受模式性能的限制,当前的气候模式在直接预报亚澳季风区的夏季降水变化方面技巧较低。采用统计—动力相结合的方法预报亚澳夏季风降水,首先从观测数据中提取具有清晰物理意义的可预报模态;然后,将国际ENSEMBLES计划提供的多模式、多集合样本耦合模式季节预测试验预测的可预报模态的主成分时间序列与对应观测得到的可预报模态的空间型组合,重构降水场,建立了针对亚澳夏季风降水的统计—动力结合的季节预测系统。分析了该系统提前1个月、4个月和7个月的回报技巧。结果表明,统计—动力预测系统的预测技巧显著优于纯动力预测的技巧。另一方面,多模式集合平均的预测技巧优于单个模式,因此针对季风区降水开展多模式集合预测是非常必要的。

Due to the limitations of model performances, the predictive skills of current climate models for the Asian-Australian summer monsoon precipitation are still poor. The prediction based on the combination of statistical and dynamic approaches is an effective way to improve the predictive skills. We used such method to identify the predictable modes of the Asian-Australian summer monsoon precipitation with clear physical interpretation from the historical observational data. Then we combined the principal components time series of these modes predicted by the coupled models, which is derived from the seasonal prediction experiments in the ENSEMBLES project, and the corresponding spatial patterns derived from the above observational analysis to reconstruct the precipitation field. These formed a statistical-dynamic seasonal prediction model for the Asian-Australian summer monsoon precipitation. We analyzed the predictive skills of the model at 1-, 4-and 7-month leads. The result shows that the forecast skills of the statistical-dynamic prediction model are higher than those of the simple dynamic predictions. In addition, the predictive skills of the Multi-Model Ensemble (MME) mean are superior to those of any individual models. Therefore, it is very necessary to implement multi-model ensemble prediction for the monsoon precipitation.