Statistically Downscaled CMIP6 Projections Show Stronger Warming for Germany

Climate modelling output that was provided under the latest Coupled Model Intercomparison Project (CMIP6) shows significant changes in model-specific Equilibrium Climate Sensitivity (ECS) as compared to CMIP5. The newer versions of many Global Climate Models (GCMs) report higher ECS values that result in stronger global warming than previously estimated. At the same time, the multi-GCM spread of ECS is significantly larger than under CMIP5. Here, we analyse how the differences between CMIP5 and CMIP6 affect climate projections for Germany. We use the statistical-empirical downscaling method EPISODES in order to downscale GCM data for the scenario pairs RCP4.5/SSP2-4.5 and RCP8.5/SSP5-8.5. We use data sets of the GCMs CanESM, EC-Earth, MPI-ESM, and NorESM. The results show that the GCM-specific changes in the ECS also have an impact at the regional scale. While the temperature signal under regional climate change remains comparable for both CMIP generations in the MPI-ESM chain, the temperature signal increases by up to 3 °C for the RCP8.5/SSP5-8.5 scenario pair in the EC-Earth chain. Changes in precipitation are less pronounced and they only show notable differences at the seasonal scale. The reported changes in the climate signal will have direct consequences for society. Climate change impacts previously projected for the high-emission RCP8.5 scenario might occur equally under the new SSP2-4.5 scenario.

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A regional climate modelling projection ensemble experiment – NARCliM

Geoscientific Model Development Discussions ◽

10.5194/gmdd-6-5117-2013 ◽

2013 ◽

Vol 6 (3) ◽

pp. 5117-5139 ◽

Cited By ~ 3

Author(s):

J. P. Evans ◽

F. Ji ◽

C. Lee ◽

P. Smith ◽

D. Argüeso ◽

...

Keyword(s):

Climate Change ◽

Climate Models ◽

Planning Process ◽

Global Climate ◽

Regional Climate ◽

Global Climate Models ◽

Regional Climate Modelling ◽

Climate Projections ◽

Climate Modelling ◽

User Engagement

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CLIMATE CHANGE AND WATER RESOURCE SUSTAINABILITY INDEX FOR A WATER-STRESSED BASIN IN BRAZIL: THE CASE STUDY OF RIO VERDE GRANDE BASIN

Nativa ◽

10.31413/nativa.v6i5.5719 ◽

2018 ◽

Vol 6 (5) ◽

pp. 480

Author(s):

Mônica Carvalho de Sá ◽

Edson De Oliveira Vieira ◽

Flavia Mazzer Rodrigues ◽

Lorrana Cavalcanti Albuquerque ◽

Núbia Ribeiro Caldeira

Keyword(s):

Climate Change ◽

Water Resources ◽

Climate Models ◽

Global Climate ◽

Coupled Model ◽

Minas Gerais ◽

Global Climate Models ◽

Climate Projections ◽

Model Intercomparison ◽

Intercomparison Project

MUDANÇAS CLIMÁTICAS E A SUSTENTABILIDADES DOS RECURSOS HÍDRICOS EM BACIA HIDROGRÁFICA COM ESCASSEZ HÍDRICA NO BRASIL: O CASO DA BACIA DE RIO VERDE GRANDE A bacia de Rio Verde Grande está localizada 87% na parte norte do estado de Minas Gerais e 13% no estado da Bahia, em uma região com clima semiárido, apresentando longos e intensos períodos de seca. Esta característica climática afeta diretamente a disponibilidade de recursos hídricos e, conseqüentemente, o desenvolvimento das principais atividades da região que são pecuária e agricultura irrigada. Não há estudos que avaliem o efeito das mudanças climáticas na disponibilidade de água e na sustentabilidade de atividades com alta demanda de água na bacia de Rio Verde Grande. O objetivo deste estudo foi analisar as mudanças prováveis na disponibilidade de água na bacia de Rio Verde Grande e a sustentabilidade dos recursos hídricos para as atividades usuárias de água, utilizando séries sintéticas geradas através de programas de modelagem climática e hidrológica. Este estudo realizou as projeções climáticas utilizando os Global Climate Models do Coupled Model Intercomparison Project Phase 5. Com base nos cálculos dos índices de sustentabilidade e na comparação dos cenários atuais e futuros, observou-se que, mesmo com todas as intervenções propostas pelo Plano de Recursos Hídricos da Bacia Rio Verde Grande implementadas, houve uma redução na sustentabilidade da água Recursos em algumas sub-bacias devido à mudança climática.Palavras-chave: CMIP5, modelo WEAP, vulnerabilidade ABSTRACT: The Rio Verde Grande basin is a water-stressed basin, which is 87% in the northern part of Minas Gerais and 13% in Bahia, Brazil. It has a semi-arid climate with long and intense periods of drought. This climatic directly affects the availability of water resources and the development of the main activities in the region. There are presently no studies that evaluate the effect of climate change on the availability of water in the Rio Verde Grande basin and the sustainability of high water demand activities. The objective of this study was to analyze future changes in the availability of water in the Rio Verde Grande basin, and the sustainability of water for the major water users. This was done using a synthetic series generated through climatic and hydrological modeling programs. This study performed climate projections using the Global Climate Models of the Coupled Model Intercomparison Project Phase 5. The calculation of sustainability indexes and a comparison between current and future scenarios, it was observed that even if all the interventions proposed by the Water Resources Plan of the Rio Verde Grande basin are implemented, there will still be a reduction in the sustainability of water resources in some sub-basins, due to climate change.Keywords: CMIP5, WEAP model, vulnerability.

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Robust increase of Indian monsoon rainfall and its variability under future warming in CMIP-6 models

10.5194/esd-2020-80 ◽

2020 ◽

Author(s):

Anja Katzenberger ◽

Jacob Schewe ◽

Julia Pongratz ◽

Anders Levermann

Keyword(s):

Climate Change ◽

Global Warming ◽

Climate Models ◽

Monsoon Rainfall ◽

Global Climate ◽

Coupled Model ◽

Global Climate Models ◽

Future Climate Change ◽

Global Mean Temperature ◽

Almost All

Abstract. The Indian summer monsoon is an integral part of the global climate system. As its seasonal rainfall plays a crucial role in India's agriculture and shapes many other aspects of life, it affects the livelihood of a fifth of the world's population. It is therefore highly relevant to assess its change under potential future climate change. Global climate models within the Coupled Model Intercomparison Project Phase 5 (CMIP-5) indicated a consistent increase in monsoon rainfall and its variability under global warming. Since the range of the results of CMIP-5 was still large and the confidence in the models was limited due to partly poor representation of observed rainfall, the updates within the latest generation of climate models in CMIP-6 are of interest. Here, we analyse 32 models of the latest CMIP-6 exercise with regard to their annual mean monsoon rainfall and its variability. All of these models show a substantial increase in June-to-September (JJAS) mean rainfall under unabated climate change (SSP5-8.5) and most do also for the other three Shared Socioeconomic Pathways analyzed (SSP1-2.6, SSP2-4.5, SSP3-7.0). Moreover, the simulation ensemble indicates a linear dependence of rainfall on global mean temperature with high agreement between the models and independent of the SSP; the multi-model mean for JJAS projects an increase of 0.33 mm/day and 5.3 % per degree of global warming. This is significantly higher than in the CMIP-5 projections. Most models project that the increase will contribute to the precipitation especially in the Himalaya region and to the northeast of the Bay of Bengal, as well as the west coast of India. Interannual variability is found to be increasing in the higher-warming scenarios by almost all models. The CMIP-6 simulations largely confirm the findings from CMIP-5 models, but show an increased robustness across models with reduced uncertainties and updated magnitudes towards a stronger increase in monsoon rainfall.

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Climate Projections Over Different Climatic Regions of Afghanistan for Shared Socioeconomic Scenarios

10.21203/rs.3.rs-1202436/v1 ◽

2022 ◽

Author(s):

Mohammad Naser Sediqi ◽

Vempi Satriya Adi Hendrawan ◽

Daisuke Komori

Keyword(s):

Climate Models ◽

Global Climate ◽

Coupled Model ◽

Global Climate Models ◽

Maximum Temperature ◽

Climate Projections ◽

Climatic Regions ◽

Temperature And Precipitation ◽

Intercomparison Project ◽

Precipitation And Temperature

Abstract The global climate models (GCMs) of Coupled Model Intercomparison Project phase 6 (CMIP6) were used spatiotemporal projections of precipitation and temperature over Afghanistan for three shared socioeconomic pathways (SSP1-2.6, 2-4.5 and 5-8.5) and two future time horizons, early (2020-2059) and late (2060-2099). The Compromise Programming (CP) approach was employed to order the GCMs based on their skill to replicate precipitation and temperature climatology for the reference period (1975-2014). Three models, namely ACCESS-CM2, MPI-ESM1-2-LR, and FIO-ESM-2-0, showed the highest skill in simulating all three variables, and therefore, were chosen for the future projections. The ensemble mean of the GCMs showed an increase in maximum temperature by 1.5-2.5oC, 2.7-4.3 oC, and 4.5-5.3 oC and minimum temperature by 1.3-1.8 oC, 2.2-3.5 oC, and 4.6-5.2 oC for SSP1-2.6, SSP2-4.5, and SSP5-8.5, respectively in the later period. Meanwhile, the changes in precipitation in the range of -15-18%, -36-47% and -40-68% for SSP1-2.6, SSP2-4.5, and SSP5-8.5, respectively. The temperature and precipitation were projected to increase in the highlands and decrease over the deserts, indicating dry regions would be drier and wet regions wetter.

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Statistical Downscaling for Climate Science

Oxford Research Encyclopedia of Climate Science ◽

10.1093/acrefore/9780190228620.013.712 ◽

2019 ◽

Cited By ~ 1

Author(s):

Douglas Maraun

Keyword(s):

Statistical Downscaling ◽

Bias Correction ◽

Climate Models ◽

Global Climate ◽

Global Climate Model ◽

Regional Scale ◽

Climatic Changes ◽

Global Climate Models ◽

Small Scale ◽

Climate Projections

Global climate models are our main tool to generate quantitative climate projections, but these models do not resolve the effects of complex topography, regional scale atmospheric processes and small-scale extreme events. To understand potential regional climatic changes, and to provide information for regional-scale impact modeling and adaptation planning, downscaling approaches have been developed. Regional climate change modeling, even though it is still a matter of basic research and questioned by many researchers, is urged to provide operational results. One major downscaling class is statistical downscaling, which exploits empirical relationships between larger-scale and local weather. The main statistical downscaling approaches are perfect prog (often referred to as empirical statistical downscaling), model output statistics (which is typically some sort of bias correction), and weather generators. Statistical downscaling complements or adds to dynamical downscaling and is useful to generate user-tailored local-scale information, or to efficiently generate regional scale information about mean climatic changes from large global climate model ensembles. Further research is needed to assess to what extent the assumptions underlying statistical downscaling are met in typical applications, and to develop new methods for generating spatially coherent projections, and for including process-understanding in bias correction. The increasing resolution of global climate models will improve the representation of downscaling predictors and will, therefore, make downscaling an even more feasible approach that will still be required to tailor information for users.

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Design of a regional climate modelling projection ensemble experiment – NARCliM

Geoscientific Model Development ◽

10.5194/gmd-7-621-2014 ◽

2014 ◽

Vol 7 (2) ◽

pp. 621-629 ◽

Cited By ~ 112

Author(s):

J. P. Evans ◽

F. Ji ◽

C. Lee ◽

P. Smith ◽

D. Argüeso ◽

...

Keyword(s):

Climate Change ◽

Climate Models ◽

Selection Process ◽

Regional Climate ◽

Global Climate Models ◽

Regional Climate Modelling ◽

Future Research ◽

Climate Projections ◽

Climate Modelling ◽

Future Change

Abstract. Including the impacts of climate change in decision making and planning processes is a challenge facing many regional governments including the New South Wales (NSW) and Australian Capital Territory (ACT) governments in Australia. NARCliM (NSW/ACT Regional Climate Modelling project) is a regional climate modelling project that aims to provide a comprehensive and consistent set of climate projections that can be used by all relevant government departments when considering climate change. To maximise end user engagement and ensure outputs are relevant to the planning process, a series of stakeholder workshops were run to define key aspects of the model experiment including spatial resolution, time slices, and output variables. As with all such experiments, practical considerations limit the number of ensemble members that can be simulated such that choices must be made concerning which global climate models (GCMs) to downscale from, and which regional climate models (RCMs) to downscale with. Here a methodology for making these choices is proposed that aims to sample the uncertainty in both GCM and RCM ensembles, as well as spanning the range of future climate projections present in the GCM ensemble. The RCM selection process uses performance evaluation metrics to eliminate poor performing models from consideration, followed by explicit consideration of model independence in order to retain as much information as possible in a small model subset. In addition to these two steps the GCM selection process also considers the future change in temperature and precipitation projected by each GCM. The final GCM selection is based on a subjective consideration of the GCM independence and future change. The created ensemble provides a more robust view of future regional climate changes. Future research is required to determine objective criteria that could replace the subjective aspects of the selection process.

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Considerations in the Selection of Global Climate Models for Regional Climate Projections: The Arctic as a Case Study*

Journal of Climate ◽

10.1175/2010jcli3462.1 ◽

2011 ◽

Vol 24 (6) ◽

pp. 1583-1597 ◽

Cited By ~ 65

Author(s):

James E. Overland ◽

Muyin Wang ◽

Nicholas A. Bond ◽

John E. Walsh ◽

Vladimir M. Kattsov ◽

...

Keyword(s):

Climate Models ◽

Global Climate ◽

Regional Scale ◽

Model Performance ◽

Global Climate Models ◽

The Arctic ◽

Climate Projections ◽

Scale Evaluation ◽

Continental Scale ◽

Societal Relevance

Abstract Climate projections at regional scales are in increased demand from management agencies and other stakeholders. While global atmosphere–ocean climate models provide credible quantitative estimates of future climate at continental scales and above, individual model performance varies for different regions, variables, and evaluation metrics—a less than satisfying situation. Using the high-latitude Northern Hemisphere as a focus, the authors assess strategies for providing regional projections based on global climate models. Starting with a set of model results obtained from an “ensemble of opportunity,” the core of this procedure is to retain a subset of models through comparisons of model simulations with observations at both continental and regional scales. The exercise is more one of model culling than model selection. The continental-scale evaluation is a check on the large-scale climate physics of the models, and the regional-scale evaluation emphasizes variables of ecological or societal relevance. An additional consideration is given to the comprehensiveness of processes included in the models. In many but not all applications, different results are obtained from a reduced set of models compared to relying on the simple mean of all available models. For example, in the Arctic the top-performing models tend to be more sensitive to greenhouse forcing than the poorer-performing models. Because of the mostly unexplained inconsistencies in model performance under different selection criteria, simple and transparent evaluation methods are favored. The use of a single model is not recommended. For some applications, no model may be able to provide a suitable regional projection. The use of model evaluation strategies, as opposed to relying on simple averages of ensembles of opportunity, should be part of future synthesis activities such as the upcoming Fifth Assessment Report of the Intergovernmental Panel on Climate Change.

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Estimating the potential impact of climate change on sunflower yield in the Konya province of Turkey

The Journal of Agricultural Science ◽

10.1017/s0021859621000101 ◽

2021 ◽

pp. 1-13

Author(s):

Hudaverdi Gurkan ◽

Vakhtang Shelia ◽

Nilgun Bayraktar ◽

Y. Ersoy Yildirim ◽

Nebi Yesilekin ◽

...

Keyword(s):

Climate Change ◽

Climate Models ◽

Global Climate ◽

Model Performance ◽

Global Climate Models ◽

Climate Projections ◽

Climate Change Scenarios ◽

Adaptation To Climate Change ◽

Impact Of Climate Change ◽

The Impact

Abstract The impact of climate change on agricultural productivity is difficult to assess. However, determining the possible effects of climate change is an absolute necessity for planning by decision-makers. The aim of the study was the evaluation of the CSM-CROPGRO-Sunflower model of DSSAT4.7 and the assessment of impact of climate change on sunflower yield under future climate projections. For this purpose, a 2-year sunflower field experiment was conducted under semi-arid conditions in the Konya province of Turkey. Rainfed and irrigated treatments were used for model analysis. For the assessment of impact of climate change, three global climate models and two representative concentration pathways, i.e. 4.5 and 8.5 were selected. The evaluation of the model showed that the model was able to simulate yield reasonably well, with normalized root mean square error of 1.3% for the irrigated treatment and 17.7% for the rainfed treatment, a d-index of 0.98 and a modelling efficiency of 0.93 for the overall model performance. For the climate change scenarios, the model predicted that yield will decrease in a range of 2.9–39.6% under rainfed conditions and will increase in a range of 7.4–38.5% under irrigated conditions. Results suggest that temperature increases due to climate change will cause a shortening of plant growth cycles. Projection results also confirmed that increasing temperatures due to climate change will cause an increase in sunflower water requirements in the future. Thus, the results reveal the necessity to apply adequate water management strategies for adaptation to climate change for sunflower production.

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Climate Change Impact Assessment for Aji Basin Using Statistical Downscaling and Bias Correction of Climate Model Outputs

Current World Environment ◽

10.12944/cwe.11.2.40 ◽

2016 ◽

Vol 11 (2) ◽

pp. 670-678 ◽

Cited By ~ 1

Author(s):

N. S Vithlani ◽

H. D Rank

Keyword(s):

Climate Change ◽

Bias Correction ◽

Climate Models ◽

Climate Model ◽

Global Climate ◽

Regional Climate ◽

Climate Extremes ◽

Global Climate Models ◽

Climate Indices ◽

Climate Projections

For the future projections Global climate models (GCMs) enable development of climate projections and relate greenhouse gas forcing to future potential climate states. When focusing it on smaller scales it exhibit some limitations to overcome this problem, regional climate models (RCMs) and other downscaling methods have been developed. To ensure statistics of the downscaled output matched the corresponding statistics of the observed data, bias correction was used. Quantify future changes of climate extremes were analyzed, based on these downscaled data from two RCMs grid points. Subset of indices and models, results of bias corrected model output and raw for the present day climate were compared with observation, which demonstrated that bias correction is important for RCM outputs. Bias correction directed agreements of extreme climate indices for future climate it does not correct for lag inverse autocorrelation and fraction of wet and dry days. But, it was observed that adjusting both the biases in the mean and variability, relatively simple non-linear correction, leads to better reproduction of observed extreme daily and multi-daily precipitation amounts. Due to climate change temperature and precipitation will increased day by day.

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Connecting Climate Model Projections of Global Temperature Change with the Real World

Bulletin of the American Meteorological Society ◽

10.1175/bams-d-14-00154.1 ◽

2016 ◽

Vol 97 (6) ◽

pp. 963-980 ◽

Cited By ~ 39

Author(s):

Ed Hawkins ◽

Rowan Sutton

Keyword(s):

Climate Models ◽

Climate Model ◽

Global Climate ◽

Coupled Model ◽

Global Climate Models ◽

Global Temperature ◽

Climate Projections ◽

Reference Period ◽

Intergovernmental Panel ◽

Temperature Levels

Abstract Current state-of-the-art global climate models produce different values for Earth’s mean temperature. When comparing simulations with each other and with observations, it is standard practice to compare temperature anomalies with respect to a reference period. It is not always appreciated that the choice of reference period can affect conclusions, both about the skill of simulations of past climate and about the magnitude of expected future changes in climate. For example, observed global temperatures over the past decade are toward the lower end of the range of the phase 5 of the Coupled Model Intercomparison Project (CMIP5) simulations irrespective of what reference period is used, but exactly where they lie in the model distribution varies with the choice of reference period. Additionally, we demonstrate that projections of when particular temperature levels are reached, for example, 2 K above “preindustrial,” change by up to a decade depending on the choice of reference period. In this article, we discuss some of the key issues that arise when using anomalies relative to a reference period to generate climate projections. We highlight that there is no perfect choice of reference period. When evaluating models against observations, a long reference period should generally be used, but how long depends on the quality of the observations available. The Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) choice to use a 1986–2005 reference period for future global temperature projections was reasonable, but a case-by-case approach is needed for different purposes and when assessing projections of different climate variables. Finally, we recommend that any studies that involve the use of a reference period should explicitly examine the robustness of the conclusions to alternative choices.

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