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<p>Climate models are mathematical tools used to project the future climate scenarios, demonstrate varying degrees of predictive skill. The outputs of climate models are essential to develop adaptation and mitigation strategies. There is a lack of systematic evidence on where climate model evaluations have been conducted, the specific models that have been investigated and the reference datasets, methodologies, and performance metrics. This study aims to address these gaps by reviewing existing climate model evaluation studies in Ethiopia. Systematic review was performed by searching studies from SCOPUS and Google Scholar databases. We selected eighteen relevant papers for synthesis and analysis. The review demonstrated that climate model evaluation is increasing steadily. Most climate modeling studies have been conducted in the Awash, Blue Nile, and Omo-Gibe basins. Several studies evaluated Regional Climate Models (RCMs) instead of Global Climate Models (GCMs); only a few used the latest Coupled Model Intercomparison Project Phase 6 (CMIP6). The most frequently evaluated RCMs were SMHI-RCA4, MPI-CSI-REMO2009, CLMcom-CCLM4-8-17, and KNMI-RACMO22T. Model performance is evaluated for precipitation alone or alongside temperature in all of the studies. The studies used station, satellite, and reanalysis-based reference climate data. The major ones are Climate Research Unit (CRU), Climate Hazards Groups Infrared Precipitation with Stations (CHIRPS), European Community Medium-Range Weather Forecasts (ERA), Global Precipitation Climatology Center (GPCC) and station. Most studies used Percent Bias(PBIAS), Coefficient of Correlation (r) and Root Mean Square Error (RMSE) statistical metrics to test model performance. Two or more of these metrics are used than aggregation methods such as compromise programming. SMHI-RCA4, MPI-CSC-REMO2009 and KNMI-RACMO22T are frequently reported RCMs with better performance in simulating precipitation. However, there is a difference between studies, basins and climatic variables. Ensembles of the models are in better agreement with reference data than individual models in simulating historical precipitation. This review provides important insight for future climate evaluation endeavors in different water basins and the country.</p>

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Climate model evaluation studies in Ethiopia: a systematic review

  • 期刊论文

  • 发表日期：

    2025年6月4日

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  • Daniel Emiru Olana,
  • Dessalegn Obsi Gemeda

摘要

Climate models are mathematical tools used to project the future climate scenarios, demonstrate varying degrees of predictive skill. The outputs of climate models are essential to develop adaptation and mitigation strategies. There is a lack of systematic evidence on where climate model evaluations have been conducted, the specific models that have been investigated and the reference datasets, methodologies, and performance metrics. This study aims to address these gaps by reviewing existing climate model evaluation studies in Ethiopia. Systematic review was performed by searching studies from SCOPUS and Google Scholar databases. We selected eighteen relevant papers for synthesis and analysis. The review demonstrated that climate model evaluation is increasing steadily. Most climate modeling studies have been conducted in the Awash, Blue Nile, and Omo-Gibe basins. Several studies evaluated Regional Climate Models (RCMs) instead of Global Climate Models (GCMs); only a few used the latest Coupled Model Intercomparison Project Phase 6 (CMIP6). The most frequently evaluated RCMs were SMHI-RCA4, MPI-CSI-REMO2009, CLMcom-CCLM4-8-17, and KNMI-RACMO22T. Model performance is evaluated for precipitation alone or alongside temperature in all of the studies. The studies used station, satellite, and reanalysis-based reference climate data. The major ones are Climate Research Unit (CRU), Climate Hazards Groups Infrared Precipitation with Stations (CHIRPS), European Community Medium-Range Weather Forecasts (ERA), Global Precipitation Climatology Center (GPCC) and station. Most studies used Percent Bias(PBIAS), Coefficient of Correlation (r) and Root Mean Square Error (RMSE) statistical metrics to test model performance. Two or more of these metrics are used than aggregation methods such as compromise programming. SMHI-RCA4, MPI-CSC-REMO2009 and KNMI-RACMO22T are frequently reported RCMs with better performance in simulating precipitation. However, there is a difference between studies, basins and climatic variables. Ensembles of the models are in better agreement with reference data than individual models in simulating historical precipitation. This review provides important insight for future climate evaluation endeavors in different water basins and the country.