Evaluation and adaptation of a regional climate model for the Horn of Africa: rainfall climatology and interannual variability
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This study evaluates the ability of the Abdus Salam International Center for Theoretical Physics (ICTP) version 3 Regional Climate Model (RegCM3) to reproduce the observed rainfall amounts and distribution over the topographically varied region of the Horn of Africa. Simulations are performed for the widespread very dry 1984 and locally very wet 1996 years using all of the alternative convective schemes available in the ICTP RegCM3. Extensive comparisons of simulations for 1984 and 1996 reveal that the Emanuel scheme best captures the rainfall patterns and interannual variations for the two extreme years over the Horn of Africa. This scheme therefore is selected for specific adaptation for the region, despite its excessive rainfall estimation there and its demonstrated deficiencies over other regions of Africa not of interest in this study. To reduce rainfall overestimation by the Emanuel scheme, several sensitivity experiments are performed by varying the key parameters that control the rate of convective mass flux, the fraction of condensed water converted to precipitation, and the heating and moistening characteristics of the environment.Results show that the amount of condensed water that ultimately falls out as rain crucially affects simulated rainfall amounts. With an appropriate adjustment to this key parameter, the excessive rainfall amount produced by the Emanuel scheme is reduced substantially. Furthermore, evaluation of RegCM3 simulations for 1982–1999 shows that the modified Emanuel convective scheme not only reproduces the 18-year average rainfall realistically but also captures the interannual variability adequately over the Horn of Africa. The correlation between the modified Emanuel-simulated and Ethiopian station rainfall is quite strong (+0.66). This customized ICTP RegCM3 model now can be used with confidence for the Horn of Africa to study regional rainfall processes and variability and to dynamically downscale seasonal rainfall forecasts.
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