Predictive Ability of CFSv2 Model for Monthly Antarctic Oscillation in Boreal Spring and Analysis of Causes

The predictive ability of the Climate Forecast System version 2 (CFSv2) for the Antarctic Oscillation (AAO) is evaluated in the three boreal spring months (March, April, and May) of the period 1983–2019. Results show that the CFSv2 model has a good predictive capability for the spatial patterns of the AAO in the three spring months. Specifically, the CFSv2 model can effectively predict the interannual variability of the AAO in March but not in April and May. Results indicate that the tropical central and eastern Pacific sea surface temperature anomalies and the eastern Australia sea surface temperature anomalies are likely to be the predictable sources of the AAO in March. On the one hand, a significant relationship exists between El Niño–Southern Oscillation (ENSO) and the AAO in March, and it weakens in April and May. In March, ENSO can excite the Pacific–South American wave train from tropical Pacific to the South Pacific, thereby influencing the interannual variability of the simultaneous AAO by affecting the South Pacific sea surface temperature anomalies and the low-level cyclonic circulation anomalies. On the other hand, the March sea surface temperature anomaly east of Australia triggers an active Rossby wave train in the core area of ​​the subtropical jet, which propagates away from eastern Australia toward the southeast and arrives at the mid-high latitudes of the South Pacific. Consequently, the 30°S westerly wind weakens, and the 60°S high latitude in the Southern Hemisphere strengthens and eventually leads to changes in the AAO. CFSv2’s predictive capability for March AAO is higher than that of April and May because this model can reproduce not only the relationship between March AAO and ENSO and the sea surface temperature east of Australia but also the physical processes between them.