Global seasonal precipitation forecasts using improved sea surface temperature predictions

Abstract

Climate models driven by observed or modeled sea surface temperature (SST) or SST anomalies (SSTA) are used as a standard tool in seasonal climate predictions. This study investigates the merit of seasonal rainfall predictions obtained by using multimodel SSTA to drive a climate model over a single model case. The multimodel SSTA predictions are obtained by combining the predictions of five climate models, whereas the stand-alone model predictions come from the Predictive Ocean Atmosphere Model for Australia. The climate model used is the Australian Community Climate and Earth System Simulator (ACCESS). The use of multimodel SSTA over a single model shows marginal improvements in seasonal rainfall predictions across the entire globe. Further improvements in rainfall forecasts are possible either through improved parameterizations in the ACCESS model or by using another climate model. As a final step, the rainfall forecasts from the multimodel and the single model SSTA (two-member hierarchical rainfall forecasts, denoted as HIER) are combined and to gain further improvements over the individual multimodel or single model rainfall predictions for most of the grid cells in all seasons.