The impact of different sea-surface temperature prediction scenarios on Southern African seasonal climate forecast skill

Abstract

Retro-active forecasts produced at a 1-month lead-time by the ECHAM4.5 AGCM are statistically downscaled to South African district rainfall totals for the austral mid-summer season of December to February. The AGCM is forced with SST forecasts produced by a) statistically predicted SSTs, and b) predicted SSTs from a dynamically coupled ocean-atmosphere model. The latter SST forecasts in turn consist of an ensemble mean of SST forecasts, and also by considering the individual ensemble members of the SST forecasts. Downscaled forecast are verified over a 24-year test period from 1978/79 to 2001/02 by investigating the various AGCM configurations' attributes of discrimination (whether the forecasts are discernibly different given different outcomes) and reliability (whether the confidence communicated in the forecasts is appropriate). Rainfall forecasts produced by forcing the AGCM with dynamically predicted SSTs produce the higher skill, and ensemble mean SST forecasts lead to improved skill over forecasts that considered an ensemble distribution of SST forecasts. An hypothesis why the ensemble-mean SST produced better skilled forecasts than a distribution of SST ensemble members is presented.