Can Optimal Projection Improve Dynamical Model Forecasts?

Abstract

n optimal projection for improving the skill of dynamical model forecasts is proposed. The proposed method uses statistical optimization techniques to identify the most skillful or most predictable patterns, and then projects forecasts onto these patterns. Applying the method to seasonal mean 2-m temperature from the Ensemble-Based Predictions of Climate Changes and Their Impacts (ENSEMBLES) multimodel hindcast dataset reveals that the method improves skill only in South America and Africa, suggesting that the benefit of optimal projection is limited to certain regions, but can be substantial. Further investigation reveals that the improvement in skill comes not from optimal projection itself, but from the EOF prefiltering that is done to reduce the dimension of the optimization space. Thus, much of the improvement attributable to optimal projection can be achieved by suitable EOF filtering. Interestingly, models are found to generate patterns that project only weakly on observational datasets but are strongly correlated between models. An important by-product of the method is a concise summary of the skillful or predictable structures in a given forecast. For the ENSEMBLES dataset, the method convincingly demonstrates that most of the seasonal prediction skill over continents comes from two components, ENSO and the global warming trend. In addition, the method can be used to determine whether a pattern exists that is well predicted by one model but not by another model (complementary skill).