Retroactive skill of multi-tiered forecasts of summer rainfall over southern Africa

Sea‐surface temperature (SST) variations of the oceans surrounding southern Africa are associated with seasonal rainfall variability, especially during austral summer when the tropical atmospheric circulation is dominant over the region. Because of instabilities in the linear association between summer rainfall over southern Africa and SSTs of the tropical Indian Ocean, the skilful prediction of seasonal rainfall may best be achieved using physically based models. A two‐tiered retro‐active forecast procedure for the December–February (DJF) season is employed over a 10‐year period starting from 1987/1988. Rainfall forecasts are produced for a number of homogeneous regions over part of southern Africa. Categorized (below‐normal, near‐normal and above‐normal) statistical DJF rainfall predictions are made for the region to form the baseline skill level that has to be outscored by more elaborate methods involving general circulation models (GCMs). The GCM used here is the Centre for Ocean–Land–Atmosphere Studies (COLA) T30, with predicted global SST fields as boundary forcing and initial conditions derived from the National Centres for Environmental Prediction (NCEP) reanalysis data. Bias‐corrected GCM simulations of circulation and moisture at certain standard pressure levels are downscaled to produce rainfall forecasts at the regional level using the perfect prognosis approach. In the two‐tiered forecasting system, SST predictions for the global oceans are made first. SST anomalies of the equatorial Pacific (NIÑO3.4) and Indian oceans are predicted skilfully at 1‐ and 3‐month lead‐times using a statistical model. These retro‐active SST forecasts are accurate for pre‐1990 conditions, but predictability seems to have weakened during the 1990s. Skilful multi‐tiered rainfall forecasts are obtained when the amplitudes of large events in the global oceans (such as El Niño and La Niña episodes) are described adequately by the predicted SST fields. GCM simulations using persisted August SST anomalies instead of forecast SSTs produce skill levels similar to those of the baseline for longer lead‐times. Given high‐skill SST forecasts, the scheme has the potential to provide climate forecasts that outscore the baseline skill level substantially