Using climate regionalization to understand Climate Forecast System Version 2 (CFSv2) precipitation performance for the Conterminous United States (CONUS)

Abstract 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 larg...

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Main Authors: Satish K. Regonda, Benjamin F. Zaitchik, Hamada S. Badr, Matthew Rodell
Format: Article
Language:English
Published: Wiley 2016-06-01
Series:Geophysical Research Letters
Subjects:
Online Access:https://doi.org/10.1002/2016GL069150
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author Satish K. Regonda
Benjamin F. Zaitchik
Hamada S. Badr
Matthew Rodell
author_facet Satish K. Regonda
Benjamin F. Zaitchik
Hamada S. Badr
Matthew Rodell
author_sort Satish K. Regonda
collection DOAJ
description Abstract 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.
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spelling doaj-art-c2923fdef8fe4e06a8aa3fbf9f982b832025-08-20T01:50:59ZengWileyGeophysical Research Letters0094-82761944-80072016-06-0143126485649210.1002/2016GL069150Using climate regionalization to understand Climate Forecast System Version 2 (CFSv2) precipitation performance for the Conterminous United States (CONUS)Satish K. Regonda0Benjamin F. Zaitchik1Hamada S. Badr2Matthew Rodell3Department of Earth and Planetary Sciences The Johns Hopkins University Baltimore Maryland USADepartment of Earth and Planetary Sciences The Johns Hopkins University Baltimore Maryland USADepartment of Earth and Planetary Sciences The Johns Hopkins University Baltimore Maryland USAHydrological Sciences Laboratory NASA Goddard Space Flight Center (GSFC) Greenbelt Maryland USAAbstract 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.https://doi.org/10.1002/2016GL069150regionalizationseasonal forecastprecipitationCFSv2CONUSmodel evaluation
spellingShingle Satish K. Regonda
Benjamin F. Zaitchik
Hamada S. Badr
Matthew Rodell
Using climate regionalization to understand Climate Forecast System Version 2 (CFSv2) precipitation performance for the Conterminous United States (CONUS)
Geophysical Research Letters
regionalization
seasonal forecast
precipitation
CFSv2
CONUS
model evaluation
title Using climate regionalization to understand Climate Forecast System Version 2 (CFSv2) precipitation performance for the Conterminous United States (CONUS)
title_full Using climate regionalization to understand Climate Forecast System Version 2 (CFSv2) precipitation performance for the Conterminous United States (CONUS)
title_fullStr Using climate regionalization to understand Climate Forecast System Version 2 (CFSv2) precipitation performance for the Conterminous United States (CONUS)
title_full_unstemmed Using climate regionalization to understand Climate Forecast System Version 2 (CFSv2) precipitation performance for the Conterminous United States (CONUS)
title_short Using climate regionalization to understand Climate Forecast System Version 2 (CFSv2) precipitation performance for the Conterminous United States (CONUS)
title_sort using climate regionalization to understand climate forecast system version 2 cfsv2 precipitation performance for the conterminous united states conus
topic regionalization
seasonal forecast
precipitation
CFSv2
CONUS
model evaluation
url https://doi.org/10.1002/2016GL069150
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