Skillful prediction of winter Arctic Oscillation from previous summer in a linear empirical model

The winter Arctic Oscillation (WAO), as a primary atmospheric variability mode in the Northern Hemisphere, plays a key role in influencing mid-high-latitude climate variations. However, current dynamical seasonal forecasting systems have limited skills in predicting WAO with lead time longer than two months. In this study, we design a linear empirical model using two effective precursors from anomalies of the Arctic sea ice concentration (SIC) and the tropical sea surface temperature (SST) initiated in preceding late summer (August) which are both significantly correlated with WAO in recent four decades. This model can provide a skillful prediction of WAO at about half-year lead started from previous summer and perform much better than the dynamical models. Such a significantly prolonged lead time is owed to the stable precursor signals extracted from the SIC and SST anomalies over specific areas, which can persist from previous August and be further enhanced through autumn months. Validation results show that this model can produce a 20-year independent-validated prediction skill of 0.45 for 1999-2018 and a 39-year cross-validated skill of 0.67 for 1980-2018, providing a potentially effective tool for earlier predictions of winter climate variations at mid-high latitudes.

Automated summary

This study developed a linear empirical model using two effective precursors significantly correlated with the winter Arctic Oscillation (WAO) in the Northern Hemisphere, achieving skillful predictions with a lead time of about half a year. The stable precursor signals extracted from anomalies of the Arctic sea ice concentration and tropical sea surface temperature enable the model to outperform dynamical models in predicting WAO.