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

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.

Multi‐system seasonal predictions of Arctic sea ice

“…Relatively little skill remains both for CanSIPS (Figure 1a) and CFSv2 (Figure 1b) after lead two (the third forecast month), although there is some evidence for longer-lead skill, primarily for target months in late fall (mainly for CFSv2) and winter (for both systems). This apparently corresponds to an observed tendency for detrended cold-season anomalies in ice area and extent to be correlated from one year to the next, possibly as a result of slowly varying ocean thermal influences in regions of winter ice cover as discussed by Blanchard-Wrigglesworth et al [2011a].…”

Mechanisms influencing seasonal to inter-annual prediction skill of sea ice extent in the Arctic Ocean in MIROC

“…The observed detrended Arctic sea ice extent, based on ensemble hindcasts can be predicted up to 2-7 and 5-11 months ahead for summer and winter, respectively (e.g., Chevallier et al, 2013;Sigmond et al, 2013;Wang et al, 2013;Msadek et al, 2014;Peterson et al, 2015;Guemas et al, 2016;Sigmond et al, 2016). In these ensemble hindcasts, it is found that ice thickness and surface or subsurface water temperatures are closely related to the prediction skill, as suggested by idealised or perfect-model experiments with climate models (e.g., Blanchard-Wrigglesworth et al, 2011b;Chevallier and Salas y Mélia, 2012;Day et al, 2014a).…”

Spring Arctic sea ice as an indicator of North American summer rainfall

“…Blanchard-Wrigglesworth et al (2011) analyse the lagged correlation of the Arctic sea ice in observations and a GCM ensemble. They showed a persistence of total sea ice area anomalies for the 1-5-month time scale, longer in winter and summer months and shorter in spring and fall months.…”