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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