Subseasonal variability and predictability of the Arctic Oscillation/North Atlantic Oscillation in BCC_AGCM2.2

Abstract

The subseasonal variability and predictability of the Arctic Oscillation/North Atlantic Oscillation (AO/NAO) is evaluated using a full set of hindcasts generated from the Beijing Climate Center Atmospheric General Circulation Model version 2.2 (BCC_AGCM2.2). It is shown that the predictability of the monthly mean AO/NAO index varies seasonally, with the highest predictability during winter (December–March) and the lowest during autumn (August–November), with respect to both observations and BCC_AGCM2.2 results. As compared with the persistence prediction skill of observations, the model skillfully predicts the monthly mean AO/NAO index with a one-pentad lead time during all winter months, and with a lead time of up to two pentads in December and January. During winter, BCC_AGCM2.2 exhibits an acceptable skill in predicting the daily AO/NAO index of ∼9 days, which is higher than the persistence prediction skill of observations of ∼4 days. Further analysis suggests that improvements in the simulation of storm track activity, synoptic eddy feedback, and troposphere–stratosphere coupling in the Northern Hemisphere could help to improve the prediction skill of subseasonal AO/NAO variability by BCC_AGCM2.2 during winter. In particular, BCC_AGCM2.2 underestimates storm track activity intensity but overestimates troposphere–stratosphere coupling, as compared with observations, thus providing a clue to further improvements in model performance.