Enhancing Dynamical Seasonal Predictions through Objective Regionalization

Abstract

mproving seasonal forecasts in East Africa has great implications for food security and water resources planning in the region. Dynamically based seasonal forecast systems have much to contribute to this effort, as they have demonstrated ability to represent and, to some extent, predict large-scale atmospheric dynamics that drive interannual rainfall variability in East Africa. However, these global models often exhibit spatial biases in their placement of rainfall and rainfall anomalies within the region, which limits their direct applicability to forecast-based decision-making. This paper introduces a method that uses objective climate regionalization to improve the utility of dynamically based forecast-system predictions for East Africa. By breaking up the study area into regions that are homogenous in interannual precipitation variability, it is shown that models sometimes capture drivers of variability but misplace precipitation anomalies. These errors are evident in the pattern of homogenous regions in forecast systems relative to observation, indicating that forecasts can more meaningfully be applied at the scale of the analogous homogeneous climate region than as a direct forecast of the local grid cell. This regionalization approach was tested during the July?September (JAS) rain months, and results show an improvement in the predictions from version 4.5 of the Max Plank Institute for Meteorology?s atmosphere?ocean general circulation model (ECHAM4.5) for applicable areas of East Africa for the two test cases presented.