Skill of Multimodel ENSO Probability Forecasts

The cross-validated hindcast skills of various multimodel ensemble combination strategies are compared for probabilistic predictions of monthly SST anomalies in the ENSO-related Nino-3.4 region of the tropical Pacific Ocean. Forecast data from seven individual models of the Development of a European Multimodel Ensemble System for Seasonal-to-Interannual Prediction (DEMETER) project are used, spanning the 22-yr period of 1980-2001. Skill of the probabilistic forecasts is measured using the ranked probability skill score and rate of return, the latter being an information theory-based measure. Although skill is generally low during boreal summer relative to other times of the year, the advantage of the model forecasts over simple historical frequencies is greatest at this time. Multimodel ensemble predictions, even those using simple combination methods, generally have higher skill than single model predictions, and this advantage is greater than that expected as a result of an increase in ensemble size. Overall, slightly better performance was obtained using combination methods based on individual model skill relative to methods based on the complete joint behavior of the models. This finding is attributed to the comparatively large expected sampling error in the estimation of the relations between model errors based on the short history. A practical conclusion is that, unless some models have grossly low skill relative to the others, and until the history is much longer than two to three decades, equal, independent, or constrained joint weighting are reasonable courses.