Improvement in Prediction of the Arctic Oscillation with a Realistic Ocean Initial Condition in a CGCM

Abstract

Abstract This study verifies the impact of improved ocean initial conditions on the Arctic Oscillation (AO) forecast skill by assessing the one-month lead predictability of boreal winter AO using the Pusan National University (PNU) coupled general circulation model (CGCM). Hindcast experiments were performed on two versions of the model, one does not use assimilated ocean initial data (V1.0) and one does (V1.1), and the results were comparatively analyzed. The forecast skill of V1.1 was superior to that of V1.0 in terms of the correlation coefficient between the predicted and observed AO indices. In the regression analysis, V1.1 showed more realistic spatial similarities than V1.0 did in predicted sea surface temperature and atmospheric circulation fields. The authors suggest the relative importance of the contribution of the ocean initial condition to the AO forecast skill was because the ocean data assimilation increased the predictability of the AO, to some extent, through the improved interaction between tropical forcing induced by realistic sea surface temperature (SST) and atmospheric circulation. In V1.1, as in the observation, the cold equatorial Pacific SST anomalies generated the weakened tropical convection and Hadley circulation over the Pacific, resulting in a decelerated subtropical jet and accelerated polar front jet in the extratropics. The intensified polar front jet implies a stronger stratospheric polar vortex relevant to the positive AO phase; hence, surface manifestations of the reflected positive AO phase were then induced through the downward propagation of the stratospheric polar vortex. The results suggest that properly assimilated initial ocean conditions might contribute to improve the predictability of global oscillations, such as the AO, through large-scale tropical ocean–atmosphere interaction.