Decadal variation of predictive skill of seasonal climate over the Yangtze River and its possible causes

Abstract

Seasonal climate predictions with global climate models which are developed based on the ocean-atmosphere interactions, contribute to the water resources management and hazard mitigation. Nowadays, multi-model ensemble seasonal climate prediction system, such as North American Multi-Model Ensemble (NMME), has become an effective way to provide useful forecast information a few months ahead especially over regions with strong ocean-atmosphere coupling. Previous studies have evaluated the skill of NMME hindcasts worldwide, however, it’s still unclear that whether the NMME real-time forecasts perform as well as the hindcasts and how the changes in ocean-atmospheric teleconnections affect the prediction skill. Here we show that although selecting an appropriate time frame for the calculation of climatology can reduce errors of real-time prediction, the real-time prediction skills are lower than hindcast skills in the Yangtze River basin, with anomaly correlation decreased by 14%-51% (38%-75%) and error increased by 30%-31% (51%-55%) for seasonal precipitation (temperature) predictions up to the sixth lead-seasons, and the skill decrease larger at longer leads. The failure in representing the decadal variations of ocean-atmospheric teleconnection (especially the association with Indian Ocean surface temperature) during the real-time forecast period can partly explain the decline in the prediction skills. Our findings suggest that improved simulations of the changes in the ocean-atmospheric teleconnections are necessary for skillful seasonal climate predictions in the real-time.