Interpretation of Seasonal Climate Forecast Using Brier Skill Score, The Florida State University Superensemble, and the AMIP-I Dataset

Abstract

The superensemble technique has been proven to be successful in producing a deterministic forecast superior not only to any of the individual models going into it, but also to the multimodel ensemble forecast. Research so far has been done on the superensemble as a deterministic forecast, and it has been shown that using the superensemble method leads to a significant reduction in rms errors. This paper investigates the skill of the superensemble as a probabilistic forecast, and it compares it with that of the multimodel ensemble. Using the Atmospheric Model Intercomparison Project (AMIP I) seasonal multimodel precipitation forecasts, probability forecasts are defined for the multimodel ensemble and for the multimodel superensemble. The Brier skill score of these forecasts is calculated for different thresholds of precipitation anomaly. It is shown that both the multimodel ensemble and the superensemble probability forecasts are much better than climatological forecast and that the superensemble probability forecast outperforms the multimodel bias-removed ensemble at any threshold level.