Low Skill in Dynamical Prediction of Boreal Summer Climate: Grounds for Looking beyond Sea Surface Temperature

Abstract

Ensemble integrations of three general circulation models (Center for Ocean?Land?Atmosphere Studies, NCAR, and NCEP) have been performed over five different boreal summer seasons (June through September of 1986?88 and 1993?94) with prescribed observed sea surface temperature to assess the predictability of seasonal climate during the boreal summer. Beyond some inconsistent initialization of soil wetness among the models, there is no land surface contribution to predictability that can be assessed. The models show a rapid degradation of skill in global terrestrial surface temperature after the first month, and no skill in precipitation over land. Potential predictability is assessed by examining in tandem the models' skill as measured by their anomaly correlation coefficients, and the models' signal-to-noise ratio (essentially interannual versus intraensemble variance) as a measure of confidence in the results. Collocation of skill in anomaly simulation and a robust signal is a strong indicator of potential predictability. Predictability of interannual climate variations is found to be low outside the deep Tropics, and nil over land. With only SST as a driving boundary condition, the poor performance of these models during summer may indicate that one must turn to the land surface in order to harvest potential predictability.