Enhancing Dynamical Seasonal Predictions through Objective Regionalization

Seasonal rainfall forecasts have great implications for food security and water resources planning in Africa. Dynamically-based seasonal forecasting 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. However, the forecasting models often exhibit spatial biases in their placement of rainfall amount and anomalies, which limit their direct applicability to forecast-based decision making. Objective regionalization is used to parse our study area into regions homogenous in interannual variability and show 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 be applied at the scale of the analogous homogeneous climate region than as a direct forecast of the local grid cell. Here we present our regionalization approach during the summer rain (July-August-September) months, and results show an improvement in the Max Plank Institute for Meteorology’s Atmosphere-ocean General Circulation Model (AGCM) version 4.5 (ECHAM4.5) predictions for applicable areas of East Africa.