    2009-01-01

    "We combined a hydro-crop model with a dynamic general equilibrium (DCGE) model to assess the impacts of climate variability and change on economic growth and poverty reduction in Zambia. The hydro-crop model is first used to estimate the impact of climate variability on crop yields over the past three decades and such analysis is done at the crop level for each of Zambia's five agroecological zones, supported by the identification of zonal-level extreme weather events using a drought index a...

  14. Assessing the future of crop yield variability in the United States with downscaled climate projections (Invited)

    Science.gov (United States)

    Lobell, D. B.; Urban, D.

    2010-12-01

    One aspect of climate change of particular concern to farmers and food markets is the potential for increased year-to-year variability in crop yields. Recent episodes of food price increases following the Australian drought or Russian heat wave have heightened this concern. Downscaled climate projections that properly capture the magnitude of daily and interannual variability of weather can be useful for projecting future yield variability. Here we examine the potential magnitude and cause of changes in variability of corn yields in the United States up to 2050. Using downscaled climate projections from multiple models, we estimate a distribution of changes in mean and variability of growing season average temperature and precipitation. These projections are then fed into a model of maize yield that explicitly factors in the effect of extremely warm days. Changes in yield variability can result from a shift in mean temperatures coupled with a nonlinear crop response, a shift in climate variability, or a combination of the two. The results are decomposed into these different causes, with implications for future research to reduce uncertainties in projections of future yield variability.

  15. How EC-EARTH simulates the Earth's climate and it's variabilities

    Science.gov (United States)

    Wang, Xueli

    2010-05-01

    Recently an Earth system named EC-Earth has been developed at KNMI in collaboration with a number of EU country's meteorological institutes. It is a coupled model with IFS (Integrated Forecasting System) from ECMWF as the atmospheric model, NEMO as ocean model. H-Tessel land scheme is used as land model and LIM as thermodynamic ice model. It will be shown that the model produces the mean climate better or equal to the CMIP3 mean climate models and the major climate variabilities are reproduced by the model fairly well. In particular the ENSO and NAO are very well simulated. Just as most climate models have difficulties to simulate variabilities in the Atlantic, EC-Earth simulates well North Tropical Atlantic SST both for seasonal cycle and variability, it simulates less good equatorial SST variabilities. The SST seasonal cycle is correctly simulated but the equatorial cold tongue is too weak comparing to the observation, up to 3 degrees. Nevertheless the model simulates reasonably good west African monsoon distribution and seasonal cycle although it's a bit to week and not penetrating north enough compare to CMAP data. Experiment with future CO2 concentration shows that the rainfall at the coastal area of Guinea increased as result of increasing CO2 concentration while no or little change in the Sahel area.

  16. Assessing risks of climate variability and climate change for Indonesian rice agriculture.

    Science.gov (United States)

    Naylor, Rosamond L; Battisti, David S; Vimont, Daniel J; Falcon, Walter P; Burke, Marshall B

    2007-05-01

    El Niño events typically lead to delayed rainfall and decreased rice planting in Indonesia's main rice-growing regions, thus prolonging the hungry season and increasing the risk of annual rice deficits. Here we use a risk assessment framework to examine the potential impact of El Niño events and natural variability on rice agriculture in 2050 under conditions of climate change, with a focus on two main rice-producing areas: Java and Bali. We select a 30-day delay in monsoon onset as a threshold beyond which significant impact on the country's rice economy is likely to occur. To project the future probability of monsoon delay and changes in the annual cycle of rainfall, we use output from the Intergovernmental Panel on Climate Change AR4 suite of climate models, forced by increasing greenhouse gases, and scale it to the regional level by using empirical downscaling models. Our results reveal a marked increase in the probability of a 30-day delay in monsoon onset in 2050, as a result of changes in the mean climate, from 9-18% today (depending on the region) to 30-40% at the upper tail of the distribution. Predictions of the annual cycle of precipitation suggest an increase in precipitation later in the crop year (April-June) of approximately 10% but a substantial decrease (up to 75% at the tail) in precipitation later in the dry season (July-September). These results indicate a need for adaptation strategies in Indonesian rice agriculture, including increased investments in water storage, drought-tolerant crops, crop diversification, and early warning systems. PMID:17483453

  17. Coupled Climate Model Appraisal a Benchmark for Future Studies

    Energy Technology Data Exchange (ETDEWEB)

    Phillips, T J; AchutaRao, K; Bader, D; Covey, C; Doutriaux, C M; Fiorino, M; Gleckler, P J; Sperber, K R; Taylor, K E

    2005-08-22

    The Program for Climate Model Diagnosis and Intercomparison (PCMDI) has produced an extensive appraisal of simulations of present-day climate by eleven representative coupled ocean-atmosphere general circulation models (OAGCMs) which were developed during the period 1995-2002. Because projections of potential future global climate change are derived chiefly from OAGCMs, there is a continuing need to test the credibility of these predictions by evaluating model performance in simulating the historically observed climate. For example, such an evaluation is an integral part of the periodic assessments of climate change that are reported by the Intergovernmental Panel on Climate Change. The PCMDI appraisal thus provides a useful benchmark for future studies of this type. The appraisal mainly analyzed multi-decadal simulations of present-day climate by models that employed diverse representations of climate processes for atmosphere, ocean, sea ice, and land, as well as different techniques for coupling these components (see Table). The selected models were a subset of those entered in phase 2 of the Coupled Model Intercomparison Project (CMIP2, Covey et al. 2003). For these ''CMIP2+ models'', more atmospheric or oceanic variables were provided than the minimum requirements for participation in CMIP2. However, the appraisal only considered those climate variables that were supplied from most of the CMIP2+ models. The appraisal focused on three facets of the simulations of current global climate: (1) secular trends in simulation time series which would be indicative of a problematical ''coupled climate drift''; (2) comparisons of temporally averaged fields of simulated atmospheric and oceanic climate variables with available observational climatologies; and (3) correspondences between simulated and observed modes of climatic variability. Highlights of these climatic aspects manifested by different CMIP2+ simulations are briefly

  18. Climatic variability leads to later seasonal flowering of Floridian plants.

    Directory of Open Access Journals (Sweden)

    Betsy Von Holle

    Full Text Available Understanding species responses to global change will help predict shifts in species distributions as well as aid in conservation. Changes in the timing of seasonal activities of organisms over time may be the most responsive and easily observable indicator of environmental changes associated with global climate change. It is unknown how global climate change will affect species distributions and developmental events in subtropical ecosystems or if climate change will differentially favor nonnative species. Contrary to previously observed trends for earlier flowering onset of plant species with increasing spring temperatures from mid and higher latitudes, we document a trend for delayed seasonal flowering among plants in Florida. Additionally, there were few differences in reproductive responses by native and nonnative species to climatic changes. We argue that plants in Florida have different reproductive cues than those from more northern climates. With global change, minimum temperatures have become more variable within the temperate-subtropical zone that occurs across the peninsula and this variation is strongly associated with delayed flowering among Florida plants. Our data suggest that climate change varies by region and season and is not a simple case of species responding to consistently increasing temperatures across the region. Research on climate change impacts need to be extended outside of the heavily studied higher latitudes to include subtropical and tropical systems in order to properly understand the complexity of regional and seasonal differences of climate change on species responses.

  19. Research on the Natural Variability of Climate

    Science.gov (United States)

    2004-01-01

    The scheme for ocean vertical mixing presented in Canuto, Howard, Cheng & Dubovikov and Canuto, Howard, Hogan, Cheng, Dubovikov & Montenegro [GISS mixing scheme] was extended to include the latitudinal dependence reported by Gregg, Sanford & Winkel of the input to the interior ocean turbulence field of energy from internal waves. The resulting latitude dependence was implemented in our module of code for the GISS vertical mixing scheme and tested in the global NCAR-CSM ocean model with 3 degree X 3 degree resolution and 25 levels in the vertical. With the latitude dependence, the background diffusivity decreases from approx. 0.1 sq cm/sec at midlatitudes to approx. less than 0.01 sq cm/sec at the equator. A significant improvement was seen in the tropical ocean, with tropical thermoclines being sharpened in agreement with the requirements of observational studies and the needs of ENS0 modeling. At the same time global measures nf performance such as the meridional overturning and northward heat transport were not adversely affected. Results were presented at the 2004 Layered Ocean Modeling meeting and a paper has been prepared and submitted to Geophysical Research Letters.

  20. Future Warming Patterns Linked to Today’s Climate Variability

    OpenAIRE

    Aiguo Dai

    2016-01-01

    The reliability of model projections of greenhouse gas (GHG)-induced future climate change is often assessed based on models’ ability to simulate the current climate, but there has been little evidence that connects the two. In fact, this practice has been questioned because the GHG-induced future climate change may involve additional physical processes that are not important for the current climate. Here I show that the spatial patterns of the GHG-induced future warming in the 21st century i...

  1. To what extent is climate change detection at the local scale 'clouded' by internal variability?

    Science.gov (United States)

    Aalbers, Emma; Lenderink, Geert; van Meijgaard, Erik; van den Hurk, Bart

    2016-04-01

    Internal variability, i.e. the natural variability of the climate system, has been shown to be an important source of uncertainty in climate change projections of mean and (especially) extreme climate events, next to model uncertainty and uncertainty in projections of greenhouse gas emissions. To quantify the internal variability and get a robust estimate of the forced climate change response, large ensembles of climate model simulations of the same model provide essential information. For global climate models (GCMs) a number of these single model ensembles are indeed available. So far however, the size of single model ensembles for regional climate models (RCMs) has been limited to only a few members, relatively short periods or small modeling domains. Here, we use a 16 member ensemble generated with the RCM KNMI-RACMO2 driven by the GCM EC-EARTH. The initial atmospheric state of EC-EARTH was perturbed in 1850, after which each member was run until 2100 assuming the historical emission scenario until 2005 and the RCP8.5 emission scenario from 2006 onwards. Each of the EC-EARTH members was then downscaled on a 12-km resolved domain covering Western Europe including the Alps for the period 1950-2100. For this ensemble we show the climate change signal, the noise due to internal variability and the signal-to-noise ratio, and how these depend on parameter, season, location and projection period. Using an aggregated spatial probability perspective similar to Fischer et al. (2013) we also examine whether spatially aggregated responses yield more robust changes and earlier detection times of climate change. This information is particularly relevant when the output of RCMs is applied in impact studies. Firstly, with this information we can identify which of the two - internal variability or climate change - is more important for a certain timescale, requiring potentially different coping strategies. Secondly, the internal variability can be a cause for the discrepancy

  2. The influence of model resolution on temperature variability

    Science.gov (United States)

    Klavans, Jeremy M.; Poppick, Andrew; Sun, Shanshan; Moyer, Elisabeth J.

    2016-08-01

    Understanding future changes in climate variability, which can impact human activities, is a current research priority. It is often assumed that a key part of this effort involves improving the spatial resolution of climate models; however, few previous studies comprehensively evaluate the effects of model resolution on variability. In this study, we systematically examine the sensitivity of temperature variability to horizontal atmospheric resolution in a single model (CCSM3, the Community Climate System Model 3) at three different resolutions (T85, T42, and T31), using spectral analysis to describe the frequency dependence of differences. We find that in these runs, increased model resolution is associated with reduced temperature variability at all but the highest frequencies (2-5 day periods), though with strong regional differences. (In the tropics, where temperature fluctuations are smallest, increased resolution is associated with increased variability.) At all resolutions, temperature fluctuations in CCSM3 are highly spatially correlated, implying that the changes in variability with model resolution are driven by alterations in large-scale phenomena. Because CCSM3 generally overestimates temperature variability relative to reanalysis output, the reductions in variability associated with increased resolution tend to improve model fidelity. However, the resolution-related variability differences are relatively uniform with frequency, whereas the sign of model bias changes at interannual frequencies. This discrepancy raises questions about the mechanisms underlying the improvement at subannual frequencies. The consistent response across frequencies also implies that the atmosphere plays a significant role in interannual variability.

  3. Climate change in Central America and Mexico: regional climate model validation and climate change projections

    Energy Technology Data Exchange (ETDEWEB)

    Karmalkar, Ambarish V. [University of Oxford, School of Geography and the Environment, Oxford (United Kingdom); Bradley, Raymond S. [University of Massachusetts, Department of Geosciences, Amherst, MA (United States); Diaz, Henry F. [NOAA/ESRL/CIRES, Boulder, CO (United States)

    2011-08-15

    Central America has high biodiversity, it harbors high-value ecosystems and it's important to provide regional climate change information to assist in adaptation and mitigation work in the region. Here we study climate change projections for Central America and Mexico using a regional climate model. The model evaluation shows its success in simulating spatial and temporal variability of temperature and precipitation and also in capturing regional climate features such as the bimodal annual cycle of precipitation and the Caribbean low-level jet. A variety of climate regimes within the model domain are also better identified in the regional model simulation due to improved resolution of topographic features. Although, the model suffers from large precipitation biases, it shows improvements over the coarse-resolution driving model in simulating precipitation amounts. The model shows a dry bias in the wet season and a wet bias in the dry season suggesting that it's unable to capture the full range of precipitation variability. Projected warming under the A2 scenario is higher in the wet season than that in the dry season with the Yucatan Peninsula experiencing highest warming. A large reduction in precipitation in the wet season is projected for the region, whereas parts of Central America that receive a considerable amount of moisture in the form of orographic precipitation show significant decreases in precipitation in the dry season. Projected climatic changes can have detrimental impacts on biodiversity as they are spatially similar, but far greater in magnitude, than those observed during the El Nino events in recent decades that adversely affected species in the region. (orig.)

  4. Climate change in Central America and Mexico: regional climate model validation and climate change projections

    Science.gov (United States)

    Karmalkar, Ambarish V.; Bradley, Raymond S.; Diaz, Henry F.

    2011-08-01

    Central America has high biodiversity, it harbors high-value ecosystems and it's important to provide regional climate change information to assist in adaptation and mitigation work in the region. Here we study climate change projections for Central America and Mexico using a regional climate model. The model evaluation shows its success in simulating spatial and temporal variability of temperature and precipitation and also in capturing regional climate features such as the bimodal annual cycle of precipitation and the Caribbean low-level jet. A variety of climate regimes within the model domain are also better identified in the regional model simulation due to improved resolution of topographic features. Although, the model suffers from large precipitation biases, it shows improvements over the coarse-resolution driving model in simulating precipitation amounts. The model shows a dry bias in the wet season and a wet bias in the dry season suggesting that it's unable to capture the full range of precipitation variability. Projected warming under the A2 scenario is higher in the wet season than that in the dry season with the Yucatan Peninsula experiencing highest warming. A large reduction in precipitation in the wet season is projected for the region, whereas parts of Central America that receive a considerable amount of moisture in the form of orographic precipitation show significant decreases in precipitation in the dry season. Projected climatic changes can have detrimental impacts on biodiversity as they are spatially similar, but far greater in magnitude, than those observed during the El Niño events in recent decades that adversely affected species in the region.

  5. Utilizing Satellite Precipitation Products to Understand the Link Between Climate Variability and Malaria

    Science.gov (United States)

    Maggioni, V.; Mousam, A.; Delamater, P. L.; Cash, B. A.; Quispe, A.

    2015-12-01

    Malaria is a public health threat to people globally leading to 198 million cases and 584,000 deaths annually. Outbreaks of vector borne diseases such as malaria can be significantly impacted by climate variables such as precipitation. For example, an increase in rainfall has the potential to create pools of water that can serve as breeding locations for mosquitos. Peru is a country that is currently controlling malaria, but has not been able to completely eliminate the disease. Despite the various initiatives in order to control malaria - including regional efforts to improve surveillance, early detection, prompt treatment, and vector management - malaria cases in Peru have risen between 2011 and 2014. The purpose of this study is to test the hypothesis that climate variability plays a fundamental role in malaria occurrence over a 12-year period (2003-2014) in Peru. When analyzing climate variability, it is important to obtain high-quality, high-resolution data for a time series long enough to draw conclusion about how climate variables have been and are changing. Remote sensing is a powerful tool for measuring and monitoring climate variables continuously in time and space. A widely used satellite-based precipitation product, the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA), available globally since 1998, was used to obtain 3-hourly data with a spatial resolution of 0.25° x 0.25°. The precipitation data was linked to weekly (2003-2014) malaria cases collected by health centers and available at a district level all over Peru to investigate the relationship between precipitation and the seasonal and annual variations in malaria incidence. Further studies will incorporate additional climate variables such as temperature, humidity, soil moisture, and surface pressure from remote sensing data products and climate models. Ultimately, this research will help us to understand if climate variability impacts malaria incidence

  6. Tropical interannual variability in a global coupled GCM: Sensitivity to mean climate state

    Energy Technology Data Exchange (ETDEWEB)

    Moore, A.M. [Bureau of Meterology Research Centre, Melbourne, Victoria (Australia)

    1995-04-01

    A global coupled ocean-atmosphere-sea ice general circulation model is used to study interannual variability in the Tropics. Flux correction is used to control the mean climate of the coupled system, and in one configuration of the coupled model, interannual variability in the tropical Pacific is dominated by westward moving anomalies. Through a series of experiments in which the equatorial ocean wave speeds and ocean-atmosphere coupling strength are varied, it is demonstrated that these westward moving disturbances are probably some manifestation of what Neelin describes as an {open_quotes}SST mode.{close_quotes} By modifying the flux correction procedure, the mean climate of the coupled model can be changed. A fairly modest change in the mean climate is all that is required to excite eastward moving anomalies in place of the westward moving SST modes found previously. The apparent sensitivity of the nature of tropical interannual variability to the mean climate state in a coupled general circulation model such as that used here suggests that caution is advisable if we try to use such models to answer questions relating to changes in ENSO-like variability associated with global climate change. 41 refs., 23 figs., 1 tab.

  7. The Community Climate System Model: CCSM3

    Energy Technology Data Exchange (ETDEWEB)

    Collins, W D; Blackmon, M; Bitz, C; Bonan, G; Bretherton, C S; Carton, J A; Chang, P; Doney, S; Hack, J J; Kiehl, J T; Henderson, T; Large, W G; McKenna, D; Santer, B D; Smith, R D

    2004-12-27

    A new version of the Community Climate System Model (CCSM) has been developed and released to the climate community. CCSM3 is a coupled climate model with components representing the atmosphere, ocean, sea ice, and land surface connected by a flux coupler. CCSM3 is designed to produce realistic simulations over a wide range of spatial resolutions, enabling inexpensive simulations lasting several millennia or detailed studies of continental-scale climate change. This paper will show results from the configuration used for climate-change simulations with a T85 grid for atmosphere and land and a 1-degree grid for ocean and sea-ice. The new system incorporates several significant improvements in the scientific formulation. The enhancements in the model physics are designed to reduce or eliminate several systematic biases in the mean climate produced by previous editions of CCSM. These include new treatments of cloud processes, aerosol radiative forcing, land-atmosphere fluxes, ocean mixed-layer processes, and sea-ice dynamics. There are significant improvements in the sea-ice thickness, polar radiation budgets, equatorial sea-surface temperatures, ocean currents, cloud radiative effects, and ENSO teleconnections. CCSM3 can produce stable climate simulations of millenial duration without ad hoc adjustments to the fluxes exchanged among the component models. Nonetheless, there are still systematic biases in the ocean-atmosphere fluxes in western coastal regions, the spectrum of ENSO variability, the spatial distribution of precipitation in the Pacific and Indian Oceans, and the continental precipitation and surface air temperatures. We conclude with the prospects for extending CCSM to a more comprehensive model of the Earth's climate system.

  8. A stochastic analysis of the influence of soil and climatic variability on the estimate of pesticide ground water polution potential

    Science.gov (United States)

    Jury, William A.; Gruber, Joachim

    1989-12-01

    Soil and climatic variability contribute in an unknown manner to the leaching of pesticides below the surface soil zone where degradation occurs at maximum levels. In this paper we couple the climatic variability model of Eagleson (1978) to the soil variability transport model of Jury (1982) to produce a probability density distribution of residual mass fraction (RMF) remaining after leaching below the surface degradation zone. Estimates of the RMF distribution are shown to be much more sensitive to soil variability than climatic variability, except when the residence time of the chemical is shorter than one year. When soil variability dominates climatic variability, the applied water distribution may be replaced by a constant average water application rate without serious error. Simulations of leaching are run with 10 pesticides in two climates and in two representative soil types with a range of soil variability. Variability in soil or climate act to produce a nonnegligible probability of survival of a small value of residual mass even for relatively immobile compounds which are predicted to degrade completely by a simple model which neglects variability. However, the simpler model may still be useful for screening pesticides for groundwater pollution potential if somewhat larger residual masses of a given compound are tolerated. Monte Carlo simulations of the RMF distribution agreed well with model predictions over a wide range of pesticide properties.

  9. Climate variability in West Greenland during the past 1500 years

    DEFF Research Database (Denmark)

    dos Santos Ribeiro, Sofia Isabel; Moros, Matthias; Ellegaard, Marianne;

    2012-01-01

    document late-Holocene climate variability in West Greenland as inferred from a marine sediment record from the outer Disko Bay. Organic-walled dinoflagellate cysts and other palynomorphs were used to reconstruct environmental changes in the area through the last c. 1500 years at 30–40 years resolution....... Sea ice cover and primary productivity were identified as the two main factors driving dinoflagellate cyst community changes through time. Our data provide evidence for an opposite climate trend in West Greenland relative to the NE Atlantic region from c. AD 500 to 1050. For the same period, sea......Ribeiro, S., Moros, M., Ellegaard, M. & Kuijpers, A. 2012 (January): Climate variability in West Greenland during the past 1500 years: evidence from a high-resolution marine palynological record from Disko Bay. Boreas, Vol. 41, pp. 68–83. 10.1111/j.1502-3885.2011.00216.x. ISSN 0300-9483. Here we...

  10. Regional climate simulations over Vietnam using the WRF model

    Science.gov (United States)

    Raghavan, S. V.; Vu, M. T.; Liong, S. Y.

    2015-07-01

    We present an analysis of the present-day (1961-1990) regional climate simulations over Vietnam. The regional climate model Weather Research and Forecasting (WRF) was driven by the global reanalysis ERA40. The performance of the regional climate model in simulating the observed climate is evaluated with a main focus on precipitation and temperature. The regional climate model was able to reproduce the observed spatial patterns of the climate, although with some biases. The model also performed better in reproducing the extreme precipitation and the interannual variability. Overall, the WRF model was able to simulate the main regional signatures of climate variables, seasonal cycles, and frequency distributions. This study is an evaluation of the present-day climate simulations of a regional climate model at a resolution of 25 km. Given that dynamical downscaling has become common for studying climate change and its impacts, the study highlights that much more improvements in modeling might be necessary to yield realistic simulations of climate at high resolutions before they can be used for impact studies at a local scale. The need for a dense network of observations is also realized as observations at high resolutions are needed when it comes to evaluations and validations of models at sub-regional and local scales.

  11. Regional climate simulations over Vietnam using the WRF model

    Science.gov (United States)

    Raghavan, S. V.; Vu, M. T.; Liong, S. Y.

    2016-10-01

    We present an analysis of the present-day (1961-1990) regional climate simulations over Vietnam. The regional climate model Weather Research and Forecasting (WRF) was driven by the global reanalysis ERA40. The performance of the regional climate model in simulating the observed climate is evaluated with a main focus on precipitation and temperature. The regional climate model was able to reproduce the observed spatial patterns of the climate, although with some biases. The model also performed better in reproducing the extreme precipitation and the interannual variability. Overall, the WRF model was able to simulate the main regional signatures of climate variables, seasonal cycles, and frequency distributions. This study is an evaluation of the present-day climate simulations of a regional climate model at a resolution of 25 km. Given that dynamical downscaling has become common for studying climate change and its impacts, the study highlights that much more improvements in modeling might be necessary to yield realistic simulations of climate at high resolutions before they can be used for impact studies at a local scale. The need for a dense network of observations is also realized as observations at high resolutions are needed when it comes to evaluations and validations of models at sub-regional and local scales.

  12. Climate Variability Impacts on Watershed Nutrient Delivery and Reservoir Production

    Science.gov (United States)

    White, J. D.; Prochnow, S. J.; Zygo, L. M.; Byars, B. W.

    2005-05-01

    Reservoirs in agricultural dominated watersheds tend to exhibit pulse-system behavior especially if located in climates dominated by summer convective precipitation inputs. Concentration and bulk mass of nutrient and sediment inputs into reservoir systems vary in terms of timing and magnitude of delivery from watershed sources to reservoirs under these climate conditions. Reservoir management often focuses on long-term average inputs without considering short and long-term impacts of variation in loading. In this study we modeled a watershed-reservoir system to assess how climate variability affects reservoir primary production through shifts in external loading and internal recycling of limiting nutrients. The Bosque watershed encompasses 423,824 ha in central Texas which delivers water to Lake Waco, a 2900 ha reservoir that is the primary water source for the city of Waco and surrounding areas. Utilizing the Soil Water Assessment Tool for the watershed and river simulations and the CE-Qual-2e model for the reservoir, hydrologic and nutrient dynamics were simulated for a 10 year period encompassing two ENSO cycles. The models were calibrated based on point measurement of water quality attributes for a two year time period. Results indicated that watershed delivery of nutrients was affected by the presence and density of small flood-control structure in the watershed. However, considerable nitrogen and phosphorus loadings were derived from soils in the upper watershed which have had long-term waste-application from concentrated animal feeding operations. During El Niño years, nutrient and sediment loads increased by 3 times above non-El Niño years. The simulated response within the reservoir to these nutrient and sediment loads had both direct and indirect. Productivity evaluated from chlorophyll a and algal biomass increased under El Niño conditions, however species composition shifts were found with an increase in cyanobacteria dominance. In non-El Niño years

  13. High dimensional decision dilemmas in climate models

    Directory of Open Access Journals (Sweden)

    A. Bracco

    2013-10-01

    Full Text Available An important source of uncertainty in climate models is linked to the calibration of model parameters. Interest in systematic and automated parameter optimization procedures stems from the desire to improve the model climatology and to quantify the average sensitivity associated with potential changes in the climate system. Building upon on the smoothness of the response of an atmospheric circulation model (AGCM to changes of four adjustable parameters, Neelin et al. (2010 used a quadratic metamodel to objectively calibrate the AGCM. The metamodel accurately estimates global spatial averages of common fields of climatic interest, from precipitation, to low and high level winds, from temperature at various levels to sea level pressure and geopotential height, while providing a computationally cheap strategy to explore the influence of parameter settings. Here, guided by the metamodel, the ambiguities or dilemmas related to the decision making process in relation to model sensitivity and optimization are examined. Simulations of current climate are subject to considerable regional-scale biases. Those biases may vary substantially depending on the climate variable considered, and/or on the performance metric adopted. Common dilemmas are associated with model revisions yielding improvement in one field or regional pattern or season, but degradation in another, or improvement in the model climatology but degradation in the interannual variability representation. Challenges are posed to the modeler by the high dimensionality of the model output fields and by the large number of adjustable parameters. The use of the metamodel in the optimization strategy helps visualize trade-offs at a regional level, e.g., how mismatches between sensitivity and error spatial fields yield regional errors under minimization of global objective functions.

  14. High dimensional decision dilemmas in climate models

    Directory of Open Access Journals (Sweden)

    A. Bracco

    2013-05-01

    Full Text Available An important source of uncertainty in climate models is linked to the calibration of model parameters. Interest in systematic and automated parameter optimization procedures stems from the desire to improve the model climatology and to quantify the average sensitivity associated with potential changes in the climate system. Neelin et al. (2010 used a quadratic metamodel to objectively calibrate an atmospheric circulation model (AGCM around four adjustable parameters. The metamodel accurately estimates global spatial averages of common fields of climatic interest, from precipitation, to low and high level winds, from temperature at various levels to sea level pressure and geopotential height, while providing a computationally cheap strategy to explore the influence of parameter settings. Here, guided by the metamodel, the ambiguities or dilemmas related to the decision making process in relation to model sensitivity and optimization are examined. Simulations of current climate are subject to considerable regional-scale biases. Those biases may vary substantially depending on the climate variable considered, and/or on the performance metric adopted. Common dilemmas are associated with model revisions yielding improvement in one field or regional pattern or season, but degradation in another, or improvement in the model climatology but degradation in the interannual variability representation. Challenges are posed to the modeler by the high dimensionality of the model output fields and by the large number of adjustable parameters. The use of the metamodel in the optimization strategy helps visualize trade-offs at a regional level, e.g. how mismatches between sensitivity and error spatial fields yield regional errors under minimization of global objective functions.

  15. High dimensional decision dilemmas in climate models

    Science.gov (United States)

    Bracco, A.; Neelin, J. D.; Luo, H.; McWilliams, J. C.; Meyerson, J. E.

    2013-10-01

    An important source of uncertainty in climate models is linked to the calibration of model parameters. Interest in systematic and automated parameter optimization procedures stems from the desire to improve the model climatology and to quantify the average sensitivity associated with potential changes in the climate system. Building upon on the smoothness of the response of an atmospheric circulation model (AGCM) to changes of four adjustable parameters, Neelin et al. (2010) used a quadratic metamodel to objectively calibrate the AGCM. The metamodel accurately estimates global spatial averages of common fields of climatic interest, from precipitation, to low and high level winds, from temperature at various levels to sea level pressure and geopotential height, while providing a computationally cheap strategy to explore the influence of parameter settings. Here, guided by the metamodel, the ambiguities or dilemmas related to the decision making process in relation to model sensitivity and optimization are examined. Simulations of current climate are subject to considerable regional-scale biases. Those biases may vary substantially depending on the climate variable considered, and/or on the performance metric adopted. Common dilemmas are associated with model revisions yielding improvement in one field or regional pattern or season, but degradation in another, or improvement in the model climatology but degradation in the interannual variability representation. Challenges are posed to the modeler by the high dimensionality of the model output fields and by the large number of adjustable parameters. The use of the metamodel in the optimization strategy helps visualize trade-offs at a regional level, e.g., how mismatches between sensitivity and error spatial fields yield regional errors under minimization of global objective functions.

  16. Biophysical transport model suggests climate variability determines distribution of Walleye Pollock early life stages in the eastern Bering Sea through effects on spawning

    Science.gov (United States)

    Petrik, Colleen M.; Duffy-Anderson, Janet T.; Mueter, Franz; Hedstrom, Katherine; Curchitser, Enrique N.

    2015-11-01

    The eastern Bering Sea recently experienced an anomalously warm period followed by an anomalously cold period. These periods varied with respect to sea ice extent, water temperature, wind patterns, and ocean circulation. The distributions of Walleye Pollock early life stages also differed between periods, with larval stages found further eastward on the shelf in warm years. Statistical analyses indicated that these spatial distributions were more closely related to temperature than to other covariates, though a mechanism has not been identified. The objective of this study was to determine if variable transport could be driving the observed differences in pollock distributions. An individual-based model of pollock early life stages was developed by coupling a hydrodynamic model to a particle-tracking model with biology and behavior. Simulation experiments were performed with the model to investigate the effects of wind on transport, ice presence on time of spawning, and water temperature on location of spawning. This modeling approach benefited from the ability to individually test mechanisms to quantitatively assess the impact of each on the distribution of pollock. Neither interannual variability in advection nor advances or delays in spawning time could adequately represent the observed differences in distribution between warm and cold years. Changes to spawning areas, particularly spatial contractions of spawning areas in cold years, resulted in modeled distributions that were most similar to observations. The location of spawning pollock in reference to cross-shelf circulation patterns is important in determining the distribution of eggs and larvae, warranting further study on the relationship between spawning adults and the physical environment. The different distributions of pollock early life stages between warm and cold years may ultimately affect recruitment by influencing the spatial overlap of pollock juveniles with prey and predators.

  17. Modeling Earth's Climate

    Science.gov (United States)

    Pallant, Amy; Lee, Hee-Sun; Pryputniewicz, Sara

    2012-01-01

    Systems thinking suggests that one can best understand a complex system by studying the interrelationships of its component parts rather than looking at the individual parts in isolation. With ongoing concern about the effects of climate change, using innovative materials to help students understand how Earth's systems connect with each other is…

  18. Long-term ERP time series as indicators for global climate variability and climate change

    Science.gov (United States)

    Lehmann, E.; Grötzsch, A.; Ulbrich, U.; Leckebusch, G. C.; Nevir, P.; Thomas, M.

    2009-04-01

    This study assesses whether variations in observed Earth orientation parameters (EOPs, IERS) such as length-of day (LOD EOP C04) and polar motion (PM EOP C04) can be applied as climate indicators. Data analyses suggest that observed EOPs are differently affected by parameters associated with the atmosphere and ocean. On interannual time scales the varying ocean-atmosphere effects on EOPs are in particular pronounced during episodes of the coupled ocean-atmosphere phenomenon El Niño-Southern Oscillation (ENSO). Observed ENSO anomalies of spatial patterns of parameters affected by atmosphere and ocean (climate indices and sea surface temperatures) are related to LOD and PM variability and associated with possible physical background processes. Present time analyses (1962 - 2000) indicate that the main source of the varying ENSO signal on observed LOD can be associated with anomalies of the relative angular momentum (AAM) related to variations in location and strength of jet streams of the upper troposphere. While on interannual time scales observed LOD and AAM are highly correlated (r=0.75), results suggest that strong El Niño events affect the observed LOD - AAM relation differently strong (explained variance 71%- 98%). Accordingly, the relation between AAM and ocean sea surface temperatures (SST) in the NIÑO 3.4 region differs (explained variances 15%-73%). Corresponding analysis is conducted on modelled EOPs (ERA40 reanalysis, ECHAM5-OM1) to obtain Earth rotation parameters undisturbed by core-mantle activities, and to study rotational variations under climate variability and change. A total of 91 strong El Niño events are analysed in coupled ocean-atmosphere ECHAM5-OM1 scenarios concerning the 20th century (20C), climate warming (A1B) and pre-industrial climate variability. Analyses on a total of 61 strong El Niño events covering a time period of 505 simulation years under pre-industrial climate conditions indicate a range of El Niño events with a strong or

  19. Development and application of downscaled hydroclimatic predictor variables for use in climate vulnerability and assessment studies.

    OpenAIRE

    James H. Thorne; Boynton, Ryan; Flint, Lorriane; Flint, Alan; N'goc Le, Thuy

    2012-01-01

    This paper outlines the production of 270 meter grid‐scale maps for 14 climate and derivative hydrologic variables for a region that encompasses the State of California and all the streams that flow into it. The paper describes the Basin Characterization Model (BCM), a map‐based, mechanistic model used to process the hydrological variables. Three historic and three future time periods of 30 years (1911–1940, 1941–1970, 1971–2000, 2010–2039, 2040–2069, and 2...

  20. Wind climate from the regional climate model REMO

    DEFF Research Database (Denmark)

    Larsén, Xiaoli Guo; Mann, Jakob; Berg, Jacob;

    2010-01-01

    Selected outputs from simulations with the regional climate model REMO from the Max Planck Institute, Hamburg, Germany were studied in connection with wind energy resource assessment. It was found that the mean wind characteristics based on observations from six mid-latitude stations are well...... described by the standard winds derived from the REMO pressure data. The mean wind parameters include the directional wind distribution, directional and omni-directional mean values and Weibull fitting parameters, spectral analysis and interannual variability of the standard winds. It was also found that...

  1. Potential Impacts of Land-Use on Climate Variability and Extremes

    Institute of Scientific and Technical Information of China (English)

    Huqiang ZHANG; LI Yaohui; GAO Xuejie

    2009-01-01

    This study aims at exploring potential impacts of land-use vegetation change (LUC) on regional climate variability and extremes.Results from a pair of Australian Bureau of Meteorology Research Centre (BMRC)climate model 54-yr (1949-2002) integrations have been analysed.In the model experiments,two vegetation datasets are used,with one representing current vegetation coverage in China and the other approximating its potential coverage without human intervention.The model results show potential impacts of LUC on climate variability and extremes.There are statistically significant changes of surface interannual climate variability simulated by the model.Using different vegetation datasets,significant changes in correlation coefficients between tropical Pacific Nifio3.4 SST and precipitation and surface temperature over East Asia are identified,which indicate that changes in vegetation coverage may alter ENSO impacts on regional climate variability.Because of the lack of slowly varying surface processes when forests are removed and less rainfall is received following LUC,the ENSO signal simulated by the model becomes stronger.Results furthermore show that land-use could modulate characteristics of decadal variations in this region.When using current vegetation coverage,the model gives better simulation of observed climate variations in the region than the case using potential vegetation coverage.In addition,results suggest that land-use could be a potential factor contributing to the prolonged drought in central-west China.Changes in local climate extremes,including precipitation and surface temperature maxima and minima,are also identified.Overall,this study has illustrated the importance of further investigation of such important issues in future land-use studies.

  2. Mid-Holocene regional reorganization of climate variability

    Directory of Open Access Journals (Sweden)

    K. W. Wirtz

    2009-01-01

    Full Text Available We integrate 130 globally distributed proxy time series to refine the understanding of climate variability during the Holocene. Cyclic anomalies and temporal trends in periodicity from the Lower to the Upper Holocene are extracted by combining Lomb-Scargle Fourier-transformed spectra with bootstrapping. Results were cross-checked by counting events in the time series. Main outcomes are: First, the propensity of the climate system to fluctuations is a region specific property. Many records of adjacent sites reveal a similar change in variability although they belong to different proxy types (e.g., δ18O, lithic composition. Secondly, at most sites, irreversible change occured in the Mid-Holocene. We suggest that altered ocean circulation together with slightly modified coupling intensity between regional climate subsystems around the 5.5 kyr BP event (termination of the African Humid Period were responsible for the shift. Fluctuations especially intensified along a pan-American corridor. This may have led to an unequal crisis probability for early human civilizations in the Old and New World. Our study did not produce evidence for millennial scale cyclicity in some solar activity proxies for the Upper Holocene, nor for a privileged role of the prominent 250, 550, 900 and 1450 yr cycles. This lack of global periodicities corroborates the regional character of climate variability.

  3. POTENTIAL IMPACTS OF CLIMATIC VARIABILITY ON INDIAN HIMALAYAN REGION

    Directory of Open Access Journals (Sweden)

    Kavita Tariyal

    2014-12-01

    Full Text Available The Himalayan region represents enormous variability of climates, hydrological and ecological systems, plus a diversity of cultures and communities. It is an essentiality to the ecological security of the Indian landmass, through providing forest cover, feeding recurrent rivers that are the source of potable water, irrigation, and hydropower, conserving biodiversity, providing a rich foundation for high value agriculture, and spectacular landscapes for sustainable tourism. Increasing concentration of greenhouse gases in the troposphere and the consequential global warming is posing a great environmental threat to water and food security at universal level. Change in climate may affect exposures to air pollutants by affecting weather, anthropogenic emissions, and by changing the distribution and types of airborne allergens. This potential variability in climate will have a serious impact on several ecosystem services, such as cleaning water and removing carbon from the atmosphere. Various services of ecosystems viz. land and water resources, agriculture, biodiversity will experience a wide range of stresses together with pests and pathogens, invasive species, atmospheric pollution, acute events, wildfires and floods. Direct stresses posed due to climate change may get intensified through high temperatures, reduced water availability, and altered frequency of extreme events and severe storms. Climate change will potentially make a threat on the availability of, and access to, water resources. The Himalayan ecosystem is vulnerable to the impacts and consequences of a changes on account of natural causes, b climate change resulting from human-induced emissions and c developmental paradigms of the modern society. Adaptation factors in the element of ‘sustainability’ into development initiatives and provides for additional measures and resources to safeguard environmental gains against climate impacts.

  4. A Pedagogical "Toy" Climate Model

    CERN Document Server

    Katz, J I

    2010-01-01

    A "toy" model, simple and elementary enough for an undergraduate class, of the temperature dependence of the greenhouse (mid-IR) absorption by atmospheric water vapor implies a bistable climate system. The stable states are glaciation and warm interglacials, while intermediate states are unstable. This is in qualitative accord with the paleoclimatic data. The present climate may be unstable, with or without anthropogenic interventions such as CO$_2$ emission, unless there is additional stabilizing feedback such as "geoengineering".

  5. A Pedagogical "Toy" Climate Model

    OpenAIRE

    Katz, J. I.

    2010-01-01

    A "toy" model, simple and elementary enough for an undergraduate class, of the temperature dependence of the greenhouse (mid-IR) absorption by atmospheric water vapor implies a bistable climate system. The stable states are glaciation and warm interglacials, while intermediate states are unstable. This is in qualitative accord with the paleoclimatic data. The present climate may be unstable, with or without anthropogenic interventions such as CO$_2$ emission, unless there is additional stabil...

  6. Evaluating the variability in surface water reservoir planning characteristics during climate change impacts assessment

    Science.gov (United States)

    Soundharajan, Bankaru-Swamy; Adeloye, Adebayo J.; Remesan, Renji

    2016-07-01

    This study employed a Monte-Carlo simulation approach to characterise the uncertainties in climate change induced variations in storage requirements and performance (reliability (time- and volume-based), resilience, vulnerability and sustainability) of surface water reservoirs. Using a calibrated rainfall-runoff (R-R) model, the baseline runoff scenario was first simulated. The R-R inputs (rainfall and temperature) were then perturbed using plausible delta-changes to produce simulated climate change runoff scenarios. Stochastic models of the runoff were developed and used to generate ensembles of both the current and climate-change-perturbed future runoff scenarios. The resulting runoff ensembles were used to force simulation models of the behaviour of the reservoir to produce 'populations' of required reservoir storage capacity to meet demands, and the performance. Comparing these parameters between the current and the perturbed provided the population of climate change effects which was then analysed to determine the variability in the impacts. The methodology was applied to the Pong reservoir on the Beas River in northern India. The reservoir serves irrigation and hydropower needs and the hydrology of the catchment is highly influenced by Himalayan seasonal snow and glaciers, and Monsoon rainfall, both of which are predicted to change due to climate change. The results show that required reservoir capacity is highly variable with a coefficient of variation (CV) as high as 0.3 as the future climate becomes drier. Of the performance indices, the vulnerability recorded the highest variability (CV up to 0.5) while the volume-based reliability was the least variable. Such variabilities or uncertainties will, no doubt, complicate the development of climate change adaptation measures; however, knowledge of their sheer magnitudes as obtained in this study will help in the formulation of appropriate policy and technical interventions for sustaining and possibly enhancing

  7. Human activity and climate variability project - annual report 2002

    International Nuclear Information System (INIS)

    Work is well underway on identifying the spatial and temporal extent, direction and range of trace element transport across Tasmania through analysis of lake sediments; A follow up investigation of sedimentation and pollution in the Nattai River catchment following the devastating 2001 bushfires in the region has been completed; The project has been extended to include investigations of evidence of human impacts in the highly sensitive and ecologically important Great Lakes of coastal NSW. This has involved the expansion of our collaboration to include Geoscience Australia; Contributions have been made to the IGBP HITE project. Further contributions will be made as the evidence gathered is drawn together and interpreted; Over the coming year, focus will be placed on completion of the investigation of the extent of aerial transport of trace elements across Tasmania over the last 200 years as well as evidence for human activity and impacts on the Great Lakes region of NSW. Further investigation of potential climate signals from sites in northern Australia will also be made. The first 12 months of data for all ACE-Asia radon and fine particle sites is now available with preliminary analyses performed; The seasonal variability of background radon concentration at each of the radon monitoring sites has been characterised for the available data; Major components related to industrial pollution and soil sources in China have been identified and quantified; Regional and seasonal variations and trends in aerosol constituents have been measured and compared across more than 2.8Mk2 of sampling area; The Hok Tsui and Kosan detectors were visited for general maintenance and recalibration; A grant application to the APN has been submitted in support of regional inventory analyses based on radon time series; Progress on the processing and interpretation of radon data was presented at the Cape Grim Science Meeting (6-7 February 2002) and the 7th Biennial SPERA Conference on

  8. A Climate System Model, Numerical Simulation and Climate Predictability

    Institute of Scientific and Technical Information of China (English)

    ZENG Qingcun; WANG Huijun; LIN Zhaohui; ZHOU Guangqing; YU Yongqiang

    2007-01-01

    @@ The implementation of the project has lasted for more than 20 years. As a result, the following key innovative achievements have been obtained, ranging from the basic theory of climate dynamics, numerical model development and its related computational theory to the dynamical climate prediction using the climate system models:

  9. Objective calibration of regional climate models

    Science.gov (United States)

    Bellprat, O.; Kotlarski, S.; Lüthi, D.; SchäR, C.

    2012-12-01

    Climate models are subject to high parametric uncertainty induced by poorly confined model parameters of parameterized physical processes. Uncertain model parameters are typically calibrated in order to increase the agreement of the model with available observations. The common practice is to adjust uncertain model parameters manually, often referred to as expert tuning, which lacks objectivity and transparency in the use of observations. These shortcomings often haze model inter-comparisons and hinder the implementation of new model parameterizations. Methods which would allow to systematically calibrate model parameters are unfortunately often not applicable to state-of-the-art climate models, due to computational constraints facing the high dimensionality and non-linearity of the problem. Here we present an approach to objectively calibrate a regional climate model, using reanalysis driven simulations and building upon a quadratic metamodel presented by Neelin et al. (2010) that serves as a computationally cheap surrogate of the model. Five model parameters originating from different parameterizations are selected for the optimization according to their influence on the model performance. The metamodel accurately estimates spatial averages of 2 m temperature, precipitation and total cloud cover, with an uncertainty of similar magnitude as the internal variability of the regional climate model. The non-linearities of the parameter perturbations are well captured, such that only a limited number of 20-50 simulations are needed to estimate optimal parameter settings. Parameter interactions are small, which allows to further reduce the number of simulations. In comparison to an ensemble of the same model which has undergone expert tuning, the calibration yields similar optimal model configurations, but leading to an additional reduction of the model error. The performance range captured is much wider than sampled with the expert-tuned ensemble and the presented

  10. A transient stochastic weather generator incorporating climate model uncertainty

    Science.gov (United States)

    Glenis, Vassilis; Pinamonti, Valentina; Hall, Jim W.; Kilsby, Chris G.

    2015-11-01

    Stochastic weather generators (WGs), which provide long synthetic time series of weather variables such as rainfall and potential evapotranspiration (PET), have found widespread use in water resources modelling. When conditioned upon the changes in climatic statistics (change factors, CFs) predicted by climate models, WGs provide a useful tool for climate impacts assessment and adaption planning. The latest climate modelling exercises have involved large numbers of global and regional climate models integrations, designed to explore the implications of uncertainties in the climate model formulation and parameter settings: so called 'perturbed physics ensembles' (PPEs). In this paper we show how these climate model uncertainties can be propagated through to impact studies by testing multiple vectors of CFs, each vector derived from a different sample from a PPE. We combine this with a new methodology to parameterise the projected time-evolution of CFs. We demonstrate how, when conditioned upon these time-dependent CFs, an existing, well validated and widely used WG can be used to generate non-stationary simulations of future climate that are consistent with probabilistic outputs from the Met Office Hadley Centre's Perturbed Physics Ensemble. The WG enables extensive sampling of natural variability and climate model uncertainty, providing the basis for development of robust water resources management strategies in the context of a non-stationary climate.

  11. Evidence of multidecadal climate variability in the Gulf of Mexico

    Science.gov (United States)

    Poore, Richard Z.; Brock, John C.

    2011-01-01

    The northern Gulf of Mexico coastal region is vulnerable to a variety of natural hazards, many of which are linked to climate and climate variability. Hurricanes, which are one such climate-related hazard, are a major recurring problem, and the active hurricane seasons of 2004 and 2005 raised interest in better understanding the controls and risks of hurricanes. Examination of historical records reveals intervals of alternating low and high hurricane activity that appear to be related to changes in average sea-surface temperature in the North Atlantic Ocean. Analyses of instrumental temperature records from the North Atlantic show decadal-scale oscillations of slightly higher versus slightly lower average temperature extending back in time for over 100 years. This oscillation is known as the Atlantic Multidecadal Oscillation (AMO).

  12. Adaptation to climate change and climate variability:The importance of understanding agriculture as performance

    NARCIS (Netherlands)

    Crane, T.A.; Roncoli, C.; Hoogenboom, G.

    2011-01-01

    Most climate change studies that address potential impacts and potential adaptation strategies are largely based on modelling technologies. While models are useful for visualizing potential future outcomes and evaluating options for potential adaptation, they do not adequately represent and integrat

  13. Does Irrigation Buffer Agriculture from Climatic Variability? - Evidence from India

    Science.gov (United States)

    Fishman, R.

    2010-12-01

    One of the key potential benefits of water storage and irrigation is the buffering of agricultural production from natural fluctuations in rainfall, be they intra-seasonal, inter-annual or decadal, by storing excess rainfall for times when it is deficient. Economically, the ability to protect food production and income from climatic and weather variability has always been important, especially in developing countries. This ability can be a key asset in adaptation to the uncertainties and enhanced variability in precipitation that is predicted to accompany climate change. It is therefore important to investigate empirically how well irrigation of different kinds has performed in this regard. We use agricultural production statistics in India, a country whose fortune has always been at the mercy of the stochastic monsoon rains, to investigate this question statistically, and study the performance of both surface and groundwater irrigation in different hydro-geologies.

  14. Monofractal nature of air temperature signals reveals their climate variability

    CERN Document Server

    Deliège, Adrien

    2014-01-01

    We use the discrete "wavelet transform microscope" to show that the surface air temperature signals of weather stations selected in Europe are monofractal. This study reveals that the information obtained in this way are richer than previous works studying long range correlations in meteorological stations. The approach presented here allows to bind the H\\"older exponents with the climate variability. We also establish that such a link does not exist with methods previously carried out.

  15. Influence of solar and cosmic-ray variability on climate

    CERN Document Server

    Badruddin,; Singh, M

    2013-01-01

    We analyze solar, geomagnetic and cosmic ray flux data along with rainfall and temperature data for almost five solar cycles. We provide evidence of significant influence of solar variability on climate. Specifically, we demonstrate association between lower (higher) rainfall and higher (lower) temperatures with increasing (decreasing) solar activity and decreasing (increasing) cosmic ray intensities. We propose a plausible scenario that accounts the results of our analysis.

  16. POTENTIAL IMPACTS OF CLIMATIC VARIABILITY ON INDIAN HIMALAYAN REGION

    OpenAIRE

    Kavita Tariyal; Dhanesh Mohan Bartwal

    2014-01-01

    The Himalayan region represents enormous variability of climates, hydrological and ecological systems, plus a diversity of cultures and communities. It is an essentiality to the ecological security of the Indian landmass, through providing forest cover, feeding recurrent rivers that are the source of potable water, irrigation, and hydropower, conserving biodiversity, providing a rich foundation for high value agriculture, and spectacular landscapes for sustainable tourism. Increasing concentr...

  17. The ESA climate change initiative: Satellite data records for essential climate variables

    DEFF Research Database (Denmark)

    Hollmann, R.; Merchant, C.J.; Saunders, R.;

    2013-01-01

    The European Space Agency (ESA) has launched the Climate Change Initiative (CCI) to provide satellite-based climate data records (CDRs) that meet the challenging requirements of the climate community. The aim is to realize the full potential of the long-term Earth observation (EO) archives...... targeting the generation of satellite derived climate data records. One focus of the CCI is to provide products for climate modelers who increasingly use satellite data to initialize, constrain, and validate models on a wide range of space and time scales....... that both ESA and third parties have established. This includes aspects of producing a CDR, which involve data acquisition, calibration, algorithm development, validation, maintenance, and provision of the data to the climate research community. The CCI is consistent with several international efforts...

  18. Model confirmation in climate economics.

    Science.gov (United States)

    Millner, Antony; McDermott, Thomas K J

    2016-08-01

    Benefit-cost integrated assessment models (BC-IAMs) inform climate policy debates by quantifying the trade-offs between alternative greenhouse gas abatement options. They achieve this by coupling simplified models of the climate system to models of the global economy and the costs and benefits of climate policy. Although these models have provided valuable qualitative insights into the sensitivity of policy trade-offs to different ethical and empirical assumptions, they are increasingly being used to inform the selection of policies in the real world. To the extent that BC-IAMs are used as inputs to policy selection, our confidence in their quantitative outputs must depend on the empirical validity of their modeling assumptions. We have a degree of confidence in climate models both because they have been tested on historical data in hindcasting experiments and because the physical principles they are based on have been empirically confirmed in closely related applications. By contrast, the economic components of BC-IAMs often rely on untestable scenarios, or on structural models that are comparatively untested on relevant time scales. Where possible, an approach to model confirmation similar to that used in climate science could help to build confidence in the economic components of BC-IAMs, or focus attention on which components might need refinement for policy applications. We illustrate the potential benefits of model confirmation exercises by performing a long-run hindcasting experiment with one of the leading BC-IAMs. We show that its model of long-run economic growth-one of its most important economic components-had questionable predictive power over the 20th century.

  19. Model confirmation in climate economics.

    Science.gov (United States)

    Millner, Antony; McDermott, Thomas K J

    2016-08-01

    Benefit-cost integrated assessment models (BC-IAMs) inform climate policy debates by quantifying the trade-offs between alternative greenhouse gas abatement options. They achieve this by coupling simplified models of the climate system to models of the global economy and the costs and benefits of climate policy. Although these models have provided valuable qualitative insights into the sensitivity of policy trade-offs to different ethical and empirical assumptions, they are increasingly being used to inform the selection of policies in the real world. To the extent that BC-IAMs are used as inputs to policy selection, our confidence in their quantitative outputs must depend on the empirical validity of their modeling assumptions. We have a degree of confidence in climate models both because they have been tested on historical data in hindcasting experiments and because the physical principles they are based on have been empirically confirmed in closely related applications. By contrast, the economic components of BC-IAMs often rely on untestable scenarios, or on structural models that are comparatively untested on relevant time scales. Where possible, an approach to model confirmation similar to that used in climate science could help to build confidence in the economic components of BC-IAMs, or focus attention on which components might need refinement for policy applications. We illustrate the potential benefits of model confirmation exercises by performing a long-run hindcasting experiment with one of the leading BC-IAMs. We show that its model of long-run economic growth-one of its most important economic components-had questionable predictive power over the 20th century. PMID:27432964

  20. NASA Scientific Forum on Climate Variability and Global Change: UNISPACE 3

    Science.gov (United States)

    Schiffer, Robert A.; Unninayar, Sushel

    1999-01-01

    The Forum on Climate Variability and Global Change is intended to provide a glimpse into some of the advances made in our understanding of key scientific and environmental issues resulting primarily from improved observations and modeling on a global basis. This publication contains the papers presented at the forum.

  1. Using Weather Data and Climate Model Output in Economic Analyses of Climate Change

    Energy Technology Data Exchange (ETDEWEB)

    Auffhammer, Maximilian [University of California at Berkeley; Hsiang, Solomon M. [Princeton University; Schlenker, Wolfram [Columbia University; Sobel, Adam H. [Columbia University

    2013-06-28

    Economists are increasingly using weather data and climate model output in analyses of the economic impacts of climate change. This article introduces a set of weather data sets and climate models that are frequently used, discusses the most common mistakes economists make in using these products, and identifies ways to avoid these pitfalls. We first provide an introduction to weather data, including a summary of the types of datasets available, and then discuss five common pitfalls that empirical researchers should be aware of when using historical weather data as explanatory variables in econometric applications. We then provide a brief overview of climate models and discuss two common and significant errors often made by economists when climate model output is used to simulate the future impacts of climate change on an economic outcome of interest.

  2. Climate simulations for 1880-2003 with GISS modelE

    CERN Document Server

    Hansen, J; Bauer, S; Baum, E; Cairns, B; Canuto, V; Chandler, M; Cheng, Y; Cohen, A; Faluvegi, G; Fleming, E; Friend, A; Genio, A D; Hall, T; Jackman, C; Jonas, J; Kelley, M; Kharecha, P; Kiang, N Y; Koch, D; Labow, G; Lacis, A; Lerner, J; Lo, K; Menon, S; Miller, R; Nazarenko, L; Novakov, T; Oinas, V; Perlwitz, J; Rind, D; Romanou, A; Ruedy, R; Russell, G; Sato, M; Schmidt, G A; Schmunk, R; Shindell, D; Stone, P; Streets, D; Sun, S; Tausnev, N; Thresher, D; Unger, N; Yao, M; Zhang, S; Perlwitz, Ja.; Perlwitz, Ju.

    2006-01-01

    We carry out climate simulations for 1880-2003 with GISS modelE driven by ten measured or estimated climate forcings. An ensemble of climate model runs is carried out for each forcing acting individually and for all forcing mechanisms acting together. We compare side-by-side simulated climate change for each forcing, all forcings, observations, unforced variability among model ensemble members, and, if available, observed variability. Discrepancies between observations and simulations with all forcings are due to model deficiencies, inaccurate or incomplete forcings, and imperfect observations. Although there are notable discrepancies between model and observations, the fidelity is sufficient to encourage use of the model for simulations of future climate change. By using a fixed well-documented model and accurately defining the 1880-2003 forcings, we aim to provide a benchmark against which the effect of improvements in the model, climate forcings, and observations can be tested. Principal model deficiencies...

  3. An attempt to assess the energy related climate variability

    Energy Technology Data Exchange (ETDEWEB)

    Iotova, A. [Bulgarian Academy of Sciences, Sofia (Bulgaria). National Inst. of Meteorology and Hydrology

    1995-12-31

    A lot of efforts are directed now to study the interactions between energy and climate because of their significant importance for our planet. Globally, energy related emissions of Greenhouse Gases (GHGs) contribute for atmospheric warming. On regional level, where it is more difficult to determine concrete direction of climate variability and change, the role of energy remains considerable being not so direct as in the case of emissions` impact. Still there is essential necessity for further analyses and assessments of energy related climate variations and change in order to understand better and to quantify the energy - climate relations. In the presentation an attempt is made to develop approach for assessment of energy related climate variations on regional level. For this purpose, data and results from the research within Bulgarian Case Study (BCS) in the DECADES Inter-Agency Project framework are used. Considering the complex nature of the examined interconnections and the medium stage of the Study`s realisation, at the moment the approach can be presented in conceptual form. Correspondingly, the obtained results are illustrative and preliminary

  4. Climate reconstructions of the NH mean temperature: Can underestimation of trends and variability be avoided?

    Science.gov (United States)

    Christiansen, Bo

    2010-05-01

    Knowledge about the climate in the period before instrumental records are available is based on climate proxies obtained from tree-rings, sediments, ice-cores etc. Reconstructing the climate from such proxies is therefore necessary for studies of climate variability and for placing recent climate change into a longer term perspective. More than a decade ago pioneering attempts at using a multi-proxy dataset to reconstruct the Northern Hemisphere (NH) mean temperature resulted in the much published "hockey-stick"; a NH mean temperature that did not vary much before the rapid increase in the last century. Subsequent reconstructions show some differences but the overall "hockey-stick" structure seems to be a persistent feature However, there has been an increasing awareness of the fact that the applied reconstruction methods underestimate the low-frequency variability and trends. The recognition of the inadequacies of the reconstruction methods has to a large degree originated from pseudo-proxy studies, i.e., from long climate model experiments where artificial proxies have been generated and reconstructions based on these have been compared to the known model climate. It has also been found that reconstructions contain a large element of stochasticity which is revealed as broad distributions of skills. This means that it is very difficult to draw conclusions from a single or a few realizations. Climate reconstruction methods are based on variants of linear regression models relating temperatures and proxies. In this contribution we review some of the theory of linear regression and error-in-variables models to identify the sources of the underestimation of variability. Based on the gained insight we formulate a reconstruction method supposed to minimize this underestimation. The method is tested by applying it to an ensemble of surrogate temperature fields based on two climate simulations covering the last 500 and 1000 years. Compared to the RegEM TTLS method and a

  5. Impacts of Climatic Variability on Vibrio parahaemolyticus Outbreaks in Taiwan.

    Science.gov (United States)

    Hsiao, Hsin-I; Jan, Man-Ser; Chi, Hui-Ju

    2016-02-01

    This study aimed to investigate and quantify the relationship between climate variation and incidence of Vibrio parahaemolyticus in Taiwan. Specifically, seasonal autoregressive integrated moving average (ARIMA) models (including autoregression, seasonality, and a lag-time effect) were employed to predict the role of climatic factors (including temperature, rainfall, relative humidity, ocean temperature and ocean salinity) on the incidence of V. parahaemolyticus in Taiwan between 2000 and 2011. The results indicated that average temperature (+), ocean temperature (+), ocean salinity of 6 months ago (+), maximum daily rainfall (current (-) and one month ago (-)), and average relative humidity (current and 9 months ago (-)) had significant impacts on the incidence of V. parahaemolyticus. Our findings offer a novel view of the quantitative relationship between climate change and food poisoning by V. parahaemolyticus in Taiwan. An early warning system based on climate change information for the disease control management is required in future. PMID:26848675

  6. Energy-balance climate models

    Science.gov (United States)

    North, G. R.; Cahalan, R. F.; Coakley, J. A., Jr.

    1980-01-01

    An introductory survey of the global energy balance climate models is presented with an emphasis on analytical results. A sequence of increasingly complicated models involving ice cap and radiative feedback processes are solved and the solutions and parameter sensitivities are studied. The model parameterizations are examined critically in light of many current uncertainties. A simple seasonal model is used to study the effects of changes in orbital elements on the temperature field. A linear stability theorem and a complete nonlinear stability analysis for the models are developed. Analytical solutions are also obtained for the linearized models driven by stochastic forcing elements. In this context the relation between natural fluctuation statistics and climate sensitivity is stressed.

  7. Impacts of Climate and Management Variables on the Contamination of Preharvest Leafy Greens with Escherichia coli.

    Science.gov (United States)

    Liu, Cheng; Hofstra, Nynke; Franz, Eelco

    2016-01-01

    The observed seasonality of foodborne disease suggests that climatic conditions play a role and that changes in the climate may affect the presence of pathogens. However, it is hard to determine whether this effect is direct or whether it works indirectly through other factors, such as farm management. This study aimed to identify the climate and management variables that are associated with the contamination (presence and concentration) of leafy green vegetables with E. coli. This study used data about E. coli contamination from 562 leafy green vegetables (lettuce and spinach) samples taken between 2011 and 2013 from 23 open-field farms in Belgium, Brazil, Egypt, Norway, and Spain. Mixed-effect logistic and linear regression models were used to study the statistical relationship between the dependent and independent variables. Climate variables and agricultural management practices together had a systematic influence on E. coli presence and concentration. The variables important for E. coli presence included the minimum temperature of the sampling day (odds ratio = 1.47), region, and application of inorganic fertilizer. The variables important for concentration (R(2) = 0.75) were the maximum temperature during the 3 days before sampling and the region. Temperature had a stronger influence (had a significant parameter estimate and the highest R(2)) than did management practices on E. coli presence and concentration. Region was a variable that masked many management variables, including rainwater, surface water, manure, inorganic fertilizer, and spray irrigation. Climate variables had a positive relationship with E. coli presence and concentration. Temperature, irrigation water type, fertilizer type, and irrigation method should be systematically considered in future studies of fresh produce safety. PMID:26735025

  8. Assessment of Human Health Vulnerability to Climate Variability and Change in Cuba

    OpenAIRE

    Bultó, Paulo Lázaro Ortíz; Rodríguez, Antonio Pérez; Valencia, Alina Rivero; Vega, Nicolás León; Gonzalez, Manuel Díaz; Carrera, Alina Pérez

    2006-01-01

    In this study we assessed the potential effects of climate variability and change on population health in Cuba. We describe the climate of Cuba as well as the patterns of climate-sensitive diseases of primary concern, particularly dengue fever. Analyses of the associations between climatic anomalies and disease patterns highlight current vulnerability to climate variability. We describe current adaptations, including the application of climate predictions to prevent disease outbreaks. Finally...

  9. Land surface phenological response to decadal climate variability across Australia using satellite remote sensing

    Directory of Open Access Journals (Sweden)

    M. Broich

    2014-05-01

    Full Text Available Land surface phenological cycles of vegetation greening and browning are influenced by variability in climatic forcing. Quantitative information on phenological cycles and their variability is important for agricultural applications, wildfire fuel accumulation, land management, land surface modeling, and climate change studies. Most phenology studies have focused on temperature-driven Northern Hemisphere systems, where phenology shows annually reoccurring patterns. Yet, precipitation-driven non-annual phenology of arid and semi-arid systems (i.e. drylands received much less attention, despite the fact that they cover more than 30% of the global land surface. Here we focused on Australia, the driest inhabited continent with one of the most variable rainfall climates in the world and vast areas of dryland systems. Detailed and internally consistent studies investigating phenological cycles and their response to climate variability across the entire continent designed specifically for Australian dryland conditions are missing. To fill this knowledge gap and to advance phenological research, we used existing methods more effectively to study geographic and climate-driven variability in phenology over Australia. We linked derived phenological metrics with rainfall and the Southern Oscillation Index (SOI. We based our analysis on Enhanced Vegetation Index (EVI data from the MODerate Resolution Imaging Spectroradiometer (MODIS from 2000 to 2013, which included extreme drought and wet years. We conducted a continent-wide investigation of the link between phenology and climate variability and a more detailed investigation over the Murray–Darling Basin (MDB, the primary agricultural area and largest river catchment of Australia. Results showed high inter- and intra-annual variability in phenological cycles. Phenological cycle peaks occurred not only during the austral summer but at any time of the year, and their timing varied by more than a month in

  10. Changing Seasonality of Tundra Vegetation and Associated Climatic Variables

    Science.gov (United States)

    Bhatt, U. S.; Walker, D. A.; Raynolds, M. K.; Bieniek, P.; Epstein, H. E.; Comiso, J. C.; Pinzon, J.; Tucker, C. J.; Steele, M.; Ermold, W. S.; Zhang, J.

    2014-12-01

    This study documents changes in the seasonality of tundra vegetation productivity and its associated climate variables using long-term data sets. An overall increase of Pan-Arctic tundra greenness potential corresponds to increased land surface temperatures and declining sea ice concentrations. While sea ice has continued to decline, summer land surface temperature and vegetation productivity increases have stalled during the last decade in parts of the Arctic. To understand the processes behind these features we investigate additional climate parameters. This study employs remotely sensed weekly 25-km sea ice concentration, weekly surface temperature, and bi-weekly NDVI from 1982 to 2013. Maximum NDVI (MaxNDVI, Maximum Normalized Difference Vegetation Index), Time Integrated NDVI (TI-NDVI), Summer Warmth Index (SWI, sum of degree months above freezing during May-August), ocean heat content (PIOMAS, model incorporating ocean data assimilation), and snow water equivalent (GlobSnow, assimilated snow data set) are explored. We analyzed the data for the full period (1982-2013) and for two sub-periods (1982-1998 and 1999-2013), which were chosen based on the declining Pan-Arctic SWI since 1998. MaxNDVI has increased from 1982-2013 over most of the Arctic but has declined from 1999 to 2013 over western Eurasia, northern Canada, and southwest Alaska. TI-NDVI has trends that are similar to those for MaxNDVI for the full period but displays widespread declines over the 1999-2013 period. Therefore, as the MaxNDVI has continued to increase overall for the Arctic, TI-NDVI has been declining since 1999. SWI has large relative increases over the 1982-2013 period in eastern Canada and Greenland and strong declines in western Eurasia and southern Canadian tundra. Weekly Pan-Arctic tundra land surface temperatures warmed throughout the summer during the 1982-1998 period but display midsummer declines from 1999-2013. Weekly snow water equivalent over Arctic tundra has declined over

  11. Climate Change Hotspots Identification in China through the CMIP5 Global Climate Model Ensemble

    Directory of Open Access Journals (Sweden)

    Huanghe Gu

    2014-01-01

    Full Text Available China is one of the countries vulnerable to adverse climate changes. The potential climate change hotspots in China throughout the 21st century are identified in this study by using a multimodel, multiscenario climate model ensemble that includes Phase Five of the Coupled Model Intercomparison Project (CMIP5 atmosphere-ocean general circulation models. Both high (RCP8.5 and low (RCP4.5 greenhouse gas emission trajectories are tested, and both the mean and extreme seasonal temperature and precipitation are considered in identifying regional climate change hotspots. Tarim basin and Tibetan Plateau in West China are identified as persistent regional climate change hotspots in both the RCP4.5 and RCP8.5 scenarios. The aggregate impacts of climate change increase throughout the 21st century and are more significant in RCP8.5 than in RCP4.5. Extreme hot event and mean temperature are two climate variables that greatly contribute to the hotspots calculation in all regions. The contribution of other climate variables exhibits a notable subregional variability. South China is identified as another hotspot based on the change of extreme dry event, especially in SON and DJF, which indicates that such event will frequently occur in the future. Our results can contribute to the designing of national and cross-national adaptation and mitigation policies.

  12. Saharan dust, climate variability, and asthma in Grenada, the Caribbean

    Science.gov (United States)

    Akpinar-Elci, Muge; Martin, Francis E.; Behr, Joshua G.; Diaz, Rafael

    2015-11-01

    Saharan dust is transported across the Atlantic and interacts with the Caribbean seasonal climatic conditions, becoming respirable and contributing to asthma presentments at the emergency department. This study investigated the relationships among dust, climatic variables, and asthma-related visits to the emergency room in Grenada. All asthma visits to the emergency room ( n = 4411) over 5 years (2001-2005) were compared to the dust cover and climatic variables for the corresponding period. Variation in asthma was associated with change in dust concentration ( R 2 = 0.036, p population sizes, industrialization level, and economies. Therefore, different than from the studies in Trinidad and Barbados, Grenada is a non-industrialized low-income small island without major industrialized air pollution addition; asthma visits were inversely related to mean sea level pressure ( R 2 = 0.123, p = 0.006) and positively correlated with relative humidity ( R 2 = 0.593, p = 0.85). Saharan dust in conjunction with seasonal humidity allows for inhalable particulate matter that exacerbates asthma among residents in the Caribbean island of Grenada. These findings contribute evidence suggesting a broader public health impact from Saharan dust. Thus, this research may inform strategic planning of resource allocation among the Caribbean public health agencies.

  13. Analysis of ENSO-based climate variability in modulating drought risks over western Rajasthan in India

    Indian Academy of Sciences (India)

    Poulomi Ganguli; M Janga Reddy

    2013-02-01

    This paper investigates the role of El Niño-Southern Oscillation (ENSO)-based climate variability in modulating multivariate drought risks in the drought-prone region of Western Rajasthan in India. Droughts are multivariate phenomenon, often characterized by severity, duration and peak. By using multivariate ENSO index, annual drought events are partitioned into three climatic states – El Niño, La Niña and neutral phases. For multivariate probabilistic representation of drought characteristics, trivariate copulas are employed, which have the ability to preserve the dependence structure of drought variables under uncertain environment. The first copula model is developed without accounting the climate state information to obtain joint and conditional return periods of drought characteristics. Then, copula-based models are developed for each climate state to estimate the joint and conditional probabilities of drought characteristics under each ENSO state. Results of the study suggest that the inclusion of ENSO-based climate variability is helpful in knowing the associated drought risks, and useful for management of water resources in the region.

  14. Climate Variability, Andean Livelihood Strategies, Development and Adaptation in the Andean Region

    Science.gov (United States)

    Valdivia, C.; Quiroz, R.; Zorogastua, P.; Baigorrea, G.

    2002-05-01

    Development programs in the Andes have failed to recognize climate variability as an element that is crucial to the adoption of new alternatives. Dairy, potatoes, improved sheep, forages are all part of the history of development in this region. A combination of climate variability, changes in the economy, the political environment, and land tenure reform shape rural livelihoods and welfare. Diversification, linking to markets, and networking are some elements that contribute to the resilience of families in the Andes. Strategies change, are flexible, and may incorporate non-agricultural activities. While some farmers are able to improve their welfare through the life cycle, others become poorer. Climate variability increases the vulnerability of some groups; in other cases, because of diversification and assets, households build economic portfolios that are more resilient to the elements. The many projects provide insights into how in the long run households improve their environment, hinting at mechanisms to adapt to climate change. In order to understand changing composition of portfolios in future scenarios of spatial heterogeneous areas such as mountains (Andes), estimates of models predicting climate change at a global scale are not useful because their resolution. Therefore, downscaling tools are useful. Spatial heterogeneity is assessed through agroecozoning. Both production and the impact on some environmental indicators are simulated through process-based models, for the Ilave-Huenque watershed in Peru that help in discussing scenarios of adaptation.

  15. Modelling Solar and Stellar Brightness Variabilities

    Science.gov (United States)

    Yeo, K. L.; Shapiro, A. I.; Krivova, N. A.; Solanki, S. K.

    2016-04-01

    Total and spectral solar irradiance, TSI and SSI, have been measured from space since 1978. This is accompanied by the development of models aimed at replicating the observed variability by relating it to solar surface magnetism. Despite significant progress, there remains persisting controversy over the secular change and the wavelength-dependence of the variation with impact on our understanding of the Sun's influence on the Earth's climate. We highlight the recent progress in TSI and SSI modelling with SATIRE. Brightness variations have also been observed for Sun-like stars. Their analysis can profit from knowledge of the solar case and provide additional constraints for solar modelling. We discuss the recent effort to extend SATIRE to Sun-like stars.

  16. Climate change or variable weather: Rethinking Danish homeowners' perceptions of floods and climate

    DEFF Research Database (Denmark)

    Baron, Nina; Petersen, Lars Kjerulf

    2015-01-01

    Climate scenarios predict that an effect of climate change will be more areas at risk of extensive flooding. This article builds on a qualitative case study of homeowners in the flood-prone area of Lolland in Denmark and uses the theories of Tim Ingold and Bruno Latour to rethink the way we under...... approaches gives new insights as to why people living in flood-prone areas are not very concerned about climate change.......Climate scenarios predict that an effect of climate change will be more areas at risk of extensive flooding. This article builds on a qualitative case study of homeowners in the flood-prone area of Lolland in Denmark and uses the theories of Tim Ingold and Bruno Latour to rethink the way we...... understand homeowners’ perception of climate change and local flood risk. Ingold argues that those perceptions are shaped by people’s experiences with and connections to their local landscape. People experience the local variability of the weather, and not global climate change as presented in statistical...

  17. Integrated approaches to climate-crop modelling: needs and challenges.

    Science.gov (United States)

    Betts, Richard A

    2005-11-29

    This paper discusses the need for a more integrated approach to modelling changes in climate and crops, and some of the challenges posed by this. While changes in atmospheric composition are expected to exert an increasing radiative forcing of climate change leading to further warming of global mean temperatures and shifts in precipitation patterns, these are not the only climatic processes which may influence crop production. Changes in the physical characteristics of the land cover may also affect climate; these may arise directly from land use activities and may also result from the large-scale responses of crops to seasonal, interannual and decadal changes in the atmospheric state. Climate models used to drive crop models may, therefore, need to consider changes in the land surface, either as imposed boundary conditions or as feedbacks from an interactive climate-vegetation model. Crops may also respond directly to changes in atmospheric composition, such as the concentrations of carbon dioxide (CO2), ozone (03) and compounds of sulphur and nitrogen, so crop models should consider these processes as well as climate change. Changes in these, and the responses of the crops, may be intimately linked with meteorological processes so crop and climate models should consider synergies between climate and atmospheric chemistry. Some crop responses may occur at scales too small to significantly influence meteorology, so may not need to be included as feedbacks within climate models. However, the volume of data required to drive the appropriate crop models may be very large, especially if short-time-scale variability is important. Implementation of crop models within climate models would minimize the need to transfer large quantities of data between separate modelling systems. It should also be noted that crop responses to climate change may interact with other impacts of climate change, such as hydrological changes. For example, the availability of water for irrigation

  18. Sensitivity of climate models: Comparison of simulated and observed patterns for past climates

    International Nuclear Information System (INIS)

    Predicting the potential climatic effects of increased concentrations of atmospheric carbon dioxide requires the continuing development of climate models. Confidence in the predictions will be much enhanced once the models are thoroughly tested in terms of their ability to simulate climates that differ significantly from today's climate. As one index of the magnitude of past climate change, the global mean temperature increase during the past 18,000 years is similar to that predicted for carbon dioxide--doubling. Simulating the climatic changes of the past 18,000 years, as well as the warmer-than-present climate of 6000 years ago and the climate of the last interglacial, around 126,000 years ago, provides an excellent opportunity to test the models that are being used in global climate change research. During the past several years, we have used paleoclimatic data to test the accuracy of the National Center for Atmospheric Research, Community Climate Model, Version 0, after changing its boundary conditions to those appropriate for past climates. We have assembled regional and near-global paleoclimatic data sets of pollen, lake level, and marine plankton data and calibrated many of the data in terms of climatic variables. We have also developed methods that permit direct quantitative comparisons between the data and model results. Our research has shown that comparing the model results with the data is an evolutionary process, because the models, the data, and the methods for comparison are continually being improved. During 1992, we have completed new modeling experiments, further analyzed previous model experiments, compiled new paleodata, made new comparisons between data and model results, and participated in workshops on paleoclimatic modeling

  19. Climate Model Diagnostic Analyzer Web Service System

    Science.gov (United States)

    Lee, S.; Pan, L.; Zhai, C.; Tang, B.; Jiang, J. H.

    2014-12-01

    We have developed a cloud-enabled web-service system that empowers physics-based, multi-variable model performance evaluations and diagnoses through the comprehensive and synergistic use of multiple observational data, reanalysis data, and model outputs. We have developed a methodology to transform an existing science application code into a web service using a Python wrapper interface and Python web service frameworks. The web-service system, called Climate Model Diagnostic Analyzer (CMDA), currently supports (1) all the observational datasets from Obs4MIPs and a few ocean datasets from NOAA and Argo, which can serve as observation-based reference data for model evaluation, (2) many of CMIP5 model outputs covering a broad range of atmosphere, ocean, and land variables from the CMIP5 specific historical runs and AMIP runs, and (3) ECMWF reanalysis outputs for several environmental variables in order to supplement observational datasets. Analysis capabilities currently supported by CMDA are (1) the calculation of annual and seasonal means of physical variables, (2) the calculation of time evolution of the means in any specified geographical region, (3) the calculation of correlation between two variables, (4) the calculation of difference between two variables, and (5) the conditional sampling of one physical variable with respect to another variable. A web user interface is chosen for CMDA because it not only lowers the learning curve and removes the adoption barrier of the tool but also enables instantaneous use, avoiding the hassle of local software installation and environment incompatibility. CMDA will be used as an educational tool for the summer school organized by JPL's Center for Climate Science in 2014. In order to support 30+ simultaneous users during the school, we have deployed CMDA to the Amazon cloud environment. The cloud-enabled CMDA will provide each student with a virtual machine while the user interaction with the system will remain the same

  20. Tropical climate variability: interactions across the Pacific, Indian, and Atlantic Oceans

    Science.gov (United States)

    Kajtar, Jules B.; Santoso, Agus; England, Matthew H.; Cai, Wenju

    2016-06-01

    Complex interactions manifest between modes of tropical climate variability across the Pacific, Indian, and Atlantic Oceans. For example, the El Niño-Southern Oscillation (ENSO) extends its influence on modes of variability in the tropical Indian and Atlantic Oceans, which in turn feed back onto ENSO. Interactions between pairs of modes can alter their strength, periodicity, seasonality, and ultimately their predictability, yet little is known about the role that a third mode plays. Here we examine the interactions and relative influences between pairs of climate modes using ensembles of 100-year partially coupled experiments in an otherwise fully coupled general circulation model. In these experiments, the air-sea interaction over each tropical ocean basin, as well as pairs of ocean basins, is suppressed in turn. We find that Indian Ocean variability has a net damping effect on ENSO and Atlantic Ocean variability, and conversely they each promote Indian Ocean variability. The connection between the Pacific and the Atlantic is most clearly revealed in the absence of Indian Ocean variability. Our model runs suggest a weak damping influence by Atlantic variability on ENSO, and an enhancing influence by ENSO on Atlantic variability.

  1. Mapping Temperate Vegetation Climate Adaptation Variability Using Normalized Land Surface Phenology

    Directory of Open Access Journals (Sweden)

    Liang Liang

    2016-04-01

    Full Text Available Climate influences geographic differences of vegetation phenology through both contemporary and historical variability. The latter effect is embodied in vegetation heterogeneity underlain by spatially varied genotype and species compositions tied to climatic adaptation. Such long-term climatic effects are difficult to map and therefore often neglected in evaluating spatially explicit phenological responses to climate change. In this study we demonstrate a way to indirectly infer the portion of land surface phenology variation that is potentially contributed by underlying genotypic differences across space. The method undertaken normalized remotely sensed vegetation start-of-season (or greenup onset with a cloned plants-based phenological model. As the geography of phenological model prediction (first leaf represents the instantaneous effect of contemporary climate, the normalized land surface phenology potentially reveals vegetation heterogeneity that is related to climatic adaptation. The study was done at the continental scale for the conterminous U.S., with a focus on the eastern humid temperate domain. Our findings suggest that, in an analogous scenario, if a uniform contemporary climate existed everywhere, spring vegetation greenup would occur earlier in the north than in the south. This is in accordance with known species-level clinal variations—for many temperate plant species, populations adapted to colder climates require less thermal forcing to initiate growth than those in warmer climates. This study, for the first time, shows that such geographic adaption relationships are supported at the ecosystem level. Mapping large-scale vegetation climate adaptation patterns contributes to our ability to better track geographically varied phenological responses to climate change.

  2. Population variability complicates the accurate detection of climate change responses.

    Science.gov (United States)

    McCain, Christy; Szewczyk, Tim; Bracy Knight, Kevin

    2016-06-01

    The rush to assess species' responses to anthropogenic climate change (CC) has underestimated the importance of interannual population variability (PV). Researchers assume sampling rigor alone will lead to an accurate detection of response regardless of the underlying population fluctuations of the species under consideration. Using population simulations across a realistic, empirically based gradient in PV, we show that moderate to high PV can lead to opposite and biased conclusions about CC responses. Between pre- and post-CC sampling bouts of modeled populations as in resurvey studies, there is: (i) A 50% probability of erroneously detecting the opposite trend in population abundance change and nearly zero probability of detecting no change. (ii) Across multiple years of sampling, it is nearly impossible to accurately detect any directional shift in population sizes with even moderate PV. (iii) There is up to 50% probability of detecting a population extirpation when the species is present, but in very low natural abundances. (iv) Under scenarios of moderate to high PV across a species' range or at the range edges, there is a bias toward erroneous detection of range shifts or contractions. Essentially, the frequency and magnitude of population peaks and troughs greatly impact the accuracy of our CC response measurements. Species with moderate to high PV (many small vertebrates, invertebrates, and annual plants) may be inaccurate 'canaries in the coal mine' for CC without pertinent demographic analyses and additional repeat sampling. Variation in PV may explain some idiosyncrasies in CC responses detected so far and urgently needs more careful consideration in design and analysis of CC responses. PMID:26725404

  3. Atlantic near-term climate variability and the role of a resolved Gulf Stream

    Science.gov (United States)

    Siqueira, Leo; Kirtman, Ben P.

    2016-04-01

    There is a continually increasing demand for near-term (i.e., lead times up to a couple of decades) climate information. This demand is partly driven by the need to have robust forecasts and is partly driven by the need to assess how much of the ongoing climate change is due to natural variability and how much is due to anthropogenic increases in greenhouse gases or other external factors. Here we discuss results from a set of state-of-the-art climate model experiments in comparison with observational estimates that show that an assessment of predictability requires models that capture the variability of major oceanic fronts, which are, at best, poorly resolved and may even be absent in the near-term prediction of Intergovernmental Panel on Climate Change class models. This is the first time that air-sea interactions associated with resolved Gulf Stream sea surface temperature have been identified in the context of a state-of-the-art global coupled climate model with inferred near-term predictability.

  4. Tools for Assessing the Impacts of Climate Variability and Change on Wildfire Regimes in Forests

    Directory of Open Access Journals (Sweden)

    Hety Herawati

    2015-04-01

    Full Text Available Fire is an intrinsic element of many forest ecosystems; it shapes their ecological processes, determines species composition and influences landscape structure. However, wildfires may: have undesirable effects on biodiversity and vegetation coverage; produce carbon emissions to the atmosphere; release smoke affecting human health; and cause loss of lives and property. There have been increasing concerns about the potential impacts of climate variability and change on forest fires. Climate change can alter factors that influence the occurrence of fire ignitions, fuel availability and fuel flammability. This review paper aims to identify tools and methods used for gathering information about the impacts of climate variability and change on forest fires, forest fuels and the probability of fires. Tools to assess the impacts of climate variability and change on forest fires include: remote sensing, dynamic global vegetation and landscape models, integrated fire-vegetation models, fire danger rating systems, empirical models and fire behavior models. This review outlines each tool in terms of its characteristics, spatial and temporal resolution, limitations and applicability of the results. To enhance and improve tool performance, each must be continuously tested in all types of forest ecosystems.

  5. Performance of climate envelope models in retrodicting recent changes in bird population size from observed climatic change

    OpenAIRE

    Green, Rhys E.; Collingham, Yvonne C.; Willis, Stephen G; Gregory, Richard D; Smith, Ken W.; Huntley, Brian

    2008-01-01

    Twenty-five-year population trends of 42 bird species rare as breeders in the UK were examined in relation to changes in climatic suitability simulated using climatic envelope models. The effects of a series of potential ‘nuisance’ variables were also assessed. A statistically significant positive correlation was found across species between population trend and climate suitability trend. The demonstration that climate envelope models are able to retrodict species' population trends provides ...

  6. Climate Variability and Yields of Major Staple Food Crops in Northern Ghana

    Science.gov (United States)

    Amikuzuno, J.

    2012-12-01

    Climate variability, the short-term fluctuations in average weather conditions, and agriculture affect each other. Climate variability affects the agroecological and growing conditions of crops and livestock, and is recently believed to be the greatest impediment to the realisation of the first Millennium Development Goal of reducing poverty and food insecurity in arid and semi-arid regions of developing countries. Conversely, agriculture is a major contributor to climate variability and change by emitting greenhouse gases and reducing the agroecology's potential for carbon sequestration. What however, is the empirical evidence of this inter-dependence of climate variability and agriculture in Sub-Sahara Africa? In this paper, we provide some insight into the long run relationship between inter-annual variations in temperature and rainfall, and annual yields of the most important staple food crops in Northern Ghana. Applying pooled panel data of rainfall, temperature and yields of the selected crops from 1976 to 2010 to cointegration and Granger causality models, there is cogent evidence of cointegration between seasonal, total rainfall and crop yields; and causality from rainfall to crop yields in the Sudano-Guinea Savannah and Guinea Savannah zones of Northern Ghana. This suggests that inter-annual yields of the crops have been influenced by the total mounts of rainfall in the planting season. Temperature variability over the study period is however stationary, and is suspected to have minimal effect if any on crop yields. Overall, the results confirm the appropriateness of our attempt in modelling long-term relationships between the climate and crop yield variables.

  7. Hierarchical Climate Modeling for Cosmoclimatology

    Science.gov (United States)

    Ohfuchi, Wataru

    2010-05-01

    It has been reported that there are correlations among solar activity, amount of galactic cosmic ray, amount of low clouds and surface air temperature (Svensmark and Friis-Chistensen, 1997). These correlations seem to exist for current climate change, Little Ice Age, and geological time scale climate changes. Some hypothetic mechanisms have been argued for the correlations but it still needs quantitative studies to understand the mechanism. In order to decrease uncertainties, only first principles or laws very close to first principles should be used. Our group at Japan Agency for Marine-Earth Science and Technology has started modeling effort to tackle this problem. We are constructing models from galactic cosmic ray inducing ionization, to aerosol formation, to cloud formation, to global climate. In this talk, we introduce our modeling activities. For aerosol formation, we use molecular dynamics. For cloud formation, we use a new cloud microphysics model called "super droplet method". We also try to couple a nonhydrostatic atmospheric regional cloud resolving model and a hydrostatic atmospheric general circulation model.

  8. A framework for modeling uncertainty in regional climate change (Invited)

    Science.gov (United States)

    Monier, E.; Gao, X.; Scott, J. R.; Sokolov, A. P.; Schlosser, C. A.

    2013-12-01

    In this study, we present a new modeling framework and a large ensemble of climate projections to investigate the uncertainty in regional climate change over the United States associated with four dimensions of uncertainty. The sources of uncertainty considered in this framework are the emissions projections (using different climate policies), the climate system response (represented by different values of climate sensitivity and net aerosol forcing), natural variability (by perturbing initial conditions) and structural uncertainty (using different climate models). The modeling framework revolves around the Massachusetts Institute of Technology (MIT) Integrated Global System Model (IGSM), an integrated assessment model with an intermediate complexity earth system model (with a two-dimensional zonal-mean atmosphere). Regional climate change over the United States is obtained through a two-pronged approach. First, we use the IGSM-CAM framework which links the IGSM to the National Center for Atmospheric Research (NCAR) Community Atmosphere Model (CAM). Secondly, we use a pattern-scaling method that extends the IGSM zonal mean based on climate change patterns from various climate models. Results show that uncertainty in temperature changes are mainly driven by policy choices and the range of climate sensitivity considered. Meanwhile, the four sources of uncertainty contribute more equally to precipitation changes, with natural variability having a large impact in the first part of the 21st century. Overall, the choice of policy is the largest driver of uncertainty in future projections of climate change over the United States. In light of these results, we recommend that when investigating climate change impacts over specific regions, studies consider all four sources of uncertainty analyzed in this paper.

  9. A Framework for Benefit-Cost Analysis of Adaptation to Climate Change and Climate Variability

    International Nuclear Information System (INIS)

    The potential damages of climate change and climate variability are dependent upon the responses or adaptations that people make to their changing environment. By adapting the management of resources, the mix and methods of producing goods and services, choices of leisure activities, and other behavior, people can lessen the damages that would otherwise result. A framework for assessing the benefits and costs of adaptation to both climate change and climate variability is described in the paper. The framework is also suitable for evaluating the economic welfare effects of climate change, allowing for autonomous adaptation by private agents. The paper also briefly addresses complications introduced by uncertainty regarding the benefits of adaptation and irreversibility of investments in adaptation. When investment costs are irreversible and there is uncertainty about benefits, the usual net present value criterion for evaluating the investment gives the wrong decision. If delaying an adaptation project is possible, and if delay will permit learning about future benefits of adaptation, it may be preferable to delay the project even if the expected net present value is positive. Implications of this result for adaptation policy are discussed in the paper. 11 refs

  10. Climate variability and human impact in South America during the last 2000 years: synthesis and perspectives from pollen records

    NARCIS (Netherlands)

    S.G.A. Flantua; H. Hooghiemstra; M. Vuille; H. Behling; J.F. Carson; W.D. Gosling; I. Hoyos; M.P. Ledru; E. Montoya; F. Mayle; A. Maldonado; V. Rull; M.S. Tonello; B.S. Whitney; C. González-Arango

    2016-01-01

    An improved understanding of present-day climate variability and change relies on high-quality data sets from the past 2 millennia. Global efforts to model regional climate modes are in the process of being validated against, and integrated with, records of past vegetation change. For South America,

  11. Impact of climatic change on ocean carbon fluxes. Role of the decadal variability

    International Nuclear Information System (INIS)

    Since the industrial revolution, oceans have absorbed roughly one quarter of the anthropogenic emissions of CO2, slowing down climate change. The evolution of the ocean carbon sink, paralleled to the anthropogenic CO2 emissions, is ruled by the CO2 as well as climate. Influence of atmospheric CO2 in the recent evolution of the ocean carbon sink is well understood whilst this is not the case for the climate's one. Indeed, some authors claim that the recent variations of the ocean CO2 sink can be attributed to climate change, whereas some others suggest that these latter are controlled by a decadal variability, which is poorly understood. In this thesis, we address question relative to the role of the decadal variability of the ocean carbon fluxes through the mean of numerical modeling. On one hand, we have demonstrated that ocean carbon fluxes exhibit decadal fluctuations within the high latitudes oceans. These fluctuations displays modes of 10 to 50-year long which account for 20 to 40% of the year-to-year variability. Thanks to Detection and Attribution methods applied to RECCAP project's reconstructions (1960-2005), we have then assessed whether the occurrence of fluctuations at decadal time scale could hamper the detection of the climate contribution to the recent evolution of ocean carbon fluxes. We have shown that the climate contribution is indeed not detected in the high latitude oceans due to the presence of decadal mode of variability. In the low latitude oceans instead, the weaker fluctuations of ocean carbon fluxes at decadal time scale favor the detection of climate influence in the recent variations of the CO2 fluxes. (author)

  12. Climate Change Modelling and Its Roles to Chinese Crops Yield

    Institute of Scientific and Technical Information of China (English)

    JU Hui; LIN Er-da; Tim Wheeler; Andrew Challinor; JIANG Shuai

    2013-01-01

    Climate has been changing in the last fifty years in China and will continue to change regardless any efforts for mitigation. Agriculture is a climate-dependent activity and highly sensitive to climate changes and climate variability. Understanding the interactions between climate change and agricultural production is essential for society stable development of China. The first mission is to fully understand how to predict future climate and link it with agriculture production system. In this paper, recent studies both domestic and international are reviewed in order to provide an overall image of the progress in climate change researches. The methods for climate change scenarios construction are introduced. The pivotal techniques linking crop model and climate models are systematically assessed and climate change impacts on Chinese crops yield among model results are summarized. The study found that simulated productions of grain crop inherit uncertainty from using different climate models, emission scenarios and the crops simulation models. Moreover, studies have different spatial resolutions, and methods for general circulation model (GCM) downscaling which increase the uncertainty for regional impacts assessment. However, the magnitude of change in crop production due to climate change (at 700 ppm CO2 eq correct) appears within ±10%for China in these assessments. In most literatures, the three cereal crop yields showed decline under climate change scenarios and only wheat in some region showed increase. Finally, the paper points out several gaps in current researches which need more studies to shorten the distance for objective recognizing the impacts of climate change on crops. The uncertainty for crop yield projection is associated with climate change scenarios, CO2 fertilization effects and adaptation options. Therefore, more studies on the fields such as free air CO2 enrichment experiment and practical adaptations implemented need to be carried out.

  13. The Monash University Interactive Simple Climate Model

    Science.gov (United States)

    Dommenget, D.

    2013-12-01

    The Monash university interactive simple climate model is a web-based interface that allows students and the general public to explore the physical simulation of the climate system with a real global climate model. It is based on the Globally Resolved Energy Balance (GREB) model, which is a climate model published by Dommenget and Floeter [2011] in the international peer review science journal Climate Dynamics. The model simulates most of the main physical processes in the climate system in a very simplistic way and therefore allows very fast and simple climate model simulations on a normal PC computer. Despite its simplicity the model simulates the climate response to external forcings, such as doubling of the CO2 concentrations very realistically (similar to state of the art climate models). The Monash simple climate model web-interface allows you to study the results of more than a 2000 different model experiments in an interactive way and it allows you to study a number of tutorials on the interactions of physical processes in the climate system and solve some puzzles. By switching OFF/ON physical processes you can deconstruct the climate and learn how all the different processes interact to generate the observed climate and how the processes interact to generate the IPCC predicted climate change for anthropogenic CO2 increase. The presentation will illustrate how this web-base tool works and what are the possibilities in teaching students with this tool are.

  14. Potential impacts of climate variability on dengue hemorrhagic fever in Honduras, 2010.

    Science.gov (United States)

    Zambrano, L I; Sevilla, C; Reyes-García, S Z; Sierra, M; Kafati, R; Rodriguez-Morales, A J; Mattar, S

    2012-12-01

    Climate change and variability are affecting human health and disease direct or indirectly through many mechanisms. Dengue is one of those diseases that is strongly influenced by climate variability; however its study in Central America has been poorly approached. In this study, we assessed potential associations between macroclimatic and microclimatic variation and dengue hemorrhagic fever (DHF) cases in the main hospital of Honduras during 2010. In this year, 3,353 cases of DHF were reported in the Hospital Escuela, Tegucigalpa. Climatic periods marked a difference of 158% in the mean incidence of cases, from El Niño weeks (-99% of cases below the mean incidence) to La Niña months (+59% of cases above it) (pHonduras. However, it is necessary to extend these studies in this and other countries in the Central America region, because these models can be applied for surveillance as well as for prediction of dengue.

  15. Climate variability as observed by the Nimbus-7 ERB

    Science.gov (United States)

    Ardanuy, P. E.; Kyle, H. L.

    1986-01-01

    Limits to the accuracy of the Earth Radiation Budget (ERB) data being obtained by the Nimbus-7 satellite are discussed with emphasis on the implications for the measured variabilities in the global climate. Error analyses are performed for both wide and narrow field of view instruments and the success of in-flight calibration efforts is noted. Alterations in the ERB due to the eruptions of El Chichon in 1982 and the 1982-1983 ENSO event are summarized, particularly the teleconnections which were observed during ENSO.

  16. Holocene climate variability from continental, marine and glacial records

    OpenAIRE

    Debret, Maxime

    2008-01-01

    The aim of this thesis is to characterize Holocene climate variability (10 000-0 years) by the analysis of marine, continental and glacial records. North and South Atlantic and southern ocean records allowed to identify two millenial frequencies. The first are present during the first part of the Holocene (10 000-5 000 years) and are comparable to frequencies observed in solar activity, whereas the second ones, during the late Holocene (5 000-10 000 years), suggest an internal oceanic forcing...

  17. Climatic information improves statistical individual-tree mortality models for three key species of Sichuan Province, China

    OpenAIRE

    Qiu, Shuai; Xu, Ming; Li, Renqiang; Zheng, Yunpu; Clark, Daniel; Cui, Xiaowei; Liu, Lixiang; Lai, Changhong; Zhang, Wen; Liu, Bo

    2015-01-01

    Key message Climate variables improve individual-tree mortality models for fir, oak and birch.• Context Climate is considered as an important driver of tree mortality, but few studies have included climate factors in models to explore their importance for modelling individual-tree mortality.• Aims To measure the performance of climate-based models, we built individual-tree mortality models using individual, stand, and climate variables for fir (Abies faxoniana Rehd. et Wils.), oak (Quercus aq...

  18. Climate system model, numerical simulation and climate predictability

    Institute of Scientific and Technical Information of China (English)

    2010-01-01

    @@ Thanks to its work of past more than 20 years,a research team led by Prof.ZENG Qingcun and Prof.WANG Huijun from the CAS Institute of Atmospheric Physics (IAP) has scored innovative achievements in their studies of basic theory of climate dynamics,numerical model development,its related computational theory,and the dynamical climate prediction using the climate system models.Their work received a second prize of the National Award for Natural Sciences in 2005.

  19. Assessing Regional Scale Variability in Extreme Value Statistics Under Altered Climate Scenarios

    Energy Technology Data Exchange (ETDEWEB)

    Brunsell, Nathaniel [University of Kansas; Mechem, David [University of Kansas; Ma, Chunsheng [Wichita State University

    2015-02-20

    Recent studies have suggested that low-frequency modes of climate variability can significantly influence regional climate. The climatology associated with extreme events has been shown to be particularly sensitive. This has profound implications for droughts, heat waves, and food production. We propose to examine regional climate simulations conducted over the continental United States by applying a recently developed technique which combines wavelet multi–resolution analysis with information theory metrics. This research is motivated by two fundamental questions concerning the spatial and temporal structure of extreme events. These questions are 1) what temporal scales of the extreme value distributions are most sensitive to alteration by low-frequency climate forcings and 2) what is the nature of the spatial structure of variation in these timescales? The primary objective is to assess to what extent information theory metrics can be useful in characterizing the nature of extreme weather phenomena. Specifically, we hypothesize that (1) changes in the nature of extreme events will impact the temporal probability density functions and that information theory metrics will be sensitive these changes and (2) via a wavelet multi–resolution analysis, we will be able to characterize the relative contribution of different timescales on the stochastic nature of extreme events. In order to address these hypotheses, we propose a unique combination of an established regional climate modeling approach and advanced statistical techniques to assess the effects of low-frequency modes on climate extremes over North America. The behavior of climate extremes in RCM simulations for the 20th century will be compared with statistics calculated from the United States Historical Climatology Network (USHCN) and simulations from the North American Regional Climate Change Assessment Program (NARCCAP). This effort will serve to establish the baseline behavior of climate extremes, the

  20. Model for eclipsing cataclysmic variables

    International Nuclear Information System (INIS)

    A new and improved model was developed to derive the elements of eclipsing cataclysmic variables (CVs). Roche geometry was used to simulate the eclipse of a flat accretion disk, which was divided into multiple equipotential rings of black-body spectral contribution. The temperature of each ring was adjusted, using a chi-square fitting routine, until the data and the simulations achieved a best-fit value. Only the rising side of the eclipse light curve was used, in order to avoid asymmetries in the disk produced by the shock bulge and hot spot. Time-resolved spectroscopic data for two eclipsing cataclysmic variables (CVs), LX Serpentis and DQ Herculis, were obtained on the 1.3 meter McGraw-Hill telescope at Kitt Peak, Arizona. Seven sets of eclipse data for each star were phase binned in order to suppress the random variability common to such CV systems. The phase of the Balmer-line eclipse minimum leads photometric minimum by 0.0066 cycles for both CV systems. The phase of the Balmer-line minimum is used as the phase of conjunction in the model. From analysis of the data, a mass ratio of 1.06 +/- 0.1 and an inclination angle of 80 +/- 30 were found for LX Ser, while the results for DQ Her were 0.76 +/- 0.1 and 87 +/- 30, respectively. Two hot, roughly 90000K, rings were found in each accretion disk system

  1. Regionalization of climate model results for the North Sea

    Energy Technology Data Exchange (ETDEWEB)

    Kauker, F.

    1999-07-01

    A dynamical downscaling is presented that allows an estimation of potential effects of climate change on the North Sea. Therefore, the ocean general circulation model OPYC is adapted for application on a shelf by adding a lateral boundary formulation and a tide model. In this set-up the model is forced, first, with data from the ECMWF reanalysis for model validation and the study of the natural variability, and, second, with data from climate change experiments to estimate the effects of climate change on the North Sea. (orig.)

  2. Climate variability, weather and enteric disease incidence in New Zealand: time series analysis.

    Directory of Open Access Journals (Sweden)

    Aparna Lal

    Full Text Available BACKGROUND: Evaluating the influence of climate variability on enteric disease incidence may improve our ability to predict how climate change may affect these diseases. OBJECTIVES: To examine the associations between regional climate variability and enteric disease incidence in New Zealand. METHODS: Associations between monthly climate and enteric diseases (campylobacteriosis, salmonellosis, cryptosporidiosis, giardiasis were investigated using Seasonal Auto Regressive Integrated Moving Average (SARIMA models. RESULTS: No climatic factors were significantly associated with campylobacteriosis and giardiasis, with similar predictive power for univariate and multivariate models. Cryptosporidiosis was positively associated with average temperature of the previous month (β =  0.130, SE =  0.060, p <0.01 and inversely related to the Southern Oscillation Index (SOI two months previously (β =  -0.008, SE =  0.004, p <0.05. By contrast, salmonellosis was positively associated with temperature (β  = 0.110, SE = 0.020, p<0.001 of the current month and SOI of the current (β  = 0.005, SE = 0.002, p<0.050 and previous month (β  = 0.005, SE = 0.002, p<0.05. Forecasting accuracy of the multivariate models for cryptosporidiosis and salmonellosis were significantly higher. CONCLUSIONS: Although spatial heterogeneity in the observed patterns could not be assessed, these results suggest that temporally lagged relationships between climate variables and national communicable disease incidence data can contribute to disease prediction models and early warning systems.

  3. Assessing the impact of climate variability and human activities on streamflow variation

    Science.gov (United States)

    Chang, Jianxia; Zhang, Hongxue; Wang, Yimin; Zhu, Yuelu

    2016-04-01

    Water resources in river systems have been changing under the impact of both climate variability and human activities. Assessing the respective impact on decadal streamflow variation is important for water resource management. By using an elasticity-based method and calibrated TOPMODEL and VIC hydrological models, we quantitatively isolated the relative contributions that human activities and climate variability made to decadal streamflow changes in the Jinghe basin, located in the northwest of China. This is an important watershed of the Shaanxi province that supplies drinking water for a population of over 6 million people. The results showed that the maximum value of the moisture index (E0/P) was 1.91 and appeared in 1991-2000, and the decreased speed of streamflow was higher since 1990 compared with 1960-1990. The average annual streamflow from 1990 to 2010 was reduced by 26.96 % compared with the multiyear average value (from 1960 to 2010). The estimates of the impacts of climate variability and human activities on streamflow decreases from the hydrological models were similar to those from the elasticity-based method. The maximum contribution value of human activities was 99 % when averaged over the three methods, and appeared in 1981-1990 due to the effects of soil and water conservation measures and irrigation water withdrawal. Climate variability made the greatest contribution to streamflow reduction in 1991-2000, the values of which was 40.4 %. We emphasized various source of errors and uncertainties that may occur in the hydrological model (parameter and structural uncertainty) and elasticity-based method (model parameter) in climate change impact studies.

  4. Multidecadal climate variability of global lands and oceans

    Science.gov (United States)

    McCabe, G.J.; Palecki, M.A.

    2006-01-01

    Principal components analysis (PCA) and singular value decomposition (SVD) are used to identify the primary modes of decadal and multidecadal variability in annual global Palmer Drought Severity Index (PDSI) values and sea-surface temperature (SSTs). The PDSI and SST data for 1925-2003 were detrended and smoothed (with a 10-year moving average) to isolate the decadal and multidecadal variability. The first two principal components (PCs) of the PDSI PCA explained almost 38% of the decadal and multidecadal variance in the detrended and smoothed global annual PDSI data. The first two PCs of detrended and smoothed global annual SSTs explained nearly 56% of the decadal variability in global SSTs. The PDSI PCs and the SST PCs are directly correlated in a pairwise fashion. The first PDSI and SST PCs reflect variability of the detrended and smoothed annual Pacific Decadal Oscillation (PDO), as well as detrended and smoothed annual Indian Ocean SSTs. The second set of PCs is strongly associated with the Atlantic Multidecadal Oscillation (AMO). The SVD analysis of the cross-covariance of the PDSI and SST data confirmed the close link between the PDSI and SST modes of decadal and multidecadal variation and provided a verification of the PCA results. These findings indicate that the major modes of multidecadal variations in SSTs and land-surface climate conditions are highly interrelated through a small number of spatially complex but slowly varying teleconnections. Therefore, these relations may be adaptable to providing improved baseline conditions for seasonal climate forecasting. Published in 2006 by John Wiley & Sons, Ltd.

  5. Potential impact of climatic variability on the epidemiology of dengue in Risaralda, Colombia, 2010-2011.

    Science.gov (United States)

    Quintero-Herrera, Liseth L; Ramírez-Jaramillo, Valeria; Bernal-Gutiérrez, Sergio; Cárdenas-Giraldo, Erika V; Guerrero-Matituy, Edwin A; Molina-Delgado, Anderson H; Montoya-Arias, Cindy P; Rico-Gallego, Jhon A; Herrera-Giraldo, Albert C; Botero-Franco, Shirley; Rodríguez-Morales, Alfonso J

    2015-01-01

    Dengue continues to be the most important viral vector-borne disease in the world, particularly in Asia and Latin America, and is significantly affected by climate variability. The influence of climate in an endemic region of Colombia, from 2010 to 2011, was assessed. Epidemiological surveillance data (weekly cases) were collected, and incidence rates were calculated. Poisson regression models were used to assess the influence of the macroclimatic variable ONI (Oscillation Niño Index) and the microclimatic variable pluviometry (mm of rain for Risaralda) on the dengue incidence rate, adjusting by year and week. During the study period, 13,650 cases were reported. In 2010, the rates ranged from 8.6 cases/100,000 pop. up to a peak of 75.3 cases/100,000 pop. for a cumulative rate of 456.2 cases/100,000 pop. in that week. The climate variability in 2010 was higher (ONI 1.6, El Niño to -1.5, La Niña) than in 2011 (ONI -1.4, La Niña to -0.2, Neutral). The mean pluviometry was 248.45mm (min 135.9-max 432.84). During El Niño, cases were significantly higher (mean 433.81) than during the climate neutral period (142.48) and during the La Niña (52.80) phases (ANOVA F=66.59; pdengue incidence rate, after adjusting by year and week (pdengue in Risaralda. This association with climate change and variability should be considered in the elements influencing disease epidemiology. In addition, predictive models should be developed further with more available data from disease surveillance.

  6. Long-term measurements of solar spectral irradiance variability: toward the establishment of a climate record

    Science.gov (United States)

    Richard, Erik; Harder, Jerald; Pilewskie, Peter; Fontenla, Juan; Woods, Thomas; Brown, Steven; Lykke, Keith

    Knowledge of the top of the atmosphere (TOA) solar spectral irradiance (SSI) is crucial in interpreting the spectrally dependent radiative processes throughout Earth's climate system. Where this energy is deposited into the atmosphere and surface, how the climate responds to solar variability, and the mechanisms of climate response, are highly dependent on how the incident solar radiation is distributed with wavelength. In order to advance understanding of how natural and anthropogenic process affect Earth's climate system there is a strong scientific imperative to maintain accurate, long-term records of climate forcing and response. The contin-uation of SSI measurements provides a unique opportunity to characterize poorly understood wavelength dependent climate processes. Coupled chemistry-climate models require realistic assessments of the magnitudes and long-term trends in SSI for the interpretation and quantifi-cation of solar forcing in climate change scenarios. This places stringent requirements on the absolute calibration of the instrument (tied directly to international standards) and the ability to maintain that calibration on-orbit (long-term stability). The Spectral Irradiance Monitor (SIM) is a solar spectral radiometer that continuously monitors the SSI from 200 nm -2400 nm, a wavelength region encompassing 96% of the total solar irradiance. The SIM instrument is included as part of the Total and Spectral Solar Irradiance Sensor (TSIS) to continue the mea-surement of SSI, which began with the SOlar Radiation and Climate Experiment (SORCE), launched in 2003. SORCE SIM measurements have characterized SSI variability during the descending phase of Solar Cycle (SC) 23, but the determination of multi-solar cycle dependen-cies remains a key climatic uncertainty. Analysis of the measured spectral irradiance variability during the SORCE mission has resulted in a number of instrument design refinements central to maintaining, on-orbit, the long-term absolute

  7. EVALUATING SHORT-TERM CLIMATE VARIABILITY IN THE LATE HOLOCENE OF THE NORTHERN GREAT PLAINS

    Energy Technology Data Exchange (ETDEWEB)

    Joseph H. Hartman

    1999-09-01

    This literature study investigated methods and areas to deduce climate change and climate patterns, looking for short-term cycle phenomena and the means to interpret them. Many groups are actively engaged in intensive climate-related research. Ongoing research might be (overly) simplified into three categories: (1) historic data on weather that can be used for trend analysis and modeling; (2) detailed geological, biological (subfossil), and analytical (geochemical, radiocarbon, etc.) studies covering the last 10,000 years (about since last glaciation); and (3) geological, paleontological, and analytical (geochemical, radiometric, etc.) studies over millions of years. Of importance is our ultimate ability to join these various lines of inquiry into an effective means of interpretation. At this point, the process of integration is fraught with methodological troubles and misconceptions about what each group can contribute. This project has met its goals to the extent that it provided an opportunity to study resource materials and consider options for future effort toward the goal of understanding the natural climate variation that has shaped our current civilization. A further outcome of this project is a proposed methodology based on ''climate sections'' that provides spatial and temporal correlation within a region. The method would integrate cultural and climate data to establish the climate history of a region with increasing accuracy with progressive study and scientific advancement (e. g., better integration of regional and global models). The goal of this project is to better understand natural climatic variations in the recent past (last 5000 years). The information generated by this work is intended to provide better context within which to examine global climate change. The ongoing project will help to establish a basis upon which to interpret late Holocene short-term climate variability as evidenced in various studies in the northern

  8. Glacier response to North Atlantic climate variability during the Holocene

    Directory of Open Access Journals (Sweden)

    N. L. Balascio

    2015-05-01

    Full Text Available Small glaciers and ice caps respond rapidly to climate variations and records of their past extent provide information on the natural envelope of past climate variability. Millennial-scale trends in Holocene glacier size are well documented and correspond with changes in Northern Hemisphere summer insolation. However, there is only sparse and fragmentary evidence for higher frequency variations in glacier size because in many Northern Hemisphere regions glacier advances of the past few hundred years were the most extensive and destroyed the geomorphic evidence of ice growth and retreat during the past several thousand years. Thus, most glacier records have been of limited use for investigating centennial scale climate forcing and feedback mechanisms. Here we report a continuous record of glacier activity for the last 9.5 ka from southeast Greenland, derived from high-resolution measurements on a proglacial lake sediment sequence. Physical and geochemical parameters show that the glaciers responded to previously documented Northern Hemisphere climatic excursions, including the "8.2 ka" cooling event, the Holocene Thermal Maximum, Neoglacial cooling, and 20th Century warming. In addition, the sediments indicate centennial-scale oscillations in glacier size during the late Holocene. Beginning at 4.1 ka, a series of abrupt glacier advances occurred, each lasting ~100 years and followed by a period of retreat, that were superimposed on a gradual trend toward larger glacier size. Thus, while declining summer insolation caused long-term cooling and glacier expansions during the late Holocene, climate system dynamics resulted in repeated episodes of glacier expansion and retreat on multi-decadal to centennial timescales. These episodes coincided with ice rafting events in the North Atlantic Ocean and periods of regional ice cap expansion, which confirms their regional significance and indicates that considerable glacier activity on these timescales is a

  9. Cross-scale impact of climate temporal variability on ecosystem water and carbon fluxes

    Science.gov (United States)

    Paschalis, Athanasios; Fatichi, Simone; Katul, Gabriel G.; Ivanov, Valeriy Y.

    2015-09-01

    While the importance of ecosystem functioning is undisputed in the context of climate change and Earth system modeling, the role of short-scale temporal variability of hydrometeorological forcing (~1 h) on the related ecosystem processes remains to be fully understood. Various impacts of meteorological forcing variability on water and carbon fluxes across a range of scales are explored here using numerical simulations. Synthetic meteorological drivers that highlight dynamic features of the short temporal scale in series of precipitation, temperature, and radiation are constructed. These drivers force a mechanistic ecohydrological model that propagates information content into the dynamics of water and carbon fluxes for an ensemble of representative ecosystems. The focus of the analysis is on a cross-scale effect of the short-scale forcing variability on the modeled evapotranspiration and ecosystem carbon assimilation. Interannual variability of water and carbon fluxes is emphasized in the analysis. The main study inferences are summarized as follows: (a) short-scale variability of meteorological input does affect water and carbon fluxes across a wide range of time scales, spanning from the hourly to the annual and longer scales; (b) different ecosystems respond to the various characteristics of the short-scale variability of the climate forcing in various ways, depending on dominant factors limiting system productivity; (c) whenever short-scale variability of meteorological forcing influences primarily fast processes such as photosynthesis, its impact on the slow-scale variability of water and carbon fluxes is small; and (d) whenever short-scale variability of the meteorological forcing impacts slow processes such as movement and storage of water in the soil, the effects of the variability can propagate to annual and longer time scales.

  10. Impacts of Multi-Scale Solar Activity on Climate.Part Ⅱ: Dominant Timescales in Decadal-Centennial Climate Variability

    Institute of Scientific and Technical Information of China (English)

    Hengyi WENG

    2012-01-01

    Part Ⅱ of this study detects the dominant decadal-centennial timescales in four SST indices up to the 2010/2011 winter and tries to relate them to the observed 11-yr and 88-yr solar activity with the sunspot number up to Solar Cycle 24.To explore plausible solar origins of the observed decadal-centennial timescales in the SSTs and climate variability in general,we design a simple one-dimensional dynamical system forced by an annual cycle modulated by a small-amplitude single- or multi-scale “solar activity.” Results suggest that nonlinear harmonic and subharmonic resonance of the system to the forcing and period-doubling bifurcations are responsible for the dominant timescales in the system,including the 60-yr timescale that dominates the Atlantic Multidecadal Oscillation.The dominant timescales in the forced system depend on the system's parameter setting.Scale enhancement among the dominant response timescales may result in dramatic amplifications over a few decades and extreme values of the time series on various timescales.Three possible energy sources for such amplifications and extremes are proposed.Dynamical model results suggest that solar activity may play an important yet not well recognized role in the observed decadal-centennial climate variability.The atmospheric dynamical amplifying mechanism shown in Part Ⅰ and the nonlinear resonant and bifurcation mechanisms shown in Part Ⅱ help us to understand the solar source of the multi-scale climate change in the 20th century and the fact that different solar influenced dominant timescales for recurrent climate extremes for a given region or a parameter setting.Part Ⅱ also indicates that solar influences on climate cannot be linearly compared with non-cyclic or sporadic thermal forcings because they cannot exert their influences on climate in the same way as the sun does.

  11. Climate variability and wildfire risk and occurrence in northern Spain

    Science.gov (United States)

    Garcia Codron, J. C.; Rasilla, D.; Diego, C.; Carracedo, V.

    2009-04-01

    In spite of their reputation of wetness, wildfires are a frequent event in Cantabria (Northern Spain), but their seasonality does not match the typical warm season maximum generalized in most of the Iberian Peninsula. They occur at the end of the winter and the beginning of the spring (January to March), being mostly anthropogenically triggered due to the necessity of preparing pastures in the uplands. However, catastrophic episodes of generalized burning are controlled by different atmospheric mechanisms, namely the occurrence of "Suradas", a downslope windstorms which combines high winds speeds and low humidities, and long periods of drought in late fall and winter. This contribution analyzes long term trends (1961 onwards) of several climatic variables during the highest wildfire risk period in order to assess to what extent the occurrence of wildfires may be linked to the recent climatic variability. Raw meteorological values of temperature, humidity, wind speed and precipitation are transformed into a well-known meteorological fire weather index, the Canadian Forest Fire Index (FWI). Besides, monthly values of the Palmer Drought Severity Index we used to assess the spatial and temporal magnitude and intensity of droughts. Our results show that the regional climate has become warmer and drier, due to the combined effects of increases in temperatures, sunshine duration, and the decrease in relative humidity and precipitation, variables that are likely to play an important role in drought. Unknown in the 60s, 70s and most of the 80s, drought has become a relatively frequent phenomenon during the last two decades, and, in fact, the two most extreme episodes of drought at century scale, during 1989-1990 and 1993, occur in the 90. However, both the frequency and the intensity of "Suradas" have reduced, and consequently, the high fire risk episodes are now less frequent, but their absolute maximum values remain unchanged. Those regional climate trends are strongly

  12. Validation of CMIP5 multimodel ensembles through the smoothness of climate variables

    KAUST Repository

    Lee, Myoungji

    2015-05-14

    Smoothness is an important characteristic of a spatial process that measures local variability. If climate model outputs are realistic, then not only the values at each grid pixel but also the relative variation over nearby pixels should represent the true climate. We estimate the smoothness of long-term averages for land surface temperature anomalies in the Coupled Model Intercomparison Project Phase 5 (CMIP5), and compare them by climate regions and seasons. We also compare the estimated smoothness of the climate outputs in CMIP5 with those of reanalysis data. The estimation is done through the composite likelihood approach for locally self-similar processes. The composite likelihood that we consider is a product of conditional likelihoods of neighbouring observations. We find that the smoothness of the surface temperature anomalies in CMIP5 depends primarily on the modelling institution and on the climate region. The seasonal difference in the smoothness is generally small, except for some climate regions where the average temperature is extremely high or low.

  13. Precipitation variability and the sugarcane climate demand in Brazil

    Science.gov (United States)

    Pereira, V. R.; de Avila, A. M. H.; Blain, G.; Zullo, J., Jr.

    2014-12-01

    This study presents the precipitation variability in São Paulo state/Brazil considering the climate demand for high sugarcane productivity. The Brazilian sugarcane and the bioethanol chain are facing an increase demand in response of the biofuel industry expansion. The productivity improvement is the key point to face the challenges about the land expansion in the Brazilian agriculture. The sugarcane phenology is climate dependent even being efficient in the decarboxylation process. The sprouting, growing, yield and the sugar content are determined by the climate. The accumulated rainy days during the pre harvest or more than 180 days of dry period can reduce the sugar content during the maturation process. Daily rainfall time series for the period 1960-2003 from 210 rain gauges at São Paulo state - the major Brazilian producer - are used. We subset the time series in the annual, seasonal, ten-day totals and dry and wet spells analysis. We used the mann- kendall non-parametric test to calculate the trends. The annual, the seasonal totals and the dry and wet spells did not showed a significant change in time. However, the ten-day total analysis in the beginning of the rainy season - i.e. in October - showed an interesting changing pattern - 24% of gauges showed a significant negative trend (p_value<0.1). These gauges are located in specific regions with the highest sugarcane production. Also, the October totals showed significant and negative trends (p_value<0.1) for more than 95% of precipitation gauges. These results are strongly indicating a longer dry season in the last twenty years. These changes in the precipitation variability can be related with the instability of the sugarcane market in Brazil in the last years.

  14. Local variability mediates vulnerability of trout populations to land use and climate change

    Science.gov (United States)

    Penaluna, Brooke E.; Dunham, Jason B.; Railsback, Steve F.; Arismendi, Ivan; Johnson, Sherri L.; Bilby, Robert E; Safeeq, Mohammad; Skaugset, Arne E.

    2015-01-01

    Land use and climate change occur simultaneously around the globe. Fully understanding their separate and combined effects requires a mechanistic understanding at the local scale where their effects are ultimately realized. Here we applied an individual-based model of fish population dynamics to evaluate the role of local stream variability in modifying responses of Coastal Cutthroat Trout (Oncorhynchus clarkii clarkii) to scenarios simulating identical changes in temperature and stream flows linked to forest harvest, climate change, and their combined effects over six decades. We parameterized the model for four neighboring streams located in a forested headwater catchment in northwestern Oregon, USA with multi-year, daily measurements of stream temperature, flow, and turbidity (2007–2011), and field measurements of both instream habitat structure and three years of annual trout population estimates. Model simulations revealed that variability in habitat conditions among streams (depth, available habitat) mediated the effects of forest harvest and climate change. Net effects for most simulated trout responses were different from or less than the sum of their separate scenarios. In some cases, forest harvest countered the effects of climate change through increased summer flow. Climate change most strongly influenced trout (earlier fry emergence, reductions in biomass of older trout, increased biomass of young-of-year), but these changes did not consistently translate into reductions in biomass over time. Forest harvest, in contrast, produced fewer and less consistent responses in trout. Earlier fry emergence driven by climate change was the most consistent simulated response, whereas survival, growth, and biomass were inconsistent. Overall our findings indicate a host of local processes can strongly influence how populations respond to broad scale effects of land use and climate change.

  15. Local Variability Mediates Vulnerability of Trout Populations to Land Use and Climate Change.

    Science.gov (United States)

    Penaluna, Brooke E; Dunham, Jason B; Railsback, Steve F; Arismendi, Ivan; Johnson, Sherri L; Bilby, Robert E; Safeeq, Mohammad; Skaugset, Arne E

    2015-01-01

    Land use and climate change occur simultaneously around the globe. Fully understanding their separate and combined effects requires a mechanistic understanding at the local scale where their effects are ultimately realized. Here we applied an individual-based model of fish population dynamics to evaluate the role of local stream variability in modifying responses of Coastal Cutthroat Trout (Oncorhynchus clarkii clarkii) to scenarios simulating identical changes in temperature and stream flows linked to forest harvest, climate change, and their combined effects over six decades. We parameterized the model for four neighboring streams located in a forested headwater catchment in northwestern Oregon, USA with multi-year, daily measurements of stream temperature, flow, and turbidity (2007-2011), and field measurements of both instream habitat structure and three years of annual trout population estimates. Model simulations revealed that variability in habitat conditions among streams (depth, available habitat) mediated the effects of forest harvest and climate change. Net effects for most simulated trout responses were different from or less than the sum of their separate scenarios. In some cases, forest harvest countered the effects of climate change through increased summer flow. Climate change most strongly influenced trout (earlier fry emergence, reductions in biomass of older trout, increased biomass of young-of-year), but these changes did not consistently translate into reductions in biomass over time. Forest harvest, in contrast, produced fewer and less consistent responses in trout. Earlier fry emergence driven by climate change was the most consistent simulated response, whereas survival, growth, and biomass were inconsistent. Overall our findings indicate a host of local processes can strongly influence how populations respond to broad scale effects of land use and climate change.

  16. A Review on Evaluation Methods of Climate Modeling

    Institute of Scientific and Technical Information of China (English)

    ZHAO; Zong-Ci; LUO; Yong; HUANG; Jian-Bin

    2013-01-01

    There is scientific progress in the evaluation methods of recent Earth system models(ESMs).Methods range from single variable to multi-variables,multi-processes,multi-phenomena quantitative evaluations in five layers(spheres)of the Earth system,from climatic mean assessment to climate change(such as trends,periodicity,interdecadal variability),extreme values,abnormal characters and quantitative evaluations of phenomena,from qualitative assessment to quantitative calculation of reliability and uncertainty for model simulations.Researchers started considering independence and similarity between models in multi-model use,as well as the quantitative evaluation of climate prediction and projection efect and the quantitative uncertainty contribution analysis.In this manuscript,the simulations and projections by both CMIP5 and CMIP3 that have been published after 2007 are reviewed and summarized.

  17. Streamflow estimation using WRF-Hydro with dynamically downscaled climate variables over southern tropical Indian region

    Science.gov (United States)

    Davis, S.; Sudheer, K. P.; Gunthe, S. S.

    2015-12-01

    Indian summer monsoon rainfall (ISMR; June to September), which constitutes around 80% of India's annual rainfall, has shown an increasing trend in intensity and frequency of extreme events (Goswami et al., 2006). It is a widely recognized fact that the increasing temperature in association with anthropogenic activities can affect the hydrological cycle, which leads to extreme events. In addition a shift in extremes of the spatial pattern of ISMR has recently been observed (Ghosh et al., 2011). Such changes in rainfall on temporal and spatial scale can further affect the stream flow over a given region subsequently making water resource management a difficult task (Mondal and Mujumdar, 2015). The hydrological models used for the stream flow estimation are dependent on various climate variables as input data. These climate variables could be obtained through either observational networks or climate model outputs. Due to the scarcity of the observational data over the Indian region and the coarse resolution of global climate model output, which is used as input to hydrologic models, large uncertainties are introduced in stream flow output (Overgaard et al., 2007). In the present study we have used the Weather Research and Forecasting (WRF) model (Skamarock et al. 2008) to downscale the essential climate variables (surface temperature, precipitation, relative humidity, etc.) as an input for its coupled hydrological extension, WRF Hydro (NCAR user's guide). We will present the results obtained from the WRF-hydro simulation to estimate the stream flow over the Thamirabarani river basin in Southern Tropical Indian region. Preliminary simulations using WRF to estimate the precipitation showed the reasonable quantitative agreement with observed values. An attempt will be made to demonstrate how these results can further be used for developing flood-forecasting techniques and for local regional water resource management.

  18. Climate change and health modeling: horses for courses

    OpenAIRE

    Ebi, Kristie L.; Rocklov, Joacim

    2014-01-01

    Mathematical and statistical models are needed to understand the extent to which weather, climate variability, and climate change are affecting current and may affect future health burdens in the context of other risk factors and a range of possible development pathways, and the temporal and spatial patterns of any changes. Such understanding is needed to guide the design and the implementation of adaptation and mitigation measures. Because each model projection captures only a narrow range o...

  19. Mixing parameterizations in ocean climate modeling

    Science.gov (United States)

    Moshonkin, S. N.; Gusev, A. V.; Zalesny, V. B.; Byshev, V. I.

    2016-03-01

    Results of numerical experiments with an eddy-permitting ocean circulation model on the simulation of the climatic variability of the North Atlantic and the Arctic Ocean are analyzed. We compare the ocean simulation quality with using different subgrid mixing parameterizations. The circulation model is found to be sensitive to a mixing parametrization. The computation of viscosity and diffusivity coefficients by an original splitting algorithm of the evolution equations for turbulence characteristics is found to be as efficient as traditional Monin-Obukhov parameterizations. At the same time, however, the variability of ocean climate characteristics is simulated more adequately. The simulation of salinity fields in the entire study region improves most significantly. Turbulent processes have a large effect on the circulation in the long-term through changes in the density fields. The velocity fields in the Gulf Stream and in the entire North Atlantic Subpolar Cyclonic Gyre are reproduced more realistically. The surface level height in the Arctic Basin is simulated more faithfully, marking the Beaufort Gyre better. The use of the Prandtl number as a function of the Richardson number improves the quality of ocean modeling.

  20. Spatial and Temporal Variation in the Effects of Climatic Variables on Dugong Calf Production.

    Science.gov (United States)

    Fuentes, Mariana M P B; Delean, Steven; Grayson, Jillian; Lavender, Sally; Logan, Murray; Marsh, Helene

    2016-01-01

    Knowledge of the relationships between environmental forcing and demographic parameters is important for predicting responses from climatic changes and to manage populations effectively. We explore the relationships between the proportion of sea cows (Dugong dugon) classified as calves and four climatic drivers (rainfall anomaly, Southern Oscillation El Niño Index [SOI], NINO 3.4 sea surface temperature index, and number of tropical cyclones) at a range of spatially distinct locations in Queensland, Australia, a region with relatively high dugong density. Dugong and calf data were obtained from standardized aerial surveys conducted along the study region. A range of lagged versions of each of the focal climatic drivers (1 to 4 years) were included in a global model containing the proportion of calves in each population crossed with each of the lagged versions of the climatic drivers to explore relationships. The relative influence of each predictor was estimated via Gibbs variable selection. The relationships between the proportion of dependent calves and the climatic drivers varied spatially and temporally, with climatic drivers influencing calf counts at sub-regional scales. Thus we recommend that the assessment of and management response to indirect climatic threats on dugongs should also occur at sub-regional scales. PMID:27355367

  1. Spatial and Temporal Variation in the Effects of Climatic Variables on Dugong Calf Production

    Science.gov (United States)

    Fuentes, Mariana M. P. B.; Delean, Steven; Grayson, Jillian; Lavender, Sally; Logan, Murray; Marsh, Helene

    2016-01-01

    Knowledge of the relationships between environmental forcing and demographic parameters is important for predicting responses from climatic changes and to manage populations effectively. We explore the relationships between the proportion of sea cows (Dugong dugon) classified as calves and four climatic drivers (rainfall anomaly, Southern Oscillation El Niño Index [SOI], NINO 3.4 sea surface temperature index, and number of tropical cyclones) at a range of spatially distinct locations in Queensland, Australia, a region with relatively high dugong density. Dugong and calf data were obtained from standardized aerial surveys conducted along the study region. A range of lagged versions of each of the focal climatic drivers (1 to 4 years) were included in a global model containing the proportion of calves in each population crossed with each of the lagged versions of the climatic drivers to explore relationships. The relative influence of each predictor was estimated via Gibbs variable selection. The relationships between the proportion of dependent calves and the climatic drivers varied spatially and temporally, with climatic drivers influencing calf counts at sub-regional scales. Thus we recommend that the assessment of and management response to indirect climatic threats on dugongs should also occur at sub-regional scales. PMID:27355367

  2. Impact of bushfire and climate variability on streamflow from forested catchments in southeast Australia

    Directory of Open Access Journals (Sweden)

    Y. Zhou

    2013-04-01

    Full Text Available Most of the surface water for natural environmental and human water uses in southeast Australia is sourced from forested catchments located in the higher rainfall areas. Water yield of these catchments is mainly affected by climatic conditions, but it is also greatly affected by vegetation cover change. Bushfires are a major natural disturbance in forested catchments and potentially modify the water yield of the catchments through changes to evapotranspiration (ET, interception and soil moisture storage. This paper quantifies the impacts of bushfire and climate variability on streamflow from three southeast Australian catchments where Ash Wednesday bushfires occurred in February 1983. The hydrological models used here include AWRA-L, Xinanjiang and GR4J. The three models are first calibrated against streamflow data from the pre-bushfire period and they are used to simulate runoff for the post-bushfire period with the calibrated parameters. The difference between the observed and model simulated runoff for the post-bushfire period provides an estimate of the impact of bushfire on streamflow. The hydrological modelling results for the three catchments indicate that there is a substantial increase in streamflow in the first 15 yr after the 1983 bushfires. The increase in streamflow is attributed to initial decreases in ET and interception resulting from the fires, followed by logging activity. After 15 yr, streamflow dynamics are more heavily influenced by climate effects, although some impact from fire and logging regeneration may still occur. It is shown that hydrological models provide reasonable consistent estimates of forest disturbance and climate impacts on streamflow for the three catchments. The results might be used by forest managers to understand the relationship between forest disturbance and climate variability impacts on water yield in the context of climate change.

  3. Climate Variability and Household Adaptation Strategies in Southern Ethiopia

    Directory of Open Access Journals (Sweden)

    Wassie Berhanu

    2015-05-01

    Full Text Available This paper examines the determinants and implied economic impacts of climate change adaptation strategies in the context of traditional pastoralism. It is based on econometric analysis of survey data generated from household level interviews in southern Ethiopian rangelands. Pastoralists’ perception of climate change in the region is found to be very consistent with the actually recorded trends of increased temperature and the evident secular declines in precipitation. Not only long-term declines, trends in the region’s rainfall also appear to have taken a shift towards the direction of more unpredictability. Pastoralist adaptation response strategies broadly involve adjustments in pastoral practices and shifts to non-pastoral livelihoods. Results of the estimated models confirm that pastoral mobility is still quite essential in the present context of climate-induced household vulnerabilities. Increased mobility and diversification of pastoral herd portfolios in favor of a drought-tolerant species (camel are found to be positively associated with pastoral household net income. A policy stance that ignores the detrimental impacts of the currently pervasive private rangeland enclosures or intends to hasten pastoralist sedentarization in the area is simply untenable in the present context of climate-induced risks and pastoral livelihood vulnerability.

  4. Climate variability and change and human intervention effects on an over-exploited aquifer

    Science.gov (United States)

    Loukas, Athanasios; Zagoriti, Katerina; Mylopoulos, Nikitas; Sidiropoulos, Pantelis; Vasiliades, Lampros

    2013-04-01

    In this paper, the influence of climate variability, change and human intervention on an over-exploited aquifer, is examined under various operational scenarios for water resources management. The aquifer of Lake Karla watershed in Central Greece is in an over-exploitation status due to the intense agricultural activities. The draining of old lake Karla at 1962 and the uncontrolled use of irrigation wells after 1980s has led to serious environmental problems as water scarcity, water and ground pollution and land subsidence. The partial restoration of Lake Karla and the accompanying projects are under construction in order to reverse this situation. A modeling system, consisting of a statistical downscaling component, a hydrological model, a groundwater model, a reservoir operation model and a module for the estimation of water demands, has been developed and applied on Lake Karla watershed. The outputs of the Canadian Centre for Climate Modelling Analysis Global Circulation Model CGCM3 were applied for three socioeconomic scenarios, namely SRES B1, SRES A1B and SRES A2 for the assessment of climate change impacts on water resources. The statistical downscaling module has been applied to estimate monthly precipitation and temperature time series for present conditions and the future climate period 2009-2058. The hydrological model was applied in a semi-distributed mode to simulate the hydrological cycle components, to simulate the reservoir operation and to produce time series of recharge data, which were imported as input to the groundwater model. The last one was applied for simulation of groundwater resources for different climate scenarios under three operational management scenarios: the first being the natural aquifer without human intervention, the second with the human intervention (reservoir simulation and management) and the third is the second scenario plus the future management plans for the urban water needs of the nearby city of Volos, as they belong

  5. Adaptation to climatic variability and change. Report of the task force on climate adaptation

    International Nuclear Information System (INIS)

    A critique and interpretation is presented of what is known and available about adaptation to climate changes, not based on any particular climate scenario. It is assumed that variability is a fact of climate and that changes in climatic conditions are possible and are constantly occurring. Emphasis is on adaptation with regard to economic and social activities in Canada. A series of linked objectives are addressed, relating to demonstration of the significance of adaptation, consideration of case studies of adaptation (past and potential future) in Canada, clarification of the meaning of adaptation and the forms it takes, assessment of policy implications, and identification of research priorities. The basic facts on global climate change are reviewed, including long-term temperature variations, and adaptation is discussed as a public policy response. Examples of adaptation in Canada are given in the areas of Great Lakes property, power generation, and transportation; Atlantic Canada communities and fisheries; forestry; the construction industry; the energy industry; recreation and tourism; agriculture; urb