Verification and Improvement of the Capability of ENSEMBLES to Predict the Winter Arctic Oscillation

The winter Arctic Oscillation (AO) is important for understanding the Northern Hemisphere climate variability and predictability. However, ENSEMBLES models produce inconsistent predictions when applied to the interannual variability of the 1962- 2006 winter AO. Compared to the conventional prediction approach, which focuses on predicting the anomalies of a variable relative to climatology, the year-to-year increment (the difference in the variable between the current year and the previous year, called DY) of winter AO (DY_AOI) is treated as predictand in this study. Then the interannual increment approach is applied to dynamical-statistical model, which demonstrates good capability in improving the DY_AOI prediction of ENSEMBLES. Next, the improved final AOI is obtained by adding the improved DY_AOI to the preceding observed AOI. Because the interannual increment approach can amplify prediction signals and takes advantage from the previous observed AOI, this method shows promise for significantly improving the interannual variability prediction capabilities of the winter AO during 1962- 2006 in the ENSEMBLES models. Therefore, this study offers important insights for AO predictions, even other climate variables predictions.