Reduction of Model Systematic Error by Statistical Correction for Dynamical Seasonal Predictions

Singular value decomposition analysis (SVDA) is used to analyze an ensemble of three 34-yr general circulation model (GCM) simulations forced with observed sea surface temperature. It is demonstrated how statistical postprocessing based on the leading SVDA modes of simulated and observed fields, primarily precipitation, can be applied to improve the skill of the simulation. For a given limited prediction region, the GCM has the potential to nonlinearly transform the SST information from around the globe and produce a dynamic solution over the region that can be statistically corrected to account for such features as systematic shifts in the location of anomaly dipoles. This paper does not address the separate question of whether the current generation of GCMs contain information above that which could be extracted using linear statistical relationships with SST.