Monthly and seasonal numerical forecasts by using the anomaly ocean-atmosphere coupled filtered model

Abstract

Case experiments of monthly predictions of eight winter months during 1976–1977 and 1982–1983 El Nino events are performed by using a three-layer anomalous filtered model (AFM), in which transient Rossby waves are filtered. The results show that this model predicts successfully the large-scale patterns of the monthly mean surface temperature anomalies. The correlation coefficients between observations and predictions are basically higher than those of persistence predictions. By comparison with the anomalous general circulation model (AGCM) the AFM gives almost the identical results, but the computation time required for running the AFM is nearly 100 times less than that required for running the AGCM. It is also shown that the results of the three-layer model are better than those of the one-layer model. In the meanwhile, four seasonal forecasts are also carried out by using the same model. It seems that the AFM possesses potential ability in predicting large-scale circulation anomalies.