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: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2016-06-01
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| Series: | Geophysical Research Letters |
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| Online Access: | https://doi.org/10.1002/2016GL069150 |
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| _version_ | 1850274944167968768 |
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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. |
| format | Article |
| id | doaj-art-c2923fdef8fe4e06a8aa3fbf9f982b83 |
| institution | OA Journals |
| issn | 0094-8276 1944-8007 |
| language | English |
| publishDate | 2016-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | Geophysical Research Letters |
| 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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