Prospects and Limitations of Seasonal Atmospheric GCM Predictions

Climate simulations and hindcast experiments of increasingly large ensemble size are being performed to determine the predictive capability of atmospheric general circulation models (AGCMs) on seasonal or longer timescales. These have exhibited large sensitivity to anomalous boundary forcing associated with global sea surface temperatures (SSTs). Large-scale patterns of climate anomalies are at times generated in the extratropics when the AGCM is forced by the SSTs associated with El Niño events. It remains to be determined whether on average such results imply useful predictive skill for seasonal means in the extratropics. Indeed, given the prospects for small, if not negligible, skill in the extratropics as revealed in variance tests of boundary-forced potential predictability, one is forced to question and examine the limits of AGCM methods. These issues are addressed within the context of a large ensemble of climate simulations using an AGCM forced with observed SSTs for the 1982–93 period. From the analysis of the model data it is argued that the impact of interannual changes in SSTs is to create a shift in the extratropical-mean state, although this shift is small and resides within the envelope of atmospheric states attained with climatological SSTs. This effect does not have any appreciable impact on the total variance of seasonal-mean atmospheric states and confirms the conclusions drawn from earlier studies. A reliable detection of the boundary-forced shift in the mean state, however, is shown to be feasible when a sufficiently large ensemble of model runs is considered. The shift in the mean state has a certain probability of being in phase with the observed seasonal anomalies. Indeed, the benefit of generating the ensemble prediction lies in the fact that it is the ensemble-mean response that nature has the greatest probability of selecting. Nonetheless, to the extent that the observed anomalies are at least partly the result of natural variability, AGCM-based seasonal predictions will be inherently probabilistic. Implications for AGCM simulations of the extratropical response to the boundary forcing, and for seasonal-mean predictions in general, are discussed.