Predictive Skill of AGCM Seasonal Climate Forecasts Subject to Different SST Prediction Methodologies

Abstract This study examines skill of retrospective forecasts using the ECHAM4.5 atmospheric general circulation model (AGCM) forced with predicted sea surface temperatures (SSTs) from methods of varying complexity. The SST fields are predicted in three ways: persisted observed SST anomalies, empirically predicted SSTs, and predicted SSTs from a dynamically coupled ocean–atmosphere model. Investigation of relative skill of the three sets of retrospective forecasts focuses on the ensemble mean, which constitutes the portion of the model response attributable to the prescribed boundary conditions. The anomaly correlation skill analyses for precipitation and 2-m air temperature indicate that dynamically predicted SSTs generally improve upon persisted and empirically predicted SSTs when they are used as boundary forcing in the AGCM predictions. This is particularly the case for precipitation forecasts. The skill differences in these experiments are ascribed to the skill of SST predictions in the tropical ocean basins. The multiscenario forecast by averaging the three retrospective experiments performs, overall, as well as or better than the best of the three individual experiments in specific seasons and regions. The advantage of multiscenario forecast manifests both in the deterministic and probabilistic skill. In particular, the multiscenario precipitation forecast for the December–February season demonstrates better skill than the best of the three scenarios over several regions, such as the western United States and southeastern South America. These results suggest the potential value in producing superensembles spanning different SST prediction scenarios.