气候变化领域集成服务门户(CCPortal): Evaluating CMIP6 model fidelity at simulating non-Gaussian temperature distribution tails

CCPortal

DOI	10.1088/1748-9326/ab8cd0
	Evaluating CMIP6 model fidelity at simulating non-Gaussian temperature distribution tails
	Catalano A.J.; Loikith P.C.; Neelin J.D.
发表日期	2020
ISSN	17489318
卷号	15 期号:7
英文摘要	Under global warming, changes in extreme temperatures will manifest in more complex ways in locations where temperature distribution tails deviate from Gaussian. Confidence in global climate model (GCM) projections of temperature extremes and associated impacts therefore relies on the realism of simulated temperature distribution tail behavior under current climate conditions. This study evaluates the ability of the latest state-of-the-art ensemble of GCMs from the Coupled Model Intercomparison Project phase six (CMIP6), to capture historical global surface temperature distribution tail shape in hemispheric winter and summer seasons. Comparisons with a global reanalysis product reveal strong agreement on coherent spatial patterns of longer- and shorter-than-Gaussian tails for both sides of the temperature distribution, suggesting that CMIP6 GCMs are broadly capturing tail behavior for plausible physical and dynamical reasons. On a global scale, most GCMs are reasonably skilled at capturing historical tail shape, exhibiting high pattern correlations with reanalysis and low values of normalized centered root mean square difference, with multi-model mean values generally outperforming individual GCMs in these metrics. A division of the domain into sub-regions containing robust shift ratio patterns indicates higher performance over Australia and an overestimation of the degree to which tails deviate from Gaussian over southeastern Asia in all cases, whereas model skill over other regions varies depending on season and tail of the temperature distribution. For example, model performance during boreal winter indicates robust agreement (>85% models) with reanalysis for shorter-than-Gaussian warm tails over the Northern Hemisphere, whereas cold-tail shape is generally mischaracterized by GCMs over western Russia. Although there is spatial and model variability, overall, results highlight the capability of the CMIP6 ensemble in capturing seasonal temperature distribution deviations from Gaussianity, boosting confidence in model utility and providing insight into the complexity of future changes in temperature extremes. © 2020 The Author(s). Published by IOP Publishing Ltd.
英文关键词	climate models; CMIP6; temperature extremes
语种	英语
scopus关键词	Gaussian distribution; Global warming; Temperature distribution; Coherent spatial patterns; Coupled Model Intercomparison Project; Extreme temperatures; Global surface temperature; Modeling variability; Northern Hemispheres; Root mean square differences; Temperature extremes; Climate models; air temperature; climate change; CMIP; extreme event; Gaussian method; global warming; seasonality; spatial distribution
来源期刊	Environmental Research Letters
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/153920
作者单位	Department of Geography, Portland State University, Portland, OR, United States; Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, CA, United States
推荐引用方式 GB/T 7714	Catalano A.J.,Loikith P.C.,Neelin J.D.. Evaluating CMIP6 model fidelity at simulating non-Gaussian temperature distribution tails[J],2020,15(7).
APA	Catalano A.J.,Loikith P.C.,&Neelin J.D..(2020).Evaluating CMIP6 model fidelity at simulating non-Gaussian temperature distribution tails.Environmental Research Letters,15(7).
MLA	Catalano A.J.,et al."Evaluating CMIP6 model fidelity at simulating non-Gaussian temperature distribution tails".Environmental Research Letters 15.7(2020).