摘要
Climate prediction using a coupled model with a one-tier scheme is an important research direction. In this study, based on 1974- 2001 hindcasts obtained from the "Development of a European Multimodel Ensemble system for seasonal to inTERannual prediction" (DEMETER) project, the capability of coupled general circulation models (CGCMs) to predict six climatic factors that have a close relationship with the western North Pacific typhoon activity is investigated over summer (June-October). Results indicate that all six DEMETER CGCMs well predict the six factors. Using the statistical relationship between these six factors and the typhoon frequency, the ability of the CGCMs to predict typhoon frequency is further explored. It is found that the six CGCMs also well predict the variability in typhoon frequency. Comparison analysis shows that the prediction skill of the statistical downscaling method is much better than that of the raw CGCMs. In addition, the six-model ensemble has the best prediction performance. This study suggests that combining a multi-model ensemble and statistical downscaling greatly improves the CGCM prediction skill, and will be an important research direction for typhoon prediction.
Climate prediction using a coupled model with a one-tier scheme is an important research direction. In this study, based on 1974- 2001 hindcasts obtained from the ＂Development of a European Multimodel Ensemble system for seasonal to inTERannual pre- diction＂ （DEMETER） project, the capability of coupled general circulation models （CGCMs） to predict six climatic factors that have a close relationship with the western North Pacific typhoon activity is investigated over summer （June-October）. Results indicate that all six DEMETER CGCMs well predict the six factors. Using the statistical relationship between these six factors and the typhoon frequency, the ability of the CGCMs to predict typhoon frequency is further explored. It is found that the six CGCMs also well predict the variability in typhoon frequency. Comparison analysis shows that the prediction skill of the statisti- cal downscaling method is much better than that of the raw CGCMs. In addition, the six-model ensemble has the best prediction performance. This study suggests that combining a multi-model ensemble and statistical downscaling greatly improves the CGCM prediction skill, and will be an important research direction for typhoon prediction.
基金
supported by the Special Fund forPublic Welfare Industry (meteorology) (GYHY200906018)
the Key Program of the Chinese Academy of Sciences (KZCX2-YW-Q03-3)
the National Basic Research Program of China (2009CB421406)
the Norwegian Research Council Project "East-Asia DecCen."
