DOI: 10.1175/JCLI-D-13-00598.1
Scopus记录号: 2-s2.0-84905197383
论文题名: An analogue approach to identify heavy precipitation events: Evaluation and application to CMIP5 climate models in the United States
作者: Gao X. ; Schlosser C.A. ; Xie P. ; Monier E. ; Entekhabi D.
刊名: Journal of Climate
ISSN: 8948755
出版年: 2014
卷: 27, 期: 15 起始页码: 5941
结束页码: 5963
语种: 英语
Scopus关键词: Climate models
; Climatology
; Computer simulation
; NASA
; Precipitation (chemical)
; Atmospheric circulation
; Climate model simulations
; Climate prediction centers
; Coupled Model Intercomparison Project
; Extreme events
; Model evaluation/performance
; North America
; Research and application
; Precipitation (meteorology)
; annual variation
; atmospheric circulation
; climate modeling
; detection method
; extreme event
; identification method
; performance assessment
; precipitation intensity
; weather forecasting
; United States
英文摘要: An analogue method is presented to detect the occurrence of heavy precipitation events without relying on modeled precipitation. The approach is based on using composites to identify distinct large-scale atmospheric conditions associated with widespread heavy precipitation events across local scales. These composites, exemplified in the south-central, midwestern, and western United States, are derived through the analysis of 27-yr (1979-2005) Climate Prediction Center (CPC) gridded station data and the NASA Modern-Era Retrospective Analysis for Research and Applications (MERRA). Circulation features and moisture plumes associated with heavy precipitation events are examined. The analogues are evaluated against the relevant daily meteorological fields from the MERRA reanalysis and achieve a success rate of around 80% in detecting observed heavy events within one or two days. The method also captures the observed interannual variations of seasonal heavy events with higher correlation and smaller RMSE than MERRA precipitation. When applied to the same 27-yr twentieth-century climate model simulations from Phase 5 of the Coupled Model Intercomparison Project (CMIP5), the analogue method produces a more consistent and less uncertain number of seasonal heavy precipitation events with observation as opposed to using model-simulated precipitation. The analogue method also performs better than model-based precipitation in characterizing the statistics (minimum, lower and upper quartile, median, and maximum) of year-to-year seasonal heavy precipitation days. These results indicate the capability of CMIP5 models to realistically simulate large-scale atmospheric conditions associated with widespread local-scale heavy precipitation events with a credible frequency. Overall, the presented analyses highlight the improved diagnoses of the analogue method against an evaluation that considers modeled precipitation alone to assess heavy precipitation frequency. © 2014 American Meteorological Society. 资助项目: NSF, National Aeronautics and Space Administration
; NASA, National Aeronautics and Space Administration
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/51138
Appears in Collections: 气候变化事实与影响
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作者单位: Joint Program on the Science and Policy of Global Change, Massachusetts Institute of Technology, Cambridge, MA, United States; NOAA/Climate Prediction Center, College Park, MD, United States; Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA, United States
Recommended Citation:
Gao X.,Schlosser C.A.,Xie P.,et al. An analogue approach to identify heavy precipitation events: Evaluation and application to CMIP5 climate models in the United States[J]. Journal of Climate,2014-01-01,27(15)