ALBERT

Description: From ensembles of 80 AGCM simulations for every December–January–February (DJF) seasonal mean in the 1980–2000 period, interannual variability in atmospheric response to interannual variations in observed sea surface temperature (SST) is analyzed. A unique facet of this study is the use of large ensemble size that allows identification of the atmospheric response to SSTs for each DJF in the analysis period. The motivation of this study was to explore what atmospheric response patterns beyond the canonical response to El Niño–Southern Oscillation (ENSO) SST anomalies exist, and to which SST forcing such patterns may be related. A practical motivation for this study was to seek sources of atmospheric predictability that may lead to improvements in seasonal predictability efforts. This analysis was based on the EOF technique applied to the ensemble mean 200-mb height response. The dominant mode of the atmospheric response was indeed the canonical atmospheric response to ENSO; however, this mode only explained 53% of interannual variability of the ensemble means (often referred to as the external variability). The second mode, explaining 19% of external variability, was related to a general increase (decrease) in the 200-mb heights related to a Tropicwide warming (cooling) in SSTs. The third dominant mode, explaining 12% of external variability, was similar to the mode identified as the “nonlinear” response to ENSO in earlier studies. The realism of different atmospheric response patterns was also assessed from a comparison of anomaly correlations computed between different renditions of AGCM-simulated atmospheric responses and the observed 200-mb height anomalies. For example, the anomaly correlation between the atmospheric response reconstructed from the first mode alone and the observations was compared with the anomaly correlation when the atmospheric response was reconstructed including modes 2 and 3. If the higher-order atmospheric response patterns obtained from the AGCM simulations had observational counterparts, their inclusion in the reconstructed atmospheric response should lead to higher anomaly correlations. Indeed, at some geographical regions, an increase in anomaly correlation with the inclusion of higher modes was found, and it is concluded that the higher-order atmospheric response patterns found in this study may be realistic and may represent additional sources of atmospheric seasonal predictability.