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Using climate regionalization to understand Climate Forecast System Version 2 (CFSv2) precipitation performance for the Conterminous United States (CONUS)
Author(s) -
Regonda Satish K.,
Zaitchik Benjamin F.,
Badr Hamada S.,
Rodell Matthew
Publication year - 2016
Publication title -
geophysical research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.007
H-Index - 273
eISSN - 1944-8007
pISSN - 0094-8276
DOI - 10.1002/2016gl069150
Subject(s) - climate forecast system , climatology , teleconnection , precipitation , forecast skill , environmental science , spatial ecology , gcm transcription factors , quantitative precipitation forecast , scale (ratio) , common spatial pattern , climate model , meteorology , climate change , general circulation model , geography , geology , cartography , statistics , mathematics , ecology , oceanography , biology
Dynamically based seasonal forecasts are prone to systematic spatial biases due to imperfections in the underlying global climate model (GCM). This can result in low‐forecast skill when the GCM misplaces teleconnections or fails to resolve geographic barriers, even if the prediction of large‐scale dynamics is accurate. To characterize and address this issue, this study applies objective climate regionalization to identify discrepancies between the Climate Forecast System Version 2 (CFSv2) and precipitation observations across the Contiguous United States (CONUS). Regionalization shows that CFSv2 1 month forecasts capture the general spatial character of warm season precipitation variability but that forecast regions systematically differ from observation in some transition zones. CFSv2 predictive skill for these misclassified areas is systematically reduced relative to correctly regionalized areas and CONUS as a whole. In these incorrectly regionalized areas, higher skill can be obtained by using a regional‐scale forecast in place of the local grid cell prediction.