2002 Volume 80 Issue 6 Pages 1415-1426
In this paper model-generated data sets are examined to address the question of seasonal precipitation forecast skill of the Asian and the North American monsoon systems. In this context the seasonal climate forecast data from a set of coupled atmosphere-ocean models were used. The main question we ask is if there is any useful skill in predicting seasonal anomalies beyond those of climatology. The methodology for prediction is the ‘FSU Superensemble’ which is applied here to the anomalies of the predicted multimodel data sets and the observed (analysis) fields. The skills of seasonal forecasts are evaluated using two different types of parameters: anomaly correlations and root mean square errors. Comparison of skill of the coupled model forecasts and the AMIP hindcasts yields encouraging results. It is noted that the superensemble based anomaly forecasts have somewhat higher skill compared to the bias-removed ensemble mean of member models, individually bias removed ensemble mean of the member models and the climatology. This skill comes partly from the forecast performance of multimodels and partly from the training component built into this system that is based on past collective performance of these multimodels. These components are separated to assess the improvements of the superensemble. Though skill of the forecasts from the superensemble is found to be higher than that of the bias-removed ensemble mean and has shown some usefulness over the climatology, the issue of forecasting a season in advance in quantitative terms still remains a challenge and demands further advancement in climate modeling studies.