Implications of regional improvement in global climate models for agricultural impact research
Global climate models (GCMs) have become increasingly important for climate change science and provide the basis for most impact studies. Since impact models are highly sensitive to input climate data, GCM skill is crucial for getting better short-, medium- and long-term outlooks for agricultural pr...
| Main Authors: | , , , |
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| Format: | Journal Article |
| Language: | Inglés |
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IOP Publishing
2013
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| Online Access: | https://hdl.handle.net/10568/28987 |
| _version_ | 1855538249582247936 |
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| author | Ramírez Villegas, Julián Armando Challinor, Andrew J. Thornton, Philip K. Jarvis, Andy |
| author_browse | Challinor, Andrew J. Jarvis, Andy Ramírez Villegas, Julián Armando Thornton, Philip K. |
| author_facet | Ramírez Villegas, Julián Armando Challinor, Andrew J. Thornton, Philip K. Jarvis, Andy |
| author_sort | Ramírez Villegas, Julián Armando |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Global climate models (GCMs) have become increasingly important for climate change science and provide the basis for most impact studies. Since impact models are highly sensitive to input climate data, GCM skill is crucial for getting better short-, medium- and long-term outlooks for agricultural production and food security. The Coupled Model Intercomparison Project (CMIP) phase 5 ensemble is likely to underpin the majority of climate impact assessments over the next few years. We assess 24 CMIP3 and 26 CMIP5 simulations of present climate against climate observations for five tropical regions, as well as regional improvements in model skill and, through literature review, the sensitivities of impact estimates to model error. Climatological means of seasonal mean temperatures depict mean errors between 1 and 18C (2–130% with respect to mean), whereas seasonal precipitation and wet-day frequency depict larger errors, often offsetting observed means and variability beyond 100%. Simulated interannual climate variability in GCMs warrants particular attention, given that no single GCM matches observations in more than 30% of the areas for monthly precipitation and wet-day frequency, 50% for diurnal range and 70% for mean temperatures. We report improvements in mean climate skill of 5–15% for climatological mean temperatures, 3–5% for diurnal range and 1–2% in precipitation. At these improvement rates, we estimate that at least 5–30 years of CMIP work is required to improve regional temperature simulations and at least 30–50 years for precipitation simulations, for these to be directly input into impact models. We conclude with some recommendations for the use of CMIP5 in agricultural impact studies |
| format | Journal Article |
| id | CGSpace28987 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2013 |
| publishDateRange | 2013 |
| publishDateSort | 2013 |
| publisher | IOP Publishing |
| publisherStr | IOP Publishing |
| record_format | dspace |
| spelling | CGSpace289872024-05-01T08:19:24Z Implications of regional improvement in global climate models for agricultural impact research Ramírez Villegas, Julián Armando Challinor, Andrew J. Thornton, Philip K. Jarvis, Andy agriculture impact models climate Global climate models (GCMs) have become increasingly important for climate change science and provide the basis for most impact studies. Since impact models are highly sensitive to input climate data, GCM skill is crucial for getting better short-, medium- and long-term outlooks for agricultural production and food security. The Coupled Model Intercomparison Project (CMIP) phase 5 ensemble is likely to underpin the majority of climate impact assessments over the next few years. We assess 24 CMIP3 and 26 CMIP5 simulations of present climate against climate observations for five tropical regions, as well as regional improvements in model skill and, through literature review, the sensitivities of impact estimates to model error. Climatological means of seasonal mean temperatures depict mean errors between 1 and 18C (2–130% with respect to mean), whereas seasonal precipitation and wet-day frequency depict larger errors, often offsetting observed means and variability beyond 100%. Simulated interannual climate variability in GCMs warrants particular attention, given that no single GCM matches observations in more than 30% of the areas for monthly precipitation and wet-day frequency, 50% for diurnal range and 70% for mean temperatures. We report improvements in mean climate skill of 5–15% for climatological mean temperatures, 3–5% for diurnal range and 1–2% in precipitation. At these improvement rates, we estimate that at least 5–30 years of CMIP work is required to improve regional temperature simulations and at least 30–50 years for precipitation simulations, for these to be directly input into impact models. We conclude with some recommendations for the use of CMIP5 in agricultural impact studies 2013-06-01 2013-05-13T15:57:55Z 2013-05-13T15:57:55Z Journal Article https://hdl.handle.net/10568/28987 en Open Access IOP Publishing Ramirez-Villegas J, Challinor A, Thornton P, Jarvis A. 2013. Implications of regional improvement in global climate models for agricultural impact research. Environmental Research Letters 8 024018 |
| spellingShingle | agriculture impact models climate Ramírez Villegas, Julián Armando Challinor, Andrew J. Thornton, Philip K. Jarvis, Andy Implications of regional improvement in global climate models for agricultural impact research |
| title | Implications of regional improvement in global climate models for agricultural impact research |
| title_full | Implications of regional improvement in global climate models for agricultural impact research |
| title_fullStr | Implications of regional improvement in global climate models for agricultural impact research |
| title_full_unstemmed | Implications of regional improvement in global climate models for agricultural impact research |
| title_short | Implications of regional improvement in global climate models for agricultural impact research |
| title_sort | implications of regional improvement in global climate models for agricultural impact research |
| topic | agriculture impact models climate |
| url | https://hdl.handle.net/10568/28987 |
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