Study of East Asian winter climate predictability by using European multi model ensemble prediction

In short-term climate prediction, the multi-model ensemble prediction is widely used as a practical approach. In this paper, the predictability of anomalies for the winter atmospheric circulation and climate in East Asian area (0°-60°N, 70°-140°E) is evaluated by using the 1980-2001 hindcast data from the DEMETER multi-model ensemble prediction system. The climate variables used are 500 hPa geopotential height, 850 hPa wind, surface air temperature and precipitation. In this paper, the Ensemble Mean (EM) is used as the primary method to construct the multi-model ensemble prediction. To correct the model predictions, the modes in the prediction space are calibrated by using the Empirical Orthogonal Function. A group of new Synthetic Data Sets are generated and then used as inputs for the Synthetic Ensemble Mean or Synthetic Superensemble (SEM/SSE) method. The results show that, in East Asia, the winter climate anomalies predictability is larger in the tropics than in the middle-high latitudes; besides, the predictability is larger in oceans than in inland areas. Multi-model ensembles, both EM and SEM/SSE, could generally improve the predictability of winter climate anomalies in East Asia, suggesting the multi-model ensembles’ advantages against individual models used in the DEMETER project. The two types of method used for multi-model ensemble construction could also influence final prediction results. For geopotential height, wind, and precipitation anomalies, the prediction skill of the SEM/SSE method is better than that of the EM method; while for winter surface air temperature anomaly, the prediction skill of the EM method is better than that of the SEM/SSE method.