气候变化领域集成服务门户(CCPortal): Identifying credible and diverse GCMs for regional climate change studies

CCPortal

DOI	10.1007/s10584-019-02411-y
	Identifying credible and diverse GCMs for regional climate change studies—case study: Northeastern United States
	Karmalkar A.V.; Thibeault J.M.; Bryan A.M.; Seth A.
发表日期	2019
ISSN	0165-0009
起始页码	367
结束页码	386
卷号	154 期号:2020-03-04
英文摘要	Climate data obtained from global climate models (GCMs) form the basis of most studies of regional climate change and its impacts. Using the northeastern U.S. as a test case, we develop a framework to systematically sub-select reliable models for use in climate change studies in the region. Model performance over the historical period is evaluated first for a wide variety of standard and process metrics including large-scale atmospheric circulation features that drive regional climate variability. The inclusion of process-based metrics allows identification of credible models in capturing key processes relevant for the climate of the northeastern U.S. Model performance is then used in conjunction with the assessment of redundancy in model projections, especially in summer precipitation, to eliminate models that have better performing counterparts. Finally, we retain some mixed-performing models to maintain the range of climate model uncertainty, required by the fact that model biases are not strongly related to their respective projections. This framework leads to the retention of 16 of 36 CMIP5 GCMs that (a) have a satisfactory historical performance for a variety of metrics and (b) provide diverse climate projections consistent with uncertainties in the multi-model ensemble (MME). Overall, the models show significant variations in their performance across metrics and seasons with none emerging as the best model in all metrics. The retained set reduces the number of models by more than one half, easing the computational burden of using the entire CMIP5 MME, while still maintaining a wide range of projections for risk assessment. The retention of some mixed-performing models to maintain ensemble uncertainty suggests a potential to narrow the ranges in temperature and precipitation. But any further refinement should be based on a more detailed analysis of models in capturing regional climate variability and extremes to avoid providing overconfident projections. © 2019, Springer Nature B.V.
语种	英语
scopus关键词	Climate change; Digital storage; Risk assessment; Uncertainty analysis; Atmospheric circulation; Computational burden; Global climate model; Historical performance; Model uncertainties; Multi-model ensemble; Regional climate changes; Summer precipitation; Climate models; atmospheric circulation; climate change; climate modeling; CMIP; global climate; identification method; precipitation (climatology); regional climate; risk assessment; seasonal variation; United States
来源期刊	Climatic Change
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/147466
作者单位	Northeast Climate Adaptation Science Center andDepartment of Geosciences, University of Massachusetts Amherst, Massachusetts, Amherst, MA, United States; Department of Geography, University of Connecticut, Storrs, CT, United States; Northeast Climate Adaptation Science Center, U.S. Geological Survey, University of Massachusetts Amherst, Massachusetts, Amherst, MA, United States
推荐引用方式 GB/T 7714	Karmalkar A.V.,Thibeault J.M.,Bryan A.M.,等. Identifying credible and diverse GCMs for regional climate change studies—case study: Northeastern United States[J],2019,154(2020-03-04).
APA	Karmalkar A.V.,Thibeault J.M.,Bryan A.M.,&Seth A..(2019).Identifying credible and diverse GCMs for regional climate change studies—case study: Northeastern United States.Climatic Change,154(2020-03-04).
MLA	Karmalkar A.V.,et al."Identifying credible and diverse GCMs for regional climate change studies—case study: Northeastern United States".Climatic Change 154.2020-03-04(2019).