Evaluating the Sensitivity of Agricultural Model Performance to Different Climate Inputs: Supplemental Material

Science.gov (United States)

Glotter, Michael J.; Ruane, Alex C.; Moyer, Elisabeth J.; Elliott, Joshua W.

2015-01-01

Projections of future food production necessarily rely on models, which must themselves be validated through historical assessments comparing modeled and observed yields. Reliable historical validation requires both accurate agricultural models and accurate climate inputs. Problems with either may compromise the validation exercise. Previous studies have compared the effects of different climate inputs on agricultural projections but either incompletely or without a ground truth of observed yields that would allow distinguishing errors due to climate inputs from those intrinsic to the crop model. This study is a systematic evaluation of the reliability of a widely used crop model for simulating U.S. maize yields when driven by multiple observational data products. The parallelized Decision Support System for Agrotechnology Transfer (pDSSAT) is driven with climate inputs from multiple sources reanalysis, reanalysis that is bias corrected with observed climate, and a control dataset and compared with observed historical yields. The simulations show that model output is more accurate when driven by any observation-based precipitation product than when driven by non-bias-corrected reanalysis. The simulations also suggest, in contrast to previous studies, that biased precipitation distribution is significant for yields only in arid regions. Some issues persist for all choices of climate inputs: crop yields appear to be oversensitive to precipitation fluctuations but under sensitive to floods and heat waves. These results suggest that the most important issue for agricultural projections may be not climate inputs but structural limitations in the crop models themselves.

The influence of cirrus cloud-radiative forcing on climate and climate sensitivity in a general circulation model

International Nuclear Information System (INIS)

Lohmann, U.; Roeckner, E.

1994-01-01

Six numerical experiments have been performed with a general circulation model (GCM) to study the influence of high-level cirrus clouds and global sea surface temperature (SST) perturbations on climate and climate sensitivity. The GCM used in this investigation is the third-generation ECHAM3 model developed jointly by the Max-Planck-Institute for Meteorology and the University of Hamburg. It is shown that the model is able to reproduce many features of the observed cloud-radiative forcing with considerable skill, such as the annual mean distribution, the response to seasonal forcing and the response to observed SST variations in the equatorial Pacific. In addition to a reference experiment where the cirrus emissivity is computed as a function of the cloud water content, two sensitivity experiments have been performed in which the cirrus emissivity is either set to zero everywhere above 400 hPa ('transparent cirrus') or set to one ('black cirrus'). These three experiments are repeated identically, except for prescribing a globally uniform SST warming of 4 K. (orig.)

Impact of Greenland and Antarctic ice sheet interactions on climate sensitivity

Energy Technology Data Exchange (ETDEWEB)

Goelzer, H.; Huybrechts, P. [Vrije Universiteit Brussel, Earth System Sciences and Departement Geografie, Brussels (Belgium); Loutre, M.F.; Goosse, H.; Fichefet, T. [Universite Catholique de Louvain, Georges Lemaitre Centre for Earth and Climate Research (TECLIM), Earth and Life Institute, Louvain-la-Neuve (Belgium); Mouchet, A. [Universite de Liege, Laboratoire de Physique Atmospherique et Planetaire, Liege (Belgium)

2011-09-15

We use the Earth system model of intermediate complexity LOVECLIM to show the effect of coupling interactive ice sheets on the climate sensitivity of the model on a millennial time scale. We compare the response to a 2 x CO{sub 2} warming scenario between fully coupled model versions including interactive Greenland and Antarctic ice sheet models and model versions with fixed ice sheets. For this purpose an ensemble of different parameter sets have been defined for LOVECLIM, covering a wide range of the model's sensitivity to greenhouse warming, while still simulating the present-day climate and the climate evolution over the last millennium within observational uncertainties. Additional freshwater fluxes from the melting ice sheets have a mitigating effect on the model's temperature response, leading to generally lower climate sensitivities of the fully coupled model versions. The mitigation is effectuated by changes in heat exchange within the ocean and at the sea-air interface, driven by freshening of the surface ocean and amplified by sea-ice-related feedbacks. The strength of the effect depends on the response of the ice sheets to the warming and on the model's climate sensitivity itself. The effect is relatively strong in model versions with higher climate sensitivity due to the relatively large polar amplification of LOVECLIM. With the ensemble approach in this study we cover a wide range of possible model responses. (orig.)

Sensitivity of climate models: Comparison of simulated and observed patterns for past climates

International Nuclear Information System (INIS)

Prell, W.L.; Webb, T. III.

1992-08-01

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

Sensitivity of Hydrologic Response to Climate Model Debiasing Procedures

Science.gov (United States)

Channell, K.; Gronewold, A.; Rood, R. B.; Xiao, C.; Lofgren, B. M.; Hunter, T.

2017-12-01

Climate change is already having a profound impact on the global hydrologic cycle. In the Laurentian Great Lakes, changes in long-term evaporation and precipitation can lead to rapid water level fluctuations in the lakes, as evidenced by unprecedented change in water levels seen in the last two decades. These fluctuations often have an adverse impact on the region's human, environmental, and economic well-being, making accurate long-term water level projections invaluable to regional water resources management planning. Here we use hydrological components from a downscaled climate model (GFDL-CM3/WRF), to obtain future water supplies for the Great Lakes. We then apply a suite of bias correction procedures before propagating these water supplies through a routing model to produce lake water levels. Results using conventional bias correction methods suggest that water levels will decline by several feet in the coming century. However, methods that reflect the seasonal water cycle and explicitly debias individual hydrological components (overlake precipitation, overlake evaporation, runoff) imply that future water levels may be closer to their historical average. This discrepancy between debiased results indicates that water level forecasts are highly influenced by the bias correction method, a source of sensitivity that is commonly overlooked. Debiasing, however, does not remedy misrepresentation of the underlying physical processes in the climate model that produce these biases and contribute uncertainty to the hydrological projections. This uncertainty coupled with the differences in water level forecasts from varying bias correction methods are important for water management and long term planning in the Great Lakes region.

The Milankovitch theory and climate sensitivity. I - Equilibrium climate model solutions for the present surface conditions. II - Interaction between the Northern Hemisphere ice sheets and the climate system

Science.gov (United States)

Neeman, Binyamin U.; Ohring, George; Joseph, Joachim H.

1988-01-01

A seasonal climate model was developed to test the climate sensitivity and, in particular, the Milankovitch (1941) theory. Four climate model versions were implemented to investigate the range of uncertainty in the parameterizations of three basic feedback mechanisms: the ice albedo-temperature, the outgoing long-wave radiation-temperature, and the eddy transport-meridional temperature gradient. It was found that the differences between the simulation of the present climate by the four versions were generally small, especially for annually averaged results. The climate model was also used to study the effect of growing/shrinking of a continental ice sheet, bedrock sinking/uplifting, and sea level changes on the climate system, taking also into account the feedback effects on the climate of the building of the ice caps.

Sensitivity of Alpine Snow and Streamflow Regimes to Climate Changes

Science.gov (United States)

Rasouli, K.; Pomeroy, J. W.; Marks, D. G.; Bernhardt, M.

2014-12-01

Understanding the sensitivity of hydrological processes to climate change in alpine areas with snow dominated regimes is of paramount importance as alpine basins show both high runoff efficiency associated with the melt of the seasonal snowpack and great sensitivity of snow processes to temperature change. In this study, meteorological data measured in a selection of alpine headwaters basins including Reynolds Mountain East, Idaho, USA, Wolf Creek, Yukon in Canada, and Zugspitze Mountain, Germany with climates ranging from arctic to continental temperate were used to study the snow and streamflow sensitivity to climate change. All research sites have detailed multi-decadal meteorological and snow measurements. The Cold Regions Hydrological Modelling platform (CRHM) was used to create a model representing a typical alpine headwater basin discretized into hydrological response units with physically based representations of snow redistribution by wind, complex terrain snowmelt energetics and runoff processes in alpine tundra. The sensitivity of snow hydrology to climate change was investigated by changing air temperature and precipitation using weather generating methods based on the change factors obtained from different climate model projections for future and current periods. The basin mean and spatial variability of peak snow water equivalent, sublimation loss, duration of snow season, snowmelt rates, streamflow peak, and basin discharge were assessed under varying climate scenarios and the most sensitive hydrological mechanisms to the changes in the different alpine climates were detected. The results show that snow hydrology in colder alpine climates is more resilient to warming than that in warmer climates, but that compensatory factors to warming such as reduced blowing snow sublimation loss and reduced melt rate should also be assessed when considering climate change impacts on alpine hydrology.

Sensitivity of hydrological modeling to meteorological data and implications for climate change studies

International Nuclear Information System (INIS)

Roy, L.G.; Roy, R.; Desrochers, G.E.; Vaillancourt, C.; Chartier, I.

2008-01-01

There are uncertainties associated with the use of hydrological models. This study aims to analyse one source of uncertainty associated with hydrological modeling, particularly in the context of climate change studies on water resources. Additional intent of this study is to compare the ability of some meteorological data sources, used in conjunction with an hydrological model, to reproduce the hydrologic regime of a watershed. A case study on a watershed of south-western Quebec, Canada using five different sources of meteorological data as input to an offline hydrological model are presented in this paper. Data used came from weather stations, NCEP reanalysis, ERA40 reanalysis and two Canadian Regional Climate Model (CRCM) runs driven by NCEP and ERA40 reanalysis, providing atmospheric driving boundary conditions to this limited-area climate model. To investigate the sensitivity of simulated streamflow to different sources of meteorological data, we first calibrated the hydrological model with each of the meteorological data sets over the 1961-1980 period. The five different sets of parameters of the hydrological model were then used to simulate streamflow of the 1981-2000 validation period with the five meteorological data sets as inputs. The 25 simulated streamflow series have been compared to the observed streamflow of the watershed. The five meteorological data sets do not have the same ability, when used with the hydrological model, to reproduce streamflow. Our results show also that the hydrological model parameters used may have an important influence on results such as water balance, but it is linked with the differences that may have in the characteristics of the meteorological data used. For climate change impacts assessments on water resources, we have found that there is an uncertainty associated with the meteorological data used to calibrate the model. For expected changes on mean annual flows of the Chateauguay River, our results vary from a small

Eocene climate and Arctic paleobathymetry: A tectonic sensitivity study using GISS ModelE-R

Science.gov (United States)

Roberts, C. D.; Legrande, A. N.; Tripati, A. K.

2009-12-01

The early Paleogene (65-45 million years ago, Ma) was a ‘greenhouse’ interval with global temperatures warmer than any other time in the last 65 Ma. This period was characterized by high levels of CO2, warm high-latitudes, warm surface-and-deep oceans, and an intensified hydrological cycle. Sediments from the Arctic suggest that the Eocene surface Arctic Ocean was warm, brackish, and episodically enabled the freshwater fern Azolla to bloom. The precise mechanisms responsible for the development of these conditions remain uncertain. We present equilibrium climate conditions derived from a fully-coupled, water-isotope enabled, general circulation model (GISS ModelE-R) configured for the early Eocene. We also present model-data comparison plots for key climatic variables (SST and δ18O) and analyses of the leading modes of variability in the tropical Pacific and North Atlantic regions. Our tectonic sensitivity study indicates that Northern Hemisphere climate would have been very sensitive to the degree of oceanic exchange through the seaways connecting the Arctic to the Atlantic and Tethys. By restricting these seaways, we simulate freshening of the surface Arctic Ocean to ~6 psu and warming of sea-surface temperatures by 2°C in the North Atlantic and 5-10°C in the Labrador Sea. Our results may help explain the occurrence of low-salinity tolerant taxa in the Arctic Ocean during the Eocene and provide a mechanism for enhanced warmth in the north western Atlantic. We also suggest that the formation of a volcanic land-bridge between Greenland and Europe could have caused increased ocean convection and warming of intermediate waters in the Atlantic. If true, this result is consistent with the theory that bathymetry changes may have caused thermal destabilisation of methane clathrates in the Atlantic.

Sensitivity of streamflow to climate change in California

Science.gov (United States)

Grantham, T.; Carlisle, D.; Wolock, D.; McCabe, G. J.; Wieczorek, M.; Howard, J.

2015-12-01

Trends of decreasing snowpack and increasing risk of drought are looming challenges for California water resource management. Increasing vulnerability of the state's natural water supplies threatens California's social-economic vitality and the health of its freshwater ecosystems. Despite growing awareness of potential climate change impacts, robust management adaptation has been hindered by substantial uncertainty in future climate predictions for the region. Down-scaled global climate model (GCM) projections uniformly suggest future warming of the region, but projections are highly variable with respect to the direction and magnitude of change in regional precipitation. Here we examine the sensitivity of California surface water supplies to climate variation independently of GCMs. We use a statistical approach to construct predictive models of monthly streamflow based on historical climate and river basin features. We then propagate an ensemble of synthetic climate simulations through the models to assess potential streamflow responses to changes in temperature and precipitation in different months and regions of the state. We also consider the range of streamflow change predicted by bias-corrected downscaled GCMs. Our results indicate that the streamflow in the xeric and coastal mountain regions of California is more sensitive to changes in precipitation than temperature, whereas streamflow in the interior mountain region responds strongly to changes in both temperature and precipitation. Mean climate projections for 2025-2075 from GCM ensembles are highly variable, indicating streamflow changes of -50% to +150% relative to baseline (1980-2010) for most months and regions. By quantifying the sensitivity of streamflow to climate change, rather than attempting to predict future hydrologic conditions based on uncertain GCM projections, these results should be more informative to water managers seeking to assess, and potentially reduce, the vulnerability of surface

Validating predictions from climate envelope models

Science.gov (United States)

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

2013-01-01

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

Validating predictions from climate envelope models.

Directory of Open Access Journals (Sweden)

James I Watling

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

The influence of the albedo-temperature feedback on climate sensitivity

NARCIS (Netherlands)

Bintanja, R.; Oerlemans, J.

1995-01-01

A vertically integrated, zonally averaged energy-balance climate model coupled to a two-dimensional ocean model with prescribed overturning pattern is employed to assess the seasonally and latitudinally varying response of the climate system to changes in radiative forcing. Since the sensitivity

Implications for Climate Sensitivity from the Response to Individual Forcings

Science.gov (United States)

Marvel, Kate; Schmidt, Gavin A.; Miller, Ron L.; Nazarenko, Larissa

2015-01-01

Climate sensitivity to doubled CO2 is a widely-used metric of the large-scale response to external forcing. Climate models predict a wide range for two commonly used definitions: the transient climate response (TCR: the warming after 70 years of CO2 concentrations that riseat 1 per year), and the equilibrium climate sensitivity (ECS: the equilibrium temperature change following a doubling of CO2 concentrations). Many observational datasets have been used to constrain these values, including temperature trends over the recent past 16, inferences from paleo-climate and process-based constraints from the modern satellite eras. However, as the IPCC recently reported different classes of observational constraints produce somewhat incongruent ranges. Here we show that climate sensitivity estimates derived from recent observations must account for the efficacy of each forcing active during the historical period. When we use single forcing experiments to estimate these efficacies and calculate climate sensitivity from the observed twentieth-century warming, our estimates of both TCR and ECS are revised upward compared to previous studies, improving the consistency with independent constraints.

Uncertainty, Sensitivity Analysis, and Causal Identification in the Arctic using a Perturbed Parameter Ensemble of the HiLAT Climate Model

Energy Technology Data Exchange (ETDEWEB)

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

2018-02-12

Coupled climate models have a large number of input parameters that can affect output uncertainty. We conducted a sensitivity analysis of sea ice proper:es and Arc:c related climate variables to 5 parameters in the HiLAT climate model: air-ocean turbulent exchange parameter (C), conversion of water vapor to clouds (cldfrc_rhminl) and of ice crystals to snow (micro_mg_dcs), snow thermal conduc:vity (ksno), and maximum snow grain size (rsnw_mlt). We used an elementary effect (EE) approach to rank their importance for output uncertainty. EE is an extension of one-at-a-time sensitivity analyses, but it is more efficient in sampling multi-dimensional parameter spaces. We looked for emerging relationships among climate variables across the model ensemble, and used causal discovery algorithms to establish potential pathways for those relationships.

Sensitivity of simulated regional Arctic climate to the choice of coupled model domain

Directory of Open Access Journals (Sweden)

Dmitry V. Sein

2014-07-01

Full Text Available The climate over the Arctic has undergone changes in recent decades. In order to evaluate the coupled response of the Arctic system to external and internal forcing, our study focuses on the estimation of regional climate variability and its dependence on large-scale atmospheric and regional ocean circulations. A global ocean–sea ice model with regionally high horizontal resolution is coupled to an atmospheric regional model and global terrestrial hydrology model. This way of coupling divides the global ocean model setup into two different domains: one coupled, where the ocean and the atmosphere are interacting, and one uncoupled, where the ocean model is driven by prescribed atmospheric forcing and runs in a so-called stand-alone mode. Therefore, selecting a specific area for the regional atmosphere implies that the ocean–atmosphere system can develop ‘freely’ in that area, whereas for the rest of the global ocean, the circulation is driven by prescribed atmospheric forcing without any feedbacks. Five different coupled setups are chosen for ensemble simulations. The choice of the coupled domains was done to estimate the influences of the Subtropical Atlantic, Eurasian and North Pacific regions on northern North Atlantic and Arctic climate. Our simulations show that the regional coupled ocean–atmosphere model is sensitive to the choice of the modelled area. The different model configurations reproduce differently both the mean climate and its variability. Only two out of five model setups were able to reproduce the Arctic climate as observed under recent climate conditions (ERA-40 Reanalysis. Evidence is found that the main source of uncertainty for Arctic climate variability and its predictability is the North Pacific. The prescription of North Pacific conditions in the regional model leads to significant correlation with observations, even if the whole North Atlantic is within the coupled model domain. However, the inclusion of the

Climate Change Sensitivity of Multi-Species Afforestation in Semi-Arid Benin

Directory of Open Access Journals (Sweden)

Florent Noulèkoun

2018-06-01

Full Text Available The early growth stage is critical in the response of trees to climate change and variability. It is not clear, however, what climate metrics are best to define the early-growth sensitivity in assessing adaptation strategies of young forests to climate change. Using a combination of field experiments and modelling, we assessed the climate sensitivity of two promising afforestation species, Jatropha curcas L. and Moringa oleifera Lam., by analyzing their predicted climate–growth relationships in the initial two years after planting on degraded cropland in the semi-arid zone of Benin. The process-based WaNuLCAS model (version 4.3, World Agroforestry Centre, Bogor, Indonesia was used to simulate aboveground biomass growth for each year in the climate record (1981–2016, either as the first or as the second year of tree growth. Linear mixed models related the annual biomass growth to climate indicators, and climate sensitivity indices quantified climate–growth relationships. In the first year, the length of dry spells had the strongest effect on tree growth. In the following year, the annual water deficit and length of dry season became the strongest predictors. Simulated rooting depths greater than those observed in the experiments enhanced biomass growth under extreme dry conditions and reduced sapling sensitivity to drought. Projected increases in aridity implied significant growth reduction, but a multi-species approach to afforestation using species that are able to develop deep-penetrating roots should increase the resilience of young forests to climate change. The results illustrate that process-based modelling, combined with field experiments, can be effective in assessing the climate–growth relationships of tree species.

Thermodynamics of climate change: generalized sensitivities

Directory of Open Access Journals (Sweden)

V. Lucarini

2010-10-01

Full Text Available Using a recent theoretical approach, we study how global warming impacts the thermodynamics of the climate system by performing experiments with a simplified yet Earth-like climate model. The intensity of the Lorenz energy cycle, the Carnot efficiency, the material entropy production, and the degree of irreversibility of the system change monotonically with the CO₂ concentration. Moreover, these quantities feature an approximately linear behaviour with respect to the logarithm of the CO₂ concentration in a relatively wide range. These generalized sensitivities suggest that the climate becomes less efficient, more irreversible, and features higher entropy production as it becomes warmer, with changes in the latent heat fluxes playing a predominant role. These results may be of help for explaining recent findings obtained with state of the art climate models regarding how increases in CO₂ concentration impact the vertical stratification of the tropical and extratropical atmosphere and the position of the storm tracks.

Sensitivity of the weather research and forecasting model to parameterization schemes for regional climate of Nile River Basin

Science.gov (United States)

Tariku, Tebikachew Betru; Gan, Thian Yew

2018-06-01

Regional climate models (RCMs) have been used to simulate rainfall at relatively high spatial and temporal resolutions useful for sustainable water resources planning, design and management. In this study, the sensitivity of the RCM, weather research and forecasting (WRF), in modeling the regional climate of the Nile River Basin (NRB) was investigated using 31 combinations of different physical parameterization schemes which include cumulus (Cu), microphysics (MP), planetary boundary layer (PBL), land-surface model (LSM) and radiation (Ra) schemes. Using the European Centre for Medium-Range Weather Forecast (ECMWF) ERA-Interim reanalysis data as initial and lateral boundary conditions, WRF was configured to model the climate of NRB at a resolution of 36 km with 30 vertical levels. The 1999-2001 simulations using WRF were compared with satellite data combined with ground observation and the NCEP reanalysis data for 2 m surface air temperature (T2), rainfall, short- and longwave downward radiation at the surface (SWRAD, LWRAD). Overall, WRF simulated more accurate T2 and LWRAD (with correlation coefficients >0.8 and low root-mean-square error) than SWRAD and rainfall for the NRB. Further, the simulation of rainfall is more sensitive to PBL, Cu and MP schemes than other schemes of WRF. For example, WRF simulated less biased rainfall with Kain-Fritsch combined with MYJ than with YSU as the PBL scheme. The simulation of T2 is more sensitive to LSM and Ra than to Cu, PBL and MP schemes selected, SWRAD is more sensitive to MP and Ra than to Cu, LSM and PBL schemes, and LWRAD is more sensitive to LSM, Ra and PBL than Cu, and MP schemes. In summary, the following combination of schemes simulated the most representative regional climate of NRB: WSM3 microphysics, KF cumulus, MYJ PBL, RRTM longwave radiation and Dudhia shortwave radiation schemes, and Noah LSM. The above configuration of WRF coupled to the Noah LSM has also been shown to simulate representative regional

Sensitivity of the weather research and forecasting model to parameterization schemes for regional climate of Nile River Basin

Science.gov (United States)

Tariku, Tebikachew Betru; Gan, Thian Yew

2017-08-01

Regional climate models (RCMs) have been used to simulate rainfall at relatively high spatial and temporal resolutions useful for sustainable water resources planning, design and management. In this study, the sensitivity of the RCM, weather research and forecasting (WRF), in modeling the regional climate of the Nile River Basin (NRB) was investigated using 31 combinations of different physical parameterization schemes which include cumulus (Cu), microphysics (MP), planetary boundary layer (PBL), land-surface model (LSM) and radiation (Ra) schemes. Using the European Centre for Medium-Range Weather Forecast (ECMWF) ERA-Interim reanalysis data as initial and lateral boundary conditions, WRF was configured to model the climate of NRB at a resolution of 36 km with 30 vertical levels. The 1999-2001 simulations using WRF were compared with satellite data combined with ground observation and the NCEP reanalysis data for 2 m surface air temperature (T2), rainfall, short- and longwave downward radiation at the surface (SWRAD, LWRAD). Overall, WRF simulated more accurate T2 and LWRAD (with correlation coefficients >0.8 and low root-mean-square error) than SWRAD and rainfall for the NRB. Further, the simulation of rainfall is more sensitive to PBL, Cu and MP schemes than other schemes of WRF. For example, WRF simulated less biased rainfall with Kain-Fritsch combined with MYJ than with YSU as the PBL scheme. The simulation of T2 is more sensitive to LSM and Ra than to Cu, PBL and MP schemes selected, SWRAD is more sensitive to MP and Ra than to Cu, LSM and PBL schemes, and LWRAD is more sensitive to LSM, Ra and PBL than Cu, and MP schemes. In summary, the following combination of schemes simulated the most representative regional climate of NRB: WSM3 microphysics, KF cumulus, MYJ PBL, RRTM longwave radiation and Dudhia shortwave radiation schemes, and Noah LSM. The above configuration of WRF coupled to the Noah LSM has also been shown to simulate representative regional

Sensitivity of Regulated Flow Regimes to Climate Change in the Western United States

Energy Technology Data Exchange (ETDEWEB)

Zhou, Tian [Pacific Northwest National Laboratory, Richland, Washington; Voisin, Nathalie [Pacific Northwest National Laboratory, Richland, Washington; Leng, Guoyong [Pacific Northwest National Laboratory, Richland, Washington; Huang, Maoyi [Pacific Northwest National Laboratory, Richland, Washington; Kraucunas, Ian [Pacific Northwest National Laboratory, Richland, Washington

2018-03-01

Water management activities or flow regulations modify water fluxes at the land surface and affect water resources in space and time. We hypothesize that flow regulations change the sensitivity of river flow to climate change with respect to unmanaged water resources. Quantifying these changes in sensitivity could help elucidate the impacts of water management at different spatiotemporal scales and inform climate adaptation decisions. In this study, we compared the emergence of significant changes in natural and regulated river flow regimes across the Western United States from simulations driven by multiple climate models and scenarios. We find that significant climate change-induced alterations in natural flow do not cascade linearly through water management activities. At the annual time scale, 50% of the Hydrologic Unit Code 4 (HUC4) sub-basins over the Western U.S. regions tend to have regulated flow regime more sensitive to the climate change than natural flow regime. Seasonality analyses show that the sensitivity varies remarkably across the seasons. We also find that the sensitivity is related to the level of water management. For 35% of the HUC4 sub-basins with the highest level of water management, the summer and winter flows tend to show a heightened sensitivity to climate change due to the complexity of joint reservoir operations. We further demonstrate that the impacts of considering water management in models are comparable to those that arises from uncertainties across climate models and emission scenarios. This prompts further climate adaptation studies research about nonlinearity effects of climate change through water management activities.

Natural Ocean Carbon Cycle Sensitivity to Parameterizations of the Recycling in a Climate Model

Science.gov (United States)

Romanou, A.; Romanski, J.; Gregg, W. W.

2014-01-01

Sensitivities of the oceanic biological pump within the GISS (Goddard Institute for Space Studies ) climate modeling system are explored here. Results are presented from twin control simulations of the air-sea CO2 gas exchange using two different ocean models coupled to the same atmosphere. The two ocean models (Russell ocean model and Hybrid Coordinate Ocean Model, HYCOM) use different vertical coordinate systems, and therefore different representations of column physics. Both variants of the GISS climate model are coupled to the same ocean biogeochemistry module (the NASA Ocean Biogeochemistry Model, NOBM), which computes prognostic distributions for biotic and abiotic fields that influence the air-sea flux of CO2 and the deep ocean carbon transport and storage. In particular, the model differences due to remineralization rate changes are compared to differences attributed to physical processes modeled differently in the two ocean models such as ventilation, mixing, eddy stirring and vertical advection. GISSEH(GISSER) is found to underestimate mixed layer depth compared to observations by about 55% (10 %) in the Southern Ocean and overestimate it by about 17% (underestimate by 2%) in the northern high latitudes. Everywhere else in the global ocean, the two models underestimate the surface mixing by about 12-34 %, which prevents deep nutrients from reaching the surface and promoting primary production there. Consequently, carbon export is reduced because of reduced production at the surface. Furthermore, carbon export is particularly sensitive to remineralization rate changes in the frontal regions of the subtropical gyres and at the Equator and this sensitivity in the model is much higher than the sensitivity to physical processes such as vertical mixing, vertical advection and mesoscale eddy transport. At depth, GISSER, which has a significant warm bias, remineralizes nutrients and carbon faster thereby producing more nutrients and carbon at depth, which

Sensitivity of Distributions of Climate System Properties to Surface Temperature Datasets

Science.gov (United States)

Libardoni, A. G.; Forest, C. E.

2011-12-01

Predictions of climate change from models depend strongly on the representation of climate system properties emerging from the processes and feedbacks in the models. The quality of any model prediction can be evaluated by determining how well its output reproduces the observed climate system. With this evaluation, the reliability of climate projections derived from the model and provided for policy makers is assessed and quantified. In this study, surface temperature, upper-air temperature, and ocean heat content data are used to constrain the distributions of the parameters that define three climate system properties in the MIT Integrated Global Systems Model: climate sensitivity, the rate of ocean heat uptake into the deep ocean, and net anthropogenic aerosol forcing. In particular, we explore the sensitivity of the distributions to the surface temperature dataset used to estimate the likelihood of model output given the observed climate records. In total, five different reconstructions of past surface temperatures are used and the resulting parameter distribution functions differ from each other. Differences in estimates of climate sensitivity mode and mean are as great as 1 K between the datasets, with an overall range of 1.2 to 5.3 K using the 5-95 confidence intervals. Ocean effective diffusivity is poorly constrained regardless of which dataset is used. All distributions show broad distributions and only three show signs of a distribution mode. When a mode is present, they tend to be for low diffusivity values. Distributions for the net aerosol forcing show similar shapes and cluster into two groups that are shifted by approximately 0.1 watts per square meter. However, the overall spread of forcing values from the 5-95 confidence interval, -0.19 to -0.83 watts per square meter, is small compared to other uncertainties in climate forcings. Transient climate response estimates derived from these distributions range between 0.87 and 2.41 K. Similar to the

Projected Crop Production under Regional Climate Change Using Scenario Data and Modeling: Sensitivity to Chosen Sowing Date and Cultivar

Directory of Open Access Journals (Sweden)

Sulin Tao

2016-02-01

Full Text Available A sensitivity analysis of the responses of crops to the chosen production adaptation options under regional climate change was conducted in this study. Projections of winter wheat production for different sowing dates and cultivars were estimated for a major economic and agricultural province of China from 2021 to 2080 using the World Food Study model (WOFOST under representative concentration pathways (RCPs scenarios. A modeling chain was established and a correction method was proposed to reduce the bias of the resulting model-simulated climate data. The results indicated that adjusting the sowing dates and cultivars could mitigate the influences of climate change on winter wheat production in Jinagsu. The yield gains were projected from the chosen sowing date and cultivar. The following actions are recommended to ensure high and stable yields under future climate changes: (i advance the latest sowing date in some areas of northern Jiangsu; and (ii use heat-tolerant or heat-tolerant and drought-resistant varieties in most areas of Jiangsu rather than the currently used cultivar. Fewer of the common negative effects of using a single climate model occurred when using the sensitivity analysis because our bias correction method was effective for scenario data and because the WOFOST performed well for Jiangsu after calibration.

Short ensembles: an efficient method for discerning climate-relevant sensitivities in atmospheric general circulation models

Directory of Open Access Journals (Sweden)

H. Wan

2014-09-01

Full Text Available This paper explores the feasibility of an experimentation strategy for investigating sensitivities in fast components of atmospheric general circulation models. The basic idea is to replace the traditional serial-in-time long-term climate integrations by representative ensembles of shorter simulations. The key advantage of the proposed method lies in its efficiency: since fewer days of simulation are needed, the computational cost is less, and because individual realizations are independent and can be integrated simultaneously, the new dimension of parallelism can dramatically reduce the turnaround time in benchmark tests, sensitivities studies, and model tuning exercises. The strategy is not appropriate for exploring sensitivity of all model features, but it is very effective in many situations. Two examples are presented using the Community Atmosphere Model, version 5. In the first example, the method is used to characterize sensitivities of the simulated clouds to time-step length. Results show that 3-day ensembles of 20 to 50 members are sufficient to reproduce the main signals revealed by traditional 5-year simulations. A nudging technique is applied to an additional set of simulations to help understand the contribution of physics–dynamics interaction to the detected time-step sensitivity. In the second example, multiple empirical parameters related to cloud microphysics and aerosol life cycle are perturbed simultaneously in order to find out which parameters have the largest impact on the simulated global mean top-of-atmosphere radiation balance. It turns out that 12-member ensembles of 10-day simulations are able to reveal the same sensitivities as seen in 4-year simulations performed in a previous study. In both cases, the ensemble method reduces the total computational time by a factor of about 15, and the turnaround time by a factor of several hundred. The efficiency of the method makes it particularly useful for the development of

Range position and climate sensitivity: The structure of among-population demographic responses to climatic variation

Science.gov (United States)

Amburgey, Staci M.; Miller, David A. W.; Grant, Evan H. Campbell; Rittenhouse, Tracy A. G.; Benard, Michael F.; Richardson, Jonathan L.; Urban, Mark C.; Hughson, Ward; Brand, Adrianne B,; Davis, Christopher J.; Hardin, Carmen R.; Paton, Peter W. C.; Raithel, Christopher J.; Relyea, Rick A.; Scott, A. Floyd; Skelly, David K.; Skidds, Dennis E.; Smith, Charles K.; Werner, Earl E.

2018-01-01

Species’ distributions will respond to climate change based on the relationship between local demographic processes and climate and how this relationship varies based on range position. A rarely tested demographic prediction is that populations at the extremes of a species’ climate envelope (e.g., populations in areas with the highest mean annual temperature) will be most sensitive to local shifts in climate (i.e., warming). We tested this prediction using a dynamic species distribution model linking demographic rates to variation in temperature and precipitation for wood frogs (Lithobates sylvaticus) in North America. Using long-term monitoring data from 746 populations in 27 study areas, we determined how climatic variation affected population growth rates and how these relationships varied with respect to long-term climate. Some models supported the predicted pattern, with negative effects of extreme summer temperatures in hotter areas and positive effects on recruitment for summer water availability in drier areas. We also found evidence of interacting temperature and precipitation influencing population size, such as extreme heat having less of a negative effect in wetter areas. Other results were contrary to predictions, such as positive effects of summer water availability in wetter parts of the range and positive responses to winter warming especially in milder areas. In general, we found wood frogs were more sensitive to changes in temperature or temperature interacting with precipitation than to changes in precipitation alone. Our results suggest that sensitivity to changes in climate cannot be predicted simply by knowing locations within the species’ climate envelope. Many climate processes did not affect population growth rates in the predicted direction based on range position. Processes such as species-interactions, local adaptation, and interactions with the physical landscape likely affect the responses we observed. Our work highlights the

Evaluating the sensitivity of Eurasian forest biomass to climate change using a dynamic vegetation model

International Nuclear Information System (INIS)

Shuman, J K; Shugart, H H

2009-01-01

Climate warming could strongly influence the structure and composition of the Eurasian boreal forest. Temperature related changes have occurred, including shifts in treelines and changes in regeneration. Dynamic vegetation models are well suited to the further exploration of the impacts that climate change may have on boreal forests. Using the individual-based gap model FAREAST, forest composition and biomass are simulated at over 2000 sites across Eurasia. Biomass output is compared to detailed forest data from a representative sample of Russian forests and a sensitivity analysis is performed to evaluate the impact that elevated temperatures and modified precipitation will have on forest biomass and composition in Eurasia. Correlations between model and forest inventory biomass are strong for several boreal tree species. A significant relationship is shown between altered precipitation and biomass. This analysis showed that a modest increase in temperature of 2 deg. C across 200 years had no significant effect on biomass; however further exploration with increased warming reflective of values measured within Siberia, or at an increased rate, are warranted. Overall, FAREAST accurately simulates forest biomass and composition at sites throughout a large geographic area with widely varying climatic conditions and produces reasonable biomass responses to simulated climatic shifts. These results indicate that this model is robust and useful in making predictions regarding the effect of future climate change on boreal forest structure across Eurasia.

Climate sensitivity of DSSAT under different agriculture practice scenarios in China

Science.gov (United States)

Xia, L.; Robock, A.

2014-12-01

Crop yields are sensitive to both agricultural practice and climate changes. Under different agricultural practice scenarios, crop yield may have different climate sensitivities. Since it is important to understand how future climate changes affect agriculture productivity and what the potential adaptation strategies would be to compensate for possible negative impacts on crop production, we performed experiments to study climate sensitivity under different agricultural practice scenarios for rice, maize and wheat in the top four production provinces in China using the Decision Support System for Agrotechnology Transfer (DSSAT) crop model. The agricultural practice scenarios include four categories: different amounts of nitrogen fertilizer or no nitrogen stress; irrigation turned on or off, or no water stress; all possible seeds in the DSSAT cultivar data base; and different planting dates. For the climate sensitivity test, the control climate is from 1998 to 2007, and we individually modify four climate variables: daily maximum and minimum temperature by +2 °C and -2 °C, daily precipitation by +20% and -20%, and daily solar radiation by + 20% and -20%. With more nitrogen fertilizer applied, crops are more sensitive to temperature changes as well as precipitation changes because of their release from nitrogen limitation. With irrigation turned on, crop yield sensitivity to temperature decreases in most of the regions depending on the amount of the local precipitation, since more water is available and soil temperature varies less with higher soil moisture. Those results indicate that there could be possible agriculture adaptation strategies under certain future climate scenarios. For example, increasing nitrogen fertilizer usage by a certain amount might compensate for the negative impact on crop yield from climate changes. However, since crops are more sensitive to climate changes when there is more nitrogen fertilizer applied, if the climate changes are

Lessons on climate sensitivity from past climate changes

NARCIS (Netherlands)

von der Heydt, A.S.; Dijkstra, H.A.; van de Wal, R.S.W.; Caballero, R.; Crucifix, M.; Foster, G.L.; Huber, M.; Kohler, P.; Rohling, E.; Valdes, P.J.; Ashwin, P.; Bathiany, S.; Berends, T.; van Bree, L.G.J.; Ditlevsen, P.; Ghil, M.; Haywood, A.; Katzav, J.K.; Lohmann, G.; Lohmann, J.; Lucarini, V.; Marzocchi, A.; Palike, H.; Ruvalcaba Baroni, I.; Simon, D.; Sluijs, A.; Stap, L.B.; Tantet, A.; Viebahn, J.; Ziegler, M.

2016-01-01

Over the last decade, our understanding of climate sensitivity has improved considerably. The climate system shows variability on many timescales, is subject to non-stationary forcing and it is most likely out of equilibrium with the changes in the radiative forcing. Slow and fast feedbacks

Internal Variability and Disequilibrium Confound Estimates of Climate Sensitivity From Observations

Science.gov (United States)

Marvel, Kate; Pincus, Robert; Schmidt, Gavin A.; Miller, Ron L.

2018-02-01

An emerging literature suggests that estimates of equilibrium climate sensitivity (ECS) derived from recent observations and energy balance models are biased low because models project more positive climate feedback in the far future. Here we use simulations from the Coupled Model Intercomparison Project Phase 5 (CMIP5) to show that across models, ECS inferred from the recent historical period (1979-2005) is indeed almost uniformly lower than that inferred from simulations subject to abrupt increases in CO2 radiative forcing. However, ECS inferred from simulations in which sea surface temperatures are prescribed according to observations is lower still. ECS inferred from simulations with prescribed sea surface temperatures is strongly linked to changes to tropical marine low clouds. However, feedbacks from these clouds are a weak constraint on long-term model ECS. One interpretation is that observations of recent climate changes constitute a poor direct proxy for long-term sensitivity.

Plio-Pleistocene climate sensitivity evaluated using high-resolution CO2 records.

Science.gov (United States)

Martínez-Botí, M A; Foster, G L; Chalk, T B; Rohling, E J; Sexton, P F; Lunt, D J; Pancost, R D; Badger, M P S; Schmidt, D N

2015-02-05

Theory and climate modelling suggest that the sensitivity of Earth's climate to changes in radiative forcing could depend on the background climate. However, palaeoclimate data have thus far been insufficient to provide a conclusive test of this prediction. Here we present atmospheric carbon dioxide (CO2) reconstructions based on multi-site boron-isotope records from the late Pliocene epoch (3.3 to 2.3 million years ago). We find that Earth's climate sensitivity to CO2-based radiative forcing (Earth system sensitivity) was half as strong during the warm Pliocene as during the cold late Pleistocene epoch (0.8 to 0.01 million years ago). We attribute this difference to the radiative impacts of continental ice-volume changes (the ice-albedo feedback) during the late Pleistocene, because equilibrium climate sensitivity is identical for the two intervals when we account for such impacts using sea-level reconstructions. We conclude that, on a global scale, no unexpected climate feedbacks operated during the warm Pliocene, and that predictions of equilibrium climate sensitivity (excluding long-term ice-albedo feedbacks) for our Pliocene-like future (with CO2 levels up to maximum Pliocene levels of 450 parts per million) are well described by the currently accepted range of an increase of 1.5 K to 4.5 K per doubling of CO2.

Plio-Pleistocene climate sensitivity evaluated using high-resolution CO2 records

Science.gov (United States)

Martínez-Botí, M. A.; Foster, G. L.; Chalk, T. B.; Rohling, E. J.; Sexton, P. F.; Lunt, D. J.; Pancost, R. D.; Badger, M. P. S.; Schmidt, D. N.

2015-02-01

Theory and climate modelling suggest that the sensitivity of Earth's climate to changes in radiative forcing could depend on the background climate. However, palaeoclimate data have thus far been insufficient to provide a conclusive test of this prediction. Here we present atmospheric carbon dioxide (CO2) reconstructions based on multi-site boron-isotope records from the late Pliocene epoch (3.3 to 2.3 million years ago). We find that Earth's climate sensitivity to CO2-based radiative forcing (Earth system sensitivity) was half as strong during the warm Pliocene as during the cold late Pleistocene epoch (0.8 to 0.01 million years ago). We attribute this difference to the radiative impacts of continental ice-volume changes (the ice-albedo feedback) during the late Pleistocene, because equilibrium climate sensitivity is identical for the two intervals when we account for such impacts using sea-level reconstructions. We conclude that, on a global scale, no unexpected climate feedbacks operated during the warm Pliocene, and that predictions of equilibrium climate sensitivity (excluding long-term ice-albedo feedbacks) for our Pliocene-like future (with CO2 levels up to maximum Pliocene levels of 450 parts per million) are well described by the currently accepted range of an increase of 1.5 K to 4.5 K per doubling of CO2.

The Sensitivity of Evapotranspiration Models to Errors in Model ...

African Journals Online (AJOL)

Five evapotranspiration (Et) model-the penman, Blaney - Criddel, Thornthwaite, the Blaney –Morin-Nigeria, and the Jensen and Haise models – were analyzed for parameter sensitivity under Nigerian Climatic conditions. The sensitivity of each model to errors in any of its measured parameters (variables) was based on the ...

Global terrestrial isoprene emission models: sensitivity to variability in climate and vegetation

Directory of Open Access Journals (Sweden)

A. Arneth

2011-08-01

Full Text Available Due to its effects on the atmospheric lifetime of methane, the burdens of tropospheric ozone and growth of secondary organic aerosol, isoprene is central among the biogenic compounds that need to be taken into account for assessment of anthropogenic air pollution-climate change interactions. Lack of process-understanding regarding leaf isoprene production as well as of suitable observations to constrain and evaluate regional or global simulation results add large uncertainties to past, present and future emissions estimates. Focusing on contemporary climate conditions, we compare three global isoprene models that differ in their representation of vegetation and isoprene emission algorithm. We specifically aim to investigate the between- and within model variation that is introduced by varying some of the models' main features, and to determine which spatial and/or temporal features are robust between models and different experimental set-ups. In their individual standard configurations, the models broadly agree with respect to the chief isoprene sources and emission seasonality, with maximum monthly emission rates around 20–25 Tg C, when averaged by 30-degree latitudinal bands. They also indicate relatively small (approximately 5 to 10 % around the mean interannual variability of total global emissions. The models are sensitive to changes in one or more of their main model components and drivers (e.g., underlying vegetation fields, climate input which can yield increases or decreases in total annual emissions of cumulatively by more than 30 %. Varying drivers also strongly alters the seasonal emission pattern. The variable response needs to be interpreted in view of the vegetation emission capacities, as well as diverging absolute and regional distribution of light, radiation and temperature, but the direction of the simulated emission changes was not as uniform as anticipated. Our results highlight the need for modellers to evaluate their

The Southern Oscillation in a coupled GCM: Implications for climate sensitivity and climate change

International Nuclear Information System (INIS)

Meehl, G.A.

1990-01-01

Results are presented from a global coupled ocean-atmosphere general circulation climate model developed at the National Center for Atmospheric Research. The atmospheric part of the coupled model is a global spectral (R15, 4.5 degree latitude by 7.5 degree longitude, 9 layers in the vertical) general circulation model. The ocean is coarse-grid (5 degree latitude by 5 degree longitude, 4 layers in the vertical) global general circulation model. The coupled model includes a simple thermodynamic sea-ice model. Due mainly to inherent limitations in the ocean model, the coupled model simulates sea surface temperatures that are too low in the tropics and too high in the extratropics in the mean. In spite of these limitations, the coupled model simulates active interannual variability of the global climate system involving signals in the tropical Pacific that resemble, in some respects, the observed Southern Oscillation. These signals in the tropics are associated with teleconnections to the extratropics of both hemispheres. The implications of this model-simulated interannual variability of the coupled system relating to climate sensitivity and climate change due to an increase of atmospheric carbon dioxide are discussed

The Southern Oscillation in a coupled GCM: Implications for climate sensitivity and climate change

International Nuclear Information System (INIS)

Meehl, G.A.

1991-01-01

Results are presented from a global coupled ocean-atmosphere general circulation climate model developed at the National Center for Atmospheric Research. The atmospheric part of the coupled model is a global spectral (R15, 4.5 degree latitude by 7.5 degree longitude, 9 layers in the vertical) general circulation model. The ocean is coarse-grid (5 degree latitude by 5 degree longitude, 4 layers in the vertical) global general circulation model. The coupled model includes a simple thermodynamic sea-ice model. Due mainly to inherent limitations in the ocean model, the coupled model simulates sea surface temperatures that are too low in the tropics and too high in the extratropics in the mean. In spite of these limitations, the coupled model simulates active interannual variability of the global climate system involving signals in the tropical Pacific that resemble, in some respects, the observed Southern Oscillation. These signals in the tropics are associated with teleconnections to the extratropics of both hemispheres. The implications of this model-simulated interannual variability of the coupled system relating to climate sensitivity and climate change due to an increase of atmospheric carbon dioxide are discussed. 25 refs.; 9 figs

Deep time evidence for climate sensitivity increase with warming

DEFF Research Database (Denmark)

Shaffer, Gary; Huber, Matthew; Rondanelli, Roberto

2016-01-01

warming analogue. We obtain constrained estimates of CO2 and climate sensitivity before and during the PETM and of the PETM carbon input amount and nature. Sensitivity increased from 3.3-5.6 to 3.7-6.5K (Kelvin) into the PETM. When taken together with Last Glacial Maximum and modern estimates, this result...... world, but past warming events may provide insight. Here we employ paleoreconstructions and new climate-carbon model simulations in a novel framework to explore a wide scenario range for the Paleocene-Eocene Thermal Maximum (PETM) carbon release and global warming event 55.8Ma ago, a possible future...

What Climate Sensitivity Index Is Most Useful for Projections?

Science.gov (United States)

Grose, Michael R.; Gregory, Jonathan; Colman, Robert; Andrews, Timothy

2018-02-01

Transient climate response (TCR), transient response at 140 years (T140), and equilibrium climate sensitivity (ECS) indices are intended as benchmarks for comparing the magnitude of climate response projected by climate models. It is generally assumed that TCR or T140 would explain more variability between models than ECS for temperature change over the 21st century, since this timescale is the realm of transient climate change. Here we find that TCR explains more variability across Coupled Model Intercomparison Project phase 5 than ECS for global temperature change since preindustrial, for 50 or 100 year global trends up to the present, and for projected change under representative concentration pathways in regions of delayed warming such as the Southern Ocean. However, unexpectedly, we find that ECS correlates higher than TCR for projected change from the present in the global mean and in most regions. This higher correlation does not relate to aerosol forcing, and the physical cause requires further investigation.

Sensitivity of tropical carbon to climate change constrained by carbon dioxide variability.

Science.gov (United States)

Cox, Peter M; Pearson, David; Booth, Ben B; Friedlingstein, Pierre; Huntingford, Chris; Jones, Chris D; Luke, Catherine M

2013-02-21

The release of carbon from tropical forests may exacerbate future climate change, but the magnitude of the effect in climate models remains uncertain. Coupled climate-carbon-cycle models generally agree that carbon storage on land will increase as a result of the simultaneous enhancement of plant photosynthesis and water use efficiency under higher atmospheric CO(2) concentrations, but will decrease owing to higher soil and plant respiration rates associated with warming temperatures. At present, the balance between these effects varies markedly among coupled climate-carbon-cycle models, leading to a range of 330 gigatonnes in the projected change in the amount of carbon stored on tropical land by 2100. Explanations for this large uncertainty include differences in the predicted change in rainfall in Amazonia and variations in the responses of alternative vegetation models to warming. Here we identify an emergent linear relationship, across an ensemble of models, between the sensitivity of tropical land carbon storage to warming and the sensitivity of the annual growth rate of atmospheric CO(2) to tropical temperature anomalies. Combined with contemporary observations of atmospheric CO(2) concentration and tropical temperature, this relationship provides a tight constraint on the sensitivity of tropical land carbon to climate change. We estimate that over tropical land from latitude 30° north to 30° south, warming alone will release 53 ± 17 gigatonnes of carbon per kelvin. Compared with the unconstrained ensemble of climate-carbon-cycle projections, this indicates a much lower risk of Amazon forest dieback under CO(2)-induced climate change if CO(2) fertilization effects are as large as suggested by current models. Our study, however, also implies greater certainty that carbon will be lost from tropical land if warming arises from reductions in aerosols or increases in other greenhouse gases.

Regional climate model sensitivity to domain size

Energy Technology Data Exchange (ETDEWEB)

Leduc, Martin [Universite du Quebec a Montreal, Canadian Regional Climate Modelling and Diagnostics (CRCMD) Network, ESCER Centre, Montreal (Canada); UQAM/Ouranos, Montreal, QC (Canada); Laprise, Rene [Universite du Quebec a Montreal, Canadian Regional Climate Modelling and Diagnostics (CRCMD) Network, ESCER Centre, Montreal (Canada)

2009-05-15

Regional climate models are increasingly used to add small-scale features that are not present in their lateral boundary conditions (LBC). It is well known that the limited area over which a model is integrated must be large enough to allow the full development of small-scale features. On the other hand, integrations on very large domains have shown important departures from the driving data, unless large scale nudging is applied. The issue of domain size is studied here by using the ''perfect model'' approach. This method consists first of generating a high-resolution climatic simulation, nicknamed big brother (BB), over a large domain of integration. The next step is to degrade this dataset with a low-pass filter emulating the usual coarse-resolution LBC. The filtered nesting data (FBB) are hence used to drive a set of four simulations (LBs for Little Brothers), with the same model, but on progressively smaller domain sizes. The LB statistics for a climate sample of four winter months are compared with BB over a common region. The time average (stationary) and transient-eddy standard deviation patterns of the LB atmospheric fields generally improve in terms of spatial correlation with the reference (BB) when domain gets smaller. The extraction of the small-scale features by using a spectral filter allows detecting important underestimations of the transient-eddy variability in the vicinity of the inflow boundary, which can penalize the use of small domains (less than 100 x 100 grid points). The permanent ''spatial spin-up'' corresponds to the characteristic distance that the large-scale flow needs to travel before developing small-scale features. The spin-up distance tends to grow in size at higher levels in the atmosphere. (orig.)

Global Sensitivity of Simulated Water Balance Indicators Under Future Climate Change in the Colorado Basin

Science.gov (United States)

Bennett, Katrina E.; Urrego Blanco, Jorge R.; Jonko, Alexandra; Bohn, Theodore J.; Atchley, Adam L.; Urban, Nathan M.; Middleton, Richard S.

2018-01-01

The Colorado River Basin is a fundamentally important river for society, ecology, and energy in the United States. Streamflow estimates are often provided using modeling tools which rely on uncertain parameters; sensitivity analysis can help determine which parameters impact model results. Despite the fact that simulated flows respond to changing climate and vegetation in the basin, parameter sensitivity of the simulations under climate change has rarely been considered. In this study, we conduct a global sensitivity analysis to relate changes in runoff, evapotranspiration, snow water equivalent, and soil moisture to model parameters in the Variable Infiltration Capacity (VIC) hydrologic model. We combine global sensitivity analysis with a space-filling Latin Hypercube Sampling of the model parameter space and statistical emulation of the VIC model to examine sensitivities to uncertainties in 46 model parameters following a variance-based approach. We find that snow-dominated regions are much more sensitive to uncertainties in VIC parameters. Although baseflow and runoff changes respond to parameters used in previous sensitivity studies, we discover new key parameter sensitivities. For instance, changes in runoff and evapotranspiration are sensitive to albedo, while changes in snow water equivalent are sensitive to canopy fraction and Leaf Area Index (LAI) in the VIC model. It is critical for improved modeling to narrow uncertainty in these parameters through improved observations and field studies. This is important because LAI and albedo are anticipated to change under future climate and narrowing uncertainty is paramount to advance our application of models such as VIC for water resource management.

Regional climate model sensitivity to domain size

Science.gov (United States)

Leduc, Martin; Laprise, René

2009-05-01

Regional climate models are increasingly used to add small-scale features that are not present in their lateral boundary conditions (LBC). It is well known that the limited area over which a model is integrated must be large enough to allow the full development of small-scale features. On the other hand, integrations on very large domains have shown important departures from the driving data, unless large scale nudging is applied. The issue of domain size is studied here by using the “perfect model” approach. This method consists first of generating a high-resolution climatic simulation, nicknamed big brother (BB), over a large domain of integration. The next step is to degrade this dataset with a low-pass filter emulating the usual coarse-resolution LBC. The filtered nesting data (FBB) are hence used to drive a set of four simulations (LBs for Little Brothers), with the same model, but on progressively smaller domain sizes. The LB statistics for a climate sample of four winter months are compared with BB over a common region. The time average (stationary) and transient-eddy standard deviation patterns of the LB atmospheric fields generally improve in terms of spatial correlation with the reference (BB) when domain gets smaller. The extraction of the small-scale features by using a spectral filter allows detecting important underestimations of the transient-eddy variability in the vicinity of the inflow boundary, which can penalize the use of small domains (less than 100 × 100 grid points). The permanent “spatial spin-up” corresponds to the characteristic distance that the large-scale flow needs to travel before developing small-scale features. The spin-up distance tends to grow in size at higher levels in the atmosphere.

Modeling Uncertainty in Climate Change: A Multi-Model Comparison

Energy Technology Data Exchange (ETDEWEB)

Gillingham, Kenneth; Nordhaus, William; Anthoff, David; Blanford, Geoffrey J.; Bosetti, Valentina; Christensen, Peter; McJeon, Haewon C.; Reilly, J. M.; Sztorc, Paul

2015-10-01

The economics of climate change involves a vast array of uncertainties, complicating both the analysis and development of climate policy. This study presents the results of the first comprehensive study of uncertainty in climate change using multiple integrated assessment models. The study looks at model and parametric uncertainties for population, total factor productivity, and climate sensitivity and estimates the pdfs of key output variables, including CO₂ concentrations, temperature, damages, and the social cost of carbon (SCC). One key finding is that parametric uncertainty is more important than uncertainty in model structure. Our resulting pdfs also provide insight on tail events.

Reconstructing Holocene climate using a climate model: Model strategy and preliminary results

Science.gov (United States)

Haberkorn, K.; Blender, R.; Lunkeit, F.; Fraedrich, K.

2009-04-01

An Earth system model of intermediate complexity (Planet Simulator; PlaSim) is used to reconstruct Holocene climate based on proxy data. The Planet Simulator is a user friendly general circulation model (GCM) suitable for palaeoclimate research. Its easy handling and the modular structure allow for fast and problem dependent simulations. The spectral model is based on the moist primitive equations conserving momentum, mass, energy and moisture. Besides the atmospheric part, a mixed layer-ocean with sea ice and a land surface with biosphere are included. The present-day climate of PlaSim, based on an AMIP II control-run (T21/10L resolution), shows reasonable agreement with ERA-40 reanalysis data. Combining PlaSim with a socio-technological model (GLUES; DFG priority project INTERDYNAMIK) provides improved knowledge on the shift from hunting-gathering to agropastoral subsistence societies. This is achieved by a data assimilation approach, incorporating proxy time series into PlaSim to initialize palaeoclimate simulations during the Holocene. For this, the following strategy is applied: The sensitivities of the terrestrial PlaSim climate are determined with respect to sea surface temperature (SST) anomalies. Here, the focus is the impact of regionally varying SST both in the tropics and the Northern Hemisphere mid-latitudes. The inverse of these sensitivities is used to determine the SST conditions necessary for the nudging of land and coastal proxy climates. Preliminary results indicate the potential, the uncertainty and the limitations of the method.

Hydrological Sensitivity of Land Use Scenarios for Climate Mitigation

Science.gov (United States)

Boegh, E.; Friborg, T.; Hansen, K.; Jensen, R.; Seaby, L. P.

2014-12-01

Bringing atmospheric concentration to 550 ppm CO2 or below by 2100 will require large-scale changes to global and national energy systems, and potentially the use of land (IPCC, 2013) The Danish government aims at reducing greenhouse gas emissions (GHG) by 40 % in 1990-2020 and energy consumption to be based on 100 % renewable energy by 2035. By 2050, GHG emissions should be reduced by 80-95 %. Strategies developed to reach these goals require land use change to increase the production of biomass for bioenergy, further use of catch crops, reduced nitrogen inputs in agriculture, reduced soil tillage, afforestation and establishment of permanent grass fields. Currently, solar radiation in the growing season is not fully exploited, and it is expected that biomass production for bioenergy can be supported without reductions in food and fodder production. Impacts of climate change on the hydrological sensitivity of biomass growth and soil carbon storage are however not known. The present study evaluates the hydrological sensitivity of Danish land use options for climate mitigation in terms of crop yields (including straw for bioenergy) and net CO2 exchange for wheat, barley, maize and clover under current and future climate conditions. Hydrological sensitivity was evaluated using the agrohydrological model Daisy. Simulations during current climate conditions were in good agreement with measured dry matter, crop nitrogen content and eddy covariance fluxes of water vapour and CO2. Climate scenarios from the European ENSEMBLES database were downscaled for simulating water, nitrogen and carbon balance for 2071-2100. The biomass potential generally increase, but water stress also increases in strength and extends over a longer period, thereby increasing sensitivity to water availability. The potential of different land use scenarios to maximize vegetation cover and biomass for climate mitigation is further discussed in relation to impacts on the energy- and water balance.

The importance of mixed-phase clouds for climate sensitivity in the global aerosol-climate model ECHAM6-HAM2

OpenAIRE

Lohmann, Ulrike; Neubauer, David

2018-01-01

Clouds are important in the climate system because of their large influence on the radiation budget. On the one hand, they scatter solar radiation and with that cool the climate. On the other hand, they absorb and re-emit terrestrial radiation, which causes a warming. How clouds change in a warmer climate is one of the largest uncertainties for the equilibrium climate sensitivity (ECS). While a large spread in the cloud feedback arises from low-level clouds, it was recently shown that also mi...

Sensitivity properties of a biosphere model based on BATS and a statistical-dynamical climate model

Energy Technology Data Exchange (ETDEWEB)

Zhang, T. (Yale Univ., New Haven, CT (United States))

1994-06-01

A biosphere model based on the Biosphere-Atmosphere Transfer Scheme (BATS) and the Saltzman-Vernekar (SV) statistical-dynamical climate model is developed. Some equations of BATS are adopted either intact or with modifications, some are conceptually modified, and still others are replaced with equations of the SV model. The model is designed so that it can be run independently as long as the parameters related to the physiology and physiognomy of the vegetation, the atmospheric conditions, solar radiation, and soil conditions are given. With this stand-alone biosphere model, a series of sensitivity investigations, particularly the model sensitivity to fractional area of vegetation cover, soil surface water availability, and solar radiation for different types of vegetation, were conducted as a first step. These numerical experiments indicate that the presence of a vegetation cover greatly enhances the exchanges of momentum, water vapor, and energy between the atmosphere and the surface of the earth. An interesting result is that a dense and thick vegetation cover tends to serve as an environment conditioner or, more specifically, a thermostat and a humidistat, since the soil surface temperature, foliage temperature, and temperature and vapor pressure of air within the foliage are practically insensitive to variation of soil surface water availability and even solar radiation within a wide range. An attempt is also made to simulate the gradual deterioration of environment accompanying gradual degradation of a tropical forest to grasslands. Comparison with field data shows that this model can realistically simulate the land surface processes involving biospheric variations. 46 refs., 10 figs., 6 tabs.

An investigation of the sensitivity of a land surface model to climate change using a reduced form model

Energy Technology Data Exchange (ETDEWEB)

Lynch, A.H.; McIlwaine, S. [PAOS/CIRES, Univ. of Colorado, Boulder, CO (United States); Beringer, J. [Inst. of Arctic Biology, Univ. of Alaska, Fairbanks (United States); Bonan, G.B. [National Center for Atmospheric Research, Boulder, CO (United States)

2001-05-01

In an illustration of a model evaluation methodology, a multivariate reduced form model is developed to evaluate the sensitivity of a land surface model to changes in atmospheric forcing. The reduced form model is constructed in terms of a set of ten integrative response metrics, including the timing of spring snow melt, sensible and latent heat fluxes in summer, and soil temperature. The responses are evaluated as a function of a selected set of six atmospheric forcing perturbations which are varied simultaneously, and hence each may be thought of as a six-dimensional response surface. The sensitivities of the land surface model are interdependent and in some cases illustrate a physically plausible feedback process. The important predictors of land surface response in a changing climate are the atmospheric temperature and downwelling longwave radiation. Scenarios characterized by warming and drying produce a large relative response compared to warm, moist scenarios. The insensitivity of the model to increases in precipitation and atmospheric humidity is expected to change in applications to coupled models, since these parameters are also strongly implicated, through the representation of clouds, in the simulation of both longwave and shortwave radiation. (orig.)

The Influence of Climate Change on Atmospheric Deposition of Mercury in the Arctic—A Model Sensitivity Study

Science.gov (United States)

Hansen, Kaj M.; Christensen, Jesper H.; Brandt, Jørgen

2015-01-01

Mercury (Hg) is a global pollutant with adverse health effects on humans and wildlife. It is of special concern in the Arctic due to accumulation in the food web and exposure of the Arctic population through a rich marine diet. Climate change may alter the exposure of the Arctic population to Hg. We have investigated the effect of climate change on the atmospheric Hg transport to and deposition within the Arctic by making a sensitivity study of how the atmospheric chemistry-transport model Danish Eulerian Hemispheric Model (DEHM) reacts to climate change forcing. The total deposition of Hg to the Arctic is 18% lower in the 2090s compared to the 1990s under the applied Special Report on Emissions Scenarios (SRES-A1B) climate scenario. Asia is the major anthropogenic source area (25% of the deposition to the Arctic) followed by Europe (6%) and North America (5%), with the rest arising from the background concentration, and this is independent of the climate. DEHM predicts between a 6% increase (Status Quo scenario) and a 37% decrease (zero anthropogenic emissions scenario) in Hg deposition to the Arctic depending on the applied emission scenario, while the combined effect of future climate and emission changes results in up to 47% lower Hg deposition. PMID:26378551

Empirically Derived and Simulated Sensitivity of Vegetation to Climate Across Global Gradients of Temperature and Precipitation

Science.gov (United States)

Quetin, G. R.; Swann, A. L. S.

2017-12-01

Successfully predicting the state of vegetation in a novel environment is dependent on our process level understanding of the ecosystem and its interactions with the environment. We derive a global empirical map of the sensitivity of vegetation to climate using the response of satellite-observed greenness and leaf area to interannual variations in temperature and precipitation. Our analysis provides observations of ecosystem functioning; the vegetation interactions with the physical environment, across a wide range of climates and provide a functional constraint for hypotheses engendered in process-based models. We infer mechanisms constraining ecosystem functioning by contrasting how the observed and simulated sensitivity of vegetation to climate varies across climate space. Our analysis yields empirical evidence for multiple physical and biological mediators of the sensitivity of vegetation to climate as a systematic change across climate space. Our comparison of remote sensing-based vegetation sensitivity with modeled estimates provides evidence for which physiological mechanisms - photosynthetic efficiency, respiration, water supply, atmospheric water demand, and sunlight availability - dominate the ecosystem functioning in places with different climates. Earth system models are generally successful in reproducing the broad sign and shape of ecosystem functioning across climate space. However, this general agreement breaks down in hot wet climates where models simulate less leaf area during a warmer year, while observations show a mixed response but overall more leaf area during warmer years. In addition, simulated ecosystem interaction with temperature is generally larger and changes more rapidly across a gradient of temperature than is observed. We hypothesize that the amplified interaction and change are both due to a lack of adaptation and acclimation in simulations. This discrepancy with observations suggests that simulated responses of vegetation to

A Data-Driven Assessment of the Sensitivity of Global Ecosystems to Climate Anomalies

Science.gov (United States)

Miralles, D. G.; Papagiannopoulou, C.; Demuzere, M.; Decubber, S.; Waegeman, W.; Verhoest, N.; Dorigo, W.

2017-12-01

Vegetation is a central player in the climate system, constraining atmospheric conditions through a series of feedbacks. This fundamental role highlights the importance of understanding regional drivers of ecological sensitivity and the response of vegetation to climatic changes. While nutrient availability and short-term disturbances can be crucial for vegetation at various spatiotemporal scales, natural vegetation dynamics are overall driven by climate. At monthly scales, the interactions between vegetation and climate become complex: some vegetation types react preferentially to specific climatic changes, with different levels of intensity, resilience and lagged response. For our current Earth System Models (ESMs) being able to capture this complexity is crucial but extremely challenging. This adds uncertainty to our projections of future climate and the fate of global ecosystems. Here, following a Granger causality framework based on a non-linear random forest predictive model, we exploit the current wealth of satellite data records to uncover the main climatic drivers of monthly vegetation variability globally. Results based on three decades of satellite data indicate that water availability is the most dominant factor driving vegetation in over 60% of the vegetated land. This overall dependency of ecosystems on water availability is larger than previously reported, partly owed to the ability of our machine-learning framework to disentangle the co-linearites between climatic drivers, and to quantify non-linear impacts of climate on vegetation. Our observation-based results are then used to benchmark ESMs on their representation of vegetation sensitivity to climate and climatic extremes. Our findings indicate that the sensitivity of vegetation to climatic anomalies is ill-reproduced by some widely-used ESMs.

State-Dependence of the Climate Sensitivity in Earth System Models of Intermediate Complexity

Science.gov (United States)

Pfister, Patrik L.; Stocker, Thomas F.

2017-10-01

Growing evidence from general circulation models (GCMs) indicates that the equilibrium climate sensitivity (ECS) depends on the magnitude of forcing, which is commonly referred to as state-dependence. We present a comprehensive assessment of ECS state-dependence in Earth system models of intermediate complexity (EMICs) by analyzing millennial simulations with sustained 2×CO2 and 4×CO2 forcings. We compare different extrapolation methods and show that ECS is smaller in the higher-forcing scenario in 12 out of 15 EMICs, in contrast to the opposite behavior reported from GCMs. In one such EMIC, the Bern3D-LPX model, this state-dependence is mainly due to the weakening sea ice-albedo feedback in the Southern Ocean, which depends on model configuration. Due to ocean-mixing adjustments, state-dependence is only detected hundreds of years after the abrupt forcing, highlighting the need for long model integrations. Adjustments to feedback parametrizations of EMICs may be necessary if GCM intercomparisons confirm an opposite state-dependence.

Sensitivity of a carbon and productivity model to climatic, water, terrain, and biophysical parameters in a Rocky Mountain watershed

Energy Technology Data Exchange (ETDEWEB)

Xu, S.; Peddle, D.R.; Coburn, C.A.; Kienzle, S. [Univ. of Lethbridge, Dept. of Geography, Lethbridge, Alberta (Canada)

2008-06-15

Net primary productivity (NPP) is a key component of the terrestrial carbon cycle and is important in ecological, watershed, and forest management studies, and more broadly in global climate change research. Determining the relative importance and magnitude of uncertainty of NPP model inputs is important for proper carbon reporting over larger areas and time periods. This paper presents a systematic evaluation of the boreal ecosystem productivity simulator (BEPS) model in mountainous terrain using an established montane forest test site in Kananaskis, Alberta, in the Canadian Rocky Mountains. Model runs were based on forest (land cover, leaf area index (LAI), biomass) and climate-water inputs (solar radiation, temperature, precipitation, humidity, soil water holding capacity) derived from digital elevation model (DEM) derivatives, climate data, geographical information system (GIS) functions, and topographically corrected satellite imagery. Four sensitivity analyses were conducted as a controlled series of experiments involving (i) NPP individual parameter sensitivity for a full growing season, (ii) NPP independent variation tests (parameter {mu} {+-} 1{sigma}), (iii) factorial analyses to assess more complex multiple-factor interactions, and (iv) topographic correction. The results, validated against field measurements, showed that modeled NPP was sensitive to most inputs measured in the study area, with LAI and forest type the most important forest input, and solar radiation the most important climate input. Soil available water holding capacity expressed as a function of wetness index was only significant in conjunction with precipitation when both parameters represented a moisture-deficit situation. NPP uncertainty resulting from topographic influence was equivalent to 140 kg C ha{sup -1}{center_dot}year{sup -1}. This suggested that topographic correction of model inputs is important for accurate NPP estimation. The BEPS model, designed originally for flat

Sensitivity of a carbon and productivity model to climatic, water, terrain, and biophysical parameters in a Rocky Mountain watershed

International Nuclear Information System (INIS)

Xu, S.; Peddle, D.R.; Coburn, C.A.; Kienzle, S.

2008-01-01

Net primary productivity (NPP) is a key component of the terrestrial carbon cycle and is important in ecological, watershed, and forest management studies, and more broadly in global climate change research. Determining the relative importance and magnitude of uncertainty of NPP model inputs is important for proper carbon reporting over larger areas and time periods. This paper presents a systematic evaluation of the boreal ecosystem productivity simulator (BEPS) model in mountainous terrain using an established montane forest test site in Kananaskis, Alberta, in the Canadian Rocky Mountains. Model runs were based on forest (land cover, leaf area index (LAI), biomass) and climate-water inputs (solar radiation, temperature, precipitation, humidity, soil water holding capacity) derived from digital elevation model (DEM) derivatives, climate data, geographical information system (GIS) functions, and topographically corrected satellite imagery. Four sensitivity analyses were conducted as a controlled series of experiments involving (i) NPP individual parameter sensitivity for a full growing season, (ii) NPP independent variation tests (parameter μ ± 1σ), (iii) factorial analyses to assess more complex multiple-factor interactions, and (iv) topographic correction. The results, validated against field measurements, showed that modeled NPP was sensitive to most inputs measured in the study area, with LAI and forest type the most important forest input, and solar radiation the most important climate input. Soil available water holding capacity expressed as a function of wetness index was only significant in conjunction with precipitation when both parameters represented a moisture-deficit situation. NPP uncertainty resulting from topographic influence was equivalent to 140 kg C ha -1 ·year -1 . This suggested that topographic correction of model inputs is important for accurate NPP estimation. The BEPS model, designed originally for flat boreal forests, was shown to be

Economics of climate change : sensitivity analysis of social cost of carbon

OpenAIRE

Torniainen, Sami

2016-01-01

Social cost of carbon (SCC) is the key concept in the economics of climate change. It measures the economic cost of climate impacts. SCC has influence on how beneficial it is to prevent climate change: if the value of SCC increases, investments to low-carbon technology become more attractive and profitable. This paper examines the sensitivity of two important assumptions that affect to SCC: the choice of a discount rate and time horizon. Using the integrated assessment model, ...

Sensitivity of ocean acidification and oxygen to the uncertainty in climate change

International Nuclear Information System (INIS)

Cao, Long; Wang, Shuangjing; Zheng, Meidi; Zhang, Han

2014-01-01

Due to increasing atmospheric CO 2 concentrations and associated climate change, the global ocean is undergoing substantial physical and biogeochemical changes. Among these, changes in ocean oxygen and carbonate chemistry have great implication for marine biota. There is considerable uncertainty in the projections of future climate change, and it is unclear how the uncertainty in climate change would also affect the projection of oxygen and carbonate chemistry. To investigate this issue, we use an Earth system model of intermediate complexity to perform a set of simulations, including that which involves no radiative effect of atmospheric CO 2 and those which involve CO 2 -induced climate change with climate sensitivity varying from 0.5 °C to 4.5 °C. Atmospheric CO 2 concentration is prescribed to follow RCP 8.5 pathway and its extensions. Climate change affects carbonate chemistry and oxygen mainly through its impact on ocean temperature, ocean ventilation, and concentration of dissolved inorganic carbon and alkalinity. It is found that climate change mitigates the decrease of carbonate ions at the ocean surface but has negligible effect on surface ocean pH. Averaged over the whole ocean, climate change acts to decrease oxygen concentration but mitigates the CO 2 -induced reduction of carbonate ion and pH. In our simulations, by year 2500, every degree increase of climate sensitivity warms the ocean by 0.8 °C and reduces ocean-mean dissolved oxygen concentration by 5.0%. Meanwhile, every degree increase of climate sensitivity buffers CO 2 -induced reduction in ocean-mean carbonate ion concentration and pH by 3.4% and 0.02 units, respectively. Our study demonstrates different sensitivities of ocean temperature, carbonate chemistry, and oxygen, in terms of both the sign and magnitude to the amount of climate change, which have great implications for understanding the response of ocean biota to climate change. (letters)

Improved Analysis of Earth System Models and Observations using Simple Climate Models

Science.gov (United States)

Nadiga, B. T.; Urban, N. M.

2016-12-01

Earth system models (ESM) are the most comprehensive tools we have to study climate change and develop climate projections. However, the computational infrastructure required and the cost incurred in running such ESMs precludes direct use of such models in conjunction with a wide variety of tools that can further our understanding of climate. Here we are referring to tools that range from dynamical systems tools that give insight into underlying flow structure and topology to tools that come from various applied mathematical and statistical techniques and are central to quantifying stability, sensitivity, uncertainty and predictability to machine learning tools that are now being rapidly developed or improved. Our approach to facilitate the use of such models is to analyze output of ESM experiments (cf. CMIP) using a range of simpler models that consider integral balances of important quantities such as mass and/or energy in a Bayesian framework.We highlight the use of this approach in the context of the uptake of heat by the world oceans in the ongoing global warming. Indeed, since in excess of 90% of the anomalous radiative forcing due greenhouse gas emissions is sequestered in the world oceans, the nature of ocean heat uptake crucially determines the surface warming that is realized (cf. climate sensitivity). Nevertheless, ESMs themselves are never run long enough to directly assess climate sensitivity. So, we consider a range of models based on integral balances--balances that have to be realized in all first-principles based models of the climate system including the most detailed state-of-the art climate simulations. The models range from simple models of energy balance to those that consider dynamically important ocean processes such as the conveyor-belt circulation (Meridional Overturning Circulation, MOC), North Atlantic Deep Water (NADW) formation, Antarctic Circumpolar Current (ACC) and eddy mixing. Results from Bayesian analysis of such models using

Sensitivity of tropical climate to low-level clouds in the NCEP climate forecast system

Energy Technology Data Exchange (ETDEWEB)

Hu, Zeng-Zhen [Center for Ocean-Land-Atmosphere Studies, Calverton, MD (United States); NCEP/NWS/NOAA, Climate Prediction Center, Camp Springs, MD (United States); Huang, Bohua; Schneider, Edwin K. [Center for Ocean-Land-Atmosphere Studies, Calverton, MD (United States); George Mason University, Department of Atmospheric, Oceanic, and Earth Sciences, College of Science, Fairfax, VA (United States); Hou, Yu-Tai; Yang, Fanglin [NCEP/NWS/NOAA, Environmental Modeling Center, Camp Springs, MD (United States); Wang, Wanqiu [NCEP/NWS/NOAA, Climate Prediction Center, Camp Springs, MD (United States); Stan, Cristiana [Center for Ocean-Land-Atmosphere Studies, Calverton, MD (United States)

2011-05-15

In this work, we examine the sensitivity of tropical mean climate and seasonal cycle to low clouds and cloud liquid water path (CLWP) by prescribing them in the NCEP climate forecast system (CFS). It is found that the change of low cloud cover alone has a minor influence on the amount of net shortwave radiation reaching the surface and on the warm biases in the southeastern Atlantic. In experiments where CLWP is prescribed using observations, the mean climate in the tropics is improved significantly, implying that shortwave radiation absorption by CLWP is mainly responsible for reducing the excessive surface net shortwave radiation over the southern oceans in the CFS. Corresponding to large CLWP values in the southeastern oceans, the model generates large low cloud amounts. That results in a reduction of net shortwave radiation at the ocean surface and the warm biases in the sea surface temperature in the southeastern oceans. Meanwhile, the cold tongue and associated surface wind stress in the eastern oceans become stronger and more realistic. As a consequence of the overall improvement of the tropical mean climate, the seasonal cycle in the tropical Atlantic is also improved. Based on the results from these sensitivity experiments, we propose a model bias correction approach, in which CLWP is prescribed only in the southeastern Atlantic by using observed annual mean climatology of CLWP. It is shown that the warm biases in the southeastern Atlantic are largely eliminated, and the seasonal cycle in the tropical Atlantic Ocean is significantly improved. Prescribing CLWP in the CFS is then an effective interim technique to reduce model biases and to improve the simulation of seasonal cycle in the tropics. (orig.)

Climate of the last millennium: a sensitivity study

Energy Technology Data Exchange (ETDEWEB)

Bertrand, Cedric [Royal Meterological Inst. of Belgium, Brussels (Belgium); Loutre, Marie-France; Crucifix, Michel; Berger, Andre [Univ. catholique de Louvain, Louvain la-neuve (Belgium). Inst. d' Astronomie et de Geophysique G. Lemaitre

2002-05-01

Seventy-one sensitivity experiments have been performed using a two-dimensional sector-averaged global climate model to assess the potential impact of six different factors on the last millennium climate and in particular on the surface air temperature evolution. Both natural (i.e. solar and volcanism) and anthropogenically-induced (i.e. deforestation, additional greenhouse gases, and tropospheric aerosol burden) climate forcings have been considered. Comparisons of climate reconstructions with model results indicate that all the investigated forcings are needed to simulate the surface air temperature evolution. Due to uncertainties in historical climate forcings and temperature reconstructions, the relative importance of a particular forcing in the explanation of the recorded temperature variance is largely function of the forcing time series used. Nevertheless, our results indicate that whatever the historical solar and volcanic reconstructions may be, these externally driven natural climate forcings are unable to give climate responses comparable in magnitude and time to the late-2Oth-century temperature warming while for earlier periods combination of solar and volcanic forcings can explain the Little Ice Age and the Medieval Warm Period. Only the greenhouse gas forcing allows the model to simulate an accelerated warming rate during the last three decades. The best guess simulation (largest similarity with the reconstruction) for the period starting 1850 AD requires however to include anthropogenic sulphate forcing as well as the impact of deforestation to constrain the magnitude of the greenhouse gas twentieth century warming to better fit the observation. On the contrary, prior to 1850 AD mid-latitude land clearance tends to reinforce the Little Ice age in our simulations.

The Role of Sea Ice in 2 x CO2 Climate Model Sensitivity. Part 2; Hemispheric Dependencies

Science.gov (United States)

Rind, D.; Healy, R.; Parkinson, C.; Martinson, D.

1997-01-01

How sensitive are doubled CO2 simulations to GCM control-run sea ice thickness and extent? This issue is examined in a series of 10 control-run simulations with different sea ice and corresponding doubled CO2 simulations. Results show that with increased control-run sea ice coverage in the Southern Hemisphere, temperature sensitivity with climate change is enhanced, while there is little effect on temperature sensitivity of (reasonable) variations in control-run sea ice thickness. In the Northern Hemisphere the situation is reversed: sea ice thickness is the key parameter, while (reasonable) variations in control-run sea ice coverage are of less importance. In both cases, the quantity of sea ice that can be removed in the warmer climate is the determining factor. Overall, the Southern Hemisphere sea ice coverage change had a larger impact on global temperature, because Northern Hemisphere sea ice was sufficiently thick to limit its response to doubled CO2, and sea ice changes generally occurred at higher latitudes, reducing the sea ice-albedo feedback. In both these experiments and earlier ones in which sea ice was not allowed to change, the model displayed a sensitivity of -0.02 C global warming per percent change in Southern Hemisphere sea ice coverage.

The Early Eocene equable climate problem: can perturbations of climate model parameters identify possible solutions?

Science.gov (United States)

Sagoo, Navjit; Valdes, Paul; Flecker, Rachel; Gregoire, Lauren J

2013-10-28

Geological data for the Early Eocene (56-47.8 Ma) indicate extensive global warming, with very warm temperatures at both poles. However, despite numerous attempts to simulate this warmth, there are remarkable data-model differences in the prediction of these polar surface temperatures, resulting in the so-called 'equable climate problem'. In this paper, for the first time an ensemble with a perturbed climate-sensitive model parameters approach has been applied to modelling the Early Eocene climate. We performed more than 100 simulations with perturbed physics parameters, and identified two simulations that have an optimal fit with the proxy data. We have simulated the warmth of the Early Eocene at 560 ppmv CO2, which is a much lower CO2 level than many other models. We investigate the changes in atmospheric circulation, cloud properties and ocean circulation that are common to these simulations and how they differ from the remaining simulations in order to understand what mechanisms contribute to the polar warming. The parameter set from one of the optimal Early Eocene simulations also produces a favourable fit for the last glacial maximum boundary climate and outperforms the control parameter set for the present day. Although this does not 'prove' that this model is correct, it is very encouraging that there is a parameter set that creates a climate model able to simulate well very different palaeoclimates and the present-day climate. Interestingly, to achieve the great warmth of the Early Eocene this version of the model does not have a strong future climate change Charney climate sensitivity. It produces a Charney climate sensitivity of 2.7(°)C, whereas the mean value of the 18 models in the IPCC Fourth Assessment Report (AR4) is 3.26(°)C±0.69(°)C. Thus, this value is within the range and below the mean of the models included in the AR4.

Separating sensitivity from exposure in assessing extinction risk from climate change.

Science.gov (United States)

Dickinson, Maria G; Orme, C David L; Suttle, K Blake; Mace, Georgina M

2014-11-04

Predictive frameworks of climate change extinction risk generally focus on the magnitude of climate change a species is expected to experience and the potential for that species to track suitable climate. A species' risk of extinction from climate change will depend, in part, on the magnitude of climate change the species experiences, its exposure. However, exposure is only one component of risk. A species' risk of extinction will also depend on its intrinsic ability to tolerate changing climate, its sensitivity. We examine exposure and sensitivity individually for two example taxa, terrestrial amphibians and mammals. We examine how these factors are related among species and across regions and how explicit consideration of each component of risk may affect predictions of climate change impacts. We find that species' sensitivities to climate change are not congruent with their exposures. Many highly sensitive species face low exposure to climate change and many highly exposed species are relatively insensitive. Separating sensitivity from exposure reveals patterns in the causes and drivers of species' extinction risk that may not be evident solely from predictions of climate change. Our findings emphasise the importance of explicitly including sensitivity and exposure to climate change in assessments of species' extinction risk.

Desert dust and anthropogenic aerosol interactions in the Community Climate System Model coupled-carbon-climate model

Directory of Open Access Journals (Sweden)

N. Mahowald

2011-02-01

Full Text Available Coupled-carbon-climate simulations are an essential tool for predicting the impact of human activity onto the climate and biogeochemistry. Here we incorporate prognostic desert dust and anthropogenic aerosols into the CCSM3.1 coupled carbon-climate model and explore the resulting interactions with climate and biogeochemical dynamics through a series of transient anthropogenic simulations (20th and 21st centuries and sensitivity studies. The inclusion of prognostic aerosols into this model has a small net global cooling effect on climate but does not significantly impact the globally averaged carbon cycle; we argue that this is likely to be because the CCSM3.1 model has a small climate feedback onto the carbon cycle. We propose a mechanism for including desert dust and anthropogenic aerosols into a simple carbon-climate feedback analysis to explain the results of our and previous studies. Inclusion of aerosols has statistically significant impacts on regional climate and biogeochemistry, in particular through the effects on the ocean nitrogen cycle and primary productivity of altered iron inputs from desert dust deposition.

Computer experiments with a coarse-grid hydrodynamic climate model

International Nuclear Information System (INIS)

Stenchikov, G.L.

1990-01-01

A climate model is developed on the basis of the two-level Mintz-Arakawa general circulation model of the atmosphere and a bulk model of the upper layer of the ocean. A detailed model of the spectral transport of shortwave and longwave radiation is used to investigate the radiative effects of greenhouse gases. The radiative fluxes are calculated at the boundaries of five layers, each with a pressure thickness of about 200 mb. The results of the climate sensitivity calculations for mean-annual and perpetual seasonal regimes are discussed. The CCAS (Computer Center of the Academy of Sciences) climate model is used to investigate the climatic effects of anthropogenic changes of the optical properties of the atmosphere due to increasing CO 2 content and aerosol pollution, and to calculate the sensitivity to changes of land surface albedo and humidity

The influence of extratropical cloud phase and amount feedbacks on climate sensitivity

Science.gov (United States)

Frey, William R.; Kay, Jennifer E.

2018-04-01

Global coupled climate models have large long-standing cloud and radiation biases, calling into question their ability to simulate climate and climate change. This study assesses the impact of reducing shortwave radiation biases on climate sensitivity within the Community Earth System Model (CESM). The model is modified by increasing supercooled cloud liquid to better match absorbed shortwave radiation observations over the Southern Ocean while tuning to reduce a compensating tropical shortwave bias. With a thermodynamic mixed-layer ocean, equilibrium warming in response to doubled CO2 increases from 4.1 K in the control to 5.6 K in the modified model. This 1.5 K increase in equilibrium climate sensitivity is caused by changes in two extratropical shortwave cloud feedbacks. First, reduced conversion of cloud ice to liquid at high southern latitudes decreases the magnitude of a negative cloud phase feedback. Second, warming is amplified in the mid-latitudes by a larger positive shortwave cloud feedback. The positive cloud feedback, usually associated with the subtropics, arises when sea surface warming increases the moisture gradient between the boundary layer and free troposphere. The increased moisture gradient enhances the effectiveness of mixing to dry the boundary layer, which decreases cloud amount and optical depth. When a full-depth ocean with dynamics and thermodynamics is included, ocean heat uptake preferentially cools the mid-latitude Southern Ocean, partially inhibiting the positive cloud feedback and slowing warming. Overall, the results highlight strong connections between Southern Ocean mixed-phase cloud partitioning, cloud feedbacks, and ocean heat uptake in a climate forced by greenhouse gas changes.

Identifying a key physical factor sensitive to the performance of Madden-Julian oscillation simulation in climate models

Science.gov (United States)

Kim, Go-Un; Seo, Kyong-Hwan

2018-01-01

A key physical factor in regulating the performance of Madden-Julian oscillation (MJO) simulation is examined by using 26 climate model simulations from the World Meteorological Organization's Working Group for Numerical Experimentation/Global Energy and Water Cycle Experiment Atmospheric System Study (WGNE and MJO-Task Force/GASS) global model comparison project. For this, intraseasonal moisture budget equation is analyzed and a simple, efficient physical quantity is developed. The result shows that MJO skill is most sensitive to vertically integrated intraseasonal zonal wind convergence (ZC). In particular, a specific threshold value of the strength of the ZC can be used as distinguishing between good and poor models. An additional finding is that good models exhibit the correct simultaneous convection and large-scale circulation phase relationship. In poor models, however, the peak circulation response appears 3 days after peak rainfall, suggesting unfavorable coupling between convection and circulation. For an improving simulation of the MJO in climate models, we propose that this delay of circulation in response to convection needs to be corrected in the cumulus parameterization scheme.

Sensitivity of ring growth and carbon allocation to climatic variation vary within ponderosa pine trees.

Science.gov (United States)

Kerhoulas, Lucy P; Kane, Jeffrey M

2012-01-01

Most dendrochronological studies focus on cores sampled from standard positions (main stem, breast height), yet vertical gradients in hydraulic constraints and priorities for carbon allocation may contribute to different growth sensitivities with position. Using cores taken from five positions (coarse roots, breast height, base of live crown, mid-crown branch and treetop), we investigated how radial growth sensitivity to climate over the period of 1895-2008 varies by position within 36 large ponderosa pines (Pinus ponderosa Dougl.) in northern Arizona. The climate parameters investigated were Palmer Drought Severity Index, water year and monsoon precipitation, maximum annual temperature, minimum annual temperature and average annual temperature. For each study tree, we generated Pearson correlation coefficients between ring width indices from each position and six climate parameters. We also investigated whether the number of missing rings differed among positions and bole heights. We found that tree density did not significantly influence climatic sensitivity to any of the climate parameters investigated at any of the sample positions. Results from three types of analyses suggest that climatic sensitivity of tree growth varied with position height: (i) correlations of radial growth and climate variables consistently increased with height; (ii) model strength based on Akaike's information criterion increased with height, where treetop growth consistently had the highest sensitivity and coarse roots the lowest sensitivity to each climatic parameter; and (iii) the correlation between bole ring width indices decreased with distance between positions. We speculate that increased sensitivity to climate at higher positions is related to hydraulic limitation because higher positions experience greater xylem tensions due to gravitational effects that render these positions more sensitive to climatic stresses. The low sensitivity of root growth to all climatic variables

Sensitivity of simulated maize crop yields to regional climate in the Southwestern United States

Science.gov (United States)

Kim, S.; Myoung, B.; Stack, D.; Kim, J.; Hatzopoulos, N.; Kafatos, M.

2013-12-01

The sensitivity of maize yield to the regional climate in the Southwestern United States (SW US) has been investigated by using a crop-yield simulation model (APSIM) in conjunction with meteorological forcings (daily minimum and maximum temperature, precipitation, and radiation) from the North American Regional Reanalysis (NARR) dataset. The primary focus of this study is to look at the effects of interannual variations of atmospheric components on the crop productivity in the SW US over the 21-year period (1991 to 2011). First of all, characteristics and performance of APSIM was examined by comparing simulated maize yields with observed yields from United States Department of Agriculture (USDA) and the leaf-area index (LAI) from MODIS satellite data. Comparisons of the simulated maize yield with the available observations show that the crop model can reasonably reproduce observed maize yields. Sensitivity tests were performed to assess the relative contribution of each climate driver to regional crop yield. Sensitivity experiments show that potential crop production responds nonlinearly to climate drivers and the yield sensitivity varied among geographical locations depending on their mean climates. Lastly, a detailed analysis of both the spatial and temporal variations of each climate driver in the regions where maize is actually grown in three states (CA, AZ, and NV) in the SW US was performed.

Sensitivity of water resources in the Delaware River basin to climate variability and change

Science.gov (United States)

Ayers, Mark A.; Wolock, David M.; McCabe, Gregory J.; Hay, Lauren E.; Tasker, Gary D.

1994-01-01

Because of the greenhouse effect, projected increases in atmospheric carbon dioxide levels might cause global warming, which in turn could result in changes in precipitation patterns and evapotranspiration and in increases in sea level. This report describes the greenhouse effect; discusses the problems and uncertainties associated with the detection, prediction, and effects of climate change; and presents the results of sensitivity analyses of how climate change might affect water resources in the Delaware River basin. Sensitivity analyses suggest that potentially serious shortfalls of certain water resources in the basin could result if some scenarios for climate change come true . The results of model simulations of the basin streamflow demonstrate the difficulty in distinguishing the effects that climate change versus natural climate variability have on streamflow and water supply . The future direction of basin changes in most water resources, furthermore, cannot be precisely determined because of uncertainty in current projections of regional temperature and precipitation . This large uncertainty indicates that, for resource planning, information defining the sensitivities of water resources to a range of climate change is most relevant . The sensitivity analyses could be useful in developing contingency plans for evaluating and responding to changes, should they occur.

Emergent climate and CO2 sensitivities of net primary productivity in ecosystem models do not agree with empirical data in temperate forests of eastern North America.

Science.gov (United States)

Rollinson, Christine R; Liu, Yao; Raiho, Ann; Moore, David J P; McLachlan, Jason; Bishop, Daniel A; Dye, Alex; Matthes, Jaclyn H; Hessl, Amy; Hickler, Thomas; Pederson, Neil; Poulter, Benjamin; Quaife, Tristan; Schaefer, Kevin; Steinkamp, Jörg; Dietze, Michael C

2017-07-01

Ecosystem models show divergent responses of the terrestrial carbon cycle to global change over the next century. Individual model evaluation and multimodel comparisons with data have largely focused on individual processes at subannual to decadal scales. Thus far, data-based evaluations of emergent ecosystem responses to climate and CO 2 at multidecadal and centennial timescales have been rare. We compared the sensitivity of net primary productivity (NPP) to temperature, precipitation, and CO 2 in ten ecosystem models with the sensitivities found in tree-ring reconstructions of NPP and raw ring-width series at six temperate forest sites. These model-data comparisons were evaluated at three temporal extents to determine whether the rapid, directional changes in temperature and CO 2 in the recent past skew our observed responses to multiple drivers of change. All models tested here were more sensitive to low growing season precipitation than tree-ring NPP and ring widths in the past 30 years, although some model precipitation responses were more consistent with tree rings when evaluated over a full century. Similarly, all models had negative or no response to warm-growing season temperatures, while tree-ring data showed consistently positive effects of temperature. Although precipitation responses were least consistent among models, differences among models to CO 2 drive divergence and ensemble uncertainty in relative change in NPP over the past century. Changes in forest composition within models had no effect on climate or CO 2 sensitivity. Fire in model simulations reduced model sensitivity to climate and CO 2 , but only over the course of multiple centuries. Formal evaluation of emergent model behavior at multidecadal and multicentennial timescales is essential to reconciling model projections with observed ecosystem responses to past climate change. Future evaluation should focus on improved representation of disturbance and biomass change as well as the

Watershed sensitivity and hydrologic response to high-resolution climate model

Science.gov (United States)

Troin, M.; Caya, D.

2012-12-01

Global climate models (GCMs) are fundamental research tools to assess climate change impacts on water resources. Regional climate models (RCMs) are complementary to GCMs. The added benefit of RCMs for hydrological applications is still not well understood because watersheds respond differently to RCM experiments. It is expected that the new generation of RCMs improve the representation of climate processes making it more attractive for impact studies. Given the cost of RCMs, it is ascertain to identify whether high-resolution RCMs allow offering more details than what is simulated in GCMs or RCMs with coarser resolution to address impacts on water resources. This study aims to assess the added value of RCM with emphasis on using high-resolution climate models. More specifically is how the hydrological cycle is represented when the resolution in climate models is increased (45 vs 200km; 15 vs 45km). We used simulations from the Canadian RCM (CRCM) driven by reanalyses integrated on high-resolution domains (45 and 15km) and CRCM driven by multiple members of two GCMs (the Canadian CGCM3; the German ECHAM5) with a horizontal resolution of 45 km. CRCM data and data from their host GCMs are compared to observation over 1971-2000. Precipitation and temperature from CRCM and GCMs' simulations are inputted into the hydrological SWAT model to simulate streamflow in watersheds for the historical period. The selected watersheds are two basins in Quebec (QC) and one basin in British Columbia (BC), Canada. CRCM-45km driven by GCMs performs well in representing precipitation but shows a cold bias of 3.3°C. Such bias in temperature is more significant for the BC basin (4.5°C) due to the Rocky Mountains. For the CRCM-45km/GCM combination (CGCM3 or ECHAM5), comparable skills in reproducing the observed climate are identified even though CGCM3 analyzed alone provides more accurate indication of climatology in the basins than ECHAM5. When we compared to GCMs results, CRCM-45km

Climate sensitivity of marine energy

International Nuclear Information System (INIS)

Harrison, G.P.; Wallace, A.R.

2005-01-01

Marine energy has a significant role to play in lowering carbon emissions within the energy sector. Paradoxically, it may be susceptible to changes in climate that will result from rising carbon emissions. Wind patterns are expected to change and this will alter wave regimes. Despite a lack of definite proof of a link to global warming, wind and wave conditions have been changing over the past few decades. Changes in the wind and wave climate will affect offshore wind and wave energy conversion: where the resource is constrained, production and economic performance may suffer; alternatively, stormier climates may create survival issues. Here, a relatively simple sensitivity study is used to quantify how changes in mean wind speed - as a proxy for wider climate change - influence wind and wave energy production and economics. (author)

Estimations of climate sensitivity based on top-of-atmosphere radiation imbalance

Directory of Open Access Journals (Sweden)

B. Lin

2010-02-01

Full Text Available Large climate feedback uncertainties limit the accuracy in predicting the response of the Earth's climate to the increase of CO₂ concentration within the atmosphere. This study explores a potential to reduce uncertainties in climate sensitivity estimations using energy balance analysis, especially top-of-atmosphere (TOA radiation imbalance. The time-scales studied generally cover from decade to century, that is, middle-range climate sensitivity is considered, which is directly related to the climate issue caused by atmospheric CO₂ change. The significant difference between current analysis and previous energy balance models is that the current study targets at the boundary condition problem instead of solving the initial condition problem. Additionally, climate system memory and deep ocean heat transport are considered. The climate feedbacks are obtained based on the constraints of the TOA radiation imbalance and surface temperature measurements of the present climate. In this study, the TOA imbalance value of 0.85 W/m² is used. Note that this imbalance value has large uncertainties. Based on this value, a positive climate feedback with a feedback coefficient ranging from −1.3 to −1.0 W/m²/K is found. The range of feedback coefficient is determined by climate system memory. The longer the memory, the stronger the positive feedback. The estimated time constant of the climate is large (70~120 years mainly owing to the deep ocean heat transport, implying that the system may be not in an equilibrium state under the external forcing during the industrial era. For the doubled-CO₂ climate (or 3.7 W/m² forcing, the estimated global warming would be 3.1 K if the current estimate of 0.85 W/m² TOA net radiative heating could be confirmed. With accurate long-term measurements of TOA radiation, the analysis method suggested by this study provides a great potential in the

High-Latitude Stratospheric Sensitivity to QBO Width in a Chemistry-Climate Model with Parameterized Ozone Chemistry

Science.gov (United States)

Hurwitz, M. M.; Braesicke, P.; Pyle, J. A.

2010-01-01

In a pair of idealized simulations with a simplified chemistry-climate model, the sensitivity of the wintertime Arctic stratosphere to variability in the width of the quasi-biennial oscillation (QBO) is assessed. The width of the QBO appears to have equal influence on the Arctic stratosphere as does the phase (i.e. the Holton-Tan mechanism). In the model, a wider QBO acts like a preferential shift toward the easterly phase of the QBO, where zonal winds at 60 N tend to be relatively weaker, while 50 hPa geopotential heights and polar ozone values tend to be higher.

Disentangling Aerosol Cooling and Greenhouse Warming to Reveal Earth's Climate Sensitivity

Science.gov (United States)

Storelvmo, Trude; Leirvik, Thomas; Phillips, Petter; Lohmann, Ulrike; Wild, Martin

2015-04-01

Earth's climate sensitivity has been the subject of heated debate for decades, and recently spurred renewed interest after the latest IPCC assessment report suggested a downward adjustment of the most likely range of climate sensitivities. Here, we present a study based on the time period 1964 to 2010, which is unique in that it does not rely on global climate models (GCMs) in any way. The study uses surface observations of temperature and incoming solar radiation from approximately 1300 surface sites, along with observations of the equivalent CO2 concentration (CO2,eq) in the atmosphere, to produce a new best estimate for the transient climate sensitivity of 1.9K (95% confidence interval 1.2K - 2.7K). This is higher than other recent observation-based estimates, and is better aligned with the estimate of 1.8K and range (1.1K - 2.5K) derived from the latest generation of GCMs. The new estimate is produced by incorporating the observations in an energy balance framework, and by applying statistical methods that are standard in the field of Econometrics, but less common in climate studies. The study further suggests that about a third of the continental warming due to increasing CO2,eq was masked by aerosol cooling during the time period studied.

Sensitivity of the Eocene climate to CO2 and orbital variability

Science.gov (United States)

Keery, John S.; Holden, Philip B.; Edwards, Neil R.

2018-02-01

The early Eocene, from about 56 Ma, with high atmospheric CO2 levels, offers an analogue for the response of the Earth's climate system to anthropogenic fossil fuel burning. In this study, we present an ensemble of 50 Earth system model runs with an early Eocene palaeogeography and variation in the forcing values of atmospheric CO2 and the Earth's orbital parameters. Relationships between simple summary metrics of model outputs and the forcing parameters are identified by linear modelling, providing estimates of the relative magnitudes of the effects of atmospheric CO2 and each of the orbital parameters on important climatic features, including tropical-polar temperature difference, ocean-land temperature contrast, Asian, African and South (S.) American monsoon rains, and climate sensitivity. Our results indicate that although CO2 exerts a dominant control on most of the climatic features examined in this study, the orbital parameters also strongly influence important components of the ocean-atmosphere system in a greenhouse Earth. In our ensemble, atmospheric CO2 spans the range 280-3000 ppm, and this variation accounts for over 90 % of the effects on mean air temperature, southern winter high-latitude ocean-land temperature contrast and northern winter tropical-polar temperature difference. However, the variation of precession accounts for over 80 % of the influence of the forcing parameters on the Asian and African monsoon rainfall, and obliquity variation accounts for over 65 % of the effects on winter ocean-land temperature contrast in high northern latitudes and northern summer tropical-polar temperature difference. Our results indicate a bimodal climate sensitivity, with values of 4.36 and 2.54 °C, dependent on low or high states of atmospheric CO2 concentration, respectively, with a threshold at approximately 1000 ppm in this model, and due to a saturated vegetation-albedo feedback. Our method gives a quantitative ranking of the influence of each of the

Can climate sensitivity be estimated from short-term relationships of top-of-atmosphere net radiation and surface temperature?

International Nuclear Information System (INIS)

Lin Bing; Min Qilong; Sun Wenbo; Hu Yongxiang; Fan, Tai-Fang

2011-01-01

Increasing the knowledge in climate radiative feedbacks is critical for current climate studies. This work focuses on short-term relationships between global mean surface temperature and top-of-atmosphere (TOA) net radiation. The relationships may be used to characterize the climate feedback as suggested by some recent studies. As those recent studies, an energy balance model with ocean mixed layer and both radiative and non-radiative heat sources is used here. The significant improvement of current model is that climate system memories are considered. Based on model simulations, short-term relationship between global mean surface temperature and TOA net radiation (or the linear striation feature as suggested by previous studies) might represent climate feedbacks when the system had no memories. However, climate systems with the same short-term feedbacks but different memories would have a similar linear striation feature. This linear striation feature reflects only fast components of climate feedbacks and may not represent the total climate feedback even when the memory length of climate systems is minimal. The potential errors in the use of short-term relationships in estimations of climate sensitivity could be big. In short time scales, fast climate processes may overwhelm long-term climate feedbacks. Thus, the climate radiative feedback parameter obtained from short-term data may not provide a reliable estimate of climate sensitivity. This result also suggests that long-term observations of global surface temperature and TOA radiation are critical in the understanding of climate feedbacks and sensitivities.

Response of climate to regional emissions of ozone precursors: sensitivities and warming potentials

International Nuclear Information System (INIS)

Berntsen, T.K.; Fuglestvedt, J.S.; Joshi, M.M.; Shine, K.P.; Hauglustaine, D.A.; Li, L.

2005-01-01

The response of climate to ozone perturbations caused by regional emissions of NO x or CO has been studied through a sequence of model simulations. Changes C and OH concentrations due to emission perturbations in Europe and southeast Asia have been calculated with two global 3-D chemical tracer models(CTMs; LMDzINCA and Oslo-CTM2). The radiative transfer codes of three general circulation models (GCMs; ECHAM4, UREAD and LMD) have been used to calculate the radiative forcing of the O 3 perturbations, and for a subset of the cases full GCM simulations have been performed with ECHAM4 and UREAD. The results have been aggregated to a global number in two ways: first, through integrating the global-mean radiative forcing of a sustained step change in emissions, and second through a modified concept (SGWP*) which includes possible differences in the climate sensitivity of O 3 , CH 4 and CO 2 changes. In terms of change in global tropospheric O 3 burden the two CTMs differ by less than 30%. Both CTMs show a higher north/south gradient in the sensitivity to changes in NO x emission than for CO. We are not able to conclude whether real O 3 perturbations in general have a different climate sensitivity from CO 2 . However, in both GCMs high-latitude emission perturbations lead to climate perturbations with higher (10-30%) climate sensitivities. The calculated SGWP*, for a 100 yr time horizon, are negative for three of the four CTM/GCM combinations for European emissions (-9.6 to +6.9), while for the Asian emissions the SGWP* (H=100) is always positive (+2.9 to +25) indicating a warming. For CO the SGWP* values (3.8 and 4.4 for European and Asian emissions respectively, with only the Oslo-CTM2/ECHAM4 model combination) are less regionally dependent. Our results support the view that for NO x , regionally different weighting factors for the emissions are necessary. For CO the results are more robust and one global number may be acceptable

Quantifying Key Climate Parameter Uncertainties Using an Earth System Model with a Dynamic 3D Ocean

Science.gov (United States)

Olson, R.; Sriver, R. L.; Goes, M. P.; Urban, N.; Matthews, D.; Haran, M.; Keller, K.

2011-12-01

Climate projections hinge critically on uncertain climate model parameters such as climate sensitivity, vertical ocean diffusivity and anthropogenic sulfate aerosol forcings. Climate sensitivity is defined as the equilibrium global mean temperature response to a doubling of atmospheric CO2 concentrations. Vertical ocean diffusivity parameterizes sub-grid scale ocean vertical mixing processes. These parameters are typically estimated using Intermediate Complexity Earth System Models (EMICs) that lack a full 3D representation of the oceans, thereby neglecting the effects of mixing on ocean dynamics and meridional overturning. We improve on these studies by employing an EMIC with a dynamic 3D ocean model to estimate these parameters. We carry out historical climate simulations with the University of Victoria Earth System Climate Model (UVic ESCM) varying parameters that affect climate sensitivity, vertical ocean mixing, and effects of anthropogenic sulfate aerosols. We use a Bayesian approach whereby the likelihood of each parameter combination depends on how well the model simulates surface air temperature and upper ocean heat content. We use a Gaussian process emulator to interpolate the model output to an arbitrary parameter setting. We use Markov Chain Monte Carlo method to estimate the posterior probability distribution function (pdf) of these parameters. We explore the sensitivity of the results to prior assumptions about the parameters. In addition, we estimate the relative skill of different observations to constrain the parameters. We quantify the uncertainty in parameter estimates stemming from climate variability, model and observational errors. We explore the sensitivity of key decision-relevant climate projections to these parameters. We find that climate sensitivity and vertical ocean diffusivity estimates are consistent with previously published results. The climate sensitivity pdf is strongly affected by the prior assumptions, and by the scaling

An explanation for the different climate sensitivities of land and ocean surfaces based on the diurnal cycle

Directory of Open Access Journals (Sweden)

A. Kleidon

2017-09-01

Full Text Available Observations and climate model simulations consistently show a higher climate sensitivity of land surfaces compared to ocean surfaces. Here we show that this difference in temperature sensitivity can be explained by the different means by which the diurnal variation in solar radiation is buffered. While ocean surfaces buffer the diurnal variations by heat storage changes below the surface, land surfaces buffer it mostly by heat storage changes above the surface in the lower atmosphere that are reflected in the diurnal growth of a convective boundary layer. Storage changes below the surface allow the ocean surface–atmosphere system to maintain turbulent fluxes over day and night, while the land surface–atmosphere system maintains turbulent fluxes only during the daytime hours, when the surface is heated by absorption of solar radiation. This shorter duration of turbulent fluxes on land results in a greater sensitivity of the land surface–atmosphere system to changes in the greenhouse forcing because nighttime temperatures are shaped by radiative exchange only, which are more sensitive to changes in greenhouse forcing. We use a simple, analytic energy balance model of the surface–atmosphere system in which turbulent fluxes are constrained by the maximum power limit to estimate the effects of these different means to buffer the diurnal cycle on the resulting temperature sensitivities. The model predicts that land surfaces have a 50 % greater climate sensitivity than ocean surfaces, and that the nighttime temperatures on land increase about twice as much as daytime temperatures because of the absence of turbulent fluxes at night. Both predictions compare very well with observations and CMIP5 climate model simulations. Hence, the greater climate sensitivity of land surfaces can be explained by its buffering of diurnal variations in solar radiation in the lower atmosphere.

Cumulus Microphysics and Climate Sensitivity.

Science.gov (United States)

del Genio, Anthony D.; Kovari, William; Yao, Mao-Sung; Jonas, Jeffrey

2005-07-01

Precipitation processes in convective storms are potentially a major regulator of cloud feedback. An unresolved issue is how the partitioning of convective condensate between precipitation-size particles that fall out of updrafts and smaller particles that are detrained to form anvil clouds will change as the climate warms. Tropical Rainfall Measuring Mission (TRMM) observations of tropical oceanic convective storms indicate higher precipitation efficiency at warmer sea surface temperature (SST) but also suggest that cumulus anvil sizes, albedos, and ice water paths become insensitive to warming at high temperatures. International Satellite Cloud Climatology Project (ISCCP) data show that instantaneous cirrus and deep convective cloud fractions are positively correlated and increase with SST except at the highest temperatures, but are sensitive to variations in large-scale vertical velocity. A simple conceptual model based on a Marshall-Palmer drop size distribution, empirical terminal velocity-particle size relationships, and assumed cumulus updraft speeds reproduces the observed tendency for detrained condensate to approach a limiting value at high SST. These results suggest that the climatic behavior of observed tropical convective clouds is intermediate between the extremes required to support the thermostat and adaptive iris hypotheses.

The ice-core record - Climate sensitivity and future greenhouse warming

Science.gov (United States)

Lorius, C.; Raynaud, D.; Jouzel, J.; Hansen, J.; Le Treut, H.

1990-01-01

The prediction of future greenhouse-gas-warming depends critically on the sensitivity of earth's climate to increasing atmospheric concentrations of these gases. Data from cores drilled in polar ice sheets show a remarkable correlation between past glacial-interglacial temperature changes and the inferred atmospheric concentration of gases such as carbon dioxide and methane. These and other palaeoclimate data are used to assess the role of greenhouse gases in explaining past global climate change, and the validity of models predicting the effect of increasing concentrations of such gases in the atmosphere.

Characterizing the Sensitivity of Groundwater Storage to Climate variation in the Indus Basin

Science.gov (United States)

Huang, L.; Sabo, J. L.

2017-12-01

Indus Basin represents an extensive groundwater aquifer facing the challenge of effective management of limited water resources. Groundwater storage is one of the most important variables of water balance, yet its sensitivity to climate change has rarely been explored. To better estimate present and future groundwater storage and its sensitivity to climate change in the Indus Basin, we analyzed groundwater recharge/discharge and their historical evolution in this basin. Several methods are applied to specify the aquifer system including: water level change and storativity estimates, gravity estimates (GRACE), flow model (MODFLOW), water budget analysis and extrapolation. In addition, all of the socioeconomic and engineering aspects are represented in the hydrological system through the change of temporal and spatial distributions of recharge and discharge (e.g., land use, crop structure, water allocation, etc.). Our results demonstrate that the direct impacts of climate change will result in unevenly distributed but increasing groundwater storage in the short term through groundwater recharge. In contrast, long term groundwater storage will decrease as a result of combined indirect and direct impacts of climate change (e.g. recharge/discharge and human activities). The sensitivity of groundwater storage to climate variation is characterized by topography, aquifer specifics and land use. Furthermore, by comparing possible outcomes of different human interventions scenarios, our study reveals human activities play an important role in affecting the sensitivity of groundwater storage to climate variation. Over all, this study presents the feasibility and value of using integrated hydrological methods to support sustainable water resource management under climate change.

Reservoir Performance Under Future Climate For Basins With Different Hydrologic Sensitivities

Science.gov (United States)

Mateus, M. C.; Tullos, D. D.

2013-12-01

In addition to long-standing uncertainties related to variable inflows and market price of power, reservoir operators face a number of new uncertainties related to hydrologic nonstationarity, changing environmental regulations, and rapidly growing water and energy demands. This study investigates the impact, sensitivity, and uncertainty of changing hydrology on hydrosystem performance across different hydrogeologic settings. We evaluate the performance of reservoirs in the Santiam River basin, including a case study in the North Santiam Basin, with high permeability and extensive groundwater storage, and the South Santiam Basin, with low permeability, little groundwater storage and rapid runoff response. The modeling objective is to address the following study questions: (1) for the two hydrologic regimes, how does the flood management, water supply, and environmental performance of current reservoir operations change under future 2.5, 50 and 97.5 percentile streamflow projections; and (2) how much change in inflow is required to initiate a failure to meet downstream minimum or maximum flows in the future. We couple global climate model results with a rainfall-runoff model and a formal Bayesian uncertainty analysis to simulate future inflow hydrographs as inputs to a reservoir operations model. To evaluate reservoir performance under a changing climate, we calculate reservoir refill reliability, changes in flood frequency, and reservoir time and volumetric reliability of meeting minimum spring and summer flow target. Reservoir performance under future hydrology appears to vary with hydrogeology. We find higher sensitivity to floods for the North Santiam Basin and higher sensitivity to minimum flow targets for the South Santiam Basin. Higher uncertainty is related with basins with a more complex hydrologeology. Results from model simulations contribute to understanding of the reliability and vulnerability of reservoirs to a changing climate.

Time-dependent climate sensitivity and the legacy of anthropogenic greenhouse gas emissions.

Science.gov (United States)

Zeebe, Richard E

2013-08-20

Climate sensitivity measures the response of Earth's surface temperature to changes in forcing. The response depends on various climate processes that feed back on the initial forcing on different timescales. Understanding climate sensitivity is fundamental to reconstructing Earth's climatic history as well as predicting future climate change. On timescales shorter than centuries, only fast climate feedbacks including water vapor, lapse rate, clouds, and snow/sea ice albedo are usually considered. However, on timescales longer than millennia, the generally higher Earth system sensitivity becomes relevant, including changes in ice sheets, vegetation, ocean circulation, biogeochemical cycling, etc. Here, I introduce the time-dependent climate sensitivity, which unifies fast-feedback and Earth system sensitivity. I show that warming projections, which include a time-dependent climate sensitivity, exhibit an enhanced feedback between surface warming and ocean CO2 solubility, which in turn leads to higher atmospheric CO2 levels and further warming. Compared with earlier studies, my results predict a much longer lifetime of human-induced future warming (23,000-165,000 y), which increases the likelihood of large ice sheet melting and major sea level rise. The main point regarding the legacy of anthropogenic greenhouse gas emissions is that, even if the fast-feedback sensitivity is no more than 3 K per CO2 doubling, there will likely be additional long-term warming from slow climate feedbacks. Time-dependent climate sensitivity also helps explaining intense and prolonged warming in response to massive carbon release as documented for past events such as the Paleocene-Eocene Thermal Maximum.

Evaluating sub-national building-energy efficiency policy options under uncertainty: Efficient sensitivity testing of alternative climate, technological, and socioeconomic futures in a regional integrated-assessment model

International Nuclear Information System (INIS)

Scott, Michael J.; Daly, Don S.; Zhou, Yuyu; Rice, Jennie S.; Patel, Pralit L.; McJeon, Haewon C.; Page Kyle, G.; Kim, Son H.; Eom, Jiyong

2014-01-01

Improving the energy efficiency of building stock, commercial equipment, and household appliances can have a major positive impact on energy use, carbon emissions, and building services. Sub-national regions such as the U.S. states wish to increase energy efficiency, reduce carbon emissions, or adapt to climate change. Evaluating sub-national policies to reduce energy use and emissions is difficult because of the large uncertainties in socioeconomic factors, technology performance and cost, and energy and climate policies. Climate change itself may undercut such policies. However, assessing all of the uncertainties of large-scale energy and climate models by performing thousands of model runs can be a significant modeling effort with its accompanying computational burden. By applying fractional–factorial methods to the GCAM-USA 50-state integrated-assessment model in the context of a particular policy question, this paper demonstrates how a decision-focused sensitivity analysis strategy can greatly reduce computational burden in the presence of uncertainty and reveal the important drivers for decisions and more detailed uncertainty analysis. - Highlights: • We evaluate building energy codes and standards for climate mitigation. • We use an integrated assessment model and fractional factorial methods. • Decision criteria are energy use, CO2 emitted, and building service cost. • We demonstrate sensitivity analysis for three states. • We identify key variables to propagate with Monte Carlo or surrogate models

Uncertainties in modelling CH4 emissions from northern wetlands in glacial climates: effect of hydrological model and CH4 model structure

Directory of Open Access Journals (Sweden)

J. van Huissteden

2009-07-01

Full Text Available Methane (CH4 fluxes from northern wetlands may have influenced atmospheric CH4 concentrations at climate warming phases during the last 800 000 years and during the present global warming. Including these CH4 fluxes in earth system models is essential to understand feedbacks between climate and atmospheric composition. Attempts to model CH4 fluxes from wetlands have previously been undertaken using various approaches. Here, we test a process-based wetland CH4 flux model (PEATLAND-VU which includes details of soil-atmosphere CH4 transport. The model has been used to simulate CH4 emissions from continental Europe in previous glacial climates and the current climate. This paper presents results regarding the sensitivity of modeling glacial terrestrial CH4 fluxes to (a basic tuning parameters of the model, (b different approaches in modeling of the water table, and (c model structure. In order to test the model structure, PEATLAND-VU was compared to a simpler modeling approach based on wetland primary production estimated from a vegetation model (BIOME 3.5. The tuning parameters are the CH4 production rate from labile organic carbon and its temperature sensitivity. The modelled fluxes prove comparatively insensitive to hydrology representation, while sensitive to microbial parameters and model structure. Glacial climate emissions are also highly sensitive to assumptions about the extent of ice cover and exposed seafloor. Wetland expansion over low relief exposed seafloor areas have compensated for a decrease of wetland area due to continental ice cover.

Sensitivity of global and regional terrestrial carbon storage to the direct CO2 effect and climate change based on the CMIP5 model intercomparison.

Science.gov (United States)

Peng, Jing; Dan, Li; Huang, Mei

2014-01-01

Global and regional land carbon storage has been significantly affected by increasing atmospheric CO2 concentration and climate change. Based on fully coupled climate-carbon-cycle simulations from the Coupled Model Intercomparison Project Phase 5 (CMIP5), we investigate sensitivities of land carbon storage to rising atmospheric CO2 concentration and climate change over the world and 21 regions during the 130 years. Overall, the simulations suggest that consistently spatial positive effects of the increasing CO2 concentrations on land carbon storage are expressed with a multi-model averaged value of 1.04 PgC per ppm. The stronger positive values are mainly located in the broad areas of temperate and tropical forest, especially in Amazon basin and western Africa. However, large heterogeneity distributed for sensitivities of land carbon storage to climate change. Climate change causes decrease in land carbon storage in most tropics and the Southern Hemisphere. In these regions, decrease in soil moisture (MRSO) and enhanced drought somewhat contribute to such a decrease accompanied with rising temperature. Conversely, an increase in land carbon storage has been observed in high latitude and altitude regions (e.g., northern Asia and Tibet). The model simulations also suggest that global negative impacts of climate change on land carbon storage are predominantly attributed to decrease in land carbon storage in tropics. Although current warming can lead to an increase in land storage of high latitudes of Northern Hemisphere due to elevated vegetation growth, a risk of exacerbated future climate change may be induced due to release of carbon from tropics.

Sensitivity of global and regional terrestrial carbon storage to the direct CO2 effect and climate change based on the CMIP5 model intercomparison.

Directory of Open Access Journals (Sweden)

Jing Peng

Full Text Available Global and regional land carbon storage has been significantly affected by increasing atmospheric CO2 concentration and climate change. Based on fully coupled climate-carbon-cycle simulations from the Coupled Model Intercomparison Project Phase 5 (CMIP5, we investigate sensitivities of land carbon storage to rising atmospheric CO2 concentration and climate change over the world and 21 regions during the 130 years. Overall, the simulations suggest that consistently spatial positive effects of the increasing CO2 concentrations on land carbon storage are expressed with a multi-model averaged value of 1.04 PgC per ppm. The stronger positive values are mainly located in the broad areas of temperate and tropical forest, especially in Amazon basin and western Africa. However, large heterogeneity distributed for sensitivities of land carbon storage to climate change. Climate change causes decrease in land carbon storage in most tropics and the Southern Hemisphere. In these regions, decrease in soil moisture (MRSO and enhanced drought somewhat contribute to such a decrease accompanied with rising temperature. Conversely, an increase in land carbon storage has been observed in high latitude and altitude regions (e.g., northern Asia and Tibet. The model simulations also suggest that global negative impacts of climate change on land carbon storage are predominantly attributed to decrease in land carbon storage in tropics. Although current warming can lead to an increase in land storage of high latitudes of Northern Hemisphere due to elevated vegetation growth, a risk of exacerbated future climate change may be induced due to release of carbon from tropics.

On the Baltic Sea Response to Climate Change: Model Implications

International Nuclear Information System (INIS)

Omstedt, Anders; Leppaeranta, Matti

1999-01-01

The sensitivity of the Baltic Sea to climate change is reviewed on the basis of recent model studies. In general, the presently available models indicate that the Baltic Sea is a most sensitive system to climate change, particularly in air temperature, wind, fresh water inflow and the barotropic forcing in the entrance area. Available scenarios for ice conditions and climate warming around year 2100 show 2-3 months' shortening of the ice season in the Bothnian Bay and about 0.4 m decrease in the maximum annual ice thickness. Corresponding scenarios for climate cooling show 1-2 months' longer ice season in the Bothnian Bay and 0.2 - 0.5 m increase in the maximum annual ice thickness

Disentangling Greenhouse Warming and Aerosol Cooling to Reveal Earth's Transient Climate Sensitivity

Science.gov (United States)

Storelvmo, T.

2015-12-01

Earth's climate sensitivity has been the subject of heated debate for decades, and recently spurred renewed interest after the latest IPCC assessment report suggested a downward adjustment of the most likely range of climate sensitivities. Here, we present an observation-based study based on the time period 1964 to 2010, which is unique in that it does not rely on global climate models (GCMs) in any way. The study uses surface observations of temperature and incoming solar radiation from approximately 1300 surface sites, along with observations of the equivalent CO2 concentration (CO2,eq) in the atmosphere, to produce a new best estimate for the transient climate sensitivity of 1.9K (95% confidence interval 1.2K - 2.7K). This is higher than other recent observation-based estimates, and is better aligned with the estimate of 1.8K and range (1.1K - 2.5K) derived from the latest generation of GCMs. The new estimate is produced by incorporating the observations in an energy balance framework, and by applying statistical methods that are standard in the field of Econometrics, but less common in climate studies. The study further suggests that about a third of the continental warming due to increasing CO2,eq was masked by aerosol cooling during the time period studied.

Large-Scale Features of Pliocene Climate: Results from the Pliocene Model Intercomparison Project

Science.gov (United States)

Haywood, A. M.; Hill, D.J.; Dolan, A. M.; Otto-Bliesner, B. L.; Bragg, F.; Chan, W.-L.; Chandler, M. A.; Contoux, C.; Dowsett, H. J.; Jost, A.;

Methyl bromide: ocean sources, ocean sinks, and climate sensitivity.

Science.gov (United States)

Anbar, A D; Yung, Y L; Chavez, F P

1996-03-01

perturbations to temperature or productivity can modify atmospheric CH3Br. Therefore atmospheric CH3Br should be sensitive to climate conditions. Our modeling indicates that climate-induced CH3Br variations can be larger than those resulting from small (+/- 25%) changes in the anthropogenic source, assuming that this source comprises less than half of all inputs. Future measurements of marine CH3Br, temperature, and primary production should be combined with such models to determine the relationship between marine biological activity and CH3Br production. Better understanding of the biological term is especially important to assess the importance of non-anthropogenic sources to stratospheric ozone loss and the sensitivity of these sources to global climate change.

A climate sensitive model of carbon transfer through atmosphere, vegetation and soil in managed forest ecosystems

Science.gov (United States)

Loustau, D.; Moreaux, V.; Bosc, A.; Trichet, P.; Kumari, J.; Rabemanantsoa, T.; Balesdent, J.; Jolivet, C.; Medlyn, B. E.; Cavaignac, S.; Nguyen-The, N.

2012-12-01

For predicting the future of the forest carbon cycle in forest ecosystems, it is necessary to account for both the climate and management impacts. Climate effects are significant not only at a short time scale but also at the temporal horizon of a forest life cycle e.g. through shift in atmospheric CO2 concentration, temperature and precipitation regimes induced by the enhanced greenhouse effect. Intensification of forest management concerns an increasing fraction of temperate and tropical forests and untouched forests represents only one third of the present forest area. Predicting tools are therefore needed to project climate and management impacts over the forest life cycle and understand the consequence of management on the forest ecosystem carbon cycle. This communication summarizes the structure, main components and properties of a carbon transfer model that describes the processes controlling the carbon cycle of managed forest ecosystems. The model, GO+, links three main components, (i) a module describing the vegetation-atmosphere mass and energy exchanges in 3D, (ii) a plant growth module and a (iii) soil carbon dynamics module in a consistent carbon scheme of transfer from atmosphere back into the atmosphere. It was calibrated and evaluated using observed data collected on coniferous and broadleaved forest stands. The model predicts the soil, water and energy balance of entire rotations of managed stands from the plantation to the final cut and according to a range of management alternatives. It accounts for the main soil and vegetation management operations such as soil preparation, understorey removal, thinnings and clearcutting. Including the available knowledge on the climatic sensitivity of biophysical and biogeochemical processes involved in atmospheric exchanges and carbon cycle of forest ecosystems, GO+ can produce long-term backward or forward simulations of forest carbon and water cycles under a range of climate and management scenarios. This

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

Sensitivity of Greenland Ice Sheet surface mass balance to surface albedo parameterization: a study with a regional climate model

OpenAIRE

Angelen, J. H.; Lenaerts, J. T. M.; Lhermitte, S.; Fettweis, X.; Kuipers Munneke, P.; Broeke, M. R.; Meijgaard, E.; Smeets, C. J. P. P.

2012-01-01

We present a sensitivity study of the surface mass balance (SMB) of the Greenland Ice Sheet, as modeled using a regional atmospheric climate model, to various parameter settings in the albedo scheme. The snow albedo scheme uses grain size as a prognostic variable and further depends on cloud cover, solar zenith angle and black carbon concentration. For the control experiment the overestimation of absorbed shortwave radiation (+6%) at the K-transect (west Greenland) for the period 2004–2009 is...

Sensitivity analysis with the regional climate model COSMO-CLM over the CORDEX-MENA domain

Science.gov (United States)

Bucchignani, E.; Cattaneo, L.; Panitz, H.-J.; Mercogliano, P.

2016-02-01

The results of a sensitivity work based on ERA-Interim driven COSMO-CLM simulations over the Middle East-North Africa (CORDEX-MENA) domain are presented. All simulations were performed at 0.44° spatial resolution. The purpose of this study was to ascertain model performances with respect to changes in physical and tuning parameters which are mainly related to surface, convection, radiation and cloud parameterizations. Evaluation was performed for the whole CORDEX-MENA region and six sub-regions, comparing a set of 26 COSMO-CLM runs against a combination of available ground observations, satellite products and reanalysis data to assess temperature, precipitation, cloud cover and mean sea level pressure. The model proved to be very sensitive to changes in physical parameters. The optimized configuration allows COSMO-CLM to improve the simulated main climate features of this area. Its main characteristics consist in the new parameterization of albedo, based on Moderate Resolution Imaging Spectroradiometer data, and the new parameterization of aerosol, based on NASA-GISS AOD distributions. When applying this configuration, Mean Absolute Error values for the considered variables are as follows: about 1.2 °C for temperature, about 15 mm/month for precipitation, about 9 % for total cloud cover, and about 0.6 hPa for mean sea level pressure.

The Dependencies of Ecosystem Pattern, Structure, and Dynamics on Climate, Climate Variability, and Climate Change

Science.gov (United States)

Flanagan, S.; Hurtt, G. C.; Fisk, J. P.; Rourke, O.

2012-12-01

A robust understanding of the sensitivity of the pattern, structure, and dynamics of ecosystems to climate, climate variability, and climate change is needed to predict ecosystem responses to current and projected climate change. We present results of a study designed to first quantify the sensitivity of ecosystems to climate through the use of climate and ecosystem data, and then use the results to test the sensitivity of the climate data in a state-of the art ecosystem model. A database of available ecosystem characteristics such as mean canopy height, above ground biomass, and basal area was constructed from sources like the National Biomass and Carbon Dataset (NBCD). The ecosystem characteristics were then paired by latitude and longitude with the corresponding climate characteristics temperature, precipitation, photosynthetically active radiation (PAR) and dew point that were retrieved from the North American Regional Reanalysis (NARR). The average yearly and seasonal means of the climate data, and their associated maximum and minimum values, over the 1979-2010 time frame provided by NARR were constructed and paired with the ecosystem data. The compiled results provide natural patterns of vegetation structure and distribution with regard to climate data. An advanced ecosystem model, the Ecosystem Demography model (ED), was then modified to allow yearly alterations to its mechanistic climate lookup table and used to predict the sensitivities of ecosystem pattern, structure, and dynamics to climate data. The combined ecosystem structure and climate data results were compared to ED's output to check the validity of the model. After verification, climate change scenarios such as those used in the last IPCC were run and future forest structure changes due to climate sensitivities were identified. The results of this study can be used to both quantify and test key relationships for next generation models. The sensitivity of ecosystem characteristics to climate data

Novel Methods to Explore Building Energy Sensitivity to Climate and Heat Waves Using PNNL's BEND Model

Science.gov (United States)

Burleyson, C. D.; Voisin, N.; Taylor, T.; Xie, Y.; Kraucunas, I.

2017-12-01

The DOE's Pacific Northwest National Laboratory (PNNL) has been developing the Building ENergy Demand (BEND) model to simulate energy usage in residential and commercial buildings responding to changes in weather, climate, population, and building technologies. At its core, BEND is a mechanism to aggregate EnergyPlus simulations of a large number of individual buildings with a diversity of characteristics over large spatial scales. We have completed a series of experiments to explore methods to calibrate the BEND model, measure its ability to capture interannual variability in energy demand due to weather using simulations of two distinct weather years, and understand the sensitivity to the number and location of weather stations used to force the model. The use of weather from "representative cities" reduces computational costs, but often fails to capture spatial heterogeneity that may be important for simulations aimed at understanding how building stocks respond to a changing climate (Fig. 1). We quantify the potential reduction in temperature and load biases from using an increasing number of weather stations across the western U.S., ranging from 8 to roughly 150. Using 8 stations results in an average absolute summertime temperature bias of 4.0°C. The mean absolute bias drops to 1.5°C using all available stations. Temperature biases of this magnitude translate to absolute summertime mean simulated load biases as high as 13.8%. Additionally, using only 8 representative weather stations can lead to a 20-40% bias of peak building loads under heat wave or cold snap conditions, a significant error for capacity expansion planners who may rely on these types of simulations. This analysis suggests that using 4 stations per climate zone may be sufficient for most purposes. Our novel approach, which requires no new EnergyPlus simulations, could be useful to other researchers designing or calibrating aggregate building model simulations - particularly those looking at

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.

Improved Upper Ocean/Sea Ice Modeling in the GISS GCM for Investigating Climate Change

Science.gov (United States)

1997-01-01

This project built on our previous results in which we highlighted the importance of sea ice in overall climate sensitivity by determining that for both warming and cooling climates, when sea ice was not allowed to change, climate sensitivity was reduced by 35-40%. We also modified the Goddard Institute for Space Studies (GISS) 8 deg x lO deg atmospheric General Circulation Model (GCM) to include an upper-ocean/sea-ice model involving the Semtner three-layer ice/snow thermodynamic model, the Price et al. (1986) ocean mixed layer model and a general upper ocean vertical advection/diffusion scheme for maintaining and fluxing properties across the pycnocline. This effort, in addition to improving the sea ice representation in the AGCM, revealed a number of sensitive components of the sea ice/ocean system. For example, the ability to flux heat through the ice/snow properly is critical in order to resolve the surface temperature properly, since small errors in this lead to unrestrained climate drift. The present project, summarized in this report, had as its objectives: (1) introducing a series of sea ice and ocean improvements aimed at overcoming remaining weaknesses in the GCM sea ice/ocean representation, and (2) performing a series of sensitivity experiments designed to evaluate the climate sensitivity of the revised model to both Antarctic and Arctic sea ice, determine the sensitivity of the climate response to initial ice distribution, and investigate the transient response to doubling CO2.

Semiarid watershed response in central New Mexico and its sensitivity to climate variability and change

Directory of Open Access Journals (Sweden)

E. R. Vivoni

2009-06-01

Full Text Available Hydrologic processes in the semiarid regions of the Southwest United States are considered to be highly susceptible to variations in temperature and precipitation characteristics due to the effects of climate change. Relatively little is known about the potential impacts of climate change on the basin hydrologic response, namely streamflow, evapotranspiration and recharge, in the region. In this study, we present the development and application of a continuous, semi-distributed watershed model for climate change studies in semiarid basins of the Southwest US. Our objective is to capture hydrologic processes in large watersheds, while accounting for the spatial and temporal variations of climate forcing and basin properties in a simple fashion. We apply the model to the Río Salado basin in central New Mexico since it exhibits both a winter and summer precipitation regime and has a historical streamflow record for model testing purposes. Subsequently, we use a sequence of climate change scenarios that capture observed trends for winter and summer precipitation, as well as their interaction with higher temperatures, to perform long-term ensemble simulations of the basin response. Results of the modeling exercise indicate that precipitation uncertainty is amplified in the hydrologic response, in particular for processes that depend on a soil saturation threshold. We obtained substantially different hydrologic sensitivities for winter and summer precipitation ensembles, indicating a greater sensitivity to more intense summer storms as compared to more frequent winter events. In addition, the impact of changes in precipitation characteristics overwhelmed the effects of increased temperature in the study basin. Nevertheless, combined trends in precipitation and temperature yield a more sensitive hydrologic response throughout the year.

Estimating option values of solar radiation management assuming that climate sensitivity is uncertain.

Science.gov (United States)

Arino, Yosuke; Akimoto, Keigo; Sano, Fuminori; Homma, Takashi; Oda, Junichiro; Tomoda, Toshimasa

2016-05-24

Although solar radiation management (SRM) might play a role as an emergency geoengineering measure, its potential risks remain uncertain, and hence there are ethical and governance issues in the face of SRM's actual deployment. By using an integrated assessment model, we first present one possible methodology for evaluating the value arising from retaining an SRM option given the uncertainty of climate sensitivity, and also examine sensitivities of the option value to SRM's side effects (damages). Reflecting the governance challenges on immediate SRM deployment, we assume scenarios in which SRM could only be deployed with a limited degree of cooling (0.5 °C) only after 2050, when climate sensitivity uncertainty is assumed to be resolved and only when the sensitivity is found to be high (T2x = 4 °C). We conduct a cost-effectiveness analysis with constraining temperature rise as the objective. The SRM option value is originated from its rapid cooling capability that would alleviate the mitigation requirement under climate sensitivity uncertainty and thereby reduce mitigation costs. According to our estimates, the option value during 1990-2049 for a +2.4 °C target (the lowest temperature target level for which there were feasible solutions in this model study) relative to preindustrial levels were in the range between $2.5 and $5.9 trillion, taking into account the maximum level of side effects shown in the existing literature. The result indicates that lower limits of the option values for temperature targets below +2.4 °C would be greater than $2.5 trillion.

Model confirmation in climate economics

Science.gov (United States)

Millner, Antony; McDermott, Thomas K. J.

2016-01-01

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

Sensitivity and requirement of improvements of four soybean crop simulation models for climate change studies in Southern Brazil

Science.gov (United States)

Battisti, R.; Sentelhas, P. C.; Boote, K. J.

2017-12-01

Crop growth models have many uncertainties that affect the yield response to climate change. Based on that, the aim of this study was to evaluate the sensitivity of crop models to systematic changes in climate for simulating soybean attainable yield in Southern Brazil. Four crop models were used to simulate yields: AQUACROP, MONICA, DSSAT, and APSIM, as well as their ensemble. The simulations were performed considering changes of air temperature (0, + 1.5, + 3.0, + 4.5, and + 6.0 °C), [CO2] (380, 480, 580, 680, and 780 ppm), rainfall (- 30, - 15, 0, + 15, and + 30%), and solar radiation (- 15, 0, + 15), applied to daily values. The baseline climate was from 1961 to 2014, totalizing 53 crop seasons. The crop models simulated a reduction of attainable yield with temperature increase, reaching 2000 kg ha-1 for the ensemble at + 6 °C, mainly due to shorter crop cycle. For rainfall, the yield had a higher rate of reduction when it was diminished than when rainfall was increased. The crop models increased yield variability when solar radiation was changed from - 15 to + 15%, whereas [CO2] rise resulted in yield gains, following an asymptotic response, with a mean increase of 31% from 380 to 680 ppm. The models used require further attention to improvements in optimal and maximum cardinal temperature for development rate; runoff, water infiltration, deep drainage, and dynamic of root growth; photosynthesis parameters related to soil water availability; and energy balance of soil-plant system to define leaf temperature under elevated CO2.

Sensitivity and requirement of improvements of four soybean crop simulation models for climate change studies in Southern Brazil

Science.gov (United States)

Battisti, R.; Sentelhas, P. C.; Boote, K. J.

2018-05-01

Crop growth models have many uncertainties that affect the yield response to climate change. Based on that, the aim of this study was to evaluate the sensitivity of crop models to systematic changes in climate for simulating soybean attainable yield in Southern Brazil. Four crop models were used to simulate yields: AQUACROP, MONICA, DSSAT, and APSIM, as well as their ensemble. The simulations were performed considering changes of air temperature (0, + 1.5, + 3.0, + 4.5, and + 6.0 °C), [CO2] (380, 480, 580, 680, and 780 ppm), rainfall (- 30, - 15, 0, + 15, and + 30%), and solar radiation (- 15, 0, + 15), applied to daily values. The baseline climate was from 1961 to 2014, totalizing 53 crop seasons. The crop models simulated a reduction of attainable yield with temperature increase, reaching 2000 kg ha-1 for the ensemble at + 6 °C, mainly due to shorter crop cycle. For rainfall, the yield had a higher rate of reduction when it was diminished than when rainfall was increased. The crop models increased yield variability when solar radiation was changed from - 15 to + 15%, whereas [CO2] rise resulted in yield gains, following an asymptotic response, with a mean increase of 31% from 380 to 680 ppm. The models used require further attention to improvements in optimal and maximum cardinal temperature for development rate; runoff, water infiltration, deep drainage, and dynamic of root growth; photosynthesis parameters related to soil water availability; and energy balance of soil-plant system to define leaf temperature under elevated CO2.

Comparing the sensitivity of permafrost and marine gas hydrate to climate warming

International Nuclear Information System (INIS)

Taylor, A.E.; Dallimore, S.R.; Hyndman, R.D.; Wright, F.

2005-01-01

The sensitivity of Arctic subpermafrost gas hydrate at the Mallik borehole was compared to temperate marine gas hydrate located offshore southwestern Canada. In particular, a finite element geothermal model was used to determine the sensitivity to the end of the ice age, and contemporary climate warming of a 30 m thick methane hydrate layer lying at the base of a gas hydrate stability zone prior to 13.5 kiloannum (ka) before present (BP). It was suggested that the 30 m gas-hydrate-bearing layer would have disappeared by now, according to the thermal signal alone. However, the same gas-hydrate-bearing layer underlying permafrost would persist until at least 4 ka after present, even with contemporary climate warming. The longer time for subpermafrost gas hydrate comes from the thawing pore ice at the base of permafrost, at the expense of dissociation of the deeper gas hydrate. The dissociation of underlying gas hydrate from climate surface warming is buffered by the overlying permafrost

Simulated climate change during the last 1,000 years: comparing the ECHO-G general circulation model with the MAGICC simple climate model

Energy Technology Data Exchange (ETDEWEB)

Osborn, Timothy J.; Briffa, Keith R. [University of East Anglia, Climatic Research Unit, School of Environmental Sciences, Norwich (United Kingdom); Raper, Sarah C.B. [University of East Anglia, Climatic Research Unit, School of Environmental Sciences, Norwich (United Kingdom); Manchester Metropolitan University, Dalton Research Institute, Manchester (United Kingdom)

2006-08-15

An intercomparison of eight climate simulations, each driven with estimated natural and anthropogenic forcings for the last millennium, indicates that the so-called ''Erik'' simulation of the ECHO-G coupled ocean-atmosphere climate model exhibits atypical behaviour. The ECHO-G simulation has a much stronger cooling trend from 1000 to 1700 and a higher rate of warming since 1800 than the other simulations, with the result that the overall amplitude of millennial-scale temperature variations in the ECHO-G simulation is much greater than in the other models. The MAGICC (Model for the Assessment of Greenhouse-gas-Induced Climate Change) simple climate model is used to investigate possible causes of this atypical behaviour. It is shown that disequilibrium in the initial conditions probably contributes spuriously to the cooling trend in the early centuries of the simulation, and that the omission of tropospheric sulphate aerosol forcing is the likely explanation for the anomalously large recent warming. The simple climate model results are used to adjust the ECHO-G Erik simulation to mitigate these effects, which brings the simulation into better agreement with the other seven models considered here and greatly reduces the overall range of temperature variations during the last millennium simulated by ECHO-G. Smaller inter-model differences remain which can probably be explained by a combination of the particular forcing histories and model sensitivities of each experiment. These have not been investigated here, though we have diagnosed the effective climate sensitivity of ECHO-G to be 2.39{+-}0.11 K for a doubling of CO{sub 2}. (orig.)

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.

Assessing climate-sensitive ecosystems in the southeastern United States

Science.gov (United States)

Costanza, Jennifer; Beck, Scott; Pyne, Milo; Terando, Adam; Rubino, Matthew J.; White, Rickie; Collazo, Jaime

2016-08-11

Climate change impacts ecosystems in many ways, from effects on species to phenology to wildfire dynamics. Assessing the potential vulnerability of ecosystems to future changes in climate is an important first step in prioritizing and planning for conservation. Although assessments of climate change vulnerability commonly are done for species, fewer have been done for ecosystems. To aid regional conservation planning efforts, we assessed climate change vulnerability for ecosystems in the Southeastern United States and Caribbean.First, we solicited input from experts to create a list of candidate ecosystems for assessment. From that list, 12 ecosystems were selected for a vulnerability assessment that was based on a synthesis of available geographic information system (GIS) data and literature related to 3 components of vulnerability—sensitivity, exposure, and adaptive capacity. This literature and data synthesis comprised “Phase I” of the assessment. Sensitivity is the degree to which the species or processes in the ecosystem are affected by climate. Exposure is the likely future change in important climate and sea level variables. Adaptive capacity is the degree to which ecosystems can adjust to changing conditions. Where available, GIS data relevant to each of these components were used. For example, we summarized observed and projected climate, protected areas existing in 2011, projected sea-level rise, and projected urbanization across each ecosystem’s distribution. These summaries were supplemented with information in the literature, and a short narrative assessment was compiled for each ecosystem. We also summarized all information into a qualitative vulnerability rating for each ecosystem.Next, for 2 of the 12 ecosystems (East Gulf Coastal Plain Near-Coast Pine Flatwoods and Nashville Basin Limestone Glade and Woodland), the NatureServe Habitat Climate Change Vulnerability Index (HCCVI) framework was used as an alternative approach for assessing

Estimating daily climatologies for climate indices derived from climate model data and observations

Science.gov (United States)

Mahlstein, Irina; Spirig, Christoph; Liniger, Mark A; Appenzeller, Christof

2015-01-01

Climate indices help to describe the past, present, and the future climate. They are usually closer related to possible impacts and are therefore more illustrative to users than simple climate means. Indices are often based on daily data series and thresholds. It is shown that the percentile-based thresholds are sensitive to the method of computation, and so are the climatological daily mean and the daily standard deviation, which are used for bias corrections of daily climate model data. Sample size issues of either the observed reference period or the model data lead to uncertainties in these estimations. A large number of past ensemble seasonal forecasts, called hindcasts, is used to explore these sampling uncertainties and to compare two different approaches. Based on a perfect model approach it is shown that a fitting approach can improve substantially the estimates of daily climatologies of percentile-based thresholds over land areas, as well as the mean and the variability. These improvements are relevant for bias removal in long-range forecasts or predictions of climate indices based on percentile thresholds. But also for climate change studies, the method shows potential for use. Key Points More robust estimates of daily climate characteristics Statistical fitting approach Based on a perfect model approach PMID:26042192

A dynamic, climate-driven model of Rift Valley fever

Directory of Open Access Journals (Sweden)

Joseph Leedale

2016-03-01

Full Text Available Outbreaks of Rift Valley fever (RVF in eastern Africa have previously occurred following specific rainfall dynamics and flooding events that appear to support the emergence of large numbers of mosquito vectors. As such, transmission of the virus is considered to be sensitive to environmental conditions and therefore changes in climate can impact the spatiotemporal dynamics of epizootic vulnerability. Epidemiological information describing the methods and parameters of RVF transmission and its dependence on climatic factors are used to develop a new spatio-temporal mathematical model that simulates these dynamics and can predict the impact of changes in climate. The Liverpool RVF (LRVF model is a new dynamic, process-based model driven by climate data that provides a predictive output of geographical changes in RVF outbreak susceptibility as a result of the climate and local livestock immunity. This description of the multi-disciplinary process of model development is accessible to mathematicians, epidemiological modellers and climate scientists, uniting dynamic mathematical modelling, empirical parameterisation and state-of-the-art climate information.

Improved Upper Ocean/Sea Ice Modeling in the GISS GCM for Investigating Climate Change

Science.gov (United States)

1998-01-01

This project built on our previous results in which we highlighted the importance of sea ice in overall climate sensitivity by determining that for both warming and cooling climates, when sea ice was not allowed to change, climate sensitivity was reduced by 35-40%. We also modified the GISS 8 deg x lO deg atmospheric GCM to include an upper-ocean/sea-ice model involving the Semtner three-layer ice/snow thermodynamic model, the Price et al. (1986) ocean mixed layer model and a general upper ocean vertical advection/diffusion scheme for maintaining and fluxing properties across the pycnocline. This effort, in addition to improving the sea ice representation in the AGCM, revealed a number of sensitive components of the sea ice/ocean system. For example, the ability to flux heat through the ice/snow properly is critical in order to resolve the surface temperature properly, since small errors in this lead to unrestrained climate drift. The present project, summarized in this report, had as its objectives: (1) introducing a series of sea ice and ocean improvements aimed at overcoming remaining weaknesses in the GCM sea ice/ocean representation, and (2) performing a series of sensitivity experiments designed to evaluate the climate sensitivity of the revised model to both Antarctic and Arctic sea ice, determine the sensitivity of the climate response to initial ice distribution, and investigate the transient response to doubling CO2.

Comparison of winter wheat yield sensitivity to climate variables under irrigated and rain-fed conditions

Science.gov (United States)

Xiao, Dengpan; Shen, Yanjun; Zhang, He; Moiwo, Juana P.; Qi, Yongqing; Wang, Rende; Pei, Hongwei; Zhang, Yucui; Shen, Huitao

2016-09-01

Crop simulation models provide alternative, less time-consuming, and cost-effective means of determining the sensitivity of crop yield to climate change. In this study, two dynamic mechanistic models, CERES (Crop Environment Resource Synthesis) and APSIM (Agricultural Production Systems Simulator), were used to simulate the yield of wheat ( Triticum aestivum L.) under well irrigated (CFG) and rain-fed (YY) conditions in relation to different climate variables in the North China Plain (NCP). The study tested winter wheat yield sensitivity to different levels of temperature, radiation, precipitation, and atmospheric carbon dioxide (CO2) concentration under CFG and YY conditions at Luancheng Agro-ecosystem Experimental Stations in the NCP. The results from the CERES and APSIM wheat crop models were largely consistent and suggested that changes in climate variables influenced wheat grain yield in the NCP. There was also significant variation in the sensitivity of winter wheat yield to climate variables under different water (CFG and YY) conditions. While a temperature increase of 2°C was the threshold beyond which temperature negatively influenced wheat yield under CFG, a temperature rise exceeding 1°C decreased winter wheat grain yield under YY. A decrease in solar radiation decreased wheat grain yield under both CFG and YY conditions. Although the sensitivity of winter wheat yield to precipitation was small under the CFG, yield decreased significantly with decreasing precipitation under the rainfed YY treatment. The results also suggest that wheat yield under CFG linearly increased by ≈3.5% per 60 ppm (parts per million) increase in CO2 concentration from 380 to 560 ppm, and yield under YY increased linearly by ≈7.0% for the same increase in CO2 concentration.

A sensitivity analysis of hazardous waste disposal site climatic and soil design parameters using HELP3

International Nuclear Information System (INIS)

Adelman, D.D.; Stansbury, J.

1997-01-01

The Resource Conservation and Recovery Act (RCRA) Subtitle C, Comprehensive Environmental Response, Compensation, And Liability Act (CERCLA), and subsequent amendments have formed a comprehensive framework to deal with hazardous wastes on the national level. Key to this waste management is guidance on design (e.g., cover and bottom leachate control systems) of hazardous waste landfills. The objective of this research was to investigate the sensitivity of leachate volume at hazardous waste disposal sites to climatic, soil cover, and vegetative cover (Leaf Area Index) conditions. The computer model HELP3 which has the capability to simulate double bottom liner systems as called for in hazardous waste disposal sites was used in the analysis. HELP3 was used to model 54 combinations of climatic conditions, disposal site soil surface curve numbers, and leaf area index values to investigate how sensitive disposal site leachate volume was to these three variables. Results showed that leachate volume from the bottom double liner system was not sensitive to these parameters. However, the cover liner system leachate volume was quite sensitive to climatic conditions and less sensitive to Leaf Area Index and curve number values. Since humid locations had considerably more cover liner system leachate volume than and locations, different design standards may be appropriate for humid conditions than for and conditions

Climate change sensitivity index for Pacific salmon habitat in southeast Alaska.

Directory of Open Access Journals (Sweden)

Colin S Shanley

Full Text Available Global climate change may become one of the most pressing challenges to Pacific Salmon conservation and management for southeast Alaska in the 21st Century. Predicted hydrologic change associated with climate change will likely challenge the ability of specific stocks to adapt to new flow regimes and resulting shifts in spawning and rearing habitats. Current research suggests egg-to-fry survival may be one of the most important freshwater limiting factors in Pacific Salmon's northern range due to more frequent flooding events predicted to scour eggs from mobile spawning substrates. A watershed-scale hydroclimatic sensitivity index was developed to map this hypothesis with an historical stream gauge station dataset and monthly multiple regression-based discharge models. The relative change from present to future watershed conditions predicted for the spawning and incubation period (September to March was quantified using an ensemble global climate model average (ECHAM5, HadCM3, and CGCM3.1 and three global greenhouse gas emission scenarios (B1, A1B, and A2 projected to the year 2080. The models showed the region's diverse physiography and climatology resulted in a relatively predictable pattern of change: northern mainland and steeper, snow-fed mountainous watersheds exhibited the greatest increases in discharge, an earlier spring melt, and a transition into rain-fed hydrologic patterns. Predicted streamflow increases for all watersheds ranged from approximately 1-fold to 3-fold for the spawning and incubation period, with increased peak flows in the spring and fall. The hydroclimatic sensitivity index was then combined with an index of currently mapped salmon habitat and species diversity to develop a research and conservation priority matrix, highlighting potentially vulnerable to resilient high-value watersheds. The resulting matrix and observed trends are put forth as a framework to prioritize long-term monitoring plans, mitigation

Using Impact-Relevant Sensitivities to Efficiently Evaluate and Select Climate Change Scenarios

Science.gov (United States)

Vano, J. A.; Kim, J. B.; Rupp, D. E.; Mote, P.

2014-12-01

We outline an efficient approach to help researchers and natural resource managers more effectively use global climate model information in their long-term planning. The approach provides an estimate of the magnitude of change of a particular impact (e.g., summertime streamflow) from a large ensemble of climate change projections prior to detailed analysis. These estimates provide both qualitative information as an end unto itself (e.g., the distribution of future changes between emissions scenarios for the specific impact) and a judicious, defensible evaluation structure that can be used to qualitatively select a sub-set of climate models for further analysis. More specifically, the evaluation identifies global climate model scenarios that both (1) span the range of possible futures for the variable/s most important to the impact under investigation, and (2) come from global climate models that adequately simulate historical climate, providing plausible results for the future climate in the region of interest. To identify how an ecosystem process responds to projected future changes, we methodically sample, using a simple sensitivity analysis, how an impact variable (e.g., streamflow magnitude, vegetation carbon) responds locally to projected regional temperature and precipitation changes. We demonstrate our technique over the Pacific Northwest, focusing on two types of impacts each in three distinct geographic settings: (a) changes in streamflow magnitudes in critical seasons for water management in the Willamette, Yakima, and Upper Columbia River basins; and (b) changes in annual vegetation carbon in the Oregon and Washington Coast Ranges, Western Cascades, and Columbia Basin ecoregions.

Variations in the Sensitivity of Shrub Growth to Climate Change along Arctic Environmental and Biotic Gradients

Science.gov (United States)

Beck, P. S. A.; Myers-Smith, I. H.; Elmendorf, S.; Georges, D.

2015-12-01

Despite evidence of rapid shrub expansion at many Arctic sites and the profound effects this has on ecosystem structure, biogeochemical cycling, and land-atmosphere feedbacks in the Arctic, the drivers of shrub growth remain poorly understood. The compilation of 41,576 annual shrub growth measurements made around the Arctic, allowed for the first systematic evaluation of the climate sensitivity of Arctic shrub growth, i.e. the strength of the relationship between annual shrub growth and monthly climate variables. The growth measurements were taken on 1821 plants of 25 species at 37 arctic and alpine sites, either as annual ring widths or as stem increments. We evaluated climate sensitivity of shrub growth for each genus-by-site combination in this data set based on the performance and parameters of linear mixed models that used CRU TS3.21 climate data as predictors of shrub growth between 1950 and 2010. 76% of genus-by-site combinations showed climate sensitive growth, but climate-growth relationships varied with soil moisture, species canopy height, and geographic position within the species ranges. Shrubs growing at sites with more soil moisture showed greater climate sensitivity, suggesting that water availability might limit shrub growth if continued warming isn't matched by a steady increase in soil moisture. Tall shrub species growing at their northern range limit were particularly climate sensitive causing climate sensitivity of shrubs to peak at the transition between Low and High Arctic, where carbon storage in permafrost is greatest. Local and regional studies have documented matching spatial and temporal patterns in dendrochronological measurements and satellite observations of vegetation indices both in boreal and Arctic regions. Yet the circumarctic comparison of patterns in dendrochronological and remote sensing data sets yielded poor levels of agreement. In much of the Arctic, steep environmental gradients generate fine spatial patterns of vegetation

Sensitivity and requirement of improvements of four soybean crop simulation models for climate change studies in Southern Brazil.

Science.gov (United States)

Battisti, R; Sentelhas, P C; Boote, K J

2018-05-01

Crop growth models have many uncertainties that affect the yield response to climate change. Based on that, the aim of this study was to evaluate the sensitivity of crop models to systematic changes in climate for simulating soybean attainable yield in Southern Brazil. Four crop models were used to simulate yields: AQUACROP, MONICA, DSSAT, and APSIM, as well as their ensemble. The simulations were performed considering changes of air temperature (0, + 1.5, + 3.0, + 4.5, and + 6.0 °C), [CO 2 ] (380, 480, 580, 680, and 780 ppm), rainfall (- 30, - 15, 0, + 15, and + 30%), and solar radiation (- 15, 0, + 15), applied to daily values. The baseline climate was from 1961 to 2014, totalizing 53 crop seasons. The crop models simulated a reduction of attainable yield with temperature increase, reaching 2000 kg ha -1 for the ensemble at + 6 °C, mainly due to shorter crop cycle. For rainfall, the yield had a higher rate of reduction when it was diminished than when rainfall was increased. The crop models increased yield variability when solar radiation was changed from - 15 to + 15%, whereas [CO 2 ] rise resulted in yield gains, following an asymptotic response, with a mean increase of 31% from 380 to 680 ppm. The models used require further attention to improvements in optimal and maximum cardinal temperature for development rate; runoff, water infiltration, deep drainage, and dynamic of root growth; photosynthesis parameters related to soil water availability; and energy balance of soil-plant system to define leaf temperature under elevated CO 2 .

Climate Modeling and Causal Identification for Sea Ice Predictability

Energy Technology Data Exchange (ETDEWEB)

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

2018-02-12

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

Responses of the ocean carbon cycle to climate change: Results from an earth system climate model simulation

Institute of Scientific and Technical Information of China (English)

WANG Shuang-Jing; CAO Long; LI Na

2014-01-01

Based on simulations using the University of Victoria’s Earth System Climate Model, we analyzed the responses of the ocean carbon cycle to increasing atmospheric CO2 levels and climate change from 1800 to 2500 following the RCP 8.5 scenario and its extension. Compared to simulations without climate change, the simulation with a climate sensitivity of 3.0 K shows that in 2100, due to increased atmospheric CO2 concentrations, the simulated sea surface temperature increases by 2.7 K, the intensity of the North Atlantic deep water formation reduces by4.5 Sv, and the oceanic uptake of anthropogenic CO2 decreases by 0.8 Pg C. Climate change is also found to have a large effect on the North Atlantic’s ocean column inventory of anthropogenic CO2. Between the years 1800 and 2500, compared with the simulation with no climate change, the simulation with climate change causes a reduction in the total anthropogenic CO2 column inventory over the entire ocean and in North Atlantic by 23.1% and 32.0%, respectively. A set of simulations with climate sensitivity variations from 0.5 K to 4.5 K show that with greater climate sensitivity climate change would have a greater effect in reducing the ocean’s ability to absorb CO2 from the atmosphere.

Sensitivity of inferred climate model skill to evaluation decisions: a case study using CMIP5 evapotranspiration

International Nuclear Information System (INIS)

Schwalm, Christopher R; Huntinzger, Deborah N; Michalak, Anna M; Fisher, Joshua B; Kimball, John S; Mueller, Brigitte; Zhang, Ke; Zhang Yongqiang

2013-01-01

Confrontation of climate models with observationally-based reference datasets is widespread and integral to model development. These comparisons yield skill metrics quantifying the mismatch between simulated and reference values and also involve analyst choices, or meta-parameters, in structuring the analysis. Here, we systematically vary five such meta-parameters (reference dataset, spatial resolution, regridding approach, land mask, and time period) in evaluating evapotranspiration (ET) from eight CMIP5 models in a factorial design that yields 68 700 intercomparisons. The results show that while model–data comparisons can provide some feedback on overall model performance, model ranks are ambiguous and inferred model skill and rank are highly sensitive to the choice of meta-parameters for all models. This suggests that model skill and rank are best represented probabilistically rather than as scalar values. For this case study, the choice of reference dataset is found to have a dominant influence on inferred model skill, even larger than the choice of model itself. This is primarily due to large differences between reference datasets, indicating that further work in developing a community-accepted standard ET reference dataset is crucial in order to decrease ambiguity in model skill. (letter)

A review on regional convection permitting climate modeling

Science.gov (United States)

van Lipzig, Nicole; Prein, Andreas; Brisson, Erwan; Van Weverberg, Kwinten; Demuzere, Matthias; Saeed, Sajjad; Stengel, Martin

2016-04-01

With the increase of computational resources, it has recently become possible to perform climate model integrations where at least part the of convection is resolved. Since convection-permitting models (CPMs) are performing better than models where convection is parameterized, especially for high-impact weather like extreme precipitation, there is currently strong scientific progress in this research domain (Prein et al., 2015). Another advantage of CPMs, that have a horizontal grid spacing climate model COSMO-CLM is frequently applied for CPM simulations, due to its non-hydrostatic dynamics and open international network of scientists. This presentation consists of an overview of the recent progress in CPM, with a focus on COSMO-CLM. It consists of three parts, namely the discussion of i) critical components of CPM, ii) the added value of CPM in the present-day climate and iii) the difference in climate sensitivity in CPM compared to coarser scale models. In terms of added value, the CPMs especially improve the representation of precipitation's, diurnal cycle, intensity and spatial distribution. However, an in depth-evaluation of cloud properties with CCLM over Belgium indicates a strong underestimation of the cloud fraction, causing an overestimation of high temperature extremes (Brisson et al., 2016). In terms of climate sensitivity, the CPMs indicate a stronger increase in flash floods, changes in hail storm characteristics, and reductions in the snowpack over mountains compared to coarser scale models. In conclusion, CPMs are a very promising tool for future climate research. However, additional efforts are necessary to overcome remaining deficiencies, like improving the cloud characteristics. This will be a challenging task due to compensating deficiencies that currently exist in `state-of-the-art' models, yielding a good representation of average climate conditions. In the light of using CPMs to study climate change it is necessary that these deficiencies

Marine low cloud sensitivity to an idealized climate change : The CGILS LES intercomparison

NARCIS (Netherlands)

Blossey, P.N.; Bretherton, C.S.; Zhang, M.; Cheng, A.; Endo, S.; Heus, T.; Liu, Y.; Lock, A.P.; De Roode, S.R.; Xu, K.M.

2013-01-01

Subtropical marine low cloud sensitivity to an idealized climate change is compared in six large-eddy simulation (LES) models as part of CGILS. July cloud cover is simulated at three locations over the subtropical northeast Pacific Ocean, which are typified by cold sea surface temperatures (SSTs)

Sensitivity of wave energy to climate change

OpenAIRE

Harrison, Gareth; Wallace, Robin

2005-01-01

Wave energy will have a key role in meeting renewable energy targets en route to a low carbon economy. However, in common with other renewables, it may be sensitive to changes in climate resulting from rising carbon emissions. Changes in wind patterns are widely anticipated and this will ultimately alter wave regimes. Indeed, evidence indicates that wave heights have been changing over the last 40 years, although there is no proven link to global warming. Changes in the wave climate will impa...

The Active Role of the Ocean in the Temporal Evolution of Climate Sensitivity

Science.gov (United States)

Garuba, Oluwayemi A.; Lu, Jian; Liu, Fukai; Singh, Hansi A.

2018-01-01

The temporal evolution of the effective climate sensitivity is shown to be influenced by the changing pattern of sea surface temperature (SST) and ocean heat uptake (OHU), which in turn have been attributed to ocean circulation changes. A set of novel experiments are performed to isolate the active role of the ocean by comparing a fully coupled CO2 quadrupling community Earth System Model (CESM) simulation against a partially coupled one, where the effect of the ocean circulation change and its impact on surface fluxes are disabled. The active OHU is responsible for the reduced effective climate sensitivity and weaker surface warming response in the fully coupled simulation. The passive OHU excites qualitatively similar feedbacks to CO2 quadrupling in a slab ocean model configuration due to the similar SST spatial pattern response in both experiments. Additionally, the nonunitary forcing efficacy of the active OHU (1.7) explains the very different net feedback parameters in the fully and partially coupled responses.

Chinese insurance agents in "bad barrels": a multilevel analysis of the relationship between ethical leadership, ethical climate and business ethical sensitivity.

Science.gov (United States)

Zhang, Na; Zhang, Jian

2016-01-01

The moral hazards and poor public image of the insurance industry, arising from insurance agents' unethical behavior, affect both the normal operation of an insurance company and decrease applicants' confidence in the company. Contrarily, these scandals may demonstrate that the organizations were "bad barrels" in which insurance agents' unethical decisions were supported or encouraged by the organization's leadership or climate. The present study brings two organization-level factors (ethical leadership and ethical climate) together and explores the role of ethical climate on the relationship between the ethical leadership and business ethical sensitivity of Chinese insurance agents. Through the multilevel analysis of 502 insurance agents from 56 organizations, it is found that organizational ethical leadership is positively related to the organizational ethical climate; organizational ethical climate is positively related to business ethical sensitivity, and organizational ethical climate fully mediates the relationship between organizational ethical leadership and business ethical sensitivity. Organizational ethical climate plays a completely mediating role in the relationship between organizational ethical leadership and business ethical sensitivity. The integrated model of ethical leadership, ethical climate and business ethical sensitivity makes several contributions to ethics theory, research and management.

Amazon collapse in the next century: exploring the sensitivity to climate and model formulation uncertainties

Science.gov (United States)

Booth, B.; Collins, M.; Harris, G.; Chris, H.; Jones, C.

2007-12-01

A number of recent studies have highlighted the risk of abrupt dieback of the Amazon Rain Forest as the result of climate changes over the next century. The recent 2005 Amazon drought brought wider acceptance of the idea that that climate drivers will play a significant role in future rain forest stability, yet that stability is still subject to considerable degree of uncertainty. We present a study which seeks to explore some of the underlying uncertainties both in the climate drivers of dieback and in the terrestrial land surface formulation used in GCMs. We adopt a perturbed physics approach which forms part of a wider project which is covered in an accompanying abstract submitted to the multi-model ensembles session. We first couple the same interactive land surface model to a number of different versions of the Hadley Centre atmosphere-ocean model that exhibit a wide range of different physical climate responses in the future. The rainforest extent is shown to collapse in all model cases but the timing of the collapse is dependent on the magnitude of the climate drivers. In the second part, we explore uncertainties in the terrestrial land surface model using the perturbed physics ensemble approach, perturbing uncertain parameters which have an important role in the vegetation and soil response. Contrasting the two approaches enables a greater understanding of the relative importance of climatic and land surface model uncertainties in Amazon dieback.

Constraining climate sensitivity and continental versus seafloor weathering using an inverse geological carbon cycle model.

Science.gov (United States)

Krissansen-Totton, Joshua; Catling, David C

2017-05-22

The relative influences of tectonics, continental weathering and seafloor weathering in controlling the geological carbon cycle are unknown. Here we develop a new carbon cycle model that explicitly captures the kinetics of seafloor weathering to investigate carbon fluxes and the evolution of atmospheric CO 2 and ocean pH since 100 Myr ago. We compare model outputs to proxy data, and rigorously constrain model parameters using Bayesian inverse methods. Assuming our forward model is an accurate representation of the carbon cycle, to fit proxies the temperature dependence of continental weathering must be weaker than commonly assumed. We find that 15-31 °C (1σ) surface warming is required to double the continental weathering flux, versus 3-10 °C in previous work. In addition, continental weatherability has increased 1.7-3.3 times since 100 Myr ago, demanding explanation by uplift and sea-level changes. The average Earth system climate sensitivity is  K (1σ) per CO 2 doubling, which is notably higher than fast-feedback estimates. These conclusions are robust to assumptions about outgassing, modern fluxes and seafloor weathering kinetics.

Determination of a lower bound on Earth's climate sensitivity

Directory of Open Access Journals (Sweden)

STEPHEN E. Schwartz

2013-09-01

Full Text Available Transient and equilibrium sensitivity of Earth's climate has been calculated using global temperature, forcing and heating rate data for the period 1970–2010. We have assumed increased long-wave radiative forcing in the period due to the increase of the long-lived greenhouse gases. By assuming the change in aerosol forcing in the period to be zero, we calculate what we consider to be lower bounds to these sensitivities, as the magnitude of the negative aerosol forcing is unlikely to have diminished in this period. The radiation imbalance necessary to calculate equilibrium sensitivity is estimated from the rate of ocean heat accumulation as 0.37±0.03 W m−2 (all uncertainty estimates are 1−σ. With these data, we obtain best estimates for transient climate sensitivity 0.39±0.07 K (W m−2−1 and equilibrium climate sensitivity 0.54±0.14 K (W m−2−1, equivalent to 1.5±0.3 and 2.0±0.5 K (3.7 W m−2−1, respectively. The latter quantity is equal to the lower bound of the ‘likely’ range for this quantity given by the 2007 IPCC Assessment Report. The uncertainty attached to the lower-bound equilibrium sensitivity permits us to state, within the assumptions of this analysis, that the equilibrium sensitivity is greater than 0.31 K (W m−2−1, equivalent to 1.16 K (3.7 W m−2−1, at the 95% confidence level.

Agricultural climate impacts assessment for economic modeling and decision support

Science.gov (United States)

Thomson, A. M.; Izaurralde, R. C.; Beach, R.; Zhang, X.; Zhao, K.; Monier, E.

2013-12-01

mitigation level of 3.7 W/m2, as well as consideration of different levels of climate sensitivity (2, 3, 4.5 and 6oC) and different initial conditions for addressing uncertainty. Since the CMIP 3 and CMIP5 protocols did not include this mitigation level or consider alternative levels of climate sensitivity, additional climate projections were required. These two cases will be discussed to illustrate some of the trade-offs made in development of methodologies for climate impact assessments that are intended for a specific user or audience, and oriented towards addressing a specific topic of interest and providing useable results. This involvement of stakeholders from the design phase of climate impacts methodology serves to both define the appropriate method for the question at hand and also to engage and inform the stakeholders of the myriad options and uncertainties associated with different methodology choices. This type of engagement should benefit decision making in the long run through greater stakeholder understanding of the science of future climate model projections, scenarios, the climate impacts sector models and the types of outputs that can be generated by each along with the respective uncertainties at each step of the climate impacts assessment process.

The ARM Cloud Radar Simulator for Global Climate Models: Bridging Field Data and Climate Models

Energy Technology Data Exchange (ETDEWEB)

Zhang, Yuying [Lawrence Livermore National Laboratory, Livermore, California; Xie, Shaocheng [Lawrence Livermore National Laboratory, Livermore, California; Klein, Stephen A. [Lawrence Livermore National Laboratory, Livermore, California; Marchand, Roger [University of Washington, Seattle, Washington; Kollias, Pavlos [Stony Brook University, Stony Brook, New York; Clothiaux, Eugene E. [The Pennsylvania State University, University Park, Pennsylvania; Lin, Wuyin [Brookhaven National Laboratory, Upton, New York; Johnson, Karen [Brookhaven National Laboratory, Upton, New York; Swales, Dustin [CIRES and NOAA/Earth System Research Laboratory, Boulder, Colorado; Bodas-Salcedo, Alejandro [Met Office Hadley Centre, Exeter, United Kingdom; Tang, Shuaiqi [Lawrence Livermore National Laboratory, Livermore, California; Haynes, John M. [Cooperative Institute for Research in the Atmosphere/Colorado State University, Fort Collins, Colorado; Collis, Scott [Argonne National Laboratory, Argonne, Illinois; Jensen, Michael [Brookhaven National Laboratory, Upton, New York; Bharadwaj, Nitin [Pacific Northwest National Laboratory, Richland, Washington; Hardin, Joseph [Pacific Northwest National Laboratory, Richland, Washington; Isom, Bradley [Pacific Northwest National Laboratory, Richland, Washington

2018-01-01

Clouds play an important role in Earth’s radiation budget and hydrological cycle. However, current global climate models (GCMs) have had difficulties in accurately simulating clouds and precipitation. To improve the representation of clouds in climate models, it is crucial to identify where simulated clouds differ from real world observations of them. This can be difficult, since significant differences exist between how a climate model represents clouds and what instruments observe, both in terms of spatial scale and the properties of the hydrometeors which are either modeled or observed. To address these issues and minimize impacts of instrument limitations, the concept of instrument “simulators”, which convert model variables into pseudo-instrument observations, has evolved with the goal to improve and to facilitate the comparison of modeled clouds with observations. Many simulators have (and continue to be developed) for a variety of instruments and purposes. A community satellite simulator package, the Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package (COSP; Bodas-Salcedo et al. 2011), contains several independent satellite simulators and is being widely used in the global climate modeling community to exploit satellite observations for model cloud evaluation (e.g., Klein et al. 2013; Zhang et al. 2010). This article introduces a ground-based cloud radar simulator developed by the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program for comparing climate model clouds with ARM observations from its vertically pointing 35-GHz radars. As compared to CloudSat radar observations, ARM radar measurements occur with higher temporal resolution and finer vertical resolution. This enables users to investigate more fully the detailed vertical structures within clouds, resolve thin clouds, and quantify the diurnal variability of clouds. Particularly, ARM radars are sensitive to low-level clouds, which are

Sensitivity of climate change in Europe to the Northern Atlantic warming

Energy Technology Data Exchange (ETDEWEB)

Timbal, B; Mahfouf, J F; Royer, J F [Centre National de Recherches Meteoroloques, Toulouse (France)

1996-12-31

The increase in atmospheric carbon dioxide since the beginning of the industrial revolution has raised the question of its impact on climate. Anthropogenic release of carbon dioxide is an extra source in the complex carbon cycle involving the ocean, the atmosphere and the biosphere. Three-dimensional general circulation models have been used world-wide over the last decade to perform climate research. Complete global change experiments need to couple an atmospheric model with an oceanic one and a thermodynamical and dynamical sea-ice model. Therefore realistic scenarios of greenhouse gas increases can be studied. These computer-time expensive experiments cannot be reproduced as often as necessary. A commonly used approach is to perform time-slice experiments at the equilibrium with an atmospheric GCM forced by Sea Surface Temperature (SST) anomalies. Several sensitivity experiments using higher resolutions or more sophisticated physical parameterisations can be performed. As the resolution increases, one can study the result over special areas of interest, such as Europe

Sensitivity of climate change in Europe to the Northern Atlantic warming

Energy Technology Data Exchange (ETDEWEB)

Timbal, B.; Mahfouf, J.F.; Royer, J.F. [Centre National de Recherches Meteoroloques, Toulouse (France)

1995-12-31

The increase in atmospheric carbon dioxide since the beginning of the industrial revolution has raised the question of its impact on climate. Anthropogenic release of carbon dioxide is an extra source in the complex carbon cycle involving the ocean, the atmosphere and the biosphere. Three-dimensional general circulation models have been used world-wide over the last decade to perform climate research. Complete global change experiments need to couple an atmospheric model with an oceanic one and a thermodynamical and dynamical sea-ice model. Therefore realistic scenarios of greenhouse gas increases can be studied. These computer-time expensive experiments cannot be reproduced as often as necessary. A commonly used approach is to perform time-slice experiments at the equilibrium with an atmospheric GCM forced by Sea Surface Temperature (SST) anomalies. Several sensitivity experiments using higher resolutions or more sophisticated physical parameterisations can be performed. As the resolution increases, one can study the result over special areas of interest, such as Europe

Climate sensitivity of shrub growth across the tundra biome

DEFF Research Database (Denmark)

Myers-Smith, Isla H.; Elmendorf, Sarah C.; Beck, Pieter S.A.

2015-01-01

Rapid climate warming in the tundra biome has been linked to increasing shrub dominance1–4. Shrub expansion can modify climate by altering surface albedo, energy and water balance, and permafrost2,5–8, yet the drivers of shrub growth remain poorly understood. Dendroecological data consisting...... of multi-decadal time series of annual shrub growth provide an underused resource to explore climate–growth relationships. Here, we analyse circumpolar data from 37 Arctic and alpine sites in 9 countries, including 25 species, and 42,000 annual growth records from 1,821 individuals. Our analyses...... demonstrate that the sensitivity of shrub growth to climate was: (1) heterogeneous, with European sites showing greater summer temperature sensitivity than North American sites, and (2) higher at sites with greater soil moisture and for taller shrubs (for example, alders and willows) growing at their northern...

Stochastic sensitivity analysis of nitrogen pollution to climate change in a river basin with complex pollution sources.

Science.gov (United States)

Yang, Xiaoying; Tan, Lit; He, Ruimin; Fu, Guangtao; Ye, Jinyin; Liu, Qun; Wang, Guoqing

2017-12-01

It is increasingly recognized that climate change could impose both direct and indirect impacts on the quality of the water environment. Previous studies have mostly concentrated on evaluating the impacts of climate change on non-point source pollution in agricultural watersheds. Few studies have assessed the impacts of climate change on the water quality of river basins with complex point and non-point pollution sources. In view of the gap, this paper aims to establish a framework for stochastic assessment of the sensitivity of water quality to future climate change in a river basin with complex pollution sources. A sub-daily soil and water assessment tool (SWAT) model was developed to simulate the discharge, transport, and transformation of nitrogen from multiple point and non-point pollution sources in the upper Huai River basin of China. A weather generator was used to produce 50 years of synthetic daily weather data series for all 25 combinations of precipitation (changes by - 10, 0, 10, 20, and 30%) and temperature change (increases by 0, 1, 2, 3, and 4 °C) scenarios. The generated daily rainfall series was disaggregated into the hourly scale and then used to drive the sub-daily SWAT model to simulate the nitrogen cycle under different climate change scenarios. Our results in the study region have indicated that (1) both total nitrogen (TN) loads and concentrations are insensitive to temperature change; (2) TN loads are highly sensitive to precipitation change, while TN concentrations are moderately sensitive; (3) the impacts of climate change on TN concentrations are more spatiotemporally variable than its impacts on TN loads; and (4) wide distributions of TN loads and TN concentrations under individual climate change scenario illustrate the important role of climatic variability in affecting water quality conditions. In summary, the large variability in SWAT simulation results within and between each climate change scenario highlights the uncertainty of

Sensitivity of groundwater recharge using climatic analogues and HYDRUS-1D

Directory of Open Access Journals (Sweden)

B. Leterme

2012-08-01

Full Text Available The sensitivity of groundwater recharge to different climate conditions was simulated using the approach of climatic analogue stations, i.e. stations presently experiencing climatic conditions corresponding to a possible future climate state. The study was conducted in the context of a safety assessment of a future near-surface disposal facility for low and intermediate level short-lived radioactive waste in Belgium; this includes estimation of groundwater recharge for the next millennia. Groundwater recharge was simulated using the Richards based soil water balance model HYDRUS-1D and meteorological time series from analogue stations. This study used four analogue stations for a warmer subtropical climate with changes of average annual precipitation and potential evapotranspiration from −42% to +5% and from +8% to +82%, respectively, compared to the present-day climate. Resulting water balance calculations yielded a change in groundwater recharge ranging from a decrease of 72% to an increase of 3% for the four different analogue stations. The Gijon analogue station (Northern Spain, considered as the most representative for the near future climate state in the study area, shows an increase of 3% of groundwater recharge for a 5% increase of annual precipitation. Calculations for a colder (tundra climate showed a change in groundwater recharge ranging from a decrease of 97% to an increase of 32% for four different analogue stations, with an annual precipitation change from −69% to −14% compared to the present-day climate.

Objectively combining AR5 instrumental period and paleoclimate climate sensitivity evidence

Science.gov (United States)

Lewis, Nicholas; Grünwald, Peter

2018-03-01

Combining instrumental period evidence regarding equilibrium climate sensitivity with largely independent paleoclimate proxy evidence should enable a more constrained sensitivity estimate to be obtained. Previous, subjective Bayesian approaches involved selection of a prior probability distribution reflecting the investigators' beliefs about climate sensitivity. Here a recently developed approach employing two different statistical methods—objective Bayesian and frequentist likelihood-ratio—is used to combine instrumental period and paleoclimate evidence based on data presented and assessments made in the IPCC Fifth Assessment Report. Probabilistic estimates from each source of evidence are represented by posterior probability density functions (PDFs) of physically-appropriate form that can be uniquely factored into a likelihood function and a noninformative prior distribution. The three-parameter form is shown accurately to fit a wide range of estimated climate sensitivity PDFs. The likelihood functions relating to the probabilistic estimates from the two sources are multiplicatively combined and a prior is derived that is noninformative for inference from the combined evidence. A posterior PDF that incorporates the evidence from both sources is produced using a single-step approach, which avoids the order-dependency that would arise if Bayesian updating were used. Results are compared with an alternative approach using the frequentist signed root likelihood ratio method. Results from these two methods are effectively identical, and provide a 5-95% range for climate sensitivity of 1.1-4.05 K (median 1.87 K).

Utilising temperature differences as constraints for estimating parameters in a simple climate model

International Nuclear Information System (INIS)

Bodman, Roger W; Karoly, David J; Enting, Ian G

2010-01-01

Simple climate models can be used to estimate the global temperature response to increasing greenhouse gases. Changes in the energy balance of the global climate system are represented by equations that necessitate the use of uncertain parameters. The values of these parameters can be estimated from historical observations, model testing, and tuning to more complex models. Efforts have been made at estimating the possible ranges for these parameters. This study continues this process, but demonstrates two new constraints. Previous studies have shown that land-ocean temperature differences are only weakly correlated with global mean temperature for natural internal climate variations. Hence, these temperature differences provide additional information that can be used to help constrain model parameters. In addition, an ocean heat content ratio can also provide a further constraint. A pulse response technique was used to identify relative parameter sensitivity which confirmed the importance of climate sensitivity and ocean vertical diffusivity, but the land-ocean warming ratio and the land-ocean heat exchange coefficient were also found to be important. Experiments demonstrate the utility of the land-ocean temperature difference and ocean heat content ratio for setting parameter values. This work is based on investigations with MAGICC (Model for the Assessment of Greenhouse-gas Induced Climate Change) as the simple climate model.

Climate change sensitivity of multi-species afforestation in semi-arid Benin

NARCIS (Netherlands)

Noulèkoun, Florent; Khamzina, Asia; Naab, Jesse B.; Khasanah, N.; Noordwijk, van Meine; Lamers, John P.A.

2018-01-01

The early growth stage is critical in the response of trees to climate change and variability. It is not clear, however, what climate metrics are best to define the early-growth sensitivity in assessing adaptation strategies of young forests to climate change. Using a combination of field

Effects of snow grain non-sphericity on climate simulations: Sensitivity tests with the NorESM model

Science.gov (United States)

Räisänen, Petri; Makkonen, Risto; Kirkevåg, Alf

2017-04-01

optically thick snowpack with a given snow grain effective size, the absorbing aerosol RE is smaller for non-spherical than for spherical snow grains. The reason for this is that due to the lower asymmetry parameter of the non-spherical snow grains, solar radiation does not penetrate as deep in snow as in the case of spherical snow grains. However, in a climate model simulation, the RE is sensitive to patterns of aerosol deposition and simulated snow cover. In fact, the global land-area mean absorbing aerosol RE is larger in the NONSPH than SPH experiment (0.193 vs. 0.168 W m-2), owing to later snowmelt in spring.

Designing ecological climate change impact assessments to reflect key climatic drivers.

Science.gov (United States)

Sofaer, Helen R; Barsugli, Joseph J; Jarnevich, Catherine S; Abatzoglou, John T; Talbert, Marian K; Miller, Brian W; Morisette, Jeffrey T

2017-07-01

Identifying the climatic drivers of an ecological system is a key step in assessing its vulnerability to climate change. The climatic dimensions to which a species or system is most sensitive - such as means or extremes - can guide methodological decisions for projections of ecological impacts and vulnerabilities. However, scientific workflows for combining climate projections with ecological models have received little explicit attention. We review Global Climate Model (GCM) performance along different dimensions of change and compare frameworks for integrating GCM output into ecological models. In systems sensitive to climatological means, it is straightforward to base ecological impact assessments on mean projected changes from several GCMs. Ecological systems sensitive to climatic extremes may benefit from what we term the 'model space' approach: a comparison of ecological projections based on simulated climate from historical and future time periods. This approach leverages the experimental framework used in climate modeling, in which historical climate simulations serve as controls for future projections. Moreover, it can capture projected changes in the intensity and frequency of climatic extremes, rather than assuming that future means will determine future extremes. Given the recent emphasis on the ecological impacts of climatic extremes, the strategies we describe will be applicable across species and systems. We also highlight practical considerations for the selection of climate models and data products, emphasizing that the spatial resolution of the climate change signal is generally coarser than the grid cell size of downscaled climate model output. Our review illustrates how an understanding of how climate model outputs are derived and downscaled can improve the selection and application of climatic data used in ecological modeling. © 2017 John Wiley & Sons Ltd.

Sensitivity of the Regional Climate in the Middle East and North Africa to Volcanic Perturbations

KAUST Repository

Dogar, Muhammad Mubashar; Stenchikov, Georgiy L.; Osipov, Sergey; Wyman, Bruce; Zhao, Ming

2017-01-01

The Middle East and North Africa (MENA) regional climate appears to be extremely sensitive to volcanic eruptions. Winter cooling after the 1991 Pinatubo eruption far exceeded the mean hemispheric temperature anomaly, even causing snowfall in Israel. To better understand MENA climate variability, the climate responses to the El Chichón and Pinatubo volcanic eruptions are analyzed using observations, NOAA/NCEP Climate Forecast System Reanalysis, and output from the Geophysical Fluid Dynamics Laboratory's High-Resolution Atmospheric Model (HiRAM). A multiple regression analysis both for the observations and the model output is performed on seasonal summer and winter composites to separate out the contributions from climate trends, El Niño Southern Oscillation (ENSO), North Atlantic Oscillation (NAO), Indian summer monsoon and volcanic aerosols. Strong regional temperature and precipitation responses over the MENA region are found in both winter and summer. The model and the observations both show that a positive NAO amplifies the MENA volcanic winter cooling. In boreal summer, the patterns of changing temperature and precipitation suggest a weakening and southward shift of the Intertropical Convergence Zone, caused by volcanic surface cooling and weakening of the Indian and West African monsoons. The model captures the main features of the climate response; however, it underestimates the total cooling, especially in winter, and exhibits a different spatial pattern of the NAO climate response in MENA compared to the observations. The conducted analysis sheds light on the internal mechanisms of MENA climate variability and helps to selectively diagnose the model deficiencies.

Sensitivity of the Regional Climate in the Middle East and North Africa to Volcanic Perturbations

KAUST Repository

Dogar, Muhammad Mubashar

2017-07-27

The Middle East and North Africa (MENA) regional climate appears to be extremely sensitive to volcanic eruptions. Winter cooling after the 1991 Pinatubo eruption far exceeded the mean hemispheric temperature anomaly, even causing snowfall in Israel. To better understand MENA climate variability, the climate responses to the El Chichón and Pinatubo volcanic eruptions are analyzed using observations, NOAA/NCEP Climate Forecast System Reanalysis, and output from the Geophysical Fluid Dynamics Laboratory\\'s High-Resolution Atmospheric Model (HiRAM). A multiple regression analysis both for the observations and the model output is performed on seasonal summer and winter composites to separate out the contributions from climate trends, El Niño Southern Oscillation (ENSO), North Atlantic Oscillation (NAO), Indian summer monsoon and volcanic aerosols. Strong regional temperature and precipitation responses over the MENA region are found in both winter and summer. The model and the observations both show that a positive NAO amplifies the MENA volcanic winter cooling. In boreal summer, the patterns of changing temperature and precipitation suggest a weakening and southward shift of the Intertropical Convergence Zone, caused by volcanic surface cooling and weakening of the Indian and West African monsoons. The model captures the main features of the climate response; however, it underestimates the total cooling, especially in winter, and exhibits a different spatial pattern of the NAO climate response in MENA compared to the observations. The conducted analysis sheds light on the internal mechanisms of MENA climate variability and helps to selectively diagnose the model deficiencies.

Sensitivity of the regional climate in the Middle East and North Africa to volcanic perturbations

Science.gov (United States)

Dogar, Muhammad Mubashar; Stenchikov, Georgiy; Osipov, Sergey; Wyman, Bruce; Zhao, Ming

2017-08-01

The Middle East and North Africa (MENA) regional climate appears to be extremely sensitive to volcanic eruptions. Winter cooling after the 1991 Pinatubo eruption far exceeded the mean hemispheric temperature anomaly, even causing snowfall in Israel. To better understand MENA climate variability, the climate responses to the El Chichón and Pinatubo volcanic eruptions are analyzed using observations, NOAA/National Centers for Environmental Prediction Climate Forecast System Reanalysis, and output from the Geophysical Fluid Dynamics Laboratory's High-Resolution Atmospheric Model. A multiple regression analysis both for the observations and the model output is performed on seasonal summer and winter composites to separate out the contributions from climate trends, El Niño-Southern Oscillation (ENSO), North Atlantic Oscillation (NAO), Indian summer monsoon, and volcanic aerosols. Strong regional temperature and precipitation responses over the MENA region are found in both winter and summer. The model and the observations both show that a positive NAO amplifies the MENA volcanic winter cooling. In boreal summer, the patterns of changing temperature and precipitation suggest a weakening and southward shift of the Intertropical Convergence Zone, caused by volcanic surface cooling and weakening of the Indian and West African monsoons. The model captures the main features of the climate response; however, it underestimates the total cooling, especially in winter, and exhibits a different spatial pattern of the NAO climate response in MENA compared to the observations. The conducted analysis sheds light on the internal mechanisms of MENA climate variability and helps to selectively diagnose the model deficiencies.

Sensitivity of regional climate to global temperature and forcing

International Nuclear Information System (INIS)

Tebaldi, Claudia; O’Neill, Brian; Lamarque, Jean-François

2015-01-01

The sensitivity of regional climate to global average radiative forcing and temperature change is important for setting global climate policy targets and designing scenarios. Setting effective policy targets requires an understanding of the consequences exceeding them, even by small amounts, and the effective design of sets of scenarios requires the knowledge of how different emissions, concentrations, or forcing need to be in order to produce substantial differences in climate outcomes. Using an extensive database of climate model simulations, we quantify how differences in global average quantities relate to differences in both the spatial extent and magnitude of climate outcomes at regional (250–1250 km) scales. We show that differences of about 0.3 °C in global average temperature are required to generate statistically significant changes in regional annual average temperature over more than half of the Earth’s land surface. A global difference of 0.8 °C is necessary to produce regional warming over half the land surface that is not only significant but reaches at least 1 °C. As much as 2.5 to 3 °C is required for a statistically significant change in regional annual average precipitation that is equally pervasive. Global average temperature change provides a better metric than radiative forcing for indicating differences in regional climate outcomes due to the path dependency of the effects of radiative forcing. For example, a difference in radiative forcing of 0.5 W m −2 can produce statistically significant differences in regional temperature over an area that ranges between 30% and 85% of the land surface, depending on the forcing pathway. (letter)

Implications of regional improvement in global climate models for agricultural impact research

International Nuclear Information System (INIS)

Ramirez-Villegas, Julian; Thornton, Philip K; Jarvis, Andy; Challinor, Andrew J

2013-01-01

Global climate models (GCMs) have become increasingly important for climate change science and provide the basis for most impact studies. Since impact models are highly sensitive to input climate data, GCM skill is crucial for getting better short-, medium- and long-term outlooks for agricultural production and food security. The Coupled Model Intercomparison Project (CMIP) phase 5 ensemble is likely to underpin the majority of climate impact assessments over the next few years. We assess 24 CMIP3 and 26 CMIP5 simulations of present climate against climate observations for five tropical regions, as well as regional improvements in model skill and, through literature review, the sensitivities of impact estimates to model error. Climatological means of seasonal mean temperatures depict mean errors between 1 and 18 ° C (2–130% with respect to mean), whereas seasonal precipitation and wet-day frequency depict larger errors, often offsetting observed means and variability beyond 100%. Simulated interannual climate variability in GCMs warrants particular attention, given that no single GCM matches observations in more than 30% of the areas for monthly precipitation and wet-day frequency, 50% for diurnal range and 70% for mean temperatures. We report improvements in mean climate skill of 5–15% for climatological mean temperatures, 3–5% for diurnal range and 1–2% in precipitation. At these improvement rates, we estimate that at least 5–30 years of CMIP work is required to improve regional temperature simulations and at least 30–50 years for precipitation simulations, for these to be directly input into impact models. We conclude with some recommendations for the use of CMIP5 in agricultural impact studies. (letter)

Tropical-extratropical climate interaction as revealed in idealized coupled climate model experiments

Energy Technology Data Exchange (ETDEWEB)

Yang, Haijun [Peking University, Department of Atmospheric Science and Laboratory for Severe Storm and Flood Disasters, School of Physics, Beijing (China); Liu, Zhengyu [University of Wisconsin-Madison, Center for Climatic Research and Department of the Atmospheric and Oceanic Sciences, Madison, WI (United States)

2005-06-01

Tropical-extratropical climate interactions are studied by idealized experiments with a prescribed 2 C SST anomaly at different latitude bands in a coupled climate model. Instead of focusing on intrinsic climate variability, this work investigates the mean climate adjustment to remote external forcing. The extratropical impact on tropical climate can be as strong as the tropical impact on extratropical climate, with the remote sea surface temperature (SST) response being about half the magnitude of the imposed SST change in the forcing region. The equatorward impact of extratropical climate is accomplished by both the atmospheric bridge and the oceanic tunnel. About two-thirds of the tropical SST change comes from the atmospheric bridge, while the remaining one-third comes from the oceanic tunnel. The equatorial SST increase is first driven by the reduced latent heat flux and the weakened poleward surface Ekman transport, and then enhanced by the decrease in subtropical cells' strength and the equatorward subduction of warm anomalies. In contrast, the poleward impact of tropical climate is accomplished mainly by the atmospheric bridge, which is responsible for extratropical temperature changes in both the surface and subsurface. Sensitivity experiments also show the dominant role of the Southern Hemisphere oceans in the tropical climate change. (orig.)

Ocean acidification over the next three centuries using a simple global climate carbon-cycle model: projections and sensitivities

Energy Technology Data Exchange (ETDEWEB)

Hartin, Corinne A.; Bond-Lamberty, Benjamin; Patel, Pralit; Mundra, Anupriya

2016-08-01

projections and sensitivity analyses, and it is capable of emulating both current observations and large-scale climate models under multiple emission pathways.

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.)

Modelling Interglacial Climate

DEFF Research Database (Denmark)

Pedersen, Rasmus Anker

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......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......, with maximum warming occurring in winter. The three scenarios all affect the climate beyond the Arctic, especially the mid-latitude circulation which is sensitive to the location of the ice loss. Together, the results presented in this thesis illustrate that the changes in the Arctic sea ice cover...

Sensitivity of the Palaeocene-Eocene Thermal Maximum climate to cloud properties.

Science.gov (United States)

Kiehl, Jeffrey T; Shields, Christine A

2013-10-28

The Palaeocene-Eocene Thermal Maximum (PETM) was a significant global warming event in the Earth's history (approx. 55 Ma). The cause for this warming event has been linked to increases in greenhouse gases, specifically carbon dioxide and methane. This rapid warming took place in the presence of the existing Early Eocene warm climate. Given that projected business-as-usual levels of atmospheric carbon dioxide reach concentrations of 800-1100 ppmv by 2100, it is of interest to study past climates where atmospheric carbon dioxide was higher than present. This is especially the case given the difficulty of climate models in simulating past warm climates. This study explores the sensitivity of the simulated pre-PETM and PETM periods to change in cloud condensation nuclei (CCN) and microphysical properties of liquid water clouds. Assuming lower levels of CCN for both of these periods leads to significant warming, especially at high latitudes. The study indicates that past differences in cloud properties may be an important factor in accurately simulating past warm climates. Importantly, additional shortwave warming from such a mechanism would imply lower required atmospheric CO2 concentrations for simulated surface temperatures to be in reasonable agreement with proxy data for the Eocene.

Contrasting model complexity under a changing climate in a headwaters catchment.

Science.gov (United States)

Foster, L.; Williams, K. H.; Maxwell, R. M.

2017-12-01

Alpine, snowmelt-dominated catchments are the source of water for more than 1/6th of the world's population. These catchments are topographically complex, leading to steep weather gradients and nonlinear relationships between water and energy fluxes. Recent evidence suggests that alpine systems are more sensitive to climate warming, but these regions are vastly simplified in climate models and operational water management tools due to computational limitations. Simultaneously, point-scale observations are often extrapolated to larger regions where feedbacks can both exacerbate or mitigate locally observed changes. It is critical to determine whether projected climate impacts are robust to different methodologies, including model complexity. Using high performance computing and an integrated model of a representative headwater catchment we determined the hydrologic response from 30 projected climate changes to precipitation, temperature and vegetation for the Rocky Mountains. Simulations were run with 100m and 1km resolution, and with and without lateral subsurface flow in order to vary model complexity. We found that model complexity alters nonlinear relationships between water and energy fluxes. Higher-resolution models predicted larger changes per degree of temperature increase than lower resolution models, suggesting that reductions to snowpack, surface water, and groundwater due to warming may be underestimated in simple models. Increases in temperature were found to have a larger impact on water fluxes and stores than changes in precipitation, corroborating previous research showing that mountain systems are significantly more sensitive to temperature changes than to precipitation changes and that increases in winter precipitation are unlikely to compensate for increased evapotranspiration in a higher energy environment. These numerical experiments help to (1) bracket the range of uncertainty in published literature of climate change impacts on headwater

Adaptation to Climate Change: A Comparative Analysis of Modeling Methods for Heat-Related Mortality.

Science.gov (United States)

Gosling, Simon N; Hondula, David M; Bunker, Aditi; Ibarreta, Dolores; Liu, Junguo; Zhang, Xinxin; Sauerborn, Rainer

2017-08-16

Multiple methods are employed for modeling adaptation when projecting the impact of climate change on heat-related mortality. The sensitivity of impacts to each is unknown because they have never been systematically compared. In addition, little is known about the relative sensitivity of impacts to "adaptation uncertainty" (i.e., the inclusion/exclusion of adaptation modeling) relative to using multiple climate models and emissions scenarios. This study had three aims: a ) Compare the range in projected impacts that arises from using different adaptation modeling methods; b ) compare the range in impacts that arises from adaptation uncertainty with ranges from using multiple climate models and emissions scenarios; c ) recommend modeling method(s) to use in future impact assessments. We estimated impacts for 2070-2099 for 14 European cities, applying six different methods for modeling adaptation; we also estimated impacts with five climate models run under two emissions scenarios to explore the relative effects of climate modeling and emissions uncertainty. The range of the difference (percent) in impacts between including and excluding adaptation, irrespective of climate modeling and emissions uncertainty, can be as low as 28% with one method and up to 103% with another (mean across 14 cities). In 13 of 14 cities, the ranges in projected impacts due to adaptation uncertainty are larger than those associated with climate modeling and emissions uncertainty. Researchers should carefully consider how to model adaptation because it is a source of uncertainty that can be greater than the uncertainty in emissions and climate modeling. We recommend absolute threshold shifts and reductions in slope. https://doi.org/10.1289/EHP634.

Uncertainty Quantification and Sensitivity Analysis in the CICE v5.1 Sea Ice Model

Science.gov (United States)

Urrego-Blanco, J. R.; Urban, N. M.

2015-12-01

Changes in the high latitude climate system have the potential to affect global climate through feedbacks with the atmosphere and connections with mid latitudes. Sea ice and climate models used to understand these changes have uncertainties that need to be characterized and quantified. In this work we characterize parametric uncertainty in Los Alamos Sea Ice model (CICE) and quantify the sensitivity of sea ice area, extent and volume with respect to uncertainty in about 40 individual model parameters. Unlike common sensitivity analyses conducted in previous studies where parameters are varied one-at-a-time, this study uses a global variance-based approach in which Sobol sequences are used to efficiently sample the full 40-dimensional parameter space. This approach requires a very large number of model evaluations, which are expensive to run. A more computationally efficient approach is implemented by training and cross-validating a surrogate (emulator) of the sea ice model with model output from 400 model runs. The emulator is used to make predictions of sea ice extent, area, and volume at several model configurations, which are then used to compute the Sobol sensitivity indices of the 40 parameters. A ranking based on the sensitivity indices indicates that model output is most sensitive to snow parameters such as conductivity and grain size, and the drainage of melt ponds. The main effects and interactions among the most influential parameters are also estimated by a non-parametric regression technique based on generalized additive models. It is recommended research to be prioritized towards more accurately determining these most influential parameters values by observational studies or by improving existing parameterizations in the sea ice model.

On the mechanisms of warming the mid-Pliocene and the inference of a hierarchy of climate sensitivities with relevance to the understanding of climate futures

Directory of Open Access Journals (Sweden)

D. Chandan

2018-06-01

timescale as the ice–albedo feedback becomes active, and then sensitivity further increases to 7.0 °C per doubling of CO2 on long timescales due to the feedback from the glacial isostatic adjustment of the Earth's surface in response to the melting of the polar ice sheets. Finally, once the slow feedbacks have stabilized, the sensitivity of the system drops to 3.35 °C per doubling of CO2. Our inference of the intermediate-timescale climate sensitivity suggests that the projected warming by 2300 CE, inferred using Earth system models of intermediate complexity on the basis of an extension to the RCP4.5 emission scenario in which atmospheric pCO2 stabilizes at roughly twice the PI level in year 2150 CE, could be underestimated by  ∼ 1 °C due to the absence of ice-sheet-based feedbacks in those models.

Sensitivity of health sector indicators' response to climate change in Ghana.

Science.gov (United States)

Dovie, Delali B K; Dzodzomenyo, Mawuli; Ogunseitan, Oladele A

2017-01-01

There is accumulating evidence that the emerging burden of global climate change threatens the fidelity of routine indicators for disease detection and management of risks to public health. The threat partially reflects the conservative character of the health sector and the reluctance to adopt new indicators, despite the growing awareness that existing environmental health indicators were developed to respond to risks that may no longer be relevant, and are too simplistic to also act as indicators for newer global-scale risk factors. This study sought to understand the scope of existing health indicators, while aiming to discover new indicators for building resilience against three climate sensitive diseases (cerebro spinal meningitis, malaria and diarrhea). Therefore, new potential indicators derived from human and biophysical origins were developed to complement existing health indicators, thereby creating climate-sensitive battery of robust composite indices of resilience in health planning. Using Ghana's health sector as a case study systematic international literature review, national expert consultation, and focus group outcomes yielded insights into the relevance, sensitivity and impacts of 45 indicators in 11 categories in responding to climate change. In total, 65% of the indicators were sensitive to health impacts of climate change; 24% acted directly; 31% synergistically; and 45% indirectly, with indicator relevance strongly associated with type of health response. Epidemiological indicators (e.g. morbidity) and health demographic indicators (e.g. population structure) require adjustments with external indicators (e.g. biophysical, policy) to be resilient to climate change. Therefore, selective integration of social and ecological indicators with existing public health indicators improves the fidelity of the health sector to adopt more robust planning of interdependent systems to build resilience. The study highlights growing uncertainties in

Modelling Bambara Groundnut Yield in Southern Africa: Towards a Climate-Resilient Future

Science.gov (United States)

Karunaratne, A. S.; Walker, S.; Ruane, A. C.

2015-01-01

Current agriculture depends on a few major species grown as monocultures that are supported by global research underpinning current productivity. However, many hundreds of alternative crops have the potential to meet real world challenges by sustaining humanity, diversifying agricultural systems for food and nutritional security, and especially responding to climate change through their resilience to certain climate conditions. Bambara groundnut (Vigna subterranea (L.) Verdc.), an underutilised African legume, is an exemplar crop for climate resilience. Predicted yield performances of Bambara groundnut by AquaCrop (a crop-water productivity model) were evaluated for baseline (1980-2009) and mid-century climates (2040-2069) under 20 downscaled Global Climate Models (CMIP5-RCP8.5), as well as for climate sensitivities (AgMIPC3MP) across 3 locations in Southern Africa (Botswana, South Africa, Namibia). Different land - races of Bambara groundnut originating from various semi-arid African locations showed diverse yield performances with diverse sensitivities to climate. S19 originating from hot-dry conditions in Namibia has greater future yield potential compared to the Swaziland landrace Uniswa Red-UN across study sites. South Africa has the lowest yield under the current climate, indicating positive future yield trends. Namibia reported the highest baseline yield at optimum current temperatures, indicating less yield potential in future climates. Bambara groundnut shows positive yield potential at temperatures of up to 31degC, with further warming pushing yields down. Thus, many regions in Southern Africa can utilize Bambara groundnut successfully in the coming decades. This modelling exercise supports decisions on genotypic suitability for present and future climates at specific locations.

Development and application of an interactive climate-ecosystem model system

Institute of Scientific and Technical Information of China (English)

CHEN Ming; D. Pollard

2003-01-01

A regional climate-ecosystem model system is developed in this study. It overcomes the weakness in traditional one-way coupling models and enables detailed description of interactive process between climate and natural ecosystem. It is applied to interaction study between monsoon climate and ecosystem in East Asia, with emphasis on future climate and ecosystem change scenario forced by doubled CO2. The climate tends to be warmer and wetter under doubled CO2 in Jianghuai and the Yangzi River valley, but it becomes warmer and drier in inland areas of northern and northwestern China. The largest changes and feedbacks between vegetation and climate occur in northern China. Northern inland ecosystems experience considerable degradation and desertification, indicating a marked sensitivity and vulnerability to climatic change. The strongest vegetation response to climate change occurs in northern China and the weakest in southern China. Vegetation feedbacks intensify warming and reduce drying due to increased CO2 during summer in northern China. Generally, vegetation-climate interactions are much stronger in northern China than in southern China.

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.

Learning About Climate and Atmospheric Models Through Machine Learning

Science.gov (United States)

Lucas, D. D.

2017-12-01

From the analysis of ensemble variability to improving simulation performance, machine learning algorithms can play a powerful role in understanding the behavior of atmospheric and climate models. To learn about model behavior, we create training and testing data sets through ensemble techniques that sample different model configurations and values of input parameters, and then use supervised machine learning to map the relationships between the inputs and outputs. Following this procedure, we have used support vector machines, random forests, gradient boosting and other methods to investigate a variety of atmospheric and climate model phenomena. We have used machine learning to predict simulation crashes, estimate the probability density function of climate sensitivity, optimize simulations of the Madden Julian oscillation, assess the impacts of weather and emissions uncertainty on atmospheric dispersion, and quantify the effects of model resolution changes on precipitation. This presentation highlights recent examples of our applications of machine learning to improve the understanding of climate and atmospheric models. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.

A method for climate and vegetation reconstruction through the inversion of a dynamic vegetation model

Energy Technology Data Exchange (ETDEWEB)

Garreta, Vincent; Guiot, Joel; Hely, Christelle [CEREGE, UMR 6635, CNRS, Universite Aix-Marseille, Europole de l' Arbois, Aix-en-Provence (France); Miller, Paul A.; Sykes, Martin T. [Lund University, Department of Physical Geography and Ecosystems Analysis, Geobiosphere Science Centre, Lund (Sweden); Brewer, Simon [Universite de Liege, Institut d' Astrophysique et de Geophysique, Liege (Belgium); Litt, Thomas [University of Bonn, Paleontological Institute, Bonn (Germany)

2010-08-15

Climate reconstructions from data sensitive to past climates provide estimates of what these climates were like. Comparing these reconstructions with simulations from climate models allows to validate the models used for future climate prediction. It has been shown that for fossil pollen data, gaining estimates by inverting a vegetation model allows inclusion of past changes in carbon dioxide values. As a new generation of dynamic vegetation model is available we have developed an inversion method for one model, LPJ-GUESS. When this novel method is used with high-resolution sediment it allows us to bypass the classic assumptions of (1) climate and pollen independence between samples and (2) equilibrium between the vegetation, represented as pollen, and climate. Our dynamic inversion method is based on a statistical model to describe the links among climate, simulated vegetation and pollen samples. The inversion is realised thanks to a particle filter algorithm. We perform a validation on 30 modern European sites and then apply the method to the sediment core of Meerfelder Maar (Germany), which covers the Holocene at a temporal resolution of approximately one sample per 30 years. We demonstrate that reconstructed temperatures are constrained. The reconstructed precipitation is less well constrained, due to the dimension considered (one precipitation by season), and the low sensitivity of LPJ-GUESS to precipitation changes. (orig.)

Process based model sheds light on climate sensitivity of Mediterranean tree-ring width

Directory of Open Access Journals (Sweden)

R. Touchan

2012-03-01

Full Text Available We use the process-based VS (Vaganov-Shashkin model to investigate whether a regional Pinus halepensis tree-ring chronology from Tunisia can be simulated as a function of climate alone by employing a biological model linking day length and daily temperature and precipitation (AD 1959–2004 from a climate station to ring-width variations. We check performance of the model on independent data by a validation exercise in which the model's parameters are tuned using data for 1982–2004 and the model is applied to generate tree-ring indices for 1959–1981. The validation exercise yields a highly significant positive correlation between the residual chronology and estimated growth curve (r=0.76 p<0.0001, n=23. The model shows that the average duration of the growing season is 191 days, with considerable variation from year to year. On average, soil moisture limits tree-ring growth for 128 days and temperature for 63 days. Model results depend on chosen values of parameters, in particular a parameter specifying a balance ratio between soil moisture and precipitation. Future work in the Mediterranean region should include multi-year natural experiments to verify patterns of cambial-growth variation suggested by the VS model.

Sensitivity of climate mitigation strategies to natural disturbances

International Nuclear Information System (INIS)

Le Page, Y; Hurtt, G; Thomson, A M; Bond-Lamberty, B; Patel, P; Wise, M; Calvin, K; Kyle, P; Clarke, L; Edmonds, J; Janetos, A

2013-01-01

The present and future concentration of atmospheric carbon dioxide depends on both anthropogenic and natural sources and sinks of carbon. Most proposed climate mitigation strategies rely on a progressive transition to carbon-efficient technologies to reduce industrial emissions, substantially supported by policies to maintain or enhance the terrestrial carbon stock in forests and other ecosystems. This strategy may be challenged if terrestrial sequestration capacity is affected by future climate feedbacks, but how and to what extent is little understood. Here, we show that climate mitigation strategies are highly sensitive to future natural disturbance rates (e.g. fires, hurricanes, droughts), because of the potential effect of disturbances on the terrestrial carbon balance. Generally, altered disturbance rates affect the pace of societal and technological transitions required to achieve the mitigation target, with substantial consequences on the energy sector and the global economy. An understanding of the future dynamics and consequences of natural disturbances on terrestrial carbon balance is thus essential for developing robust climate mitigation strategies and policies. (letter)

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...

Cross-scale intercomparison of climate change impacts simulated by regional and global hydrological models in eleven large river basins

Energy Technology Data Exchange (ETDEWEB)

Hattermann, F. F.; Krysanova, V.; Gosling, S. N.; Dankers, R.; Daggupati, P.; Donnelly, C.; Flörke, M.; Huang, S.; Motovilov, Y.; Buda, S.; Yang, T.; Müller, C.; Leng, G.; Tang, Q.; Portmann, F. T.; Hagemann, S.; Gerten, D.; Wada, Y.; Masaki, Y.; Alemayehu, T.; Satoh, Y.; Samaniego, L.

2017-01-04

Ideally, the results from models operating at different scales should agree in trend direction and magnitude of impacts under climate change. However, this implies that the sensitivity of impact models designed for either scale to climate variability and change is comparable. In this study, we compare hydrological changes simulated by 9 global and 9 regional hydrological models (HM) for 11 large river basins in all continents under reference and scenario conditions. The foci are on model validation runs, sensitivity of annual discharge to climate variability in the reference period, and sensitivity of the long-term average monthly seasonal dynamics to climate change. One major result is that the global models, mostly not calibrated against observations, often show a considerable bias in mean monthly discharge, whereas regional models show a much better reproduction of reference conditions. However, the sensitivity of two HM ensembles to climate variability is in general similar. The simulated climate change impacts in terms of long-term average monthly dynamics evaluated for HM ensemble medians and spreads show that the medians are to a certain extent comparable in some cases with distinct differences in others, and the spreads related to global models are mostly notably larger. Summarizing, this implies that global HMs are useful tools when looking at large-scale impacts of climate change and variability, but whenever impacts for a specific river basin or region are of interest, e.g. for complex water management applications, the regional-scale models validated against observed discharge should be used.

Community vulnerability to climate change in the context of other exposure-sensitivities in Kugluktuk, Nunavut

Directory of Open Access Journals (Sweden)

Laura Tozer

2011-07-01

Full Text Available Climate change in the Canadian north is, and will be, managed by communities that are already experiencing social, political, economic and other environmental changes. Hence, there is a need to understand vulnerability to climate change in the context of multiple exposure-sensitivities at the community level. This article responds to this perceived knowledge need based on a case study of the community of Kugluktuk in Nunavut, Canada. An established approach for vulnerability assessment is used to identify current climatic and non-climatic exposure-sensitivities along with their associated contemporary adaptation strategies. This assessment of current vulnerability is used as a basis to consider Kugluktuk's possible vulnerability to climatic change in the future. Current climate-related exposure-sensitivities in Kugluktuk relate primarily to subsistence harvesting and community infrastructure. Thinner and less stable ice conditions and unpredictable weather patterns are making travel and harvesting more dangerous and some community infrastructure is sensitive to permafrost melt and extreme weather events (e.g., flash floods. The ability of individuals and households to adapt to these and other climatic exposure-sensitivities is influenced by non-climatic factors that condition adaptive capacity including substance abuse, the erosion of traditional knowledge and youth suicide. These and other non-climatic factors often underpin adaptive capacity to deal with and adapt to changing conditions and must be considered in an assessment of vulnerability. This research argues that Northern communities are challenged by multiple exposure-sensitivities—beyond just those posed by climate—and effective adaptation to climate change requires consideration if not resolution of socio-economic and other issues in communities.

Emergent constraint on equilibrium climate sensitivity from global temperature variability

Science.gov (United States)

Cox, Peter M.; Huntingford, Chris; Williamson, Mark S.

2018-01-01

Equilibrium climate sensitivity (ECS) remains one of the most important unknowns in climate change science. ECS is defined as the global mean warming that would occur if the atmospheric carbon dioxide (CO2) concentration were instantly doubled and the climate were then brought to equilibrium with that new level of CO2. Despite its rather idealized definition, ECS has continuing relevance for international climate change agreements, which are often framed in terms of stabilization of global warming relative to the pre-industrial climate. However, the ‘likely’ range of ECS as stated by the Intergovernmental Panel on Climate Change (IPCC) has remained at 1.5-4.5 degrees Celsius for more than 25 years. The possibility of a value of ECS towards the upper end of this range reduces the feasibility of avoiding 2 degrees Celsius of global warming, as required by the Paris Agreement. Here we present a new emergent constraint on ECS that yields a central estimate of 2.8 degrees Celsius with 66 per cent confidence limits (equivalent to the IPCC ‘likely’ range) of 2.2-3.4 degrees Celsius. Our approach is to focus on the variability of temperature about long-term historical warming, rather than on the warming trend itself. We use an ensemble of climate models to define an emergent relationship between ECS and a theoretically informed metric of global temperature variability. This metric of variability can also be calculated from observational records of global warming, which enables tighter constraints to be placed on ECS, reducing the probability of ECS being less than 1.5 degrees Celsius to less than 3 per cent, and the probability of ECS exceeding 4.5 degrees Celsius to less than 1 per cent.

Emergent constraint on equilibrium climate sensitivity from global temperature variability.

Science.gov (United States)

Cox, Peter M; Huntingford, Chris; Williamson, Mark S

2018-01-17

Equilibrium climate sensitivity (ECS) remains one of the most important unknowns in climate change science. ECS is defined as the global mean warming that would occur if the atmospheric carbon dioxide (CO 2 ) concentration were instantly doubled and the climate were then brought to equilibrium with that new level of CO 2 . Despite its rather idealized definition, ECS has continuing relevance for international climate change agreements, which are often framed in terms of stabilization of global warming relative to the pre-industrial climate. However, the 'likely' range of ECS as stated by the Intergovernmental Panel on Climate Change (IPCC) has remained at 1.5-4.5 degrees Celsius for more than 25 years. The possibility of a value of ECS towards the upper end of this range reduces the feasibility of avoiding 2 degrees Celsius of global warming, as required by the Paris Agreement. Here we present a new emergent constraint on ECS that yields a central estimate of 2.8 degrees Celsius with 66 per cent confidence limits (equivalent to the IPCC 'likely' range) of 2.2-3.4 degrees Celsius. Our approach is to focus on the variability of temperature about long-term historical warming, rather than on the warming trend itself. We use an ensemble of climate models to define an emergent relationship between ECS and a theoretically informed metric of global temperature variability. This metric of variability can also be calculated from observational records of global warming, which enables tighter constraints to be placed on ECS, reducing the probability of ECS being less than 1.5 degrees Celsius to less than 3 per cent, and the probability of ECS exceeding 4.5 degrees Celsius to less than 1 per cent.

Sensitivity of the French Alps snow cover to the variation of climatic variables

Directory of Open Access Journals (Sweden)

E. Martin

Full Text Available In order to study the sensitivity of snow cover to changes in meteorological variables at a regional scale, a numerical snow model and an analysis system of the meteorological conditions adapted to relief were used. This approach has been successfully tested by comparing simulated and measured snow depth at 37 sites in the French Alps during a ten year data period. Then, the sensitivity of the snow cover to a variation in climatic conditions was tested by two different methods, which led to very similar results. To assess the impact of a particular "doubled CO₂" scenario, coherent perturbations were introduced in the input data of the snow model. It was found that although the impact would be very pronounced, it would also be extremely differentiated, dependent on the internal state of the snow cover. The most sensitive areas are the elevations below 2400 m, especially in the southern part of the French Alps.

Multi-scale enhancement of climate prediction over land by improving the model sensitivity to vegetation variability

Science.gov (United States)

Alessandri, A.; Catalano, F.; De Felice, M.; Hurk, B. V. D.; Doblas-Reyes, F. J.; Boussetta, S.; Balsamo, G.; Miller, P. A.

2017-12-01

Here we demonstrate, for the first time, that the implementation of a realistic representation of vegetation in Earth System Models (ESMs) can significantly improve climate simulation and prediction across multiple time-scales. The effective sub-grid vegetation fractional coverage vary seasonally and at interannual time-scales in response to leaf-canopy growth, phenology and senescence. Therefore it affects biophysical parameters such as the surface resistance to evapotranspiration, albedo, roughness lenght, and soil field capacity. To adequately represent this effect in the EC-Earth ESM, we included an exponential dependence of the vegetation cover on the Leaf Area Index.By comparing two sets of simulations performed with and without the new variable fractional-coverage parameterization, spanning from centennial (20th Century) simulations and retrospective predictions to the decadal (5-years), seasonal (2-4 months) and weather (4 days) time-scales, we show for the first time a significant multi-scale enhancement of vegetation impacts in climate simulation and prediction over land. Particularly large effects at multiple time scales are shown over boreal winter middle-to-high latitudes over Canada, West US, Eastern Europe, Russia and eastern Siberia due to the implemented time-varying shadowing effect by tree-vegetation on snow surfaces. Over Northern Hemisphere boreal forest regions the improved representation of vegetation-cover consistently correct the winter warm biases, improves the climate change sensitivity, the decadal potential predictability as well as the skill of forecasts at seasonal and weather time-scales. Significant improvements of the prediction of 2m temperature and rainfall are also shown over transitional land surface hot spots. Both the potential predictability at decadal time-scale and seasonal-forecasts skill are enhanced over Sahel, North American Great Plains, Nordeste Brazil and South East Asia, mainly related to improved performance in

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

1997-12-31

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

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

Potential Influence of Climate Change on the Acid-Sensitivity of High-Elevation Lakes in the Georgia Basin, British Columbia

Directory of Open Access Journals (Sweden)

Donna Strang

2015-01-01

Full Text Available Global climate models predict increased temperature and precipitation in the Georgia Basin, British Colmbia; however, little is known about the impacts on high-elevation regions. In the current study, fifty-four high-elevation lakes (754–2005 m a.s.l. were studied to investigate the potential influence of climate change on surface water acid-sensitivity. Redundancy analysis indicated that the concentration of nitrate, dissolved organic carbon, and associated metals was significantly influenced by climate parameters. Furthermore, these components differed significantly between biogeoclimatic zones. Modelled soil base cation weathering for a subset of the study lakes (n=11 was predicted to increase by 9% per 1°C increase in temperature. Changes in temperature and precipitation may potentially decrease the pH of surface waters owing to changes in anthropogenic deposition and organic acid production. In contrast, increased soil base cation weathering may increase the critical load (of acidity of high-elevation lakes. Ultimately, the determining factor will be whether enhanced base cation weathering is sufficient to buffer changes in natural and anthropogenic acidity. Mountain and high-elevation regions are considered early warning systems to climate change; as such, future monitoring is imperative to assess the potential ramifications of climate change on the hydrochemistry and acid-sensitivity of these surface waters.

Classifying multi-model wheat yield impact response surfaces showing sensitivity to temperature and precipitation change

Czech Academy of Sciences Publication Activity Database

Fronzek, S.; Pirttioja, N. K.; Carter, T. R.; Bindi, M.; Hoffmann, H.; Palosuo, T.; Ruiz-Ramos, M.; Tao, F.; Trnka, Miroslav; Acutis, M.; Asseng, S.; Baranowski, P.; Basso, B.; Bodin, P.; Buis, S.; Cammarano, D.; Deligios, P.; Destain, M. F.; Dumont, B.; Ewert, F.; Ferrise, R.; Francois, L.; Gaiser, T.; Hlavinka, Petr; Jacquemin, I.; Kersebaum, K. C.; Kollas, C.; Krzyszczak, J.; Lorite, I. J.; Minet, J.; Ines Minguez, M.; Montesino, M.; Moriondo, M.; Mueller, C.; Nendel, C.; Öztürk, I.; Perego, A.; Rodriguez, A.; Ruane, A. C.; Ruget, F.; Sanna, M.; Semenov, M. A.; Slawinski, C.; Stratonovitch, P.; Supit, I.; Waha, K.; Wang, E.; Wu, L.; Zhao, Z.; Rötter, R.

2018-01-01

Roč. 159, jan (2018), s. 209-224 ISSN 0308-521X Institutional support: RVO:86652079 Keywords : climate - change * crop models * probabilistic assessment * simulating impacts * british catchments * uncertainty * europe * productivity * calibration * adaptation * Classification * Climate change * Crop model * Ensemble * Sensitivity analysis * Wheat Subject RIV: GC - Agronomy OBOR OECD: Agronomy, plant breeding and plant protection Impact factor: 2.571, year: 2016

Climate change due to greenhouse effects in China as simulated by a regional climate model

Energy Technology Data Exchange (ETDEWEB)

Gao, X.J.; Zhao, Z.C.; Ding, Y.H.; Huang, R.H.; Giorgi, F. [National Climate Centre, Beijing (China)

2001-07-01

Impacts of greenhouse effects (2 x CO{sub 2}) upon climate change over China as simulated by a regional climate model over China (RegCM / China) have been investigated. The model was based on RegCM2 and was nested to a global coupled ocean-atmosphere model (CSIRO R21L9 AOGCM model). Results of the control run (1 x CO{sub 2}) indicated that simulations of surface air temperature and precipitation in China by RegCM are much better than that by the global coupled model because of a higher resolution. Results of sensitive experiment by RegCM with 2 x CO{sub 2} showed that the surface air temperature over China might increase remarkably due to greenhouse effect, especially in winter season and in North China. Precipitation might also increase in most parts of China due to the CO{sub 2} doubling.

Allowable CO2 concentrations under the United Nations Framework Convention on Climate Change as a function of the climate sensitivity probability distribution function

International Nuclear Information System (INIS)

Harvey, L D Danny

2007-01-01

Article 2 of the United Nations Framework Convention on Climate Change (UNFCCC) calls for stabilization of greenhouse gas (GHG) concentrations at levels that prevent dangerous anthropogenic interference (DAI) in the climate system. Until recently, the consensus viewpoint was that the climate sensitivity (the global mean equilibrium warming for a doubling of atmospheric CO 2 concentration) was 'likely' to fall between 1.5 and 4.5 K. However, a number of recent studies have generated probability distribution functions (pdfs) for climate sensitivity with the 95th percentile of the expected climate sensitivity as large as 10 K, while some studies suggest that the climate sensitivity is likely to fall in the lower half of the long-standing 1.5-4.5 K range. This paper examines the allowable CO 2 concentration as a function of the 95th percentile of the climate sensitivity pdf (ranging from 2 to 8 K) and for the following additional assumptions: (i) the 50th percentile for the pdf of the minimum sustained global mean warming that causes unacceptable harm equal to 1.5 or 2.5 K; and (ii) 1%, 5% or 10% allowable risks of unacceptable harm. For a 1% risk tolerance and the more stringent harm-threshold pdf, the allowable CO 2 concentration ranges from 323 to 268 ppmv as the 95th percentile of the climate sensitivity pdf increases from 2 to 8 K, while for a 10% risk tolerance and the less stringent harm-threshold pdf, the allowable CO 2 concentration ranges from 531 to 305 ppmv. In both cases it is assumed that non-CO 2 GHG radiative forcing can be reduced to half of its present value, otherwise; the allowable CO 2 concentration is even smaller. Accounting for the fact that the CO 2 concentration will gradually fall if emissions are reduced to zero, and that peak realized warming will then be less than the peak equilibrium warming (related to peak radiative forcing) allows the CO 2 concentration to peak at 10-40 ppmv higher than the limiting values given above for a climate

Assessing climate change impact by integrated hydrological modelling

Science.gov (United States)

Lajer Hojberg, Anker; Jørgen Henriksen, Hans; Olsen, Martin; der Keur Peter, van; Seaby, Lauren Paige; Troldborg, Lars; Sonnenborg, Torben; Refsgaard, Jens Christian

2013-04-01

Future climate may have a profound effect on the freshwater cycle, which must be taken into consideration by water management for future planning. Developments in the future climate are nevertheless uncertain, thus adding to the challenge of managing an uncertain system. To support the water managers at various levels in Denmark, the national water resources model (DK-model) (Højberg et al., 2012; Stisen et al., 2012) was used to propagate future climate to hydrological response under considerations of the main sources of uncertainty. The DK-model is a physically based and fully distributed model constructed on the basis of the MIKE SHE/MIKE11 model system describing groundwater and surface water systems and the interaction between the domains. The model has been constructed for the entire 43.000 km2 land area of Denmark only excluding minor islands. Future climate from General Circulation Models (GCM) was downscaled by Regional Climate Models (RCM) by a distribution-based scaling method (Seaby et al., 2012). The same dataset was used to train all combinations of GCM-RCMs and they were found to represent the mean and variance at the seasonal basis equally well. Changes in hydrological response were computed by comparing the short term development from the period 1990 - 2010 to 2021 - 2050, which is the time span relevant for water management. To account for uncertainty in future climate predictions, hydrological response from the DK-model using nine combinations of GCMs and RCMs was analysed for two catchments representing the various hydrogeological conditions in Denmark. Three GCM-RCM combinations displaying high, mean and low future impacts were selected as representative climate models for which climate impact studies were carried out for the entire country. Parameter uncertainty was addressed by sensitivity analysis and was generally found to be of less importance compared to the uncertainty spanned by the GCM-RCM combinations. Analysis of the simulations

Sensitivity of a coupled climate-carbon cycle model to large volcanic eruptions during the last millennium

Energy Technology Data Exchange (ETDEWEB)

Brovkin, Victor; Lorenz, Stephan J.; Jungclaus, Johann; Raddatz, Thomas; Timmreck, Claudia; Reick, Christian H.; Segschneider, Joachim; Six, Katharina (Max Planck Inst. for Meteorology Hamburg (Germany))

2010-11-15

The sensitivity of the climate-biogeochemistry system to volcanic eruptions is investigated using the comprehensive Earth System Model developed at the Max Planck Institute for Meteorology. The model includes an interactive carbon cycle with modules for terrestrial biosphere as well as ocean biogeochemistry. The volcanic forcing is based on a recent reconstruction for the last 1200 yr. An ensemble of five simulations is performed and the averaged response of the system is analysed in particular for the largest eruption of the last millennium in the year 1258. After this eruption, the global annual mean temperature drops by 1 K and recovers slowly during 10 yr. Atmospheric CO{sub 2} concentration declines during 4 yr after the eruption by ca. 2 ppmv to its minimum value and then starts to increase towards the pre-eruption level. This CO{sub 2} decrease is explained mainly by reduced heterotrophic respiration on land in response to the surface cooling, which leads to increased carbon storage in soils, mostly in tropical and subtropical regions. The ocean acts as a weak carbon sink, which is primarily due to temperature-induced solubility. This sink saturates 2 yr after the eruption, earlier than the land uptake.

Sensitivity of a coupled climate-carbon cycle model to large volcanic eruptions during the last millennium

International Nuclear Information System (INIS)

Brovkin, Victor; Lorenz, Stephan J.; Jungclaus, Johann; Raddatz, Thomas; Timmreck, Claudia; Reick, Christian H.; Segschneider, Joachim; Six, Katharina

2010-01-01

The sensitivity of the climate-biogeochemistry system to volcanic eruptions is investigated using the comprehensive Earth System Model developed at the Max Planck Institute for Meteorology. The model includes an interactive carbon cycle with modules for terrestrial biosphere as well as ocean biogeochemistry. The volcanic forcing is based on a recent reconstruction for the last 1200 yr. An ensemble of five simulations is performed and the averaged response of the system is analysed in particular for the largest eruption of the last millennium in the year 1258. After this eruption, the global annual mean temperature drops by 1 K and recovers slowly during 10 yr. Atmospheric CO 2 concentration declines during 4 yr after the eruption by ca. 2 ppmv to its minimum value and then starts to increase towards the pre-eruption level. This CO 2 decrease is explained mainly by reduced heterotrophic respiration on land in response to the surface cooling, which leads to increased carbon storage in soils, mostly in tropical and subtropical regions. The ocean acts as a weak carbon sink, which is primarily due to temperature-induced solubility. This sink saturates 2 yr after the eruption, earlier than the land uptake.

Regional model simulations of New Zealand climate

Science.gov (United States)

Renwick, James A.; Katzfey, Jack J.; Nguyen, Kim C.; McGregor, John L.

1998-03-01

Simulation of New Zealand climate is examined through the use of a regional climate model nested within the output of the Commonwealth Scientific and Industrial Research Organisation nine-level general circulation model (GCM). R21 resolution GCM output is used to drive a regional model run at 125 km grid spacing over the Australasian region. The 125 km run is used in turn to drive a simulation at 50 km resolution over New Zealand. Simulations with a full seasonal cycle are performed for 10 model years. The focus is on the quality of the simulation of present-day climate, but results of a doubled-CO2 run are discussed briefly. Spatial patterns of mean simulated precipitation and surface temperatures improve markedly as horizontal resolution is increased, through the better resolution of the country's orography. However, increased horizontal resolution leads to a positive bias in precipitation. At 50 km resolution, simulated frequency distributions of daily maximum/minimum temperatures are statistically similar to those of observations at many stations, while frequency distributions of daily precipitation appear to be statistically different to those of observations at most stations. Modeled daily precipitation variability at 125 km resolution is considerably less than observed, but is comparable to, or exceeds, observed variability at 50 km resolution. The sensitivity of the simulated climate to changes in the specification of the land surface is discussed briefly. Spatial patterns of the frequency of extreme temperatures and precipitation are generally well modeled. Under a doubling of CO2, the frequency of precipitation extremes changes only slightly at most locations, while air frosts become virtually unknown except at high-elevation sites.

Can nudging be used to quantify model sensitivities in precipitation and cloud forcing?: NUDGING AND MODEL SENSITIVITIES

Energy Technology Data Exchange (ETDEWEB)

Lin, Guangxing [Pacific Northwest National Laboratory, Atmospheric Science and Global Change Division, Richland Washington USA; Wan, Hui [Pacific Northwest National Laboratory, Atmospheric Science and Global Change Division, Richland Washington USA; Zhang, Kai [Pacific Northwest National Laboratory, Atmospheric Science and Global Change Division, Richland Washington USA; Qian, Yun [Pacific Northwest National Laboratory, Atmospheric Science and Global Change Division, Richland Washington USA; Ghan, Steven J. [Pacific Northwest National Laboratory, Atmospheric Science and Global Change Division, Richland Washington USA

2016-07-10

Efficient simulation strategies are crucial for the development and evaluation of high resolution climate models. This paper evaluates simulations with constrained meteorology for the quantification of parametric sensitivities in the Community Atmosphere Model version 5 (CAM5). Two parameters are perturbed as illustrating examples: the convection relaxation time scale (TAU), and the threshold relative humidity for the formation of low-level stratiform clouds (rhminl). Results suggest that the fidelity and computational efficiency of the constrained simulations depend strongly on 3 factors: the detailed implementation of nudging, the mechanism through which the perturbed parameter affects precipitation and cloud, and the magnitude of the parameter perturbation. In the case of a strong perturbation in convection, temperature and/or wind nudging with a 6-hour relaxation time scale leads to non-negligible side effects due to the distorted interactions between resolved dynamics and parameterized convection, while a 1-year free running simulation can satisfactorily capture the annual mean precipitation sensitivity in terms of both global average and geographical distribution. In the case of a relatively weak perturbation the large-scale condensation scheme, results from 1-year free-running simulations are strongly affected by noise associated with internal variability, while nudging winds effectively reduces the noise, and reasonably reproduces the response of precipitation and cloud forcing to parameter perturbation. These results indicate that caution is needed when using nudged simulations to assess precipitation and cloud forcing sensitivities to parameter changes in general circulation models. We also demonstrate that ensembles of short simulations are useful for understanding the evolution of model sensitivities.

Runoff sensitivity to climate change in the Nile River Basin

Science.gov (United States)

Hasan, Emad; Tarhule, Aondover; Kirstetter, Pierre-Emmanuel; Clark, Race; Hong, Yang

2018-06-01

In data scarce basins, such as the Nile River Basin (NRB) in Africa, constraints related to data availability, quality, and access often complicate attempts to estimate runoff sensitivity using conventional methods. In this paper, we show that by integrating the concept of the aridity index (AI) (derived from the Budyko curve) and climate elasticity, we can obtain the first order response of the runoff sensitivity using minimal data input and modeling expertise or experience. The concept of runoff elasticity relies on the fact that the energy available for evapotranspiration plays a major role in determining whether the precipitation received within a drainage basin generates runoff. The approach does not account for human impacts on runoff modification and or diversions. By making use of freely available gauge-corrected satellite data for precipitation, temperature, runoff, and potential evapotranspiration, we derived the sensitivity indicator (β) to determine the runoff response to changes in precipitation and temperature for four climatic zones in the NRB, namely, tropical, subtropical, semiarid and arid zones. The proposed sensitivity indicator can be partitioned into different elasticity components i.e: precipitation (εp), potential evapotranspiration (εETp), temperature (εT) and the total elasticity (εtot) . These elasticities allow robust quantification of the runoff response to the potential changes in precipitation and temperature with a high degree of accuracy. Results indicate that the tropical zone is energy-constrained with low sensitivity, (β 1.0) . The subtropical-highland zone moves between energy-limited to water-limited conditions during periods of wet and dry spells with varying sensitivity. The semiarid and arid zones are water limited, with high sensitivity, (β > 1.0) . The calculated runoff elasticities show that a 10% decrease in precipitation leads to a decrease in runoff of between 19% in the tropical zone and 30% in the arid zones

Crop Yield Simulations Using Multiple Regional Climate Models in the Southwestern United States

Science.gov (United States)

Stack, D.; Kafatos, M.; Kim, S.; Kim, J.; Walko, R. L.

2013-12-01

Agricultural productivity (described by crop yield) is strongly dependent on climate conditions determined by meteorological parameters (e.g., temperature, rainfall, and solar radiation). California is the largest producer of agricultural products in the United States, but crops in associated arid and semi-arid regions live near their physiological limits (e.g., in hot summer conditions with little precipitation). Thus, accurate climate data are essential in assessing the impact of climate variability on agricultural productivity in the Southwestern United States and other arid regions. To address this issue, we produced simulated climate datasets and used them as input for the crop production model. For climate data, we employed two different regional climate models (WRF and OLAM) using a fine-resolution (8km) grid. Performances of the two different models are evaluated in a fine-resolution regional climate hindcast experiment for 10 years from 2001 to 2010 by comparing them to the North American Regional Reanalysis (NARR) dataset. Based on this comparison, multi-model ensembles with variable weighting are used to alleviate model bias and improve the accuracy of crop model productivity over large geographic regions (county and state). Finally, by using a specific crop-yield simulation model (APSIM) in conjunction with meteorological forcings from the multi-regional climate model ensemble, we demonstrate the degree to which maize yields are sensitive to the regional climate in the Southwestern United States.

The AgMIP Coordinated Climate-Crop Modeling Project (C3MP): Methods and Protocols

Science.gov (United States)

Shukla, Sonali P.; Ruane, Alexander Clark

2014-01-01

Climate change is expected to alter a multitude of factors important to agricultural systems, including pests, diseases, weeds, extreme climate events, water resources, soil degradation, and socio-economic pressures. Changes to carbon dioxide concentration ([CO2]), temperature, and water (CTW) will be the primary drivers of change in crop growth and agricultural systems. Therefore, establishing the CTW-change sensitivity of crop yields is an urgent research need and warrants diverse methods of investigation. Crop models provide a biophysical, process-based tool to investigate crop responses across varying environmental conditions and farm management techniques, and have been applied in climate impact assessment by using a variety of methods (White et al., 2011, and references therein). However, there is a significant amount of divergence between various crop models' responses to CTW changes (Rotter et al., 2011). While the application of a site-based crop model is relatively simple, the coordination of such agricultural impact assessments on larger scales requires consistent and timely contributions from a large number of crop modelers, each time a new global climate model (GCM) scenario or downscaling technique is created. A coordinated, global effort to rapidly examine CTW sensitivity across multiple crops, crop models, and sites is needed to aid model development and enhance the assessment of climate impacts (Deser et al., 2012). To fulfill this need, the Coordinated Climate-Crop Modeling Project (C3MP) (Ruane et al., 2014) was initiated within the Agricultural Model Intercomparison and Improvement Project (AgMIP; Rosenzweig et al., 2013). The submitted results from C3MP Phase 1 (February 15, 2013-December 31, 2013) are currently being analyzed. This chapter serves to present and update the C3MP protocols, discuss the initial participation and general findings, comment on needed adjustments, and describe continued and future development. AgMIP aims to improve

Runoff and Evapotranspiration Sensitivities to a Changing Climate in the Western U.S.

Science.gov (United States)

Gao, M.; Xiao, M.; Lettenmaier, D. P.

2017-12-01

Climate change is likely to alter streamflow seasonal patterns, affect water availability, and otherwise pose challenges to water resources management. It is therefore important to understand how streamflow will respond to changes in climate. Previous studies have mostly focused on runoff sensitivity to precipitation (P) and temperature change, but runoff sensitivity to potential evapotranspiration (PET) is less well understood. In order to investigate how variations in precipitation and PET influence runoff, we conducted both statistical and model-based analyses of 84 near-natural basins in California, Oregon, and Washington. We obtained meteorological forcing data at 1/16 degree spatial resolution for each basin from the University of Washington/UCLA Experimental Surface Water Monitor, and observed runoff data from USGS. For the statistical method, we applied three estimators of the precipitation elasticity of runoff from previous studies. We also estimated the PET elasticity of runoff, using Penman-Monteith reference ET as a surrogate for PET. For the modelling method, we implemented the Sacramento Soil Moisture Accounting (SAC-SMA) Model, where PET is an explicit input. We performed experiments in which we changed P and PET by 1% individually to examine their effects on runoff, from which we computed the P and PET elasticities. We explore the spatial patterns in the elasticities of runoff and their relationships with basin characteristics and climatology. We also evaluate how well the statistical and model-based results meet the complementary relationship posited by Dooge (based on the Budyko Hypothesis) that the precipitation and PET elasticities of annual runoff should sum to one.

Carbon-Temperature-Water Change Analysis for Peanut Production Under Climate Change: A Prototype for the AgMIP Coordinated Climate-Crop Modeling Project (C3MP)

Science.gov (United States)

Ruane, Alex C.; McDermid, Sonali; Rosenzweig, Cynthia; Baigorria, Guillermo A.; Jones, James W.; Romero, Consuelo C.; Cecil, L. DeWayne

2014-01-01

Climate change is projected to push the limits of cropping systems and has the potential to disrupt the agricultural sector from local to global scales. This article introduces the Coordinated Climate-Crop Modeling Project (C3MP), an initiative of the Agricultural Model Intercomparison and Improvement Project (AgMIP) to engage a global network of crop modelers to explore the impacts of climate change via an investigation of crop responses to changes in carbon dioxide concentration ([CO2]), temperature, and water. As a demonstration of the C3MP protocols and enabled analyses, we apply the Decision Support System for Agrotechnology Transfer (DSSAT) CROPGRO-Peanut crop model for Henry County, Alabama, to evaluate responses to the range of plausible [CO2], temperature changes, and precipitation changes projected by climate models out to the end of the 21st century. These sensitivity tests are used to derive crop model emulators that estimate changes in mean yield and the coefficient of variation for seasonal yields across a broad range of climate conditions, reproducing mean yields from sensitivity test simulations with deviations of ca. 2% for rain-fed conditions. We apply these statistical emulators to investigate how peanuts respond to projections from various global climate models, time periods, and emissions scenarios, finding a robust projection of modest (20%) losses and larger uncertainty at the end of the century under the more severe representative concentration pathway (RCP8.5). This projection is not substantially altered by the selection of the AgMERRA global gridded climate dataset rather than the local historical observations, differences between the Third and Fifth Coupled Model Intercomparison Project (CMIP3 and CMIP5), or the use of the delta method of climate impacts analysis rather than the C3MP impacts response surface and emulator approach.

Benchmarking sensitivity of biophysical processes to leaf area changes in land surface models

Science.gov (United States)

Forzieri, Giovanni; Duveiller, Gregory; Georgievski, Goran; Li, Wei; Robestson, Eddy; Kautz, Markus; Lawrence, Peter; Ciais, Philippe; Pongratz, Julia; Sitch, Stephen; Wiltshire, Andy; Arneth, Almut; Cescatti, Alessandro

2017-04-01

Land surface models (LSM) are widely applied as supporting tools for policy-relevant assessment of climate change and its impact on terrestrial ecosystems, yet knowledge of their performance skills in representing the sensitivity of biophysical processes to changes in vegetation density is still limited. This is particularly relevant in light of the substantial impacts on regional climate associated with the changes in leaf area index (LAI) following the observed global greening. Benchmarking LSMs on the sensitivity of the simulated processes to vegetation density is essential to reduce their uncertainty and improve the representation of these effects. Here we present a novel benchmark system to assess model capacity in reproducing land surface-atmosphere energy exchanges modulated by vegetation density. Through a collaborative effort of different modeling groups, a consistent set of land surface energy fluxes and LAI dynamics has been generated from multiple LSMs, including JSBACH, JULES, ORCHIDEE, CLM4.5 and LPJ-GUESS. Relationships of interannual variations of modeled surface fluxes to LAI changes have been analyzed at global scale across different climatological gradients and compared with satellite-based products. A set of scoring metrics has been used to assess the overall model performances and a detailed analysis in the climate space has been provided to diagnose possible model errors associated to background conditions. Results have enabled us to identify model-specific strengths and deficiencies. An overall best performing model does not emerge from the analyses. However, the comparison with other models that work better under certain metrics and conditions indicates that improvements are expected to be potentially achievable. A general amplification of the biophysical processes mediated by vegetation is found across the different land surface schemes. Grasslands are characterized by an underestimated year-to-year variability of LAI in cold climates

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.

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 A.

2017-08-01

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 from centennial (twentieth century) simulations and retrospective predictions to the decadal (5-years), seasonal and weather 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 weather time-scales. Significant improvements of the prediction of 2 m 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

Sensitivity of the carbon cycle in the Arctic to climate change

Science.gov (United States)

McGuire, A. David; Anderson, Leif G.; Christensen, Torben R.; Dallimore, Scott; Guo, Laodong; Hayes, Daniel J.; Heimann, Martin; Lorenson, T.D.; Macdonald, Robie W.; Roulet, Nigel

2009-01-01

The recent warming in the Arctic is affecting a broad spectrum of physical, ecological, and human/cultural systems that may be irreversible on century time scales and have the potential to cause rapid changes in the earth system. The response of the carbon cycle of the Arctic to changes in climate is a major issue of global concern, yet there has not been a comprehensive review of the status of the contemporary carbon cycle of the Arctic and its response to climate change. This review is designed to clarify key uncertainties and vulnerabilities in the response of the carbon cycle of the Arctic to ongoing climatic change. While it is clear that there are substantial stocks of carbon in the Arctic, there are also significant uncertainties associated with the magnitude of organic matter stocks contained in permafrost and the storage of methane hydrates beneath both subterranean and submerged permafrost of the Arctic. In the context of the global carbon cycle, this review demonstrates that the Arctic plays an important role in the global dynamics of both CO2 and CH4. Studies suggest that the Arctic has been a sink for atmospheric CO2 of between 0 and 0.8 Pg C/yr in recent decades, which is between 0% and 25% of the global net land/ocean flux during the 1990s. The Arctic is a substantial source of CH4 to the atmosphere (between 32 and 112 Tg CH4/yr), primarily because of the large area of wetlands throughout the region. Analyses to date indicate that the sensitivity of the carbon cycle of the Arctic during the remainder of the 21st century is highly uncertain. To improve the capability to assess the sensitivity of the carbon cycle of the Arctic to projected climate change, we recommend that (1) integrated regional studies be conducted to link observations of carbon dynamics to the processes that are likely to influence those dynamics, and (2) the understanding gained from these integrated studies be incorporated into both uncoupled and fully coupled carbon–climate

Atmospheric sulfur and climate changes: a modelling study at mid and high-southern latitudes

International Nuclear Information System (INIS)

Castebrunet, H.

2007-09-01

The mid and high-southern latitudes are still marginally affected by anthropogenic sulfur emissions. They are the only regions in the world where the natural cycle of the atmospheric sulfur may still be observed. Sulfur aerosols are well-known for their radiative impact, and thus interact with climate. Climate can in turn affect atmospheric sulfur sources, distribution and chemistry. Antarctic ice cores provide information on the evolution of climate and sulfur deposition at the surface of the ice sheet at glacial-interglacial time scales. The aim of this thesis is to develop and use modeling towards a better understanding of the atmospheric sulfur cycle in antarctic and sub-antarctic regions. Ice core data are used to validate model results under glacial climate conditions. An Atmospheric General Circulation Model (AGCM) coupled to a sulfur chemistry module is used: the LMD-ZTSulfur model, version 4. An update of both the physical and chemical parts of the model. The model was first performed. The impact of there changes on modelled sulfur cycle are evaluated for modern climate. Further, boundary conditions are adapted to simulate the atmospheric circulation and sulfur cycle at the Last Glacial Maximum, approximately 20,000 years ago. In the model, sulfur is found to be highly sensitive to antarctic sea-ice coverage, which is still poorly known during the ice age. An original dataset of ice-age sea-ice coverage was developed. Its impact on the oceanic emissions of dimethyl sulfide, main precursor of sulfur aerosols at high-southern latitudes, is discussed. Using the same oceanic sulfur reservoirs as for present day climate, the model broadly reproduces the glacial deposits of sulfur aerosols on the Antarctic plateau, suggesting little impact of climate on oceanic sulfur production in the Antarctic region. Sensitivity tests were carried out to draw an up-to-date status of major uncertainties and difficulties facing future progress in understanding atmospheric

Generating synthetic wave climates for coastal modelling: a linear mixed modelling approach

Science.gov (United States)

Thomas, C.; Lark, R. M.

2013-12-01

Numerical coastline morphological evolution models require wave climate properties to drive morphological change through time. Wave climate properties (typically wave height, period and direction) may be temporally fixed, culled from real wave buoy data, or allowed to vary in some way defined by a Gaussian or other pdf. However, to examine sensitivity of coastline morphologies to wave climate change, it seems desirable to be able to modify wave climate time series from a current to some new state along a trajectory, but in a way consistent with, or initially conditioned by, the properties of existing data, or to generate fully synthetic data sets with realistic time series properties. For example, mean or significant wave height time series may have underlying periodicities, as revealed in numerous analyses of wave data. Our motivation is to develop a simple methodology to generate synthetic wave climate time series that can change in some stochastic way through time. We wish to use such time series in a coastline evolution model to test sensitivities of coastal landforms to changes in wave climate over decadal and centennial scales. We have worked initially on time series of significant wave height, based on data from a Waverider III buoy located off the coast of Yorkshire, England. The statistical framework for the simulation is the linear mixed model. The target variable, perhaps after transformation (Box-Cox), is modelled as a multivariate Gaussian, the mean modelled as a function of a fixed effect, and two random components, one of which is independently and identically distributed (iid) and the second of which is temporally correlated. The model was fitted to the data by likelihood methods. We considered the option of a periodic mean, the period either fixed (e.g. at 12 months) or estimated from the data. We considered two possible correlation structures for the second random effect. In one the correlation decays exponentially with time. In the second

Study of tropical clouds feedback to a climate warming as simulated by climate models

International Nuclear Information System (INIS)

Brient, Florent

2012-01-01

The last IPCC report affirms the predominant role of low cloud-radiative feedbacks in the inter-model spread of climate sensitivity. Understanding the mechanisms that control the behavior of low-level clouds is thus crucial. However, the complexity of coupled ocean-atmosphere models and the large number of processes potentially involved make the analysis of this response difficult. To simplify the analysis and to identify the most critical controls of cloud feedbacks, we analyze the cloud response to climate change simulated by the IPSL-CM5A model in a hierarchy of configurations. A comparison between three model configurations (coupled, atmospheric and aqua-planet) using the same physical parametrizations shows that the cloud response to global warming is dominated by a decrease of low clouds in regimes of moderate subsidence. Using a Single Column Model, forced by weak subsidence large-scale forcing, allows us to reproduce the vertical cloud profile predicted in the 3D model, as well as its response to climate change (if a stochastic forcing is added on vertical velocity). We analyze the sensitivity of this low-cloud response to external forcing and also to uncertain parameters of physical parameterizations involved on the atmospheric model. Through a moist static energy (MSE) budget, we highlight several mechanisms: (1) Robust: Over weak subsidence regimes, the Clausius-Clapeyron relationship predicts that a warmer atmosphere leads to a increase of the vertical MSE gradient, resulting on a strengthening of the import of low-MSE from the free atmosphere into the cloudy boundary layer. The MSE budget links changes of vertical advection and cloud radiative effects. (2) Physics Model Dependent: The coupling between shallow convection, turbulence and cloud schemes allows the intensification of low-MSE transport so that cloud radiative cooling becomes 'less necessary' to balance the energy budget (Robust positive low cloud-radiative feedback for the model). The

Sensitivity of summer stream temperatures to climate variability in the Pacific Northwest

Science.gov (United States)

Charles Luce; Brian Staab; Marc Kramer; Seth Wenger; Dan Isaak; Callie McConnell

2014-01-01

Estimating the thermal response of streams to a warming climate is important for prioritizing native fish conservation efforts. While there are plentiful estimates of air temperature responses to climate change, the sensitivity of streams, particularly small headwater streams, to warming temperatures is less well understood. A substantial body of literature correlates...

Modeling technical change in climate analysis: evidence from agricultural crop damages.

Science.gov (United States)

Ahmed, Adeel; Devadason, Evelyn S; Al-Amin, Abul Quasem

2017-05-01

This study accounts for the Hicks neutral technical change in a calibrated model of climate analysis, to identify the optimum level of technical change for addressing climate changes. It demonstrates the reduction to crop damages, the costs to technical change, and the net gains for the adoption of technical change for a climate-sensitive Pakistan economy. The calibrated model assesses the net gains of technical change for the overall economy and at the agriculture-specific level. The study finds that the gains of technical change are overwhelmingly higher than the costs across the agriculture subsectors. The gains and costs following technical change differ substantially for different crops. More importantly, the study finds a cost-effective optimal level of technical change that potentially reduces crop damages to a minimum possible level. The study therefore contends that the climate policy for Pakistan should consider the role of technical change in addressing climate impacts on the agriculture sector.

Climate change in Germany. Vulnerability and adaption of climate sensitive sectors; Klimawandel in Deutschland. Vulnerabilitaet und Anpassungsstrategien klimasensitiver Systeme

Energy Technology Data Exchange (ETDEWEB)

Zebisch, Marc; Grothmann, Torsten; Schroeter, Dagmar; Hasse, Clemens; Fritsch, Uta; Cramer, Wolfgang [Potsdam Institut fuer Klimaforschung, Potsdam (Germany)

2005-08-15

The objectives of this study were the following: documentation of existing knowledge on global change (and particularly climate change) in Germany and to analysis of its current and potential future impacts on seven climate-sensitive sectors (water management, agriculture, forestry, biodiversity/nature conservation, health, tourism and transport).; the evaluation of the present degree of adaptation and the adaptive capacity of these climate-sensitive sectors to global change; conclusions on the vulnerability to global change of sectors and regions in Germany by considering potential global change impacts, degrees of adaptation and adaptive capacity; and the discussion of the results of the study with decision-makers from government, administration, economy and society, in order to develop a basis for the development of strategies of adaptation to global change in Germany.

The transferability of hydrological models under nonstationary climatic conditions

Directory of Open Access Journals (Sweden)

C. Z. Li

2012-04-01

Full Text Available This paper investigates issues involved in calibrating hydrological models against observed data when the aim of the modelling is to predict future runoff under different climatic conditions. To achieve this objective, we tested two hydrological models, DWBM and SIMHYD, using data from 30 unimpaired catchments in Australia which had at least 60 yr of daily precipitation, potential evapotranspiration (PET, and streamflow data. Nash-Sutcliffe efficiency (NSE, modified index of agreement (d₁ and water balance error (WBE were used as performance criteria. We used a differential split-sample test to split up the data into 120 sub-periods and 4 different climatic sub-periods in order to assess how well the calibrated model could be transferred different periods. For each catchment, the models were calibrated for one sub-period and validated on the other three. Monte Carlo simulation was used to explore parameter stability compared to historic climatic variability. The chi-square test was used to measure the relationship between the distribution of the parameters and hydroclimatic variability. The results showed that the performance of the two hydrological models differed and depended on the model calibration. We found that if a hydrological model is set up to simulate runoff for a wet climate scenario then it should be calibrated on a wet segment of the historic record, and similarly a dry segment should be used for a dry climate scenario. The Monte Carlo simulation provides an effective and pragmatic approach to explore uncertainty and equifinality in hydrological model parameters. Some parameters of the hydrological models are shown to be significantly more sensitive to the choice of calibration periods. Our findings support the idea that when using conceptual hydrological models to assess future climate change impacts, a differential split-sample test and Monte Carlo simulation should be used to quantify uncertainties due to

The Canadian Centre for Climate Modelling and Analysis global coupled model and its climate

Energy Technology Data Exchange (ETDEWEB)

Flato, G.M.; Boer, G.J.; Lee, W.G.; McFarlane, N.A.; Ramsden, D.; Reader, M.C. [Canadian Centre for Climate Modelling and Analysis, Victoria, BC (Canada); Weaver, A.J. [School of Earth and Ocean Sciences, University of Victoria, BC (Canada)

2000-06-01

A global, three-dimensional climate model, developed by coupling the CCCma second-generation atmospheric general circulation model (GCM2) to a version of the GFDL modular ocean model (MOM1), forms the basis for extended simulations of past, current and projected future climate. The spin-up and coupling procedures are described, as is the resulting climate based on a 200 year model simulation with constant atmospheric composition and external forcing. The simulated climate is systematically compared to available observations in terms of mean climate quantities and their spatial patterns, temporal variability, and regional behavior. Such comparison demonstrates a generally successful reproduction of the broad features of mean climate quantities, albeit with local discrepancies. Variability is generally well-simulated over land, but somewhat underestimated in the tropical ocean and the extratropical storm-track regions. The modelled climate state shows only small trends, indicating a reasonable level of balance at the surface, which is achieved in part by the use of heat and freshwater flux adjustments. The control simulation provides a basis against which to compare simulated climate change due to historical and projected greenhouse gas and aerosol forcing as described in companion publications. (orig.)

Investigations of the Climate System Response to Climate Engineering in a Hierarchy of Models

Science.gov (United States)

McCusker, Kelly E.

. We show that upon cessation, an abrupt, spatially broad, and sustained warming over land occurs that is well outside the bounds of 20th century climate variability. We then use an upwelling-diffusion energy balance climate model to further show the sensitivity of these trends to background greenhouse gas emissions, termination year, and climate sensitivity. We find that the rate of warming from cessation of solar radiation management -- of critical importance for ecological and human systems -- is principally controlled by the background greenhouse gas concentrations. It follows that the only way to avoid the risk of an abrupt and dangerous warming that is inherent to the large-scale implementation of solar radiation management is to also strongly reduce greenhouse gas emissions. The climate system responds to radiative forcing on a diverse spectrum of timescales, which will affect what goals can be achieved for a given stratospheric aerosol implementation. We next investigate how different rates of stratospheric sulfate aerosol deployment affect what climate impacts can be avoided by simulating two rates of increasing stratospheric sulfate concentrations in a fully-coupled global climate model. We find that disparate goals are achieved for different rates of deployment; for a slow ramping of sulfate, land surface temperature trends remain small but sea levels continue to rise for decades, whereas a quick ramp-up of sulfate yields large land surface cooling trends and immediately reduces sea level. However, atmospheric circulation changes also act to create a large-scale subsurface ocean environment around Antarctica that is favorable for increased basal melting of ice sheet outlets, thereby leaving the potential open for increased sea level rise even in the absence of large atmospheric surface warming. We show that instead, when greenhouse gases are abruptly returned to preindustrial levels, circulation anomalies are reversed, and the subsurface ocean environment

A three-component analytic model of long-term climate change

Science.gov (United States)

Pratt, V. R.

2011-12-01

-term climate occurred around the 1960s. Analytic. It can be repeatedly differentiated and integrated symbolically. Future skill. Modeling only the temperature data up to 1975 with the same methodology yields parameters almost identical to those obtained by fitting to the current data. This model would therefore have predicted the dramatic rise during the fourth quarter of the century, in sharp contrast to the flat profile obtained by naive extrapolation of the preceding quarter-century. Under the two assumptions of business as usual and no major tipping points, and given the rationales for the three components, we feel this past performance justifies extrapolating the model at least another quarter century if not half. For 2100 the model projects a 2 degree rise, though it is surely wishful thinking to expect both assumptions to hold up that long. Calibrated sensitivity. Instead of assuming a fixed climate sensitivity the model makes it a function of delay between emission and temperature. Taking the delay to be 0 years (instantaneous response), 20 years (transient climate response), and 30 years (best fit for our model) gives respective sensitivities of 1.8, 2.7, and 3.3 degrees per doubling. No conclusion can be drawn about no-feedback sensitivity because the methodology builds in all feedbacks whatever they may be. Figures and further details at

Climate Model Diagnostic Analyzer

Science.gov (United States)

Lee, Seungwon; Pan, Lei; Zhai, Chengxing; Tang, Benyang; Kubar, Terry; Zhang, Zia; Wang, Wei

2015-01-01

The comprehensive and innovative evaluation of climate models with newly available global observations is critically needed for the improvement of climate model current-state representation and future-state predictability. A climate model diagnostic evaluation process requires physics-based multi-variable analyses that typically involve large-volume and heterogeneous datasets, making them both computation- and data-intensive. With an exploratory nature of climate data analyses and an explosive growth of datasets and service tools, scientists are struggling to keep track of their datasets, tools, and execution/study history, let alone sharing them with others. In response, we have developed a cloud-enabled, provenance-supported, web-service system called Climate Model Diagnostic Analyzer (CMDA). CMDA enables the physics-based, multivariable model performance evaluations and diagnoses through the comprehensive and synergistic use of multiple observational data, reanalysis data, and model outputs. At the same time, CMDA provides a crowd-sourcing space where scientists can organize their work efficiently and share their work with others. CMDA is empowered by many current state-of-the-art software packages in web service, provenance, and semantic search.

Can crop-climate models be accurate and precise? A case study for wheat production in Denmark

DEFF Research Database (Denmark)

Montesino San Martin, Manuel; Olesen, Jørgen E.; Porter, John Roy

2015-01-01

Crop models, used to make projections of climate change impacts, differ greatly in structural detail. Complexity of model structure has generic effects on uncertainty and error propagation in climate change impact assessments. We applied Bayesian calibration to three distinctly different empirical....... Yields predicted by the mechanistic model were generally more accurate than the empirical models for extrapolated conditions. This trend does not hold for all extrapolations; mechanistic and empirical models responded differently due to their sensitivities to distinct weather features. However, higher...... suitable for generic model ensembles for near-term agricultural impact assessments of climate change....

Sensitivity of Climate to Changes in NDVI

Science.gov (United States)

Bounoua, L.; Collatz, G. J.; Los, S. O.; Sellers, P. J.; Dazlich, D. A.; Tucker, C. J.; Randall, D. A.

1999-01-01

The sensitivity of global and regional climate to changes in vegetation density is investigated using a coupled biosphere-atmosphere model. The magnitude of the vegetation changes and their spatial distribution are based on natural decadal variability of the normalized difference vegetation index (ndvi). Different scenarios using maximum and minimum vegetation cover were derived from satellite records spanning the period 1982-1990. Albedo decreased in the northern latitudes and increased in the tropics with increased ndvi. The increase in vegetation density revealed that the vegetation's physiological response was constrained by the limits of the available water resources. The difference between the maximum and minimum vegetation scenarios resulted in a 46% increase in absorbed visible solar radiation and a similar increase in gross photosynthetic C02 uptake on a global annual basis. This caused the canopy transpiration and interception fluxes to increase, and reduced those from the soil. The redistribution of the surface energy fluxes substantially reduced the Bowen ratio during the growing season, resulting in cooler and moister near-surface climate, except when soil moisture was limiting. Important effects of increased vegetation on climate are : (1) A cooling of about 1.8 K in the northern latitudes during the growing season and a slight warming during the winter, which is primarily due to the masking of high albedo of snow by a denser canopy. and (2) A year round cooling of 0.8 K in the tropics. These results suggest that increases in vegetation density could partially compensate for parallel increases in greenhouse warming . Increasing vegetation density globally caused both evapotranspiration and precipitation to increase. Evapotranspiration, however increased more than precipitation resulting in a global soil-water deficit of about 15 %. A spectral analysis on the simulated results showed that changes in the state of vegetation could affect the low

Growing sensitivity of maize to water scarcity under climate change.

Science.gov (United States)

Meng, Qingfeng; Chen, Xinping; Lobell, David B; Cui, Zhenling; Zhang, Yi; Yang, Haishun; Zhang, Fusuo

2016-01-25

Climate change can reduce crop yields and thereby threaten food security. The current measures used to adapt to climate change involve avoiding crops yield decrease, however, the limitations of such measures due to water and other resources scarcity have not been well understood. Here, we quantify how the sensitivity of maize to water availability has increased because of the shift toward longer-maturing varieties during last three decades in the Chinese Maize Belt (CMB). We report that modern, longer-maturing varieties have extended the growing period by an average of 8 days and have significantly offset the negative impacts of climate change on yield. However, the sensitivity of maize production to water has increased: maize yield across the CMB was 5% lower with rainfed than with irrigated maize in the 1980s and was 10% lower (and even >20% lower in some areas) in the 2000s because of both warming and the increased requirement for water by the longer-maturing varieties. Of the maize area in China, 40% now fails to receive the precipitation required to attain the full yield potential. Opportunities for water saving in maize systems exist, but water scarcity in China remains a serious problem.

Sensitivity of precipitation to parameter values in the community atmosphere model version 5

Energy Technology Data Exchange (ETDEWEB)

Johannesson, Gardar; Lucas, Donald; Qian, Yun; Swiler, Laura Painton; Wildey, Timothy Michael

2014-03-01

One objective of the Climate Science for a Sustainable Energy Future (CSSEF) program is to develop the capability to thoroughly test and understand the uncertainties in the overall climate model and its components as they are being developed. The focus on uncertainties involves sensitivity analysis: the capability to determine which input parameters have a major influence on the output responses of interest. This report presents some initial sensitivity analysis results performed by Lawrence Livermore National Laboratory (LNNL), Sandia National Laboratories (SNL), and Pacific Northwest National Laboratory (PNNL). In the 2011-2012 timeframe, these laboratories worked in collaboration to perform sensitivity analyses of a set of CAM5, 2° runs, where the response metrics of interest were precipitation metrics. The three labs performed their sensitivity analysis (SA) studies separately and then compared results. Overall, the results were quite consistent with each other although the methods used were different. This exercise provided a robustness check of the global sensitivity analysis metrics and identified some strongly influential parameters.

Possible impact of climate change on meningitis in northwest Nigeria: an assessment using CMIP5 climate model simulations

Science.gov (United States)

Abdussalam, Auwal; Monaghan, Andrew; Steinhoff, Daniel; Dukic, Vanja; Hayden, Mary; Hopson, Thomas; Thornes, John; Leckebusch, Gregor

2014-05-01

Meningitis remains a major health burden throughout Sahelian Africa, especially in heavily-populated northwest Nigeria. Cases exhibit strong sensitivity to intra- and inter-annual climate variability, peaking during the hot and dry boreal spring months, raising concern that future climate change may increase the incidence of meningitis in the region. The impact of future climate change on meningitis risk in northwest Nigeria is assessed by forcing an empirical model of meningitis with monthly simulations from an ensemble of thirteen statistically downscaled global climate model projections from the Coupled Model Intercomparison Experiment Phase 5 (CMIP5) for RCPs 2.6, 6.0 and 8.5 scenarios. The results suggest future temperature increases due to climate change has the potential to significantly increase meningitis cases in both the early and late 21st century, and to increase the length of the meningitis season in the late century. March cases may increase from 23 per 100,000 people for present day (1990-2005), to 29-30 per 100,000 (p<0.01) in the early century (2020-2035) and 31-42 per 100,000 (p<0.01) in the late century (2060-2075), the range being dependent on the emissions scenario. It is noteworthy that these results represent the climatological potential for increased cases due to climate change, as we assume current prevention and treatment strategies remain similar in the future.

Increased sensitivity to climate change in disturbed ecosystems

DEFF Research Database (Denmark)

Kroël-Dulay, György; Ransijn, Johannes; Schmidt, Inger Kappel

2015-01-01

Human domination of the biosphere includes changes to disturbance regimes, which push many ecosystems towards early-successional states. Ecological theory predicts that early-successional ecosystems are more sensitive to perturbations than mature systems, but little evidence supports this relatio......Human domination of the biosphere includes changes to disturbance regimes, which push many ecosystems towards early-successional states. Ecological theory predicts that early-successional ecosystems are more sensitive to perturbations than mature systems, but little evidence supports...... this relationship for the perturbation of climate change. Here we show that vegetation (abundance, species richness and species composition) across seven European shrublands is quite resistant to moderate experimental warming and drought, and responsiveness is associated with the dynamic state of the ecosystem...

Climatic and basin factors affecting the flood frequency curve: PART I – A simple sensitivity analysis based on the continuous simulation approach

Directory of Open Access Journals (Sweden)

A. M. Hashemi

2000-01-01

Full Text Available Regionalized and at-site flood frequency curves exhibit considerable variability in their shapes, but the factors controlling the variability (other than sampling effects are not well understood. An application of the Monte Carlo simulation-based derived distribution approach is presented in this two-part paper to explore the influence of climate, described by simulated rainfall and evapotranspiration time series, and basin factors on the flood frequency curve (ffc. The sensitivity analysis conducted in the paper should not be interpreted as reflecting possible climate changes, but the results can provide an indication of the changes to which the flood frequency curve might be sensitive. A single site Neyman Scott point process model of rainfall, with convective and stratiform cells (Cowpertwait, 1994; 1995, has been employed to generate synthetic rainfall inputs to a rainfall runoff model. The time series of the potential evapotranspiration (ETp demand has been represented through an AR(n model with seasonal component, while a simplified version of the ARNO rainfall-runoff model (Todini, 1996 has been employed to simulate the continuous discharge time series. All these models have been parameterised in a realistic manner using observed data and results from previous applications, to obtain ‘reference’ parameter sets for a synthetic case study. Subsequently, perturbations to the model parameters have been made one-at-a-time and the sensitivities of the generated annual maximum rainfall and flood frequency curves (unstandardised, and standardised by the mean have been assessed. Overall, the sensitivity analysis described in this paper suggests that the soil moisture regime, and, in particular, the probability distribution of soil moisture content at the storm arrival time, can be considered as a unifying link between the perturbations to the several parameters and their effects on the standardised and unstandardised ffcs, thus revealing the

Modeling responses of the meadow steppe dominated by Leymus chinensis to climate change

International Nuclear Information System (INIS)

Wang, Yuhui; Zhou, Guangsheng; Wang, Yonghe

2007-01-01

Grassland is one of the most widespread vegetation types worldwide and plays a significant role in regional climate and global carbon cycling. Understanding the sensitivity of Chinese grassland ecosystems to climate change and elevated atmospheric CO2 and the effect of these changes on the grassland ecosystems is a key issue in global carbon cycling. China encompasses vast grassland areas of 354 million ha of 17 major grassland types, according to a national grassland survey. In this study, a process-based terrestrial model the CENTURY model was used to simulate potential changes in net primary productivity (NPP) and soil organic carbon (SOC) of the Leymus chinensis meadow steppe (LCMS) under different scenarios of climatic change and elevated atmospheric CO2. The LCMS sensitivities, its potential responses to climate change, and the change in capacity of carbon stock and sequestration in the future are evaluated. The results showed that the LCMS NPP and SOC are sensitive to climatic change and elevated CO2. In the next 100 years, with doubled CO2 concentration, if temperature increases from 2.7-3.9C and precipitation increases by 10% NPP and SOC will increase by 7-21% and 5-6% respectively. However, if temperature increases by 7.5-7.8C and precipitation increases by only 10% NPP and SOC would decrease by 24% and 8% respectively. Therefore, changes in the NPP and SOC of the meadow steppe are attributed mainly to the amount of temperature and precipitation change and the atmospheric CO2 concentration in the future

Is Convection Sensitive to Model Vertical Resolution and Why?

Science.gov (United States)

Xie, S.; Lin, W.; Zhang, G. J.

2017-12-01

Model sensitivity to horizontal resolutions has been studied extensively, whereas model sensitivity to vertical resolution is much less explored. In this study, we use the US Department of Energy (DOE)'s Accelerated Climate Modeling for Energy (ACME) atmosphere model to examine the sensitivity of clouds and precipitation to the increase of vertical resolution of the model. We attempt to understand what results in the behavior change (if any) of convective processes represented by the unified shallow and turbulent scheme named CLUBB (Cloud Layers Unified by Binormals) and the Zhang-McFarlane deep convection scheme in ACME. A short-term hindcast approach is used to isolate parameterization issues from the large-scale circulation. The analysis emphasizes on how the change of vertical resolution could affect precipitation partitioning between convective- and grid-scale as well as the vertical profiles of convection-related quantities such as temperature, humidity, clouds, convective heating and drying, and entrainment and detrainment. The goal is to provide physical insight into potential issues with model convective processes associated with the increase of model vertical resolution. This work performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.

Formulation of an ocean model for global climate simulations

Directory of Open Access Journals (Sweden)

S. M. Griffies

2005-01-01

Full Text Available This paper summarizes the formulation of the ocean component to the Geophysical Fluid Dynamics Laboratory's (GFDL climate model used for the 4th IPCC Assessment (AR4 of global climate change. In particular, it reviews the numerical schemes and physical parameterizations that make up an ocean climate model and how these schemes are pieced together for use in a state-of-the-art climate model. Features of the model described here include the following: (1 tripolar grid to resolve the Arctic Ocean without polar filtering, (2 partial bottom step representation of topography to better represent topographically influenced advective and wave processes, (3 more accurate equation of state, (4 three-dimensional flux limited tracer advection to reduce overshoots and undershoots, (5 incorporation of regional climatological variability in shortwave penetration, (6 neutral physics parameterization for representation of the pathways of tracer transport, (7 staggered time stepping for tracer conservation and numerical efficiency, (8 anisotropic horizontal viscosities for representation of equatorial currents, (9 parameterization of exchange with marginal seas, (10 incorporation of a free surface that accomodates a dynamic ice model and wave propagation, (11 transport of water across the ocean free surface to eliminate unphysical ``virtual tracer flux' methods, (12 parameterization of tidal mixing on continental shelves. We also present preliminary analyses of two particularly important sensitivities isolated during the development process, namely the details of how parameterized subgridscale eddies transport momentum and tracers.

Modeling glacial climates

Science.gov (United States)

North, G. R.; Crowley, T. J.

1984-01-01

Mathematical climate modelling has matured as a discipline to the point that it is useful in paleoclimatology. As an example a new two dimensional energy balance model is described and applied to several problems of current interest. The model includes the seasonal cycle and the detailed land-sea geographical distribution. By examining the changes in the seasonal cycle when external perturbations are forced upon the climate system it is possible to construct hypotheses about the origin of midlatitude ice sheets and polar ice caps. In particular the model predicts a rather sudden potential for glaciation over large areas when the Earth's orbital elements are only slightly altered. Similarly, the drift of continents or the change of atmospheric carbon dioxide over geological time induces radical changes in continental ice cover. With the advance of computer technology and improved understanding of the individual components of the climate system, these ideas will be tested in far more realistic models in the near future.

Terrestrial ecosystem model performance in simulating productivity and its vulnerability to climate change in the northern permafrost region

DEFF Research Database (Denmark)

Xia, Jianyang; McGuire, A. David; Lawrence, David

2017-01-01

productivity (NPP) and responses to historical climate change in permafrost regions in the Northern Hemisphere. In comparison with the satellite estimate from the Moderate Resolution Imaging Spectroradiometer (MODIS; 246 ± 6 g C m−2 yr−1), most models produced higher NPP (309 ± 12 g C m−2 yr−1) over...... and the maximum rate of carboxylation by the enzyme Rubisco at 25°C (Vcmax_25), respectively. The models also varied in their sensitivities of NPP, GPP, and CUE to historical changes in climate and atmospheric CO2 concentration. These results indicate that model predictive ability of the C cycle in permafrost...... regions can be improved by better representation of the processes controlling CUE and GPPmax as well as their sensitivity to climate change....

Sensitivity of the global submarine hydrate inventory to scenarios of future climate change

Science.gov (United States)

Hunter, S. J.; Goldobin, D. S.; Haywood, A. M.; Ridgwell, A.; Rees, J. G.

2013-04-01

The global submarine inventory of methane hydrate is thought to be considerable. The stability of marine hydrates is sensitive to changes in temperature and pressure and once destabilised, hydrates release methane into sediments and ocean and potentially into the atmosphere, creating a positive feedback with climate change. Here we present results from a multi-model study investigating how the methane hydrate inventory dynamically responds to different scenarios of future climate and sea level change. The results indicate that a warming-induced reduction is dominant even when assuming rather extreme rates of sea level rise (up to 20 mm yr-1) under moderate warming scenarios (RCP 4.5). Over the next century modelled hydrate dissociation is focussed in the top ˜100m of Arctic and Subarctic sediments beneath business-as-usual scenario (RCP 8.5), upper estimates of resulting global sea-floor methane fluxes could exceed estimates of natural global fluxes by 2100 (>30-50TgCH4yr-1), although subsequent oxidation in the water column could reduce peak atmospheric release rates to 0.75-1.4 Tg CH4 yr-1.

Different parts, different stories: climate sensitivity of growth is stronger in root collars vs. stems in tundra shrubs.

Science.gov (United States)

Ropars, Pascale; Angers-Blondin, Sandra; Gagnon, Marianne; Myers-Smith, Isla H; Lévesque, Esther; Boudreau, Stéphane

2017-08-01

Shrub densification has been widely reported across the circumpolar arctic and subarctic biomes in recent years. Long-term analyses based on dendrochronological techniques applied to shrubs have linked this phenomenon to climate change. However, the multi-stemmed structure of shrubs makes them difficult to sample and therefore leads to non-uniform sampling protocols among shrub ecologists, who will favor either root collars or stems to conduct dendrochronological analyses. Through a comparative study of the use of root collars and stems of Betula glandulosa, a common North American shrub species, we evaluated the relative sensitivity of each plant part to climate variables and assessed whether this sensitivity is consistent across three different types of environments in northwestern Québec, Canada (terrace, hilltop and snowbed). We found that root collars had greater sensitivity to climate than stems and that these differences were maintained across the three types of environments. Growth at the root collar was best explained by spring precipitation and summer temperature, whereas stem growth showed weak and inconsistent responses to climate variables. Moreover, sensitivity to climate was not consistent among plant parts, as individuals having climate-sensitive root collars did not tend to have climate-sensitive stems. These differences in sensitivity of shrub parts to climate highlight the complexity of resource allocation in multi-stemmed plants. Whereas stem initiation and growth are driven by microenvironmental variables such as light availability and competition, root collars integrate the growth of all plant parts instead, rendering them less affected by mechanisms such as competition and more responsive to signals of global change. Although further investigations are required to determine the degree to which these findings are generalizable across the tundra biome, our results indicate that consistency and caution in the choice of plant parts are a key

Compositional Stability of the Bacterial Community in a Climate-Sensitive Sub-Arctic Peatland.

Science.gov (United States)

Weedon, James T; Kowalchuk, George A; Aerts, Rien; Freriks, Stef; Röling, Wilfred F M; van Bodegom, Peter M

2017-01-01

The climate sensitivity of microbe-mediated soil processes such as carbon and nitrogen cycling offers an interesting case for evaluating the corresponding sensitivity of microbial community composition to environmental change. Better understanding of the degree of linkage between functional and compositional stability would contribute to ongoing efforts to build mechanistic models aiming at predicting rates of microbe-mediated processes. We used an amplicon sequencing approach to test if previously observed large effects of experimental soil warming on C and N cycle fluxes (50-100% increases) in a sub-arctic Sphagnum peatland were reflected in changes in the composition of the soil bacterial community. We found that treatments that previously induced changes to fluxes did not associate with changes in the phylogenetic composition of the soil bacterial community. For both DNA- and RNA-based analyses, variation in bacterial communities could be explained by the hierarchy: spatial variation (12-15% of variance explained) > temporal variation (7-11%) > climate treatment (4-9%). We conclude that the bacterial community in this environment is stable under changing conditions, despite the previously observed sensitivity of process rates-evidence that microbe-mediated soil processes can alter without concomitant changes in bacterial communities. We propose that progress in linking soil microbial communities to ecosystem processes can be advanced by further investigating the relative importance of community composition effects versus physico-chemical factors in controlling biogeochemical process rates in different contexts.

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

KAUST Repository

Imbers, Jara; Lopez, Ana; Huntingford, Chris; Allen, Myles

2014-01-01

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.

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.

Linking models of human behaviour and climate alters projected climate change

Science.gov (United States)

Beckage, Brian; Gross, Louis J.; Lacasse, Katherine; Carr, Eric; Metcalf, Sara S.; Winter, Jonathan M.; Howe, Peter D.; Fefferman, Nina; Franck, Travis; Zia, Asim; Kinzig, Ann; Hoffman, Forrest M.

2018-01-01

Although not considered in climate models, perceived risk stemming from extreme climate events may induce behavioural changes that alter greenhouse gas emissions. Here, we link the C-ROADS climate model to a social model of behavioural change to examine how interactions between perceived risk and emissions behaviour influence projected climate change. Our coupled climate and social model resulted in a global temperature change ranging from 3.4-6.2 °C by 2100 compared with 4.9 °C for the C-ROADS model alone, and led to behavioural uncertainty that was of a similar magnitude to physical uncertainty (2.8 °C versus 3.5 °C). Model components with the largest influence on temperature were the functional form of response to extreme events, interaction of perceived behavioural control with perceived social norms, and behaviours leading to sustained emissions reductions. Our results suggest that policies emphasizing the appropriate attribution of extreme events to climate change and infrastructural mitigation may reduce climate change the most.

Comparison of climate envelope models developed using expert-selected variables versus statistical selection

Science.gov (United States)

Brandt, Laura A.; Benscoter, Allison; Harvey, Rebecca G.; Speroterra, Carolina; Bucklin, David N.; Romañach, Stephanie; Watling, James I.; Mazzotti, Frank J.

2017-01-01

Climate envelope models are widely used to describe potential future distribution of species under different climate change scenarios. It is broadly recognized that there are both strengths and limitations to using climate envelope models and that outcomes are sensitive to initial assumptions, inputs, and modeling methods Selection of predictor variables, a central step in modeling, is one of the areas where different techniques can yield varying results. Selection of climate variables to use as predictors is often done using statistical approaches that develop correlations between occurrences and climate data. These approaches have received criticism in that they rely on the statistical properties of the data rather than directly incorporating biological information about species responses to temperature and precipitation. We evaluated and compared models and prediction maps for 15 threatened or endangered species in Florida based on two variable selection techniques: expert opinion and a statistical method. We compared model performance between these two approaches for contemporary predictions, and the spatial correlation, spatial overlap and area predicted for contemporary and future climate predictions. In general, experts identified more variables as being important than the statistical method and there was low overlap in the variable sets (0.9 for area under the curve (AUC) and >0.7 for true skill statistic (TSS). Spatial overlap, which compares the spatial configuration between maps constructed using the different variable selection techniques, was only moderate overall (about 60%), with a great deal of variability across species. Difference in spatial overlap was even greater under future climate projections, indicating additional divergence of model outputs from different variable selection techniques. Our work is in agreement with other studies which have found that for broad-scale species distribution modeling, using statistical methods of variable

Sensitivity of APSIM/ORYZA model due to estimation errors in solar radiation

Directory of Open Access Journals (Sweden)

Alexandre Bryan Heinemann

2012-01-01

Full Text Available Crop models are ideally suited to quantify existing climatic risks. However, they require historic climate data as input. While daily temperature and rainfall data are often available, the lack of observed solar radiation (Rs data severely limits site-specific crop modelling. The objective of this study was to estimate Rs based on air temperature solar radiation models and to quantify the propagation of errors in simulated radiation on several APSIM/ORYZA crop model seasonal outputs, yield, biomass, leaf area (LAI and total accumulated solar radiation (SRA during the crop cycle. The accuracy of the 5 models for estimated daily solar radiation was similar, and it was not substantially different among sites. For water limited environments (no irrigation, crop model outputs yield, biomass and LAI was not sensitive for the uncertainties in radiation models studied here.

Drought timing and local climate determine the sensitivity of eastern temperate forests to drought.

Science.gov (United States)

D'Orangeville, Loïc; Maxwell, Justin; Kneeshaw, Daniel; Pederson, Neil; Duchesne, Louis; Logan, Travis; Houle, Daniel; Arseneault, Dominique; Beier, Colin M; Bishop, Daniel A; Druckenbrod, Daniel; Fraver, Shawn; Girard, François; Halman, Joshua; Hansen, Chris; Hart, Justin L; Hartmann, Henrik; Kaye, Margot; Leblanc, David; Manzoni, Stefano; Ouimet, Rock; Rayback, Shelly; Rollinson, Christine R; Phillips, Richard P

2018-02-20

Projected changes in temperature and drought regime are likely to reduce carbon (C) storage in forests, thereby amplifying rates of climate change. While such reductions are often presumed to be greatest in semi-arid forests that experience widespread tree mortality, the consequences of drought may also be important in temperate mesic forests of Eastern North America (ENA) if tree growth is significantly curtailed by drought. Investigations of the environmental conditions that determine drought sensitivity are critically needed to accurately predict ecosystem feedbacks to climate change. We matched site factors with the growth responses to drought of 10,753 trees across mesic forests of ENA, representing 24 species and 346 stands, to determine the broad-scale drivers of drought sensitivity for the dominant trees in ENA. Here we show that two factors-the timing of drought, and the atmospheric demand for water (i.e., local potential evapotranspiration; PET)-are stronger drivers of drought sensitivity than soil and stand characteristics. Drought-induced reductions in tree growth were greatest when the droughts occurred during early-season peaks in radial growth, especially for trees growing in the warmest, driest regions (i.e., highest PET). Further, mean species trait values (rooting depth and ψ 50 ) were poor predictors of drought sensitivity, as intraspecific variation in sensitivity was equal to or greater than interspecific variation in 17 of 24 species. From a general circulation model ensemble, we find that future increases in early-season PET may exacerbate these effects, and potentially offset gains in C uptake and storage in ENA owing to other global change factors. © 2018 John Wiley & Sons Ltd.

Water-Constrained Electric Sector Capacity Expansion Modeling Under Climate Change Scenarios

Science.gov (United States)

Cohen, S. M.; Macknick, J.; Miara, A.; Vorosmarty, C. J.; Averyt, K.; Meldrum, J.; Corsi, F.; Prousevitch, A.; Rangwala, I.

2015-12-01

Over 80% of U.S. electricity generation uses a thermoelectric process, which requires significant quantities of water for power plant cooling. This water requirement exposes the electric sector to vulnerabilities related to shifts in water availability driven by climate change as well as reductions in power plant efficiencies. Electricity demand is also sensitive to climate change, which in most of the United States leads to warming temperatures that increase total cooling-degree days. The resulting demand increase is typically greater for peak demand periods. This work examines the sensitivity of the development and operations of the U.S. electric sector to the impacts of climate change using an electric sector capacity expansion model that endogenously represents seasonal and local water resource availability as well as climate impacts on water availability, electricity demand, and electricity system performance. Capacity expansion portfolios and water resource implications from 2010 to 2050 are shown at high spatial resolution under a series of climate scenarios. Results demonstrate the importance of water availability for future electric sector capacity planning and operations, especially under more extreme hotter and drier climate scenarios. In addition, region-specific changes in electricity demand and water resources require region-specific responses that depend on local renewable resource availability and electricity market conditions. Climate change and the associated impacts on water availability and temperature can affect the types of power plants that are built, their location, and their impact on regional water resources.

Assessing a Top-Down Modeling Approach for Seasonal Scale Snow Sensitivity

Science.gov (United States)

Luce, C. H.; Lute, A.

2017-12-01

Mechanistic snow models are commonly applied to assess changes to snowpacks in a warming climate. Such assessments involve a number of assumptions about details of weather at daily to sub-seasonal time scales. Models of season-scale behavior can provide contrast for evaluating behavior at time scales more in concordance with climate warming projections. Such top-down models, however, involve a degree of empiricism, with attendant caveats about the potential of a changing climate to affect calibrated relationships. We estimated the sensitivity of snowpacks from 497 Snowpack Telemetry (SNOTEL) stations in the western U.S. based on differences in climate between stations (spatial analog). We examined the sensitivity of April 1 snow water equivalent (SWE) and mean snow residence time (SRT) to variations in Nov-Mar precipitation and average Nov-Mar temperature using multivariate local-fit regressions. We tested the modeling approach using a leave-one-out cross-validation as well as targeted two-fold non-random cross-validations contrasting, for example, warm vs. cold years, dry vs. wet years, and north vs. south stations. Nash-Sutcliffe Efficiency (NSE) values for the validations were strong for April 1 SWE, ranging from 0.71 to 0.90, and still reasonable, but weaker, for SRT, in the range of 0.64 to 0.81. From these ranges, we exclude validations where the training data do not represent the range of target data. A likely reason for differences in validation between the two metrics is that the SWE model reflects the influence of conservation of mass while using temperature as an indicator of the season-scale energy balance; in contrast, SRT depends more strongly on the energy balance aspects of the problem. Model forms with lower numbers of parameters generally validated better than more complex model forms, with the caveat that pseudoreplication could encourage selection of more complex models when validation contrasts were weak. Overall, the split sample validations

Assessing climate change effects on long-term forest development: adjusting growth, phenology, and seed production in a gap model

NARCIS (Netherlands)

Meer, van der P.J.; Jorritsma, I.T.M.; Kramer, K.

2002-01-01

The sensitivity of forest development to climate change is assessed using a gap model. Process descriptions in the gap model of growth, phenology, and seed production were adjusted for climate change effects using a detailed process-based growth modeland a regression analysis. Simulation runs over

Climate simulations for the last interglacial period by means of climate models of different complexity

Energy Technology Data Exchange (ETDEWEB)

Montoya, M L [GKSS-Forschungszentrum Geesthacht GmbH (Germany). Inst. fuer Hydrophysik

1999-07-01

Climatic conditions during the lst interglacial (125,000 years before present) are investigated with two climate models of different complexity: The atmosphere-ocean general circulation model ECHAM-1/LSG and the climate system model of intermediate complexity CLIMBER-2. In particular the role of vegetation at the last interglacial maximum, and its importance for a consistent simulation of the Mid-Holocene climate, has been investigated (EU project ASPEN: Air-Sea Wave Processes in Climate Change Models). Comparison of the results of the two models reveals a broad agreement in most large-scale features. Nevertheless, discrepancies are also detected. Essentially, the models differ in their ocean circulation responses. Profiting of the fast turnaround time of CLIMBER-2, a number of sensitivity experiments have been performed to try to explain the possible reasons for these differences, and to analyze additional effects not included in the previous simulations. In particular, the role of vegetation at the last interglacial maximum has been investigated. Comparison of the simulated responses against CLIMAP reconstructed SSTs for Marine Isotope Stage 5e shows a satisfactory agreement within the data uncertainties. (orig.) [German] Die klimatischen Bedingungen waehrend der letzten interglazialen Periode (vor 125 000 Jahren) werden anhand zweier Klimamodelle unterschiedlicher Komplexitaet untersucht: Dem Ozean-Atmosphaere gekoppelten allgemeinen Zirkulationsmodell ECHAM-1/LSG und dem Klimasystemmodell mittlerer Komplexitaet CLIMBER-2. Inbesondere wurde die Rolle der Vegetation in der letzten interglazialen Periode und ihre Bedeutung fuer eine konsistente Simulation des mittelholozaenischen Klimas untersucht (EU-Projekt ASPEN: Air-Sea Wave Processes in Climate Change Models - 'Klimavariationen in historischen Zeiten'). Der Vergleich der Ergebnisse beider Modelle zeigt eine gute Uebereinstimmung der meisten der grossskaligen Eigenschaften, allerdings zeigen sich auch

Climate simulations for the last interglacial period by means of climate models of different complexity

Energy Technology Data Exchange (ETDEWEB)

Montoya, M.L. [GKSS-Forschungszentrum Geesthacht GmbH (Germany). Inst. fuer Hydrophysik

1999-07-01

Climatic conditions during the lst interglacial (125,000 years before present) are investigated with two climate models of different complexity: The atmosphere-ocean general circulation model ECHAM-1/LSG and the climate system model of intermediate complexity CLIMBER-2. In particular the role of vegetation at the last interglacial maximum, and its importance for a consistent simulation of the Mid-Holocene climate, has been investigated (EU project ASPEN: Air-Sea Wave Processes in Climate Change Models). Comparison of the results of the two models reveals a broad agreement in most large-scale features. Nevertheless, discrepancies are also detected. Essentially, the models differ in their ocean circulation responses. Profiting of the fast turnaround time of CLIMBER-2, a number of sensitivity experiments have been performed to try to explain the possible reasons for these differences, and to analyze additional effects not included in the previous simulations. In particular, the role of vegetation at the last interglacial maximum has been investigated. Comparison of the simulated responses against CLIMAP reconstructed SSTs for Marine Isotope Stage 5e shows a satisfactory agreement within the data uncertainties. (orig.) [German] Die klimatischen Bedingungen waehrend der letzten interglazialen Periode (vor 125 000 Jahren) werden anhand zweier Klimamodelle unterschiedlicher Komplexitaet untersucht: Dem Ozean-Atmosphaere gekoppelten allgemeinen Zirkulationsmodell ECHAM-1/LSG und dem Klimasystemmodell mittlerer Komplexitaet CLIMBER-2. Inbesondere wurde die Rolle der Vegetation in der letzten interglazialen Periode und ihre Bedeutung fuer eine konsistente Simulation des mittelholozaenischen Klimas untersucht (EU-Projekt ASPEN: Air-Sea Wave Processes in Climate Change Models - 'Klimavariationen in historischen Zeiten'). Der Vergleich der Ergebnisse beider Modelle zeigt eine gute Uebereinstimmung der meisten der grossskaligen Eigenschaften, allerdings zeigen sich

Sensitivity analysis of a coupled hydro-mechanical paleo-climate model of density-dependent groundwater flow in discretely fractured crystalline rock

International Nuclear Information System (INIS)

Normani, S.D.; Sykes, J.F.

2011-01-01

A high resolution three-dimensional sub-regional scale (104 km 2 ) density-dependent, discretely fractured groundwater flow model with hydro-mechanical coupling and pseudo-permafrost was developed from a larger 5734 km 2 regional-scale groundwater flow model of a Canadian Shield setting. The objective of the work is to determine the sensitivity of modelled groundwater system evolution to the hydro-mechanical parameters. The discrete fracture dual continuum numerical model FRAC3DVS-OPG was used for all simulations. A discrete fracture network model delineated from surface features was superimposed onto an approximate 790 000 element domain mesh with approximately 850 000 nodes. Orthogonal fracture faces (between adjacent finite element grid blocks) were used to best represent the irregular discrete fracture zone network. Interconnectivity of the permeable fracture zones is an important pathway for the possible migration and subsequent reduction in groundwater and contaminant residence times. The crystalline rock matrix between these structural discontinuities was assigned mechanical and flow properties characteristic of those reported for the Canadian Shield. The variation of total dissolved solids with depth was assigned using literature data for the Canadian Shield. Performance measures for the sensitivity analysis include equivalent freshwater heads, environmental heads, linear velocities, and depth of penetration by conservative non-decaying tracers released at the surface. A 121 000 year North American continental scale paleo-climate simulation was applied to the domain with ice-sheet histories estimated by the University of Toronto Glacial Systems Model (UofT GSM). Hydro-mechanical coupling between the rock matrix and the pore fluid, due to the ice sheet normal stress, was included in the simulations. The flow model included the influence of vertical strain and assumed that areal loads were homogeneous. Permafrost depth was applied as a permeability reduction

Deriving user-informed climate information from climate model ensemble results

Science.gov (United States)

Huebener, Heike; Hoffmann, Peter; Keuler, Klaus; Pfeifer, Susanne; Ramthun, Hans; Spekat, Arne; Steger, Christian; Warrach-Sagi, Kirsten

2017-07-01

Communication between providers and users of climate model simulation results still needs to be improved. In the German regional climate modeling project ReKliEs-De a midterm user workshop was conducted to allow the intended users of the project results to assess the preliminary results and to streamline the final project results to their needs. The user feedback highlighted, in particular, the still considerable gap between climate research output and user-tailored input for climate impact research. Two major requests from the user community addressed the selection of sub-ensembles and some condensed, easy to understand information on the strengths and weaknesses of the climate models involved in the project.

Potential Sensitivity of Québec's Breeding Birds to Climate Change

Directory of Open Access Journals (Sweden)

Jean-Luc DesGranges

2010-12-01

Full Text Available We examined the relationship between climatic factors and the distribution of breeding birds in southern Québec, Canada to identify the species whose distribution renders them potentially sensitive to climate change in the study area. We determined the degree of association between the distribution of 65 breeding bird species (601 presence-absence squares of the Atlas of the Breeding Birds of Québec and climate variables (212 climatological stations in operation for at least 20 years over the period 1953-1984 by statistically correcting for the effects of several factors that are correlated with bird distribution. Factors considered were the nature and scale of land cover patterns that included vegetation types and landscape characterization, geographical coordinates, and elevation. Canonical Correspondence Analysis (CCA was used to investigate the effect of climatic variables on breeding bird distribution. Independent variables accounted for a total of 29.1% of the variation in the species matrix. A very large portion of the variance explained by climate variables was shared with spatial variables, reflecting the relationships among latitude, longitude, elevation, and climate. After correcting for the effect of land cover variables, climatic variables still explained 11.4% of the variation in the species matrix, with temperature, i.e., warmer summers and milder winters, having a greater influence than precipitation, i.e., wetter summers. Of the 65 species, 14 appeared to be particularly climate-sensitive. Eight are insectivorous neotropical migrants and six species are at the northern limit of their range in the study area. The opposite is largely true for the eight others; they are practically absent from the southern part of the study area, except for the Dark-eyed Junco (Junco hyemalis, which is widespread there. The White-breasted Nuthatch (Sitta carolinensis is the only resident species that seemed responsive to climatic variables, i

Assessing climate impact on reinforced concrete durability with a multi-physics model

DEFF Research Database (Denmark)

Michel, Alexander; Flint, Madeleine M.

to shorter-term fluctuations in boundary conditions and therefore may underestimate climate change impacts. A highly sensitive fully-coupled, validated, multi-physics model for heat, moisture and ion transport and corrosion was used to assess a reinforced concrete structure located in coastal Norfolk...

Sensitivity analysis of the GEMS soil organic carbon model to land cover land use classification uncertainties under different climate scenarios in Senegal

Science.gov (United States)

Dieye, A.M.; Roy, David P.; Hanan, N.P.; Liu, S.; Hansen, M.; Toure, A.

2012-01-01

Spatially explicit land cover land use (LCLU) change information is needed to drive biogeochemical models that simulate soil organic carbon (SOC) dynamics. Such information is increasingly being mapped using remotely sensed satellite data with classification schemes and uncertainties constrained by the sensing system, classification algorithms and land cover schemes. In this study, automated LCLU classification of multi-temporal Landsat satellite data were used to assess the sensitivity of SOC modeled by the Global Ensemble Biogeochemical Modeling System (GEMS). The GEMS was run for an area of 1560 km2 in Senegal under three climate change scenarios with LCLU maps generated using different Landsat classification approaches. This research provides a method to estimate the variability of SOC, specifically the SOC uncertainty due to satellite classification errors, which we show is dependent not only on the LCLU classification errors but also on where the LCLU classes occur relative to the other GEMS model inputs.

Modeling climate and fuel reduction impacts on mixed-conifer forest carbon stocks in the Sierra Nevada, California

Science.gov (United States)

Matthew D. Hurteau; Timothy A. Robards; Donald Stevens; David Saah; Malcolm North; George W. Koch

2014-01-01

Quantifying the impacts of changing climatic conditions on forest growth is integral to estimating future forest carbon balance. We used a growth-and-yield model, modified for climate sensitivity, to quantify the effects of altered climate on mixed-conifer forest growth in the Lake Tahoe Basin, California. Estimates of forest growth and live tree carbon stocks were...

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.

Failure analysis of parameter-induced simulation crashes in climate models

Science.gov (United States)

Lucas, D. D.; Klein, R.; Tannahill, J.; Ivanova, D.; Brandon, S.; Domyancic, D.; Zhang, Y.

2013-08-01

Simulations using IPCC (Intergovernmental Panel on Climate Change)-class climate models are subject to fail or crash for a variety of reasons. Quantitative analysis of the failures can yield useful insights to better understand and improve the models. During the course of uncertainty quantification (UQ) ensemble simulations to assess the effects of ocean model parameter uncertainties on climate simulations, we experienced a series of simulation crashes within the Parallel Ocean Program (POP2) component of the Community Climate System Model (CCSM4). About 8.5% of our CCSM4 simulations failed for numerical reasons at combinations of POP2 parameter values. We applied support vector machine (SVM) classification from machine learning to quantify and predict the probability of failure as a function of the values of 18 POP2 parameters. A committee of SVM classifiers readily predicted model failures in an independent validation ensemble, as assessed by the area under the receiver operating characteristic (ROC) curve metric (AUC > 0.96). The causes of the simulation failures were determined through a global sensitivity analysis. Combinations of 8 parameters related to ocean mixing and viscosity from three different POP2 parameterizations were the major sources of the failures. This information can be used to improve POP2 and CCSM4 by incorporating correlations across the relevant parameters. Our method can also be used to quantify, predict, and understand simulation crashes in other complex geoscientific models.

Can model weighting improve probabilistic projections of climate change?

Energy Technology Data Exchange (ETDEWEB)

Raeisaenen, Jouni; Ylhaeisi, Jussi S. [Department of Physics, P.O. Box 48, University of Helsinki (Finland)

2012-10-15

Recently, Raeisaenen and co-authors proposed a weighting scheme in which the relationship between observable climate and climate change within a multi-model ensemble determines to what extent agreement with observations affects model weights in climate change projection. Within the Third Coupled Model Intercomparison Project (CMIP3) dataset, this scheme slightly improved the cross-validated accuracy of deterministic projections of temperature change. Here the same scheme is applied to probabilistic temperature change projection, under the strong limiting assumption that the CMIP3 ensemble spans the actual modeling uncertainty. Cross-validation suggests that probabilistic temperature change projections may also be improved by this weighting scheme. However, the improvement relative to uniform weighting is smaller in the tail-sensitive logarithmic score than in the continuous ranked probability score. The impact of the weighting on projection of real-world twenty-first century temperature change is modest in most parts of the world. However, in some areas mainly over the high-latitude oceans, the mean of the distribution is substantially changed and/or the distribution is considerably narrowed. The weights of individual models vary strongly with location, so that a model that receives nearly zero weight in some area may still get a large weight elsewhere. Although the details of this variation are method-specific, it suggests that the relative strengths of different models may be difficult to harness by weighting schemes that use spatially uniform model weights. (orig.)

On coupling global biome models with climate models

International Nuclear Information System (INIS)

Claussen, M.

1994-01-01

The BIOME model of Prentice et al. (1992), which predicts global vegetation patterns in equilibrium with climate, is coupled with the ECHAM climate model of the Max-Planck-Institut fuer Meteorologie, Hamburg. It is found that incorporation of the BIOME model into ECHAM, regardless at which frequency, does not enhance the simulated climate variability, expressed in terms of differences between global vegetation patterns. Strongest changes are seen only between the initial biome distribution and the biome distribution computed after the first simulation period, provided that the climate-biome model is started from a biome distribution that resembles the present-day distribution. After the first simulation period, there is no significant shrinking, expanding, or shifting of biomes. Likewise, no trend is seen in global averages of land-surface parameters and climate variables. (orig.)

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

78 FR 13874 - Watershed Modeling To Assess the Sensitivity of Streamflow, Nutrient, and Sediment Loads to...

Science.gov (United States)

2013-03-01

... EPA's policy to include all comments it receives in the public docket without change and to make the... Modeling To Assess the Sensitivity of Streamflow, Nutrient, and Sediment Loads to Climate Change and Urban... Loads to Climate Change and Urban Development in 20 U.S. Watersheds (EPA/600/R-12/058). EPA also is...

Anticipating the uncertain: economic modeling and climate change policy

Energy Technology Data Exchange (ETDEWEB)

Jensen, Svenn

2012-11-01

With this thesis I wish to contribute to the understanding of how uncertainty and the anticipation of future events by economic actors affect climate policies. The thesis consists of four papers. Two papers are analytical models which explicitly consider that emissions are caused by extracting scarce fossil fuels which in the future must be replaced by clean technologies. The other two are so called numerical integrated assessment models. Such models represent the world economy, the climate system and the interactions between those two quantitatively, complementing more abstract theoretical work. Should policy makers discriminate between subsidizing renewable energy sources such as wind or solar power, and technologies such as carbon capture and storage (CCS)? Focusing only on the dynamic supply of fossil fuels and hence Co{sub 2}, we find here that cheaper future renewables cause extraction to speed up, lower costs of CCS may delay it. CCS hence may dampen the dynamic inefficiency caused by the absence of comprehensive climate policies today. Does it matter whether uncertainty about future damage assessment is due to scientific complexities or stems from the political process? In paper two, I find that political and scientific uncertainties have opposing effects on the incentives to investment in renewables and the extraction of fossil fuels: The prospect of scientific learning about the climate system increases investment incentives and, ceteris paribus, slows extraction down; uncertainty about future political constellations does the opposite. The optimal carbon tax under scientific uncertainty equals expected marginal damages, whereas political uncertainty demands a tax below marginal damages that decreases over time. Does uncertainty about economic growth impact optimal climate policy today? Here we are the first to consistently analyze how uncertainty about future economic growth affects optimal emission reductions and the optimal social cost of carbon. We

Agricultural drought in a future climate: results from 15 global climate models participating in the IPCC 4th assessment

Science.gov (United States)

Wang, Guiling

2005-12-01

This study examines the impact of greenhouse gas warming on soil moisture based on predictions of 15 global climate models by comparing the after-stabilization climate in the SRESA1b experiment with the pre-industrial control climate. The models are consistent in predicting summer dryness and winter wetness in only part of the northern middle and high latitudes. Slightly over half of the models predict year-round wetness in central Eurasia and/or year-round dryness in Siberia and mid-latitude Northeast Asia. One explanation is offered that relates such lack of seasonality to the carryover effect of soil moisture storage from season to season. In the tropics and subtropics, a decrease of soil moisture is the dominant response. The models are especially consistent in predicting drier soil over the southwest North America, Central America, the Mediterranean, Australia, and the South Africa in all seasons, and over much of the Amazon and West Africa in the June July August (JJA) season and the Asian monsoon region in the December January February (DJF) season. Since the only major areas of future wetness predicted with a high level of model consistency are part of the northern middle and high latitudes during the non-growing season, it is suggested that greenhouse gas warming will cause a worldwide agricultural drought. Over regions where there is considerable consistency among the analyzed models in predicting the sign of soil moisture changes, there is a wide range of magnitudes of the soil moisture response, indicating a high degree of model dependency in terrestrial hydrological sensitivity. A major part of the inter-model differences in the sensitivity of soil moisture response are attributable to differences in land surface parameterization.

Interactions of Vegetation and Climate: Remote Observations, Earth System Models, and the Amazon Forest

Science.gov (United States)

Quetin, Gregory R.

The natural composition of terrestrial ecosystems can be shaped by climate to take advantage of local environmental conditions. Ecosystem functioning, e.g. interaction between photosynthesis and temperature, can also acclimate to different climatological states. The combination of these two factors thus determines ecological-climate interactions. The ecosystem functioning also plays a key role in predicting the carbon cycle, hydrological cycle, terrestrial surface energy balance, and the feedbacks in the climate system. Predicting the response of the Earth's biosphere to global warming requires the ability to mechanistically represent the processes controlling ecosystem functioning through photosynthesis, respiration, and water use. The physical environment in a place shapes the vegetation there, but vegetation also has the potential to shape the environment, e.g. increased photosynthesis and transpiration moisten the atmosphere. These two-way ecoclimate interactions create the potential for feedbacks between vegetation at the physical environment that depend on the vegetation and the climate of a place, and can change throughout the year. In Chapter 1, we derive a global empirical map of the sensitivity of vegetation to climate using the response of satellite-observed greenness to interannual variations in temperature and precipitation. We infer mechanisms constraining ecosystem functioning by analyzing how the sensitivity of vegetation to climate varies across climate space. Our analysis yields empirical evidence for multiple physical and biological mediators of the sensitivity of vegetation to climate at large spatial scales. In hot and wet locations, vegetation is greener in warmer years despite temperatures likely exceeding thermally optimum conditions. However, sunlight generally increases during warmer years, suggesting that the increased stress from higher atmospheric water demand is offset by higher rates of photosynthesis. The sensitivity of vegetation

Decision- rather than scenario-centred downscaling: Towards smarter use of climate model outputs

Science.gov (United States)

Wilby, Robert L.

2013-04-01

Climate model output has been used for hydrological impact assessments for at least 25 years. Scenario-led methods raise awareness about risks posed by climate variability and change to the security of supplies, performance of water infrastructure, and health of freshwater ecosystems. However, it is less clear how these analyses translate into actionable information for adaptation. One reason is that scenario-led methods typically yield very large uncertainty bounds in projected impacts at regional and river catchment scales. Consequently, there is growing interest in vulnerability-based frameworks and strategies for employing climate model output in decision-making contexts. This talk begins by summarising contrasting perspectives on climate models and principles for testing their utility for water sector applications. Using selected examples it is then shown how water resource systems may be adapted with varying levels of reliance on climate model information. These approaches include the conventional scenario-led risk assessment, scenario-neutral strategies, safety margins and sensitivity testing, and adaptive management of water systems. The strengths and weaknesses of each approach are outlined and linked to selected water management activities. These cases show that much progress can be made in managing water systems without dependence on climate models. Low-regret measures such as improved forecasting, better inter-agency co-operation, and contingency planning, yield benefits regardless of the climate outlook. Nonetheless, climate model scenarios are useful for evaluating adaptation portfolios, identifying system thresholds and fixing weak links, exploring the timing of investments, improving operating rules, or developing smarter licensing regimes. The most problematic application remains the climate change safety margin because of the very low confidence in extreme precipitation and river flows generated by climate models. In such cases, it is necessary to

A sensitive slope: estimating landscape patterns of forest resilience in a changing climate

Science.gov (United States)

Jill F. Johnstone; Eliot J.B. McIntire; Eric J. Pedersen; Gregory King; Michael J.F. Pisaric

2010-01-01

Changes in Earth's environment are expected to stimulate changes in the composition and structure of ecosystems, but it is still unclear how the dynamics of these responses will play out over time. In long-lived forest systems, communities of established individuals may be resistant to respond to directional climate change, but may be highly sensitive to climate...

Idealized climate change simulations with a high-resolution physical model: HadGEM3-GC2

Science.gov (United States)

Senior, Catherine A.; Andrews, Timothy; Burton, Chantelle; Chadwick, Robin; Copsey, Dan; Graham, Tim; Hyder, Pat; Jackson, Laura; McDonald, Ruth; Ridley, Jeff; Ringer, Mark; Tsushima, Yoko

2016-06-01

Idealized climate change simulations with a new physical climate model, HadGEM3-GC2 from The Met Office Hadley Centre are presented and contrasted with the earlier MOHC model, HadGEM2-ES. The role of atmospheric resolution is also investigated. The Transient Climate Response (TCR) is 1.9 K/2.1 K at N216/N96 and Effective Climate Sensitivity (ECS) is 3.1 K/3.2 K at N216/N96. These are substantially lower than HadGEM2-ES (TCR: 2.5 K; ECS: 4.6 K) arising from a combination of changes in the size of climate feedbacks. While the change in the net cloud feedback between HadGEM3 and HadGEM2 is relatively small, there is a change in sign of its longwave and a strengthening of its shortwave components. At a global scale, there is little impact of the increase in atmospheric resolution on the future climate change signal and even at a broad regional scale, many features are robust including tropical rainfall changes, however, there are some significant exceptions. For the North Atlantic and western Europe, the tripolar pattern of winter storm changes found in most CMIP5 models is little impacted by resolution but for the most intense storms, there is a larger percentage increase in number at higher resolution than at lower resolution. Arctic sea-ice sensitivity shows a larger dependence on resolution than on atmospheric physics.

Influences of subtropical jet and Tibetan Plateau on precipitation pattern in Asia : Insights from regional climate modeling

NARCIS (Netherlands)

Sato, Tomonori

2009-01-01

Large topographic features, like the Tibetan Plateau (TP) and the Rocky Mountains, have significant impacts on Earth's climate. Numerical experiments were carried out using a regional climate model in order to study the sensitivity of rainfall distribution to the TP's thermal/dynamic effects and

Can we trust climate models to realistically represent severe European windstorms?

Science.gov (United States)

Trzeciak, Tomasz M.; Knippertz, Peter; Owen, Jennifer S. R.

2014-05-01

Despite the enormous advances made in climate change research, robust projections of the position and the strength of the North Atlantic stormtrack are not yet possible. In particular with respect to damaging windstorms, this incertitude bears enormous risks to European societies and the (re)insurance industry. Previous studies have addressed the problem of climate model uncertainty through statistical comparisons of simulations of the current climate with (re-)analysis data and found that there is large disagreement between different climate models, different ensemble members of the same model and observed climatologies of intense cyclones. One weakness of such statistical evaluations lies in the difficulty to separate influences of the climate model's basic state from the influence of fast processes on the development of the most intense storms. Compensating effects between the two might conceal errors and suggest higher reliability than there really is. A possible way to separate influences of fast and slow processes in climate projections is through a "seamless" approach of hindcasting historical, severe storms with climate models started from predefined initial conditions and run in a numerical weather prediction mode on the time scale of several days. Such a cost-effective case-study approach, which draws from and expands on the concepts from the Transpose-AMIP initiative, has recently been undertaken in the SEAMSEW project at the University of Leeds funded by the AXA Research Fund. Key results from this work focusing on 20 historical storms and using different lead times and horizontal and vertical resolutions include: (a) Tracks are represented reasonably well by most hindcasts. (b) Sensitivity to vertical resolution is low. (c) There is a systematic underprediction of cyclone depth for a coarse resolution of T63, but surprisingly no systematic bias is found for higher-resolution runs using T127, showing that climate models are in fact able to represent the

Can feedback analysis be used to uncover the physical origin of climate sensitivity and efficacy differences?

Science.gov (United States)

Rieger, Vanessa S.; Dietmüller, Simone; Ponater, Michael

2017-10-01

Different strengths and types of radiative forcings cause variations in the climate sensitivities and efficacies. To relate these changes to their physical origin, this study tests whether a feedback analysis is a suitable approach. For this end, we apply the partial radiative perturbation method. Combining the forward and backward calculation turns out to be indispensable to ensure the additivity of feedbacks and to yield a closed forcing-feedback-balance at top of the atmosphere. For a set of CO2-forced simulations, the climate sensitivity changes with increasing forcing. The albedo, cloud and combined water vapour and lapse rate feedback are found to be responsible for the variations in the climate sensitivity. An O3-forced simulation (induced by enhanced NOx and CO surface emissions) causes a smaller efficacy than a CO2-forced simulation with a similar magnitude of forcing. We find that the Planck, albedo and most likely the cloud feedback are responsible for this effect. Reducing the radiative forcing impedes the statistical separability of feedbacks. We additionally discuss formal inconsistencies between the common ways of comparing climate sensitivities and feedbacks. Moreover, methodical recommendations for future work are given.

Assessing modeled Greenland surface mass balance in the GISS Model E2 and its sensitivity to surface albedo

Science.gov (United States)

Alexander, Patrick; LeGrande, Allegra N.; Koenig, Lora S.; Tedesco, Marco; Moustafa, Samiah E.; Ivanoff, Alvaro; Fischer, Robert P.; Fettweis, Xavier

2016-04-01

The surface mass balance (SMB) of the Greenland Ice Sheet (GrIS) plays an important role in global sea level change. Regional Climate Models (RCMs) such as the Modèle Atmosphérique Régionale (MAR) have been employed at high spatial resolution with relatively complex physics to simulate ice sheet SMB. Global climate models (GCMs) incorporate less sophisticated physical schemes and provide outputs at a lower spatial resolution, but have the advantage of modeling the interaction between different components of the earth's oceans, climate, and land surface at a global scale. Improving the ability of GCMs to represent ice sheet SMB is important for making predictions of future changes in global sea level. With the ultimate goal of improving SMB simulated by the Goddard Institute for Space Studies (GISS) Model E2 GCM, we compare simulated GrIS SMB against the outputs of the MAR model and radar-derived estimates of snow accumulation. In order to reproduce present-day climate variability in the Model E2 simulation, winds are constrained to match the reanalysis datasets used to force MAR at the lateral boundaries. We conduct a preliminary assessment of the sensitivity of the simulated Model E2 SMB to surface albedo, a parameter that is known to strongly influence SMB. Model E2 albedo is set to a fixed value of 0.8 over the entire ice sheet in the initial configuration of the model (control case). We adjust this fixed value in an ensemble of simulations over a range of 0.4 to 0.8 (roughly the range of observed summer GrIS albedo values) to examine the sensitivity of ice-sheet-wide SMB to albedo. We prescribe albedo from the Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43A3 v6 to examine the impact of a more realistic spatial and temporal variations in albedo. An age-dependent snow albedo parameterization is applied, and its impact on SMB relative to observations and the RCM is assessed.

Modelling pesticide leaching under climate change: parameter vs. climate input uncertainty

Directory of Open Access Journals (Sweden)

K. Steffens

2014-02-01

Full Text Available Assessing climate change impacts on pesticide leaching requires careful consideration of different sources of uncertainty. We investigated the uncertainty related to climate scenario input and its importance relative to parameter uncertainty of the pesticide leaching model. The pesticide fate model MACRO was calibrated against a comprehensive one-year field data set for a well-structured clay soil in south-western Sweden. We obtained an ensemble of 56 acceptable parameter sets that represented the parameter uncertainty. Nine different climate model projections of the regional climate model RCA3 were available as driven by different combinations of global climate models (GCM, greenhouse gas emission scenarios and initial states of the GCM. The future time series of weather data used to drive the MACRO model were generated by scaling a reference climate data set (1970–1999 for an important agricultural production area in south-western Sweden based on monthly change factors for 2070–2099. 30 yr simulations were performed for different combinations of pesticide properties and application seasons. Our analysis showed that both the magnitude and the direction of predicted change in pesticide leaching from present to future depended strongly on the particular climate scenario. The effect of parameter uncertainty was of major importance for simulating absolute pesticide losses, whereas the climate uncertainty was relatively more important for predictions of changes of pesticide losses from present to future. The climate uncertainty should be accounted for by applying an ensemble of different climate scenarios. The aggregated ensemble prediction based on both acceptable parameterizations and different climate scenarios has the potential to provide robust probabilistic estimates of future pesticide losses.

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

Science.gov (United States)

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

2017-12-01

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

Bayesian hierarchical models for regional climate reconstructions of the last glacial maximum

Science.gov (United States)

Weitzel, Nils; Hense, Andreas; Ohlwein, Christian

2017-04-01

Spatio-temporal reconstructions of past climate are important for the understanding of the long term behavior of the climate system and the sensitivity to forcing changes. Unfortunately, they are subject to large uncertainties, have to deal with a complex proxy-climate structure, and a physically reasonable interpolation between the sparse proxy observations is difficult. Bayesian Hierarchical Models (BHMs) are a class of statistical models that is well suited for spatio-temporal reconstructions of past climate because they permit the inclusion of multiple sources of information (e.g. records from different proxy types, uncertain age information, output from climate simulations) and quantify uncertainties in a statistically rigorous way. BHMs in paleoclimatology typically consist of three stages which are modeled individually and are combined using Bayesian inference techniques. The data stage models the proxy-climate relation (often named transfer function), the process stage models the spatio-temporal distribution of the climate variables of interest, and the prior stage consists of prior distributions of the model parameters. For our BHMs, we translate well-known proxy-climate transfer functions for pollen to a Bayesian framework. In addition, we can include Gaussian distributed local climate information from preprocessed proxy records. The process stage combines physically reasonable spatial structures from prior distributions with proxy records which leads to a multivariate posterior probability distribution for the reconstructed climate variables. The prior distributions that constrain the possible spatial structure of the climate variables are calculated from climate simulation output. We present results from pseudoproxy tests as well as new regional reconstructions of temperatures for the last glacial maximum (LGM, ˜ 21,000 years BP). These reconstructions combine proxy data syntheses with information from climate simulations for the LGM that were

Modelling climate impact on floods under future emission scenarios using an ensemble of climate model projections

Science.gov (United States)

Wetterhall, F.; Cloke, H. L.; He, Y.; Freer, J.; Pappenberger, F.

2012-04-01

Evidence provided by modelled assessments of climate change impact on flooding is fundamental to water resource and flood risk decision making. Impact models usually rely on climate projections from Global and Regional Climate Models, and there is no doubt that these provide a useful assessment of future climate change. However, cascading ensembles of climate projections into impact models is not straightforward because of problems of coarse resolution in Global and Regional Climate Models (GCM/RCM) and the deficiencies in modelling high-intensity precipitation events. Thus decisions must be made on how to appropriately pre-process the meteorological variables from GCM/RCMs, such as selection of downscaling methods and application of Model Output Statistics (MOS). In this paper a grand ensemble of projections from several GCM/RCM are used to drive a hydrological model and analyse the resulting future flood projections for the Upper Severn, UK. The impact and implications of applying MOS techniques to precipitation as well as hydrological model parameter uncertainty is taken into account. The resultant grand ensemble of future river discharge projections from the RCM/GCM-hydrological model chain is evaluated against a response surface technique combined with a perturbed physics experiment creating a probabilisic ensemble climate model outputs. The ensemble distribution of results show that future risk of flooding in the Upper Severn increases compared to present conditions, however, the study highlights that the uncertainties are large and that strong assumptions were made in using Model Output Statistics to produce the estimates of future discharge. The importance of analysing on a seasonal basis rather than just annual is highlighted. The inability of the RCMs (and GCMs) to produce realistic precipitation patterns, even in present conditions, is a major caveat of local climate impact studies on flooding, and this should be a focus for future development.

Water sensitive design as a means of adaptation for climate change and urbanisation

International Nuclear Information System (INIS)

Semadeni-Davies, Annette

2007-01-01

Full text: Full text: This presentation uses urban drainage to show how climate change impact assessments should also consider changes in resource management, land-use and technology. Although the work was carried out in Sweden (Semadeni-Davies 2004; 2007 a, b), the findings are relevant for Australia and New Zealand as these countries are currently moving away from pipe stormwater networks towards open water systems. Urban areas are characterised by high peak flows and fast response times. A central issue here is that there has been a paradigm shift in urban water management, particularly in new developments where the stormwater system is fast becoming a landscape feature. The shift is part of a worldwide trend called water sensitive design (WSD) or low impact design (LID). Ponds, wetlands, infiltration trenches, and porous paving are common examples of WSD elements intended to reduce peak stormwater and contaminant transport while maintaining low flows. Even in city centres where land values are at a premium, there has been an interest in retro-fitting for WSD. It is important for those interested in the impacts of climate change on urban areas to know this background information, as WSD may offer a means of adapting to climate change. However, there is a major stumbling block - the output from regional climate models is currently not at a sufficient spatial or temporal resolution to assess theimpact on urban drainage as the processes operate on a scale of minutes and metres (Shilling 1991). The disparity in resolution is also problematic for the design of future-proofed urban water systems as this requires information on rainfall intensity and frequency. To illustrate the effect of WSD, the potential impacts of climate change and urbanisation on flow were assessed both separately and together using DHI software (MIKE SHE, MOUSE) for Helsingborg in two related studies for combined and separate sewers. The Swedish regional climate model developed at the Rossby

Modelling the impacts of European emission and climate change scenarios on acid-sensitive catchments in Finland

Directory of Open Access Journals (Sweden)

M. Posch

2008-03-01

Full Text Available The dynamic hydro-chemical Model of Acidification of Groundwater in Catchments (MAGIC was used to predict the response of 163 Finnish lake catchments to future acidic deposition and climatic change scenarios. Future deposition was assumed to follow current European emission reduction policies and a scenario based on maximum (technologically feasible reductions (MFR. Future climate (temperature and precipitation was derived from the HadAM3 and ECHAM4/OPYC3 general circulation models under two global scenarios of the Intergovernmental Panel on Climate Change (IPCC: A2 and B2. The combinations resulting in the widest range of future changes were used for simulations, i.e., the A2 scenario results from ECHAM4/OPYC3 (highest predicted change and B2 results from HadAM3 (lowest predicted change. Future scenarios for catchment runoff were obtained from the Finnish watershed simulation and forecasting system. The potential influence of future changes in surface water organic carbon concentrations was also explored using simple empirical relationships based on temperature and sulphate deposition. Surprisingly, current emission reduction policies hardly show any future recovery; however, significant chemical recovery of soil and surface water from acidification was predicted under the MFR emission scenario. The direct influence of climate change (temperate and precipitation on recovery was negligible, as runoff hardly changed; greater precipitation is offset by increased evapotranspiration due to higher temperatures. However, two exploratory empirical DOC models indicated that changes in sulphur deposition or temperature could have a confounding influence on the recovery of surface waters from acidification, and that the corresponding increases in DOC concentrations may offset the recovery in pH due to reductions in acidifying depositions.

Effects of snow grain shape on climate simulations: sensitivity tests with the Norwegian Earth System Model

Science.gov (United States)

Räisänen, Petri; Makkonen, Risto; Kirkevåg, Alf; Debernard, Jens B.

2017-12-01

Snow consists of non-spherical grains of various shapes and sizes. Still, in radiative transfer calculations, snow grains are often treated as spherical. This also applies to the computation of snow albedo in the Snow, Ice, and Aerosol Radiation (SNICAR) model and in the Los Alamos sea ice model, version 4 (CICE4), both of which are employed in the Community Earth System Model and in the Norwegian Earth System Model (NorESM). In this study, we evaluate the effect of snow grain shape on climate simulated by NorESM in a slab ocean configuration of the model. An experiment with spherical snow grains (SPH) is compared with another (NONSPH) in which the snow shortwave single-scattering properties are based on a combination of three non-spherical snow grain shapes optimized using measurements of angular scattering by blowing snow. The key difference between these treatments is that the asymmetry parameter is smaller in the non-spherical case (0.77-0.78 in the visible region) than in the spherical case ( ≈ 0.89). Therefore, for the same effective snow grain size (or equivalently, the same specific projected area), the snow broadband albedo is higher when assuming non-spherical rather than spherical snow grains, typically by 0.02-0.03. Considering the spherical case as the baseline, this results in an instantaneous negative change in net shortwave radiation with a global-mean top-of-the-model value of ca. -0.22 W m-2. Although this global-mean radiative effect is rather modest, the impacts on the climate simulated by NorESM are substantial. The global annual-mean 2 m air temperature in NONSPH is 1.17 K lower than in SPH, with substantially larger differences at high latitudes. The climatic response is amplified by strong snow and sea ice feedbacks. It is further demonstrated that the effect of snow grain shape could be largely offset by adjusting the snow grain size. When assuming non-spherical snow grains with the parameterized grain size increased by ca. 70 %, the

Confirmation of ACRU model results for applications in land use and climate change studies

Directory of Open Access Journals (Sweden)

G. P. W. Jewitt

2010-12-01

Full Text Available The hydrological responses of a catchment are sensitive to, and strongly coupled to, land use and climate, and changes thereof. The hydrological responses to the impacts of changing land use and climate will be the result of complex interactions, where the change in one may moderate or exacerbate the effects of the other. Further difficulties in assessing these interactions are that dominant drivers of the hydrological system may vary at different spatial and temporal scales. To assess these interactions, a process-based hydrological model, sensitive to land use and climate, and changes thereof, needs to be used. For this purpose the daily time step ACRU model was selected. However, to be able to use a hydrological model such as ACRU with confidence its representation of reality must be confirmed by comparing simulated output against observations across a range of climatic conditions. Comparison of simulated against observed streamflow was undertaken in three climatically diverse South African catchments, ranging from the semi-arid, sub-tropical Luvuvhu catchment, to the winter rainfall Upper Breede catchment and the sub-humid Mgeni catchment. Not only do the climates of the catchments differ, but their primary land uses also vary. In the upper areas of the Mgeni catchment commercial plantation forestry is dominant, while in the middle reaches there are significant areas of commercial plantation sugarcane and urban areas, while the lower reaches are dominated by urban areas. The Luvuvhu catchment has a large proportion of subsistence agriculture and informal residential areas. In the Upper Breede catchment in the Western Cape, commercial orchards and vineyards are the primary land uses. Overall the ACRU model was able to represent the high, low and total flows, with satisfactory Nash-Sutcliffe efficiency indexes obtained for the selected catchments. The study concluded that the ACRU model can be used with confidence to simulate the streamflows

Confirmation of ACRU model results for applications in land use and climate change studies

Science.gov (United States)

Warburton, M. L.; Schulze, R. E.; Jewitt, G. P. W.

2010-12-01

The hydrological responses of a catchment are sensitive to, and strongly coupled to, land use and climate, and changes thereof. The hydrological responses to the impacts of changing land use and climate will be the result of complex interactions, where the change in one may moderate or exacerbate the effects of the other. Further difficulties in assessing these interactions are that dominant drivers of the hydrological system may vary at different spatial and temporal scales. To assess these interactions, a process-based hydrological model, sensitive to land use and climate, and changes thereof, needs to be used. For this purpose the daily time step ACRU model was selected. However, to be able to use a hydrological model such as ACRU with confidence its representation of reality must be confirmed by comparing simulated output against observations across a range of climatic conditions. Comparison of simulated against observed streamflow was undertaken in three climatically diverse South African catchments, ranging from the semi-arid, sub-tropical Luvuvhu catchment, to the winter rainfall Upper Breede catchment and the sub-humid Mgeni catchment. Not only do the climates of the catchments differ, but their primary land uses also vary. In the upper areas of the Mgeni catchment commercial plantation forestry is dominant, while in the middle reaches there are significant areas of commercial plantation sugarcane and urban areas, while the lower reaches are dominated by urban areas. The Luvuvhu catchment has a large proportion of subsistence agriculture and informal residential areas. In the Upper Breede catchment in the Western Cape, commercial orchards and vineyards are the primary land uses. Overall the ACRU model was able to represent the high, low and total flows, with satisfactory Nash-Sutcliffe efficiency indexes obtained for the selected catchments. The study concluded that the ACRU model can be used with confidence to simulate the streamflows of the three selected

Conceptual understanding of climate change with a globally resolved energy balance model

Energy Technology Data Exchange (ETDEWEB)

Dommenget, Dietmar [Monash University, School of Mathematical Sciences, Melbourne, VIC (Australia); Floeter, Janine [Leibniz Institute for Marine Sciences, Kiel (Germany)

2011-12-15

The future climate change projections are essentially based on coupled general circulation model (CGCM) simulations, which give a distinct global warming pattern with arctic winter amplification, an equilibrium land-sea warming contrast and an inter-hemispheric warming gradient. While these simulations are the most important tool of the Intergovernmental Panel on Climate Change (IPCC) predictions, the conceptual understanding of these predicted structures of climate change and the causes of their uncertainties is very difficult to reach if only based on these highly complex CGCM simulations. In the study presented here we will introduce a very simple, globally resolved energy balance (GREB) model, which is capable of simulating the main characteristics of global warming. The model shall give a bridge between the strongly simplified energy balance models and the fully coupled 4-dimensional complex CGCMs. It provides a fast tool for the conceptual understanding and development of hypotheses for climate change studies, which shall build a basis or starting point for more detailed studies of observations and CGCM simulations. It is based on the surface energy balance by very simple representations of solar and thermal radiation, the atmospheric hydrological cycle, sensible turbulent heat flux, transport by the mean atmospheric circulation and heat exchange with the deeper ocean. Despite some limitations in the representations of the basic processes, the models climate sensitivity and the spatial structure of the warming pattern are within the uncertainties of the IPCC models simulations. It is capable of simulating aspects of the arctic winter amplification, the equilibrium land-sea warming contrast and the inter-hemispheric warming gradient with good agreement to the IPCC models in amplitude and structure. The results give some insight into the understanding of the land-sea contrast and the polar amplification. The GREB model suggests that the regional inhomogeneous

Wind climate estimation using WRF model output: method and model sensitivities over the sea

DEFF Research Database (Denmark)

Hahmann, Andrea N.; Vincent, Claire Louise; Peña, Alfredo

2015-01-01

setup parameters. The results of the year-long sensitivity simulations show that the long-term mean wind speed simulated by the WRF model offshore in the region studied is quite insensitive to the global reanalysis, the number of vertical levels, and the horizontal resolution of the sea surface...... temperature used as lower boundary conditions. Also, the strength and form (grid vs spectral) of the nudging is quite irrelevant for the mean wind speed at 100 m. Large sensitivity is found to the choice of boundary layer parametrization, and to the length of the period that is discarded as spin-up to produce...... a wind climatology. It is found that the spin-up period for the boundary layer winds is likely larger than 12 h over land and could affect the wind climatology for points offshore for quite a distance downstream from the coast....

On coupling global biome models with climate models

OpenAIRE

Claussen, M.

1994-01-01

The BIOME model of Prentice et al. (1992; J. Biogeogr. 19: 117-134), which predicts global vegetation patterns in equilibrium with climate, was coupled with the ECHAM climate model of the Max-Planck-Institut fiir Meteorologie, Hamburg, Germany. It was found that incorporation of the BIOME model into ECHAM, regardless at which frequency, does not enhance the simulated climate variability, expressed in terms of differences between global vegetation patterns. Strongest changes are seen only betw...

Efficacy of Radiative Transfer Model Across Space, Time and Hydro-climates

Science.gov (United States)

Mohanty, B.; Neelam, M.

2017-12-01

The efficiency of radiative transfer model for better soil moisture retrievals is not yet clearly understood over natural systems with great variability and heterogeneity with respect to soil, land cover, topography, precipitation etc. However, this knowledge is important to direct and strategize future research direction and field campaigns. In this work, we present global sensitivity analysis (GSA) technique to study the influence of heterogeneity and uncertainties on radiative transfer model (RTM) and to quantify climate-soil-vegetation interactions. A framework is proposed to understand soil moisture mechanisms underlying these interactions, and influence of these interactions on soil moisture retrieval accuracy. Soil moisture dynamics is observed to play a key role in variability of these interactions, i.e., it enhances both mean and variance of soil-vegetation coupling. The analysis is conducted for different support scales (Point Scale, 800 m, 1.6 km, 3.2 km, 6.4 km, 12.8 km, and 36 km), seasonality (time), hydro-climates, aggregation (scaling) methods and across Level I and Level II ecoregions of contiguous USA (CONUS). For undisturbed natural environments such as SGP'97 (Oklahoma, USA) and SMEX04 (Arizona, USA), the sensitivity of TB to land surface variables remain nearly uniform and are not influenced by extent, support scales or averaging method. On the contrary, for anthropogenically-manipulated environments such as SMEX02 (Iowa, USA) and SMAPVEX12 (Winnipeg, Canada), the sensitivity to variables are highly influenced by the distribution of land surface heterogeneity and upscaling methods. The climate-soil-vegetation interactions analyzed across all ecoregions are presented through a probability distribution function (PDF). The intensity of these interactions are categorized accordingly to yield "hotspots", where the RTM model fails to retrieve soil moisture. A ecoregion specific scaling function is proposed for these hotspots to rectify RTM for

Climate simulations for 1880-2003 with GISS modelE

International Nuclear Information System (INIS)

Hansen, J.; Lacis, A.; Miller, R.; Schmidt, G.A.; Russell, G.; Canuto, V.; Del Genio, A.; Hall, T.; Hansen, J.; Sato, M.; Kharecha, P.; Nazarenko, L.; Aleinov, I.; Bauer, S.; Chandler, M.; Faluvegi, G.; Jonas, J.; Ruedy, R.; Lo, K.; Cheng, Y.; Lacis, A.; Schmidt, G.A.; Del Genio, A.; Miller, R.; Cairns, B.; Hall, T.; Baum, E.; Cohen, A.; Fleming, E.; Jackman, C.; Friend, A.; Kelley, M.

2007-01-01

We carry out climate simulations for 1880-2003 with GISS modelE driven by ten measured or estimated climate forcing. 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 forcing, observations, unforced variability among model ensemble members, and, if available, observed variability. Discrepancies between observations and simulations with all forcing are due to model deficiencies, inaccurate or incomplete forcing, 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 forcing, we aim to provide a benchmark against which the effect of improvements in the model, climate forcing, and observations can be tested. Principal model deficiencies include unrealistic weak tropical El Nino-like variability and a poor distribution of sea ice, with too much sea ice in the Northern Hemisphere and too little in the Southern Hemisphere. Greatest uncertainties in the forcing are the temporal and spatial variations of anthropogenic aerosols and their indirect effects on clouds. (authors)

Issues in Establishing Climate Sensitivity in Recent Studies

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John T. Fasullo

2011-09-01

Full Text Available Numerous attempts have been made to constrain climate sensitivity with observations [1-10] (with [6] as LC09, [8] as SB11. While all of these attempts contain various caveats and sources of uncertainty, some efforts have been shown to contain major errors and are demonstrably incorrect. For example, multiple studies [11-13] separately addressed weaknesses in LC09 [6]. The work of Trenberth et al. [13], for instance, demonstrated a basic lack of robustness in the LC09 method that fundamentally undermined their results. Minor changes in that study’s subjective assumptions yielded major changes in its main conclusions. Moreover, Trenberth et al. [13] criticized the interpretation of El Niño-Southern Oscillation (ENSO as an analogue for exploring the forced response of the climate system. In addition, as many cloud variations on monthly time scales result from internal atmospheric variability, such as the Madden-Julian Oscillation, cloud variability is not a deterministic response to surface temperatures. Nevertheless, many of the problems in LC09 [6] have been perpetuated, and Dessler [10] has pointed out similar issues with two more recent such attempts [7,8]. Here we briefly summarize more generally some of the pitfalls and issues involved in developing observational constraints on climate feedbacks. [...

Sources and Impacts of Modeled and Observed Low-Frequency Climate Variability

Science.gov (United States)

Parsons, Luke Alexander

Here we analyze climate variability using instrumental, paleoclimate (proxy), and the latest climate model data to understand more about the sources and impacts of low-frequency climate variability. Understanding the drivers of climate variability at interannual to century timescales is important for studies of climate change, including analyses of detection and attribution of climate change impacts. Additionally, correctly modeling the sources and impacts of variability is key to the simulation of abrupt change (Alley et al., 2003) and extended drought (Seager et al., 2005; Pelletier and Turcotte, 1997; Ault et al., 2014). In Appendix A, we employ an Earth system model (GFDL-ESM2M) simulation to study the impacts of a weakening of the Atlantic meridional overturning circulation (AMOC) on the climate of the American Tropics. The AMOC drives some degree of local and global internal low-frequency climate variability (Manabe and Stouffer, 1995; Thornalley et al., 2009) and helps control the position of the tropical rainfall belt (Zhang and Delworth, 2005). We find that a major weakening of the AMOC can cause large-scale temperature, precipitation, and carbon storage changes in Central and South America. Our results suggest that possible future changes in AMOC strength alone will not be sufficient to drive a large-scale dieback of the Amazonian forest, but this key natural ecosystem is sensitive to dry-season length and timing of rainfall (Parsons et al., 2014). In Appendix B, we compare a paleoclimate record of precipitation variability in the Peruvian Amazon to climate model precipitation variability. The paleoclimate (Lake Limon) record indicates that precipitation variability in western Amazonia is 'red' (i.e., increasing variability with timescale). By contrast, most state-of-the-art climate models indicate precipitation variability in this region is nearly 'white' (i.e., equally variability across timescales). This paleo-model disagreement in the overall

Sensitivity of intermittent streams to climate variations in the USA

Science.gov (United States)

Eng, Kenny; Wolock, David M.; Dettinger, Mike

2015-01-01

There is a great deal of interest in the literature on streamflow changes caused by climate change because of the potential negative effects on aquatic biota and water supplies. Most previous studies have primarily focused on perennial streams, and there have been only a few studies examining the effect of climate variability on intermittent streams. Our objectives in this study were to (1) identify regions of similar zero-flow behavior, and (2) evaluate the sensitivity of intermittent streams to historical variability in climate in the United States. This study was carried out at 265 intermittent streams by evaluating: (1) correlations among time series of flow metrics (number of zero-flow events, the average of the central 50% and largest 10% of flows) with climate (magnitudes, durations and intensity), and (2) decadal changes in the seasonality and long-term trends of these flow metrics. Results identified five distinct seasonality patterns in the zero-flow events. In addition, strong associations between the low-flow metrics and historical changes in climate were found. The decadal analysis suggested no significant seasonal shifts or decade-to-decade trends in the low-flow metrics. The lack of trends or changes in seasonality is likely due to unchanged long-term patterns in precipitation over the time period examined.

Representation of Northern Hemisphere winter storm tracks in climate models

Energy Technology Data Exchange (ETDEWEB)

Greeves, C.Z.; Pope, V.D.; Stratton, R.A.; Martin, G.M. [Met Office Hadley Centre for Climate Prediction and Research, Exeter (United Kingdom)

2007-06-15

Northern Hemisphere winter storm tracks are a key element of the winter weather and climate at mid-latitudes. Before projections of climate change are made for these regions, it is necessary to be sure that climate models are able to reproduce the main features of observed storm tracks. The simulated storm tracks are assessed for a variety of Hadley Centre models and are shown to be well modelled on the whole. The atmosphere-only model with the semi-Lagrangian dynamical core produces generally more realistic storm tracks than the model with the Eulerian dynamical core, provided the horizontal resolution is high enough. The two models respond in different ways to changes in horizontal resolution: the model with the semi-Lagrangian dynamical core has much reduced frequency and strength of cyclonic features at lower resolution due to reduced transient eddy kinetic energy. The model with Eulerian dynamical core displays much smaller changes in frequency and strength of features with changes in horizontal resolution, but the location of the storm tracks as well as secondary development are sensitive to resolution. Coupling the atmosphere-only model (with semi-Lagrangian dynamical core) to an ocean model seems to affect the storm tracks largely via errors in the tropical representation. For instance a cold SST bias in the Pacific and a lack of ENSO variability lead to large changes in the Pacific storm track. Extratropical SST biases appear to have a more localised effect on the storm tracks. (orig.)

Future Projections of Fire Occurrence in Brazil Using EC-Earth Climate Model

Directory of Open Access Journals (Sweden)

Patrícia Silva

Full Text Available Abstract Fire has a fundamental role in the Earth system as it influences global and local ecosystem patterns and processes, such as vegetation distribution and structure, the carbon cycle and climate. Since, in the global context, Brazil is one of the regions with higher fire activity, an assessment is here performed of the sensitivity of the wildfire regime in Brazilian savanna and shrubland areas to changes in regional climate during the 21st Century, for an intermediate scenario (RCP4.5 of climate change. The assessment is based on a spatial and temporal analysis of a meteorological fire danger index specifically developed for Brazilian biomes, which was evaluated based on regional climate simulations of temperature, relative humidity and precipitation using the Rossby Centre Regional Climate Model (RCA4 forced by the EC-Earth earth system model. Results show a systematic increase in the extreme levels of fire danger throughout the 21st Century that mainly results from the increase in maximum daily temperature, which rises by about 2 °C between 2005 and 2100. This study provides new insights about projected fire activity in Brazilian woody savannas associated to climate change and is expected to benefit the user community, from governmental policies to land management and climate researches.

Parametric sensitivity analysis of an agro-economic model of management of irrigation water

Science.gov (United States)

El Ouadi, Ihssan; Ouazar, Driss; El Menyari, Younesse

2015-04-01

The current work aims to build an analysis and decision support tool for policy options concerning the optimal allocation of water resources, while allowing a better reflection on the issue of valuation of water by the agricultural sector in particular. Thus, a model disaggregated by farm type was developed for the rural town of Ait Ben Yacoub located in the east Morocco. This model integrates economic, agronomic and hydraulic data and simulates agricultural gross margin across in this area taking into consideration changes in public policy and climatic conditions, taking into account the competition for collective resources. To identify the model input parameters that influence over the results of the model, a parametric sensitivity analysis is performed by the "One-Factor-At-A-Time" approach within the "Screening Designs" method. Preliminary results of this analysis show that among the 10 parameters analyzed, 6 parameters affect significantly the objective function of the model, it is in order of influence: i) Coefficient of crop yield response to water, ii) Average daily gain in weight of livestock, iii) Exchange of livestock reproduction, iv) maximum yield of crops, v) Supply of irrigation water and vi) precipitation. These 6 parameters register sensitivity indexes ranging between 0.22 and 1.28. Those results show high uncertainties on these parameters that can dramatically skew the results of the model or the need to pay particular attention to their estimates. Keywords: water, agriculture, modeling, optimal allocation, parametric sensitivity analysis, Screening Designs, One-Factor-At-A-Time, agricultural policy, climate change.

The global aerosol-climate model ECHAM-HAM, version 2: sensitivity to improvements in process representations

Directory of Open Access Journals (Sweden)

K. Zhang

2012-10-01

Full Text Available This paper introduces and evaluates the second version of the global aerosol-climate model ECHAM-HAM. Major changes have been brought into the model, including new parameterizations for aerosol nucleation and water uptake, an explicit treatment of secondary organic aerosols, modified emission calculations for sea salt and mineral dust, the coupling of aerosol microphysics to a two-moment stratiform cloud microphysics scheme, and alternative wet scavenging parameterizations. These revisions extend the model's capability to represent details of the aerosol lifecycle and its interaction with climate. Nudged simulations of the year 2000 are carried out to compare the aerosol properties and global distribution in HAM1 and HAM2, and to evaluate them against various observations. Sensitivity experiments are performed to help identify the impact of each individual update in model formulation.

Results indicate that from HAM1 to HAM2 there is a marked weakening of aerosol water uptake in the lower troposphere, reducing the total aerosol water burden from 75 Tg to 51 Tg. The main reason is the newly introduced κ-Köhler-theory-based water uptake scheme uses a lower value for the maximum relative humidity cutoff. Particulate organic matter loading in HAM2 is considerably higher in the upper troposphere, because the explicit treatment of secondary organic aerosols allows highly volatile oxidation products of the precursors to be vertically transported to regions of very low temperature and to form aerosols there. Sulfate, black carbon, particulate organic matter and mineral dust in HAM2 have longer lifetimes than in HAM1 because of weaker in-cloud scavenging, which is in turn related to lower autoconversion efficiency in the newly introduced two-moment cloud microphysics scheme. Modification in the sea salt emission scheme causes a significant increase in the ratio (from 1.6 to 7.7 between accumulation mode and coarse mode emission fluxes of

Coupling Climate Models and Forward-Looking Economic Models

Science.gov (United States)

Judd, K.; Brock, W. A.

2010-12-01

Authors: Dr. Kenneth L. Judd, Hoover Institution, and Prof. William A. Brock, University of Wisconsin Current climate models range from General Circulation Models (GCM’s) with millions of degrees of freedom to models with few degrees of freedom. Simple Energy Balance Climate Models (EBCM’s) help us understand the dynamics of GCM’s. The same is true in economics with Computable General Equilibrium Models (CGE’s) where some models are infinite-dimensional multidimensional differential equations but some are simple models. Nordhaus (2007, 2010) couples a simple EBCM with a simple economic model. One- and two- dimensional ECBM’s do better at approximating damages across the globe and positive and negative feedbacks from anthroprogenic forcing (North etal. (1981), Wu and North (2007)). A proper coupling of climate and economic systems is crucial for arriving at effective policies. Brock and Xepapadeas (2010) have used Fourier/Legendre based expansions to study the shape of socially optimal carbon taxes over time at the planetary level in the face of damages caused by polar ice cap melt (as discussed by Oppenheimer, 2005) but in only a “one dimensional” EBCM. Economists have used orthogonal polynomial expansions to solve dynamic, forward-looking economic models (Judd, 1992, 1998). This presentation will couple EBCM climate models with basic forward-looking economic models, and examine the effectiveness and scaling properties of alternative solution methods. We will use a two dimensional EBCM model on the sphere (Wu and North, 2007) and a multicountry, multisector regional model of the economic system. Our aim will be to gain insights into intertemporal shape of the optimal carbon tax schedule, and its impact on global food production, as modeled by Golub and Hertel (2009). We will initially have limited computing resources and will need to focus on highly aggregated models. However, this will be more complex than existing models with forward

GFDL's CM2 global coupled climate models. Part I: Formulation and simulation characteristics

Science.gov (United States)

Delworth, T.L.; Broccoli, A.J.; Rosati, A.; Stouffer, R.J.; Balaji, V.; Beesley, J.A.; Cooke, W.F.; Dixon, K.W.; Dunne, J.; Dunne, K.A.; Durachta, J.W.; Findell, K.L.; Ginoux, P.; Gnanadesikan, A.; Gordon, C.T.; Griffies, S.M.; Gudgel, R.; Harrison, M.J.; Held, I.M.; Hemler, R.S.; Horowitz, L.W.; Klein, S.A.; Knutson, T.R.; Kushner, P.J.; Langenhorst, A.R.; Lee, H.-C.; Lin, S.-J.; Lu, J.; Malyshev, S.L.; Milly, P.C.D.; Ramaswamy, V.; Russell, J.; Schwarzkopf, M.D.; Shevliakova, E.; Sirutis, J.J.; Spelman, M.J.; Stern, W.F.; Winton, M.; Wittenberg, A.T.; Wyman, B.; Zeng, F.; Zhang, R.

2006-01-01

the land model, both of which act to increase the net surface shortwave radiation in CM2.1, thereby reducing an overall cold bias present in CM2.0; and 3) a reduction of ocean lateral viscosity in the extratropics in CM2.1, which reduces sea ice biases in the North Atlantic. Both models have be en used to conduct a suite of climate change simulations for the 2007 Intergovernmental Panel on Climate Change (IPCC) assessment report and are able to simulate the main features of the observed warming of the twentieth century. The climate sensitivities of the CM2.0 and CM2.1 models are 2.9 and 3.4 K, respectively. These sensitivities are defined by coupling the atmospheric components of CM2.0 and CM2.1 to a slab ocean model and allowing the model to come into equilibrium with a doubling of atmospheric CO2. The output from a suite of integrations conducted with these models is freely available online (see http://nomads.gfdl.noaa.gov/). ?? 2006 American Meteorological Society.

Modelling the impact of climate change and atmospheric N deposition on French forests biodiversity

International Nuclear Information System (INIS)

Rizzetto, Simon; Belyazid, Salim; Gégout, Jean-Claude; Nicolas, Manuel; Alard, Didier; Corcket, Emmanuel; Gaudio, Noémie; Sverdrup, Harald; Probst, Anne

2016-01-01

A dynamic coupled biogeochemical–ecological model was used to simulate the effects of nitrogen deposition and climate change on plant communities at three forest sites in France. The three sites had different forest covers (sessile oak, Norway spruce and silver fir), three nitrogen loads ranging from relatively low to high, different climatic regions and different soil types. Both the availability of vegetation time series and the environmental niches of the understory species allowed to evaluate the model for predicting the composition of the three plant communities. The calibration of the environmental niches was successful, with a model performance consistently reasonably high throughout the three sites. The model simulations of two climatic and two deposition scenarios showed that climate change may entirely compromise the eventual recovery from eutrophication of the simulated plant communities in response to the reductions in nitrogen deposition. The interplay between climate and deposition was strongly governed by site characteristics and histories in the long term, while forest management remained the main driver of change in the short term. - Highlights: • The effects of N atmospheric deposition and climate change on vegetation were simulated. • The model ForSAFE-Veg was calibrated and validated carefully for three forests in France. • Climate has a greater influence on vegetation than N deposition in conifer forests. • N-poor ecosystems are, however, more sensitive to N deposition than to climate change. - Compared to nitrogen atmospheric deposition, climate appears to be the main driver of change in forest plant biodiversity on a century scale, except in N-poor ecosystems.

The role of soil moisture in land surface-atmosphere coupling: climate model sensitivity experiments over India

Science.gov (United States)

Williams, Charles; Turner, Andrew

2015-04-01

It is generally acknowledged that anthropogenic land use changes, such as a shift from forested land into irrigated agriculture, may have an impact on regional climate and, in particular, rainfall patterns in both time and space. India provides an excellent example of a country in which widespread land use change has occurred during the last century, as the country tries to meet its growing demand for food. Of primary concern for agriculture is the Indian summer monsoon (ISM), which displays considerable seasonal and subseasonal variability. Although it is evident that changing rainfall variability will have a direct impact on land surface processes (such as soil moisture variability), the reverse impact is less well understood. However, the role of soil moisture in the coupling between the land surface and atmosphere needs to be properly explored before any potential impact of changing soil moisture variability on ISM rainfall can be understood. This paper attempts to address this issue, by conducting a number of sensitivity experiments using a state-of-the-art climate model from the UK Meteorological Office Hadley Centre: HadGEM2. Several experiments are undertaken, with the only difference between them being the extent to which soil moisture is coupled to the atmosphere. Firstly, the land surface is fully coupled to the atmosphere, globally (as in standard model configurations); secondly, the land surface is entirely uncoupled from the atmosphere, again globally, with soil moisture values being prescribed on a daily basis; thirdly, the land surface is uncoupled from the atmosphere over India but fully coupled elsewhere; and lastly, vice versa (i.e. the land surface is coupled to the atmosphere over India but uncoupled elsewhere). Early results from this study suggest certain 'hotspot' regions where the impact of soil moisture coupling/uncoupling may be important, and many of these regions coincide with previous studies. Focusing on the third experiment, i

Evaluating climate model performance with various parameter sets using observations over the recent past

Directory of Open Access Journals (Sweden)

M. F. Loutre

2011-05-01

Full Text Available Many sources of uncertainty limit the accuracy of climate projections. Among them, we focus here on the parameter uncertainty, i.e. the imperfect knowledge of the values of many physical parameters in a climate model. Therefore, we use LOVECLIM, a global three-dimensional Earth system model of intermediate complexity and vary several parameters within a range based on the expert judgement of model developers. Nine climatic parameter sets and three carbon cycle parameter sets are selected because they yield present-day climate simulations coherent with observations and they cover a wide range of climate responses to doubled atmospheric CO₂ concentration and freshwater flux perturbation in the North Atlantic. Moreover, they also lead to a large range of atmospheric CO₂ concentrations in response to prescribed emissions. Consequently, we have at our disposal 27 alternative versions of LOVECLIM (each corresponding to one parameter set that provide very different responses to some climate forcings. The 27 model versions are then used to illustrate the range of responses provided over the recent past, to compare the time evolution of climate variables over the time interval for which they are available (the last few decades up to more than one century and to identify the outliers and the "best" versions over that particular time span. For example, between 1979 and 2005, the simulated global annual mean surface temperature increase ranges from 0.24 °C to 0.64 °C, while the simulated increase in atmospheric CO₂ concentration varies between 40 and 50 ppmv. Measurements over the same period indicate an increase in global annual mean surface temperature of 0.45 °C (Brohan et al., 2006 and an increase in atmospheric CO₂ concentration of 44 ppmv (Enting et al., 1994; GLOBALVIEW-CO2, 2006. Only a few parameter sets yield simulations that reproduce the observed key variables of the climate system over the last

Construction of a novel economy-climate model

Institute of Scientific and Technical Information of China (English)

CHOU JieMing; DONG WenJie; YE DuZheng

2007-01-01

An attempt has been made to construct a novel economy-climate model by combining climate change research with agricultural economy research to evaluate the influence of global climate change on grain yields. The insertion of a climate change factor into the economic C-D (Cobb-Dauglas) production function model yields a novel evaluation model, which connects the climate change factor to the economic variation factor, and the performance and reasonableness of the novel evaluation model are also preliminarily simulated and verified.

Modeling Bird Migration under Climate Change: A Mechanistic Approach

Science.gov (United States)

Smith, James A.

2009-01-01

How will migrating birds respond to changes in the environment under climate change? What are the implications for migratory success under the various accelerated climate change scenarios as forecast by the Intergovernmental Panel on Climate Change? How will reductions or increased variability in the number or quality of wetland stop-over sites affect migratory bird species? The answers to these questions have important ramifications for conservation biology and wildlife management. Here, we describe the use of continental scale simulation modeling to explore how spatio-temporal changes along migratory flyways affect en-route migration success. We use an individually based, biophysical, mechanistic, bird migration model to simulate the movement of shorebirds in North America as a tool to study how such factors as drought and wetland loss may impact migratory success and modify migration patterns. Our model is driven by remote sensing and climate data and incorporates important landscape variables. The energy budget components of the model include resting, foraging, and flight, but presently predation is ignored. Results/Conclusions We illustrate our model by studying the spring migration of sandpipers through the Great Plains to their Arctic breeding grounds. Why many species of shorebirds have shown significant declines remains a puzzle. Shorebirds are sensitive to stop-over quality and spacing because of their need for frequent refueling stops and their opportunistic feeding patterns. We predict bird "hydrographs that is, stop-over frequency with latitude, that are in agreement with the literature. Mean stop-over durations predicted from our model for nominal cases also are consistent with the limited, but available data. For the shorebird species simulated, our model predicts that shorebirds exhibit significant plasticity and are able to shift their migration patterns in response to changing drought conditions. However, the question remains as to whether this

The sensitivity of Alpine summer convection to surrogate climate change: an intercomparison between convection-parameterizing and convection-resolving models

Directory of Open Access Journals (Sweden)

M. Keller

2018-04-01

Full Text Available Climate models project an increase in heavy precipitation events in response to greenhouse gas forcing. Important elements of such events are rain showers and thunderstorms, which are poorly represented in models with parameterized convection. In this study, simulations with 12 km horizontal grid spacing (convection-parameterizing model, CPM and 2 km grid spacing (convection-resolving model, CRM are employed to investigate the change in the diurnal cycle of convection with warmer climate. For this purpose, simulations of 11 days in June 2007 with a pronounced diurnal cycle of convection are compared with surrogate simulations from the same period. The surrogate climate simulations mimic a future climate with increased temperatures but unchanged relative humidity and similar synoptic-scale circulation. Two temperature scenarios are compared: one with homogeneous warming (HW using a vertically uniform warming and the other with vertically dependent warming (VW that enables changes in lapse rate.The two sets of simulations with parameterized and explicit convection exhibit substantial differences, some of which are well known from the literature. These include differences in the timing and amplitude of the diurnal cycle of convection, and the frequency of precipitation with low intensities. The response to climate change is much less studied. We can show that stratification changes have a strong influence on the changes in convection. Precipitation is strongly increasing for HW but decreasing for the VW simulations. For cloud type frequencies, virtually no changes are found for HW, but a substantial reduction in high clouds is found for VW. Further, we can show that the climate change signal strongly depends upon the horizontal resolution. In particular, significant differences between CPM and CRM are found in terms of the radiative feedbacks, with CRM exhibiting a stronger negative feedback in the top-of-the-atmosphere energy budget.

The sensitivity of Alpine summer convection to surrogate climate change: an intercomparison between convection-parameterizing and convection-resolving models

Science.gov (United States)

Keller, Michael; Kröner, Nico; Fuhrer, Oliver; Lüthi, Daniel; Schmidli, Juerg; Stengel, Martin; Stöckli, Reto; Schär, Christoph

2018-04-01

Climate models project an increase in heavy precipitation events in response to greenhouse gas forcing. Important elements of such events are rain showers and thunderstorms, which are poorly represented in models with parameterized convection. In this study, simulations with 12 km horizontal grid spacing (convection-parameterizing model, CPM) and 2 km grid spacing (convection-resolving model, CRM) are employed to investigate the change in the diurnal cycle of convection with warmer climate. For this purpose, simulations of 11 days in June 2007 with a pronounced diurnal cycle of convection are compared with surrogate simulations from the same period. The surrogate climate simulations mimic a future climate with increased temperatures but unchanged relative humidity and similar synoptic-scale circulation. Two temperature scenarios are compared: one with homogeneous warming (HW) using a vertically uniform warming and the other with vertically dependent warming (VW) that enables changes in lapse rate. The two sets of simulations with parameterized and explicit convection exhibit substantial differences, some of which are well known from the literature. These include differences in the timing and amplitude of the diurnal cycle of convection, and the frequency of precipitation with low intensities. The response to climate change is much less studied. We can show that stratification changes have a strong influence on the changes in convection. Precipitation is strongly increasing for HW but decreasing for the VW simulations. For cloud type frequencies, virtually no changes are found for HW, but a substantial reduction in high clouds is found for VW. Further, we can show that the climate change signal strongly depends upon the horizontal resolution. In particular, significant differences between CPM and CRM are found in terms of the radiative feedbacks, with CRM exhibiting a stronger negative feedback in the top-of-the-atmosphere energy budget.

Meeting the Next Generation Science Standards Through "Rediscovered" Climate Model Experiments

Science.gov (United States)

Sohl, L. E.; Chandler, M. A.; Zhou, J.

2013-12-01

Since the Educational Global Climate Model (EdGCM) Project made its debut in January 2005, over 150 institutions have employed EdGCM software for a variety of uses ranging from short lab exercises to semester-long and year-long thesis projects. The vast majority of these EdGCM adoptees have been at the undergraduate and graduate levels, with few users at the K-12 level. The K-12 instructors who have worked with EdGCM in professional development settings have commented that, although EdGCM can be used to illustrate a number of the Disciplinary Core Ideas and connects to many of the Common Core State Standards across subjects and grade levels, significant hurdles preclude easy integration of EdGCM into their curricula. Time constraints, a scarcity of curriculum materials, and classroom technology are often mentioned as obstacles in providing experiences to younger grade levels in realistic climate modeling research. Given that the NGSS incorporates student performance expectations relating to Earth System Science, and to climate science and the human dimension in particular, we feel that a streamlined version of EdGCM -- one that eliminates the need to run the climate model on limited computing resources, and provides a more guided climate modeling experience -- would be highly beneficial for the K-12 community. This new tool currently under development, called EzGCM, functions through a browser interface, and presents "rediscovery experiments" that allow students to do their own exploration of model output from published climate experiments, or from sensitivity experiments designed to illustrate how climate models as well as the climate system work. The experiments include background information and sample questions, with more extensive notes for instructors so that the instructors can design their own reflection questions or follow-on activities relating to physical or human impacts, as they choose. An added benefit of the EzGCM tool is that, like EdGCM, it helps

Acceleration of global warming due to carbon-cycle feedbacks in a coupled climate model

International Nuclear Information System (INIS)

Cox, P.M.; Betts, R.A.; Jones, C.D.; Spall, S.A.; Totterdell, I.J.

2000-01-01

The continued increase in the atmospheric concentration of carbon dioxide due to anthropogenic emissions is predicted to lead to significant changes in climate. About half of the current emissions are being absorbed by the ocean and by land ecosystems, but this absorption is sensitive to climate as well as to atmospheric carbon dioxide concentrations, creating a feedback loop. General circulation models have generally excluded the feedback between climate and the biosphere, using static vegetation distributions and CO 2 concentrations from simple carbon-cycle models that do not include climate change. Here we present results from a fully coupled, three-dimensional carbon-climate model, indicating that carbon-cycle feedbacks could significantly accelerate climate change over the twenty-first century. We find that under a 'business as usual' scenario, the terrestrial biosphere acts as an overall carbon sink until about 2050, but turns into a source thereafter. By 2100, the ocean uptake rate of 5 Gt C yr -1 is balanced by the terrestrial carbon source, and atmospheric CO 2 concentrations are 250 p.p.m.v. higher in our fully coupled simulation than in uncoupled carbon models, resulting in a global-mean warming of 5.5 K, as compared to 4 K without the carbon-cycle feedback. (author)

Effects of snow grain shape on climate simulations: sensitivity tests with the Norwegian Earth System Model

Directory of Open Access Journals (Sweden)

P. Räisänen

2017-12-01

Full Text Available Snow consists of non-spherical grains of various shapes and sizes. Still, in radiative transfer calculations, snow grains are often treated as spherical. This also applies to the computation of snow albedo in the Snow, Ice, and Aerosol Radiation (SNICAR model and in the Los Alamos sea ice model, version 4 (CICE4, both of which are employed in the Community Earth System Model and in the Norwegian Earth System Model (NorESM. In this study, we evaluate the effect of snow grain shape on climate simulated by NorESM in a slab ocean configuration of the model. An experiment with spherical snow grains (SPH is compared with another (NONSPH in which the snow shortwave single-scattering properties are based on a combination of three non-spherical snow grain shapes optimized using measurements of angular scattering by blowing snow. The key difference between these treatments is that the asymmetry parameter is smaller in the non-spherical case (0.77–0.78 in the visible region than in the spherical case ( ≈  0.89. Therefore, for the same effective snow grain size (or equivalently, the same specific projected area, the snow broadband albedo is higher when assuming non-spherical rather than spherical snow grains, typically by 0.02–0.03. Considering the spherical case as the baseline, this results in an instantaneous negative change in net shortwave radiation with a global-mean top-of-the-model value of ca. −0.22 W m−2. Although this global-mean radiative effect is rather modest, the impacts on the climate simulated by NorESM are substantial. The global annual-mean 2 m air temperature in NONSPH is 1.17 K lower than in SPH, with substantially larger differences at high latitudes. The climatic response is amplified by strong snow and sea ice feedbacks. It is further demonstrated that the effect of snow grain shape could be largely offset by adjusting the snow grain size. When assuming non-spherical snow grains with the parameterized grain

Decomposing the uncertainty in climate impact projections of Dynamic Vegetation Models: a test with the forest models LANDCLIM and FORCLIM

Science.gov (United States)

Cailleret, Maxime; Snell, Rebecca; von Waldow, Harald; Kotlarski, Sven; Bugmann, Harald

2015-04-01

not so much at medium elevations. (ii) Considering climate change, the variability that is due to the GCM-RCM chains is much greater than the variability induced by the uncertainty in the initial climatic conditions. (iii) The uncertainties caused by the intrinsic stochasticity in the DVMs and by the random generation of the climate time-series are negligible. Overall, our results indicate that DVMs are quite sensitive to the climate data, highlighting particularly (1) the limitations of using one single multi-model average climate change scenario in climate impact studies and (2) the need to better consider the uncertainty in climate model outputs for projecting future vegetation changes.

Last Interglacial climate and sea-level evolution from a coupled ice sheet-climate model

Science.gov (United States)

Goelzer, Heiko; Huybrechts, Philippe; Loutre, Marie-France; Fichefet, Thierry

2016-12-01

As the most recent warm period in Earth's history with a sea-level stand higher than present, the Last Interglacial (LIG, ˜ 130 to 115 kyr BP) is often considered a prime example to study the impact of a warmer climate on the two polar ice sheets remaining today. Here we simulate the Last Interglacial climate, ice sheet, and sea-level evolution with the Earth system model of intermediate complexity LOVECLIM v.1.3, which includes dynamic and fully coupled components representing the atmosphere, the ocean and sea ice, the terrestrial biosphere, and the Greenland and Antarctic ice sheets. In this setup, sea-level evolution and climate-ice sheet interactions are modelled in a consistent framework.Surface mass balance change governed by changes in surface meltwater runoff is the dominant forcing for the Greenland ice sheet, which shows a peak sea-level contribution of 1.4 m at 123 kyr BP in the reference experiment. Our results indicate that ice sheet-climate feedbacks play an important role to amplify climate and sea-level changes in the Northern Hemisphere. The sensitivity of the Greenland ice sheet to surface temperature changes considerably increases when interactive albedo changes are considered. Southern Hemisphere polar and sub-polar ocean warming is limited throughout the Last Interglacial, and surface and sub-shelf melting exerts only a minor control on the Antarctic sea-level contribution with a peak of 4.4 m at 125 kyr BP. Retreat of the Antarctic ice sheet at the onset of the LIG is mainly forced by rising sea level and to a lesser extent by reduced ice shelf viscosity as the surface temperature increases. Global sea level shows a peak of 5.3 m at 124.5 kyr BP, which includes a minor contribution of 0.35 m from oceanic thermal expansion. Neither the individual contributions nor the total modelled sea-level stand show fast multi-millennial timescale variations as indicated by some reconstructions.

Continuous versus discontinuous albedo representations in a simple diffusive climate model

Science.gov (United States)

Simmons, P. A.; Griffel, D. H.

1988-07-01

A one-dimensional annually and zonally averaged energy-balance model, with diffusive meridional heat transport and including icealbedo feedback, is considered. This type of model is found to be very sensitive to the form of albedo used. The solutions for a discontinuous step-function albedo are compared to those for a more realistic smoothly varying albedo. The smooth albedo gives a closer fit to present conditions, but the discontinuous form gives a better representation of climates in earlier epochs.

Spatio-temporal modelling of climate-sensitive disease risk: Towards an early warning system for dengue in Brazil

Science.gov (United States)

Lowe, Rachel; Bailey, Trevor C.; Stephenson, David B.; Graham, Richard J.; Coelho, Caio A. S.; Sá Carvalho, Marilia; Barcellos, Christovam

2011-03-01

This paper considers the potential for using seasonal climate forecasts in developing an early warning system for dengue fever epidemics in Brazil. In the first instance, a generalised linear model (GLM) is used to select climate and other covariates which are both readily available and prove significant in prediction of confirmed monthly dengue cases based on data collected across the whole of Brazil for the period January 2001 to December 2008 at the microregion level (typically consisting of one large city and several smaller municipalities). The covariates explored include temperature and precipitation data on a 2.5°×2.5° longitude-latitude grid with time lags relevant to dengue transmission, an El Niño Southern Oscillation index and other relevant socio-economic and environmental variables. A negative binomial model formulation is adopted in this model selection to allow for extra-Poisson variation (overdispersion) in the observed dengue counts caused by unknown/unobserved confounding factors and possible correlations in these effects in both time and space. Subsequently, the selected global model is refined in the context of the South East region of Brazil, where dengue predominates, by reverting to a Poisson framework and explicitly modelling the overdispersion through a combination of unstructured and spatio-temporal structured random effects. The resulting spatio-temporal hierarchical model (or GLMM—generalised linear mixed model) is implemented via a Bayesian framework using Markov Chain Monte Carlo (MCMC). Dengue predictions are found to be enhanced both spatially and temporally when using the GLMM and the Bayesian framework allows posterior predictive distributions for dengue cases to be derived, which can be useful for developing a dengue alert system. Using this model, we conclude that seasonal climate forecasts could have potential value in helping to predict dengue incidence months in advance of an epidemic in South East Brazil.

Spontaneous abrupt climate change due to an atmospheric blocking-sea-ice-ocean feedback in an unforced climate model simulation.

Science.gov (United States)

Drijfhout, Sybren; Gleeson, Emily; Dijkstra, Henk A; Livina, Valerie

2013-12-03

Abrupt climate change is abundant in geological records, but climate models rarely have been able to simulate such events in response to realistic forcing. Here we report on a spontaneous abrupt cooling event, lasting for more than a century, with a temperature anomaly similar to that of the Little Ice Age. The event was simulated in the preindustrial control run of a high-resolution climate model, without imposing external perturbations. Initial cooling started with a period of enhanced atmospheric blocking over the eastern subpolar gyre. In response, a southward progression of the sea-ice margin occurred, and the sea-level pressure anomaly was locked to the sea-ice margin through thermal forcing. The cold-core high steered more cold air to the area, reinforcing the sea-ice concentration anomaly east of Greenland. The sea-ice surplus was carried southward by ocean currents around the tip of Greenland. South of 70 °N, sea ice already started melting and the associated freshwater anomaly was carried to the Labrador Sea, shutting off deep convection. There, surface waters were exposed longer to atmospheric cooling and sea surface temperature dropped, causing an even larger thermally forced high above the Labrador Sea. In consequence, east of Greenland, anomalous winds changed from north to south, terminating the event with similar abruptness to its onset. Our results imply that only climate models that possess sufficient resolution to correctly represent atmospheric blocking, in combination with a sensitive sea-ice model, are able to simulate this kind of abrupt climate change.

Regional modeling of large wildfires under current and potential future climates in Colorado and Wyoming, USA

Science.gov (United States)

West, Amanda; Kumar, Sunil; Jarnevich, Catherine S.

2016-01-01

Regional analysis of large wildfire potential given climate change scenarios is crucial to understanding areas most at risk in the future, yet wildfire models are not often developed and tested at this spatial scale. We fit three historical climate suitability models for large wildfires (i.e. ≥ 400 ha) in Colorado andWyoming using topography and decadal climate averages corresponding to wildfire occurrence at the same temporal scale. The historical models classified points of known large wildfire occurrence with high accuracies. Using a novel approach in wildfire modeling, we applied the historical models to independent climate and wildfire datasets, and the resulting sensitivities were 0.75, 0.81, and 0.83 for Maxent, Generalized Linear, and Multivariate Adaptive Regression Splines, respectively. We projected the historic models into future climate space using data from 15 global circulation models and two representative concentration pathway scenarios. Maps from these geospatial analyses can be used to evaluate the changing spatial distribution of climate suitability of large wildfires in these states. April relative humidity was the most important covariate in all models, providing insight to the climate space of large wildfires in this region. These methods incorporate monthly and seasonal climate averages at a spatial resolution relevant to land management (i.e. 1 km2) and provide a tool that can be modified for other regions of North America, or adapted for other parts of the world.

International Space Science Institute Workshop on Shallow Clouds, Water Vapor, Circulation and Climate Sensitivity

CERN Document Server

Winker, David; Bony, Sandrine; Stevens, Bjorn

2018-01-01

This volume presents a series of overview articles arising from a workshop exploring the links among shallow clouds, water vapor, circulation, and climate sensitivity. It provides a state-of-the art synthesis of understanding about the coupling of clouds and water vapor to the large-scale circulation. The emphasis is on two phenomena, namely the self-aggregation of deep convection and interactions between low clouds and the large-scale environment, with direct links to the sensitivity of climate to radiative perturbations. Each subject is approached using simulations, observations, and synthesizing theory; particular attention is paid to opportunities offered by new remote-sensing technologies, some still prospective. The collection provides a thorough grounding in topics representing one of the World Climate Research Program’s Grand Challenges. Previously published in Surveys in Geophysics, Volume 38, Issue 6, 2017 The articles “Observing Convective Aggregation”, “An Observational View of Relationshi...

Quantitative Study of Green Area for Climate Sensitive Terraced Housing Area Design in Malaysia

International Nuclear Information System (INIS)

Yeo, O T S; Saito, K; Said, I

2014-01-01

Neighbourhood plays a significant role in peoples' daily lives. Nowadays, terraced housing is common in Malaysia, and green areas in the neighborhood are not used to their maximum. The aim of the research is to quantify the types of green area that are most efficient for cooling the environment for thermal comfort and mitigation of Urban Heat Island. Spatial and environmental inputs are manipulated for the simulation using Geographic Information System (GIS) integrated with computational microclimate simulation. The outcome of this research is a climate sensitive housing environment model framework on the green area to solve the problem of Urban Heat Island

Selecting representative climate models for climate change impact studies : An advanced envelope-based selection approach

NARCIS (Netherlands)

Lutz, Arthur F.; ter Maat, Herbert W.; Biemans, Hester; Shrestha, Arun B.; Wester, Philippus; Immerzeel, Walter W.|info:eu-repo/dai/nl/290472113

2016-01-01

Climate change impact studies depend on projections of future climate provided by climate models. The number of climate models is large and increasing, yet limitations in computational capacity make it necessary to compromise the number of climate models that can be included in a climate change

Selecting representative climate models for climate change impact studies: an advanced envelope-based selection approach

NARCIS (Netherlands)

Lutz, Arthur F.; Maat, ter Herbert W.; Biemans, Hester; Shrestha, Arun B.; Wester, Philippus; Immerzeel, Walter W.

2016-01-01

Climate change impact studies depend on projections of future climate provided by climate models. The number of climate models is large and increasing, yet limitations in computational capacity make it necessary to compromise the number of climate models that can be included in a climate change

Model of urban water management towards water sensitive city: a literature review

Science.gov (United States)

Maftuhah, D. I.; Anityasari, M.; Sholihah, M.

2018-04-01

Nowadays, many cities are facing with complex issues such as climate change, social, economic, culture, and environmental problems, especially urban water. In other words, the city has to struggle with the challenge to make sure its sustainability in all aspects. This research focuses on how to ensure the city sustainability and resilience on urban water management. Many research were not only conducted in urban water management, but also in sustainability itself. Moreover, water sustainability shifts from urban water management into water sensitive city. This transition needs comprehensive aspects such as social, institutional dynamics, technical innovation, and local contents. Some literatures about model of urban water management and the transition towards water sensitivity had been reviewed in this study. This study proposed discussion about model of urban water management and the transition towards water sensitive city. Research findings suggest that there are many different models developed in urban water management, but they are not comprehensive yet and only few studies discuss about the transition towards water sensitive and resilience city. The drawbacks of previous research can identify and fulfill the gap of this study. Therefore, the paper contributes a general framework for the urban water management modelling studies.

Sensitivity of Water Scarcity Events to ENSO-Driven Climate Variability at the Global Scale

Science.gov (United States)

Veldkamp, T. I. E.; Eisner, S.; Wada, Y.; Aerts, J. C. J. H.; Ward, P. J.

2015-01-01

Globally, freshwater shortage is one of the most dangerous risks for society. Changing hydro-climatic and socioeconomic conditions have aggravated water scarcity over the past decades. A wide range of studies show that water scarcity will intensify in the future, as a result of both increased consumptive water use and, in some regions, climate change. Although it is well-known that El Niño- Southern Oscillation (ENSO) affects patterns of precipitation and drought at global and regional scales, little attention has yet been paid to the impacts of climate variability on water scarcity conditions, despite its importance for adaptation planning. Therefore, we present the first global-scale sensitivity assessment of water scarcity to ENSO, the most dominant signal of climate variability. We show that over the time period 1961-2010, both water availability and water scarcity conditions are significantly correlated with ENSO-driven climate variability over a large proportion of the global land area (> 28.1 %); an area inhabited by more than 31.4% of the global population. We also found, however, that climate variability alone is often not enough to trigger the actual incidence of water scarcity events. The sensitivity of a region to water scarcity events, expressed in terms of land area or population exposed, is determined by both hydro-climatic and socioeconomic conditions. Currently, the population actually impacted by water scarcity events consists of 39.6% (CTA: consumption-to-availability ratio) and 41.1% (WCI: water crowding index) of the global population, whilst only 11.4% (CTA) and 15.9% (WCI) of the global population is at the same time living in areas sensitive to ENSO-driven climate variability. These results are contrasted, however, by differences in growth rates found under changing socioeconomic conditions, which are relatively high in regions exposed to water scarcity events. Given the correlations found between ENSO and water availability and scarcity

Combined effects of climate models, hydrological model structures and land use scenarios on hydrological impacts of climate change

DEFF Research Database (Denmark)

Karlsson, Ida B.; Sonnenborg, Torben O.; Refsgaard, Jens Christian

2016-01-01

Impact studies of the hydrological response of future climate change are important for the water authorities when risk assessment, management and adaptation to a changing climate are carried out. The objective of this study was to model the combined effect of land use and climate changes...... use scenarios. The results revealed that even though the hydrological models all showed similar performance during calibration, the mean discharge response to climate change varied up to 30%, and the variations were even higher for extreme events (1th and 99th percentile). Land use changes appeared...... to cause little change in mean hydrological responses and little variation between hydrological models. Differences in hydrological model responses to land use were, however, significant for extremes due to dissimilarities in hydrological model structure and process equations. The climate model choice...