Predictability of the Seasonal Mean Atmospheric Circulation during Autumn, Winter, and Spring

Abstract

The predictability of the autumn, boreal winter, and spring seasons with foreknowledge of sea surface temperatures (SSTs) is studied using ensembles of seasonal simulations of three general circulation models (GCMs): the Center for Ocean?Land?Atmosphere Studies (COLA) GCM, the National Aeronautics and Space Administration Seasonal to Interannual Prediction Project (NSIPP) GCM, and the National Centers for Environmental Prediction (NCEP) GCM. Warm-minus-cold composites of the ensemble mean and observed tropical Pacific precipitation, averaged for the three warmest El Niño and three coldest La Niña winters, show large positive anomalies near the date line that extend eastward to the South American coast. The same is true for composites of the spring following the event. In the composites of the autumn preceding the event, the precipitation is weaker and shifted off the equator in the far eastern Pacific, where equatorial SSTs are too low to support convection. The corresponding boreal winter 200-hPa height composites show strong signals in the Tropics and midlatitudes of both hemispheres. The subsequent spring composites are similar, but weaker in the northern extratropics. In the preceding autumn composites, the overall height signal is quite weak, except in the southern Pacific. The model dependence of the signal (variance of ensemble means) and noise (variance about the ensemble means) of the seasonal mean 200-hPa height is small, a result that holds for all three seasons and is in contrast to earlier studies. The signal-to-noise ratio is significantly greater than unity in the Tropics (all seasons), the northern Pacific and continental North America subtropics (boreal winter and spring), and the southern Pacific subtropics (boreal autumn). Rotated empirical orthogonal function analysis of the tropical Pacific SST recovers El Niño?like dominant patterns in boreal winter and spring, but emphasizes two SST patterns in autumn, one with largest SST in the far eastern tropical Pacific and one with a maximum nearer the date line. Two methods are used to assess the precipitation and height field responses to these patterns: linear regression of the ensemble means on the principal component (PC) time series of SST and identification of patterns that optimize the signal-to-noise ratio. The two methods yield remarkably similar results. The optimal height patterns for boreal winter and spring are similar, although the spring response over the northern extratropics is somewhat weaker, and some subtle changes in phase are found in all three GCMs. The associated optimal time series have serial correlations with the leading PC of SST that exceed 0.9 for all GCMs for winter and spring. For autumn the time series of the leading two optimal patterns each has a serial correlation with the corresponding PC of SST that exceeds 0.7 for the COLA and NSIPP GCMs. The autumn 200-hPa-height leading optimal pattern (response to eastern Pacific SST) is quite weak, representing nearly uniform tropical warming. The second optimal pattern (response to central Pacific SST) shows a robust wave train in the southern Pacific, with a consistent belt of low height over northern midlatitudes.