atmospheric patterns into their positive and negative phases in any given year or sequence of years, however, is not well understood. Although models generally simulate these major modes of variability, studies reveal potentially important discrepancies in the statistics of their variations (e.g., Stenchikov et al., 2006). Recent studies have suggested that phasing of the AO may be related to stratospheric influences in some situations (Black, 2002; Cohen and Jones, 2011) and to surface forcing in others (Overland and Wang, 2010). Thus models need to include a realistic representation of the stratosphere and its interaction with the tropospheric circulation.

The present capability to predict, in detail, the large-scale modes of atmospheric variability is limited, and in some cases there may be little deterministic predictability beyond a few weeks. Experiments addressing such changes in large-scale atmospheric modes are challenged by natural variability, because many ensemble members can be required to detect significant changes in the circulation (Bhatt et al., 2008; Deser et al., 2012). Despite these challenges, models need to realistically simulate the statistics of these atmospheric variations, including their spatial patterns, frequency of occurrence, and response to varying forcings, if they are to accurately simulate sea ice variability on decadal scales. Whether fluctuations in spatial extent, intensity, and frequency of these large-scale oscillations will change as greenhouse gases continue to accumulate is a key open question. Evidence does suggest that high-latitude surface changes—which include changes in sea ice cover that may affect wind patterns (e.g., Liu et al., 2012), water vapor content, and cloud amount (e.g., Winton, 2006; Kay et al., 2012)—can then feed back onto the ice cover (Overland et al., 2012;Wu et al., 2012) and steer ocean currents. Capturing these feedbacks in coupled models is critical if decadal predictions are to be successful.

As evidenced in summer 2007, extreme events (e.g., anomalous winds as one of several key factors) may combine with preconditioning and ice-albedo feedback to result in abrupt change (e.g., a large decrease of sea ice in a short time) (Haas et al., 2008; Perovich et al., 2008; Zhang et al., 2008a; Lindsay et al., 2009; Ogi and Wallace, 2012) that can have decadal impacts. For example, drastic loss of perennial sea ice owing to persistent wind patterns in 2005 and 2007 (AMAP, 2011) may influence the long-term sea ice trends. Models do simulate extreme events of this type (e.g., Holland et al., 2006) but the realism of how simulated extreme events modify key parameters needs to be further assessed.

Another characteristic that highlights the interconnectedness of the Arctic system is the influence of the Atlantic and Pacific oceans on the Arctic Ocean. Relatively warm water masses from the Atlantic and Pacific enter the Arctic Ocean, and because they are saltier than the surface waters, reside below the mixed layer. The Arctic Ocean’s present stratification, resulting primarily from the vertical salinity profile, largely limits heat transfer to the ice cover from the deeper layers. These deeper layers contain vast quantities of heat that could melt all of the sea ice relatively quickly (e.g., Alexeev et al., 2011). As the Arctic transitions to a state



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