Skill Assessment of North American Multi-Models Ensemble (NMME) for June-September (JJAS) Seasonal Rainfall over Ethiopia — Oak Academic Publishing
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Skill Assessment of North American Multi-Models Ensemble (NMME) for June-September (JJAS) Seasonal Rainfall over Ethiopia
National Meteorological Agency of Ethiopia, Addis Ababa, Ethiopia
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Key Laboratory of Meteorological Disaster, Ministry of Education (KLME)/International Joint Research Laboratory of Climate and Environment Change (ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science & Technology (NUIST), Nanjing, China
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Key Laboratory of Meteorological Disaster, Ministry of Education (KLME)/International Joint Research Laboratory of Climate and Environment Change (ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science & Technology (NUIST), Nanjing, China
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International Research Institute for Climate and Society (IRI), The Earth Institute, Columbia University, New York, USA
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NORCE Norwegian Research Center, Bjerknes Center for Climate Research, Bergen, Norway
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International Research Institute for Climate and Society (IRI), The Earth Institute, Columbia University, New York, USA
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International Research Institute for Climate and Society (IRI), The Earth Institute, Columbia University, New York, USA
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Ethiopian Institute of Agricultural Research, Addis Ababa, Ethiopia
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Center for Earth System Modeling, Analysis, and Data, Department of Meteorology and Atmospheric Science, Pennsylvania State University, University Park, PA, USA
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CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) East Africa, Addis Ababa, Ethiopia
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Scuola Superiore Sant’Anna, Pisa, Italy
1 National Meteorological Agency of Ethiopia, Addis Ababa, Ethiopia
2 Key Laboratory of Meteorological Disaster, Ministry of Education (KLME)/International Joint Research Laboratory of Climate and Environment Change (ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science & Technology (NUIST), Nanjing, China
3 Key Laboratory of Meteorological Disaster, Ministry of Education (KLME)/International Joint Research Laboratory of Climate and Environment Change (ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science & Technology (NUIST), Nanjing, China
4 International Research Institute for Climate and Society (IRI), The Earth Institute, Columbia University, New York, USA
5 NORCE Norwegian Research Center, Bjerknes Center for Climate Research, Bergen, Norway
6 International Research Institute for Climate and Society (IRI), The Earth Institute, Columbia University, New York, USA
7 International Research Institute for Climate and Society (IRI), The Earth Institute, Columbia University, New York, USA
8 Ethiopian Institute of Agricultural Research, Addis Ababa, Ethiopia
9 Center for Earth System Modeling, Analysis, and Data, Department of Meteorology and Atmospheric Science, Pennsylvania State University, University Park, PA, USA
10 CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) East Africa, Addis Ababa, Ethiopia
In recent years, there has been increasing demand for high-resolution seasonal climate forecasts at sufficient lead times to allow response planning from users in agriculture, hydrology, disaster risk management, and health, among others. This paper examines the forecasting skill of the North American Multi-model Ensemble (NMME) over Ethiopia during the June to September (JJAS) season. The NMME, one of the multi-model seasonal forecasting systems, regularly generates monthly seasonal rainfall forecasts over the globe with 0.5 - 11.5 months lead time. The skill and predictability of seasonal rainfall are assessed using 28 years of hindcast data from the NMME models. The forecast skill is quantified using canonical correlation analysis (CCA) and root mean square error. The results show that the NMME models capture the JJAS seasonal rainfall over central, northern, and northeastern parts of Ethiopia while exhibiting weak or limited skill across western and southwestern Ethiopia. The performance of each model in predicting the JJAS seasonal rainfall is variable, showing greater skill in predicting dry conditions. Overall, the performance of the multi-model ensemble was not consistently better than any single ensemble member. The correlation of observed and predicted seasonal rainfall for the better performing models —GFDL-CM2p5-FLOR-A06, CMC2-CanCM4, GFDL-CM2p5-FLOR-B01 and NASA-GMAO-062012 — is 0.68, 0.58, 0.52, and 0.5, respectively. The COLA-RSMAS-CCSM4, CMC1- CanCM3 and NCEP-CFSv2 models exhibit less skill, with correlations less than 0.4. In general, the NMME offers promising skill to predict seasonal rainfall over Ethiopia during the June-September (JJAS) season, motivating further work to assess its performance at longer lead times.
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