Persistence and Inherent Predictability of Arctic Sea Ice in a GCM Ensemble and Observations

Abstract

Abstract The temporal characteristics of Arctic sea ice extent and area are analyzed in terms of their lagged correlation in observations and a GCM ensemble. Observations and model output generally match, exhibiting a red-noise spectrum, where significant correlation (or memory) is lost within 2–5 months. September sea ice extent is significantly correlated with extent of the previous August and July, and thus these months show a predictive skill of the summer minimum extent. Beyond this initial loss of memory, there is an increase in correlation—a reemergence of memory—that is more ubiquitous in the model than observations. There are two distinct modes of memory reemergence in the model. The first, a summer-to-summer reemergence arises within the model from the persistence of thickness anomalies and their influence on ice area. The second, which is also seen in observations, is associated with anomalies in the growth season that originate in the melt season. This reemergence stems from the several-month persistence of SSTs. In the model memory reemergence is enhanced by the sea ice albedo feedback. The same mechanisms that give rise to reemergence also enhance the 1-month lagged correlation during summer and winter. The study finds the least correlation between successive months when the sea ice is most rapidly advancing or retreating.