资源环境科技发展态势分析平台

Phase five of the Coupled Model Intercomparison Project enabled a range of decadal modeling experiments where climate models were initialized with observations and allowed to evolve freely for 10-30 years. However, climate models struggle to realistically simulate rainfall and the skill of rainfall prediction in decadal experiments is poor. Here, we examine how predictions of sea surface temperature anomaly (SSTA) indices from Coupled Model Intercomparison Project Phase 5 decadal experiments can provide skillful rainfall forecasts at interannual timescales for Australia. Forecasts of commonly used SSTA indices relevant to Australian seasonal rainfall are derived from decadal hindcasts of six different climate models and corrected for model drift. The corrected indices are then combined to form a multimodel ensemble. The resultant forecasts are used as predictors in a statistical rainfall model developed in this study. As SSTA forecasts lose skill with increasing lead time, a new methodology for predicting interannual rainfall is proposed. We allow our statistical prediction model to evolve with lead time while accounting for the loss of skill in SSTA forecasts instead of using one statistical model for all lead times. Results in this pilot study across two of the largest climate zones in Australia show that SSTA outputs from the decadal experiments provide enhanced skill in rainfall prediction over using the conventional model (based purely on lagged observed indices) up to a maximum of three years ahead. This methodology could be used more broadly for other regions around the world where rainfall variability is known to have strong links to ocean temperatures.