The Role of Sea Ice Thickness Distribution in the Arctic Sea Ice Potential Predictability: A Diagnostic Approach with a Coupled GCM

Abstract

The intrinsic seasonal predictability of Arctic sea ice is investigated in a 400-yr-long preindustrial simulation performed with the Centre National de Recherches Météorologiques Coupled Global Climate Model, version 3.3 (CNRM-CM3.3). The skill of several predictors of the pan-Arctic sea ice area was quantified: the sea ice area itself, the pan-Arctic sea ice volume, and some areal predictors built from the subgrid ice thickness distribution (ITD). Sea ice area provides a potential predictability of about 3 months, which is consistent with previous studies using model and observation data. Sea ice volume predictive skill for winter sea ice area prediction is weak. Nevertheless, there is a higher potential to predict the September ice area with the June volume anomaly than with the June area anomaly. Using ITD-based predictors, two ''regimes'' of predictability were highlighted. The first one, a ''persistence regime,'' applies to winter/early spring sea ice seasonal predictability. The winter sea ice cover can be predicted in late fall/early winter from the amount of young ice formed since the freeze-up onset in the margins. However, sea ice area itself is potentially the best predictor of winter sea ice area at seasonal time scales. The second regime is a ''memory regime.'' It applies to the predictability of summer sea ice area. An ice area anomaly in September is potentially predictable up to 6 months in advance, using the area covered by ice thicker than a critical thickness lying between 0.9 and 1.5 m. Results of this study are preliminary; however, they provide information for the design of future prediction systems and highlight the need for observations and a state-of-the-art sea ice model.

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

“…3a-c), which is consistent with Chevallier and Salas y Mélia (2012). Conversely, the OHC AO has more persistent negative correlations with the SIE AO in winter than in summer (Fig.…”

“…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).…”

Regional Arctic sea ice variations as predictor for winter climate conditions

“…A recent study by Inoue et al (2012) suggested that the meridional temperature gradient due to ice variations in the Barents Sea is the main reason for the observed SLP anomalies and the connected "warm Arctic-cold continent" temperature patterns in recent years. A number of studies showed predictability of sea ice anomalies up to 6 months and more Tietsche et al 2014;Chevallier and Salas-Mélia 2012;Blanchard-Wrigglesworth et al 2011a, b;) mainly due to persistence, reemergence and advection processes. Thus, September and even more November sea ice anomalies (Chevallier et al 2013) could lead winter ice anomalies, which in turn affect the meridional temperature gradient and thus atmospheric circulation.…”

Interannual predictability of Arctic sea ice in a global climate model: regional contrasts and temporal evolution

“…Most of analyses dealing with the Arctic sea ice cover seasonal predictability are based on statistical relationships (Walsh et al 1980;Johnson et al 1985;Drobot and Maslanik 2002;Drobot 2007), giving relatively good prediction scores. It seems, however, that non-observable variables-such as, for example, sea ice thickness (SIT) distribution-appear to be the best predictors of sea ice cover (extent or volume) (Chevallier and Salas y Mélia 2012). Further, considering the rapid changes in the Arctic environment, statistical relationships may not remain valid in the future (Holland and Stroeve 2011), justifying the increasing use of physical models.…”