Seasonal climate prediction remains a difficult challenge. During Northern Hemisphere (NH) winter the large-scale teleconnection pattern of the Arctic Oscillation (AO) explains the largest fraction of temperature variance of any other known climate mode. However the Arctic Oscillation is considered to be a result of intrinsic atmospheric dynamics or chaotic behavior and therefore is unpredictable. With the development of the Snow Advance Index, which was derived from antecedent observed snow cover, we can now explain approximately 75% of the variance of the winter AO. The high correlation between the Snow Advance Index and the winter AO demonstrates that the AO is mostly predictable, which can be exploited for skillful seasonal climate predictions.

An immediate benefit of the development of this new index is improved seasonal climate predictions. The ability to predict the winter AO is considered the single most important advance in achieving successful winter forecasts. We created cross-validated hindcasts of winter land surface temperatures using the Snow Advance Index as a predictor in the AER seasonal forecast model and compared those hindcasts with hindcasts using the observed winter AO and ENSO. Skill or accuracy of the AER model compares favorably to that of the observed winter AO and ENSO especially for the Eastern US and large portions of Northern Eurasia. And considering that the index is known four months prior to the winter AO, yet matches the skill of the winter AO so closely, demonstrates great potential for improved real-time winter forecasts.