气候变化领域集成服务门户(CCPortal): Improving US extreme precipitation simulation: sensitivity to physics parameterizations

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DOI	10.1007/s00382-020-05267-6
	Improving US extreme precipitation simulation: sensitivity to physics parameterizations
	Sun C.; Liang X.-Z.
发表日期	2020
ISSN	0930-7575
起始页码	4891
结束页码	4918
卷号	54
英文摘要	Climate models tend to underestimate rainfall intensity while producing more frequent light events, leading to significant bias in extreme precipitation simulation. To reduce this bias and better understand its underlying causes, we tested an ensemble of 25 physics configurations in the regional Climate-Weather Research and Forecasting model (CWRF). All configurations were driven by the ECMWF-Interim reanalysis and continuously integrated during 1980–2015 over the contiguous United States with 30-km grid spacing. Together they represent CWRF’s ability to simulate characteristics of US extreme precipitation, and their spread depicts the structural uncertainty from alternate physics parameterizations. The US extreme precipitation simulation was most sensitive to cumulus parameterization among all physics configurations. The ensemble cumulus parameterization (ECP) was overall the most skilled at reproducing seasonal mean spatial patterns of daily 95th percentile precipitation (P95). Other cumulus schemes severely underestimated P95, especially over the Gulf States and the Central-Midwest States in convective prevailing seasons. CWRF with ECP outperformed the driving reanalysis, which substantially underestimated P95 despite its daily atmospheric moisture data assimilation. The CWRF improvement over ERI is much larger in warm than cold seasons. Changing alone ECP closure assumptions produced two distinct clusters of convective heating/drying effects: one altered P95 mainly by changing total precipitation intensity and another by changing rainy-day frequency. Microphysics, radiation, boundary layer, and land surface processes also impacted the result, especially under mixed synoptic and convective forcings, and some of their parameterization schemes worked with ECP to further improve P95. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.
英文关键词	Climate model; Extreme precipitation; Numerical modeling
语种	英语
scopus关键词	climate modeling; computer simulation; cumulus; data assimilation; ensemble forecasting; extreme event; parameterization; precipitation intensity; synoptic meteorology; uncertainty analysis; weather forecasting; United States
来源期刊	Climate Dynamics
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/145466
作者单位	Department of Atmospheric and Oceanic Science, University of Maryland, College Park, MD, United States; Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, United States
推荐引用方式 GB/T 7714	Sun C.,Liang X.-Z.. Improving US extreme precipitation simulation: sensitivity to physics parameterizations[J],2020,54.
APA	Sun C.,&Liang X.-Z..(2020).Improving US extreme precipitation simulation: sensitivity to physics parameterizations.Climate Dynamics,54.
MLA	Sun C.,et al."Improving US extreme precipitation simulation: sensitivity to physics parameterizations".Climate Dynamics 54(2020).