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Using Climate Regionalization to Understand Climate Forecast System Version 2 (CFSv2) Precipitation Performance for the Conterminous United States (CONUS)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 SystemVersion 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.
Document ID
20170002749
Document Type
Reprint (Version printed in journal)
External Source(s)
Authors
Regonda, Satish K. (Johns Hopkins Univ. Baltimore, MD, United States)
Zaitchik, Benjamin F. (Johns Hopkins Univ. Baltimore, MD, United States)
Badr, Hamada S. (Johns Hopkins Univ. Baltimore, MD, United States)
Rodell, Matthew (NASA Goddard Space Flight Center Greenbelt, MD United States)
Date Acquired
March 31, 2017
Publication Date
June 20, 2016
Publication Information
Publication: Geophysical Research Letters
Volume: 43
Issue: 12
ISSN: 0094-8276
Subject Category
Meteorology and Climatology
Report/Patent Number
GSFC-E-DAA-TN39541
Funding Number(s)
CONTRACT_GRANT: NNX15AD50G
Distribution Limits
Public
Copyright
Other
Keywords
geographic barriers
Climate Forecast System