Diagnosing Sources of U.S. Seasonal forecast Skill

In this study we diagnose the sources for the contiguous U.S. seasonal forecast skill that are related to sea- surface temperature (SST) variations using a combination of dynamical and empirical methods. The dynamical methods include 100-member ensemble simulations with 8 atmospheric general circulation models (AGCM) forced by observed monthly global SSTs from 1950 to 1999. The empirical methods involve a suite of reductions of the AGCM simulations. These include uni- and multi-variate regression models that encapsulate the simultaneous and 1-season lag linear connections between seasonal mean tropical SST anomalies and U.S. precipitation and surface air temperature. Nearly all of the AGCM skill in U.S. precipitation and surface air temperature, arising from global SST influences, can be explained by a single degree of freedom in the tropical SST field --- that associated with the linear atmospheric signal of El Niño/Southern Oscillation (ENSO). The results support previous findings regarding the preeminence of ENSO as a U.S. skill source. The diagnostics promote the interpretation that only limited advances in U.S. seasonal prediction skill should be expected from methods seeking to capitalize on sea surface predictors alone, and that advances which may occur in future decades could be readily masked by inherent multi-decadal fluctuations in skill of coupled ocean-atmosphere systems.