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DOI | 10.1175/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
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ISSN | 0894-8755 |
EISSN | 1520-0442 |
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 |
引用统计 | |
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 |
推荐引用方式 GB/T 7714 | 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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