High-resolution and bias-corrected CMIP5 projections for climate change impact assessments
Projections of climate change are available at coarse scales (70–400 km). But agricultural and species models typically require finer scale climate data to model climate change impacts. Here, we present a global database of future climates developed by applying the delta method –a method for climate...
| Autores principales: | , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Springer
2020
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/106634 |
| _version_ | 1855523931733098496 |
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| author | Navarro-Racines, Carlos Eduardo Tarapues Montenegro, Jaime Eduardo Thornton, Philip K. Jarvis, Andy Ramírez Villegas, Julián Armando |
| author_browse | Jarvis, Andy Navarro-Racines, Carlos Eduardo Ramírez Villegas, Julián Armando Tarapues Montenegro, Jaime Eduardo Thornton, Philip K. |
| author_facet | Navarro-Racines, Carlos Eduardo Tarapues Montenegro, Jaime Eduardo Thornton, Philip K. Jarvis, Andy Ramírez Villegas, Julián Armando |
| author_sort | Navarro-Racines, Carlos Eduardo |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Projections of climate change are available at coarse scales (70–400 km). But agricultural and species models typically require finer scale climate data to model climate change impacts. Here, we present a global database of future climates developed by applying the delta method –a method for climate model bias correction. We performed a technical evaluation of the bias-correction method using a ‘perfect sibling’ framework and show that it reduces climate model bias by 50–70%. The data include monthly maximum and minimum temperatures and monthly total precipitation, and a set of bioclimatic indices, and can be used for assessing impacts of climate change on agriculture and biodiversity. The data are publicly available in the World Data Center for Climate (WDCC; cera-www.dkrz.de), as well as in the CCAFS-Climate data portal (http://ccafs-climate.org). The database has been used up to date in more than 350 studies of ecosystem and agricultural impact assessment. |
| format | Journal Article |
| id | CGSpace106634 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2020 |
| publishDateRange | 2020 |
| publishDateSort | 2020 |
| publisher | Springer |
| publisherStr | Springer |
| record_format | dspace |
| spelling | CGSpace1066342024-09-09T10:04:48Z High-resolution and bias-corrected CMIP5 projections for climate change impact assessments Navarro-Racines, Carlos Eduardo Tarapues Montenegro, Jaime Eduardo Thornton, Philip K. Jarvis, Andy Ramírez Villegas, Julián Armando agriculture food security climate change models factors precipitation Projections of climate change are available at coarse scales (70–400 km). But agricultural and species models typically require finer scale climate data to model climate change impacts. Here, we present a global database of future climates developed by applying the delta method –a method for climate model bias correction. We performed a technical evaluation of the bias-correction method using a ‘perfect sibling’ framework and show that it reduces climate model bias by 50–70%. The data include monthly maximum and minimum temperatures and monthly total precipitation, and a set of bioclimatic indices, and can be used for assessing impacts of climate change on agriculture and biodiversity. The data are publicly available in the World Data Center for Climate (WDCC; cera-www.dkrz.de), as well as in the CCAFS-Climate data portal (http://ccafs-climate.org). The database has been used up to date in more than 350 studies of ecosystem and agricultural impact assessment. 2020-01-01 2020-01-20T20:12:29Z 2020-01-20T20:12:29Z Journal Article https://hdl.handle.net/10568/106634 en Open Access Springer Navarro-Racines C, Tarapues J, Thornton P, Jarvis A, Ramirez-Villegas J. 2020. High-resolution and bias-corrected CMIP5 projections for climate change impact assessments. Scientific Data 7:7. |
| spellingShingle | agriculture food security climate change models factors precipitation Navarro-Racines, Carlos Eduardo Tarapues Montenegro, Jaime Eduardo Thornton, Philip K. Jarvis, Andy Ramírez Villegas, Julián Armando High-resolution and bias-corrected CMIP5 projections for climate change impact assessments |
| title | High-resolution and bias-corrected CMIP5 projections for climate change impact assessments |
| title_full | High-resolution and bias-corrected CMIP5 projections for climate change impact assessments |
| title_fullStr | High-resolution and bias-corrected CMIP5 projections for climate change impact assessments |
| title_full_unstemmed | High-resolution and bias-corrected CMIP5 projections for climate change impact assessments |
| title_short | High-resolution and bias-corrected CMIP5 projections for climate change impact assessments |
| title_sort | high resolution and bias corrected cmip5 projections for climate change impact assessments |
| topic | agriculture food security climate change models factors precipitation |
| url | https://hdl.handle.net/10568/106634 |
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