Evaluation of CMIP6 models in reproducing observed rainfall over Ethiopia
Ethiopia is highly susceptible to the effects of climate change and variability. This study evaluated the performances of 37 CMIP6 models against a gridded rainfall product of Ethiopia known as Enhancing National Climate Services (ENACTS) in simulating the observed rainfall from 1981 to 2014. Taylor...
| Main Authors: | , , , , , |
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| Format: | Journal Article |
| Language: | Inglés |
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IWA Publishing
2023
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/131672 |
| _version_ | 1855520889495355392 |
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| author | Berhanu, D. Alamirew, T. Taye, Meron Teferi Tibebe, D. Gebrehiwot, S. Zeleke, G. |
| author_browse | Alamirew, T. Berhanu, D. Gebrehiwot, S. Taye, Meron Teferi Tibebe, D. Zeleke, G. |
| author_facet | Berhanu, D. Alamirew, T. Taye, Meron Teferi Tibebe, D. Gebrehiwot, S. Zeleke, G. |
| author_sort | Berhanu, D. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Ethiopia is highly susceptible to the effects of climate change and variability. This study evaluated the performances of 37 CMIP6 models against a gridded rainfall product of Ethiopia known as Enhancing National Climate Services (ENACTS) in simulating the observed rainfall from 1981 to 2014. Taylor Skill Score was used for ranking the performance of individual models for mean monthly, June–September, and February–May seasonal rainfall. Comprehensive rating metrics (RM) were used to derive the overall ranks of the models. Results show that the performances of the models were not consistent in reproducing rainfall distributions at different statistical metrics and timeframes. More than 20 models simulated the largest dry bias on high topographic and rainfall-receiving areas of the country during the June–September season. The RM-based overall ranks of CMIP6 models showed that GFDL-CM4 is the best-performing model followed by GFDL-ESM4, NorESM2-MM, and CESM2 in simulating rainfall over Ethiopia. The ensemble of these four Global Climate Models showed the best performance in representing the spatiotemporal patterns of the observed rainfall relative to the ensembles of all models. Generally, this study highlighted the existence of dry bias in climate model projections for Ethiopia, which requires bias adjustment of the models, for impact assessment. |
| format | Journal Article |
| id | CGSpace131672 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | IWA Publishing |
| publisherStr | IWA Publishing |
| record_format | dspace |
| spelling | CGSpace1316722025-02-19T13:42:13Z Evaluation of CMIP6 models in reproducing observed rainfall over Ethiopia Berhanu, D. Alamirew, T. Taye, Meron Teferi Tibebe, D. Gebrehiwot, S. Zeleke, G. climate models performance assessment evaluation rainfall patterns spatial distribution trends precipitation seasonal variation datasets climate change Ethiopia is highly susceptible to the effects of climate change and variability. This study evaluated the performances of 37 CMIP6 models against a gridded rainfall product of Ethiopia known as Enhancing National Climate Services (ENACTS) in simulating the observed rainfall from 1981 to 2014. Taylor Skill Score was used for ranking the performance of individual models for mean monthly, June–September, and February–May seasonal rainfall. Comprehensive rating metrics (RM) were used to derive the overall ranks of the models. Results show that the performances of the models were not consistent in reproducing rainfall distributions at different statistical metrics and timeframes. More than 20 models simulated the largest dry bias on high topographic and rainfall-receiving areas of the country during the June–September season. The RM-based overall ranks of CMIP6 models showed that GFDL-CM4 is the best-performing model followed by GFDL-ESM4, NorESM2-MM, and CESM2 in simulating rainfall over Ethiopia. The ensemble of these four Global Climate Models showed the best performance in representing the spatiotemporal patterns of the observed rainfall relative to the ensembles of all models. Generally, this study highlighted the existence of dry bias in climate model projections for Ethiopia, which requires bias adjustment of the models, for impact assessment. 2023-08-01 2023-08-30T15:02:30Z 2023-08-30T15:02:30Z Journal Article https://hdl.handle.net/10568/131672 en Open Access IWA Publishing Berhanu, D.; Alamirew, T.; Taye, Meron Teferi; Tibebe, D.; Gebrehiwot, S.; Zeleke, G. 2023. Evaluation of CMIP6 models in reproducing observed rainfall over Ethiopia. Journal of Water and Climate Change, 14(8):2583-2605. [doi: https://doi.org/10.2166/wcc.2023.502] |
| spellingShingle | climate models performance assessment evaluation rainfall patterns spatial distribution trends precipitation seasonal variation datasets climate change Berhanu, D. Alamirew, T. Taye, Meron Teferi Tibebe, D. Gebrehiwot, S. Zeleke, G. Evaluation of CMIP6 models in reproducing observed rainfall over Ethiopia |
| title | Evaluation of CMIP6 models in reproducing observed rainfall over Ethiopia |
| title_full | Evaluation of CMIP6 models in reproducing observed rainfall over Ethiopia |
| title_fullStr | Evaluation of CMIP6 models in reproducing observed rainfall over Ethiopia |
| title_full_unstemmed | Evaluation of CMIP6 models in reproducing observed rainfall over Ethiopia |
| title_short | Evaluation of CMIP6 models in reproducing observed rainfall over Ethiopia |
| title_sort | evaluation of cmip6 models in reproducing observed rainfall over ethiopia |
| topic | climate models performance assessment evaluation rainfall patterns spatial distribution trends precipitation seasonal variation datasets climate change |
| url | https://hdl.handle.net/10568/131672 |
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