GSTDTAP  > 气候变化
DOI10.1175/JCLI-D-17-0404.1
Large-Scale Predictors for Extreme Hourly Precipitation Events in Convection-Permitting Climate Simulations
Chan, Steven C.1; Kendon, Elizabeth J.2; Roberts, Nigel3; Blenkinsop, Stephen1; Fowler, Hayley J.1
2018-03-01
发表期刊JOURNAL OF CLIMATE
ISSN0894-8755
EISSN1520-0442
出版年2018
卷号31期号:6页码:2115-2131
文章类型Article
语种英语
国家England
英文摘要

Midlatitude extreme precipitation events are caused by well-understood meteorological drivers, such as vertical instability and low pressure systems. In principle, dynamical weather and climate models behave in the same way, although perhaps with the sensitivities to the drivers varying between models. Unlike parameterized convection models (PCMs), convection-permitting models (CPMs) are able to realistically capture subdaily extreme precipitation. CPMs are computationally expensive; being able to diagnose the occurrence of subdaily extreme precipitation from large-scale drivers, with sufficient skill, would allow effective targeting of CPM downscaling simulations. Here the regression relationships are quantified between the occurrence of extreme hourly precipitation events and vertical stability and circulation predictors in southern United Kingdom 1.5-km CPM and 12-km PCM present-and future-climate simulations. Overall, the large-scale predictors demonstrate skill in predicting the occurrence of extreme hourly events in both the 1.5-and 12-km simulations. For the present-climate simulations, extreme occurrences in the 12-km model are less sensitive to vertical stability than in the 1.5-km model, consistent with understanding the limitations of cumulus parameterization. In the future-climate simulations, the regression relationship is more similar between the two models, which may be understood from changes to the large-scale circulation patterns and land surface climate. Overall, regression analysis offers a promising avenue for targeting CPM simulations. The authors also outline which events would be missed by adopting such a targeted approach.


领域气候变化
收录类别SCI-E
WOS记录号WOS:000427447000002
WOS关键词INGREDIENTS-BASED METHODOLOGY ; MODEL ; REANALYSIS ; REGRESSION ; MESOSCALE ; ENSEMBLE ; RAINFALL ; FUTURE ; STORMS ; WINTER
WOS类目Meteorology & Atmospheric Sciences
WOS研究方向Meteorology & Atmospheric Sciences
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文献类型期刊论文
条目标识符http://119.78.100.173/C666/handle/2XK7JSWQ/19836
专题气候变化
作者单位1.Newcastle Univ, Sch Engn, Newcastle Upon Tyne, Tyne & Wear, England;
2.Met Off Hadley Ctr, Exeter, Devon, England;
3.MetOff Reading, Reading, Berks, England
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Chan, Steven C.,Kendon, Elizabeth J.,Roberts, Nigel,et al. Large-Scale Predictors for Extreme Hourly Precipitation Events in Convection-Permitting Climate Simulations[J]. JOURNAL OF CLIMATE,2018,31(6):2115-2131.
APA Chan, Steven C.,Kendon, Elizabeth J.,Roberts, Nigel,Blenkinsop, Stephen,&Fowler, Hayley J..(2018).Large-Scale Predictors for Extreme Hourly Precipitation Events in Convection-Permitting Climate Simulations.JOURNAL OF CLIMATE,31(6),2115-2131.
MLA Chan, Steven C.,et al."Large-Scale Predictors for Extreme Hourly Precipitation Events in Convection-Permitting Climate Simulations".JOURNAL OF CLIMATE 31.6(2018):2115-2131.
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