Retro-active 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 durin g austral summer when the tropical atmospheric circulation is dominant over the region. Because of instabilities in the linear association between sum mer rainfall over southern Africa and SSTs of the tropical Indian Ocean, th e skilful prediction of seasonal rainfall may best be achieved using physic ally based models. A two-tiered retro-active forecast procedure for the Dec ember-February (DJF) season is employed over a 10-year period starting from 1987/1988. Rainfall forecasts are produced for a number of homogeneous reg ions over part of southern Africa. Categorized (below-normal, near-normal a nd above-normal) statistical DJF rainfall predictions are made for the regi on to form the baseline skill level that has to be outscored by more elabor ate 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 t he National Centres for Environmental Prediction (NCEP) reanalysis data. Bi as-corrected GCM simulations of circulation and moisture at certain standar d pressure levels are downscaled to produce rainfall forecasts at the regio nal 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 (NINO3.4) and Indi an oceans are predicted skilfully at 1- and 3-month lead-times using a stat istical model. These retro-active SST forecasts are accurate for pre-1990 c onditions, but predictability seems to have weakened during the 1990s. Skil ful multi-tiered rainfall forecasts are obtained when the amplitudes of lar ge events in the global oceans (such as Ei Nino and La Nina episodes) are d escribed adequately by the predicted SST fields. GCM simulations using pers isted August SST anomalies instead of forecast SSTs produce skill levels si milar to those of the baseline for longer lead-times. Given high-skill SST forecasts, the scheme has the potential to provide climate forecasts that o utscore the baseline skill level substantially. Copyright (C) 2001 Royal Me teorological Society.