No 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.

International audienc

No abstract

Since 2008, the Study of Environmental Arctic Change Sea Ice Outlook has solicited predictions of September sea-ice extent from the Arctic research community. Individuals and teams employ a variety of modeling, statistical, and heuristic approaches to make these predictions. Viewed as monthly ensembles each with one or two dozen individual predictions, they display a bimodal pattern of success. In years when observed ice extent is near its trend, the median predictions tend to be accurate. In years when the observed extent is anomalous, the median and most individual predictions are less accurate. Statistical analysis suggests that year-to-year variability, rather than methods, dominate the variation in ensemble prediction success. Furthermore, ensemble predictions do not improve as the season evolves. We consider the role of initial ice, atmosphere and ocean conditions, and summer storms and weather in contributing to the challenge of sea-ice prediction.

This study uses coupled climate model experiments to identify the influence of atmospheric physics [Community Atmosphere Model, versions 4 and 5 (CAM4; CAM5)] and ocean model complexity (slab ocean, full-depth ocean) on the equilibrium Arctic climate response to an instantaneous CO 2 doubling. In slab ocean model (SOM) experiments using CAM4 and CAM5, local radiative feedbacks, not atmospheric heat flux convergence, are the dominant control on the Arctic surface response to increased greenhouse gas forcing. Equilibrium Arctic surface air temperature warming and amplification are greater in the CAM5 SOM experiment than in the equivalent CAM4 SOM experiment. Larger 2 3 CO 2 radiative forcing, more positive Arctic surface albedo feedbacks, and less negative Arctic shortwave cloud feedbacks all contribute to greater Arctic surface warming and sea ice loss in CAM5 as compared to CAM4. When CAM4 is coupled to an active full-depth ocean model, Arctic Ocean horizontal heat flux convergence increases in response to the instantaneous CO 2 doubling. Though this increased ocean northward heat transport slightly enhances Arctic sea ice extent loss, the representation of atmospheric processes (CAM4 versus CAM5) has a larger influence on the equilibrium Arctic surface climate response than the degree of ocean coupling (slab ocean versus fulldepth ocean). These findings underscore that local feedbacks can be more important than northward heat transport for explaining the equilibrium Arctic surface climate response and response differences in coupled climate models. That said, the processes explaining the equilibrium climate response differences here may be different than the processes explaining intermodel spread in transient climate projections.* The climate sensitivity is the equilibrium global surface temperature response to a CO 2 doubling. The CCSM4 climate sensitivity was estimated using a regression between the globally averaged TOA radiation imbalance and surface temperature (Gregory et al. 2004), a method with known deficiencies (Winton et al. 2010). The CAM4 and CAM5 climate sensitivities are estimated from the average surface temperature increase during the last 10 years of the slab ocean model runs. The CAM4 hi climate sensitivity is from Bitz et al. (2012b).

[1] The recent sharp decline in Arctic sea ice has triggered an increase in the interest of Arctic sea ice predictability, not least driven by the potential of significant human industrial activity in the region. In this study we quantify how long Arctic sea ice predictability is dominated by dependence on its initial conditions versus dependence on its secular decline in a state-of-the-art global circulation model (GCM) under a 'perfect model' assumption. We demonstrate initialvalue predictability of pan-Arctic sea ice area is continuous for 1-2 years, after which predictability is intermittent in the 2-4 year range. Predictability of area at these longer lead times is associated with strong area-thickness coupling in the summer season. Initial-value predictability of pan-Arctic sea ice volume is significant continuously for 3-4 years, after which time predictability from secular trends dominates. Thus we conclude predictability of Arctic sea ice beyond 3 years is dominated by climate forcing rather than initial conditions. Additionally, we find that forecast of summer conditions are equally good from the previous September or January initial conditions. Citation: BlanchardWrigglesworth, E., C. M. Bitz, and M. M. Holland (2011), Influence of initial conditions and climate forcing on predicting Arctic sea ice, Geophys. Res. Lett., 38, L18503,

[1] Rapid Arctic sea ice retreat has fueled speculation about the possibility of threshold (or 'tipping point') behavior and irreversible loss of the sea ice cover. We test sea ice reversibility within a state-of-the-art atmosphereocean global climate model by increasing atmospheric carbon dioxide until the Arctic Ocean becomes ice-free throughout the year and subsequently decreasing it until the initial ice cover returns. Evidence for irreversibility in the form of hysteresis outside the envelope of natural variability is explored for the loss of summer and winter ice in both hemispheres. We find no evidence of irreversibility or multiple ice-cover states over the full range of simulated sea ice conditions between the modern climate and that with an annually ice-free Arctic Ocean. Summer sea ice area recovers as hemispheric temperature cools along a trajectory that is indistinguishable from the trajectory of summer sea ice loss, while the recovery of winter ice area appears to be slowed due to the long response times of the ocean near the modern winter ice edge. The results are discussed in the context of previous studies that assess the plausibility of sea ice tipping points by other methods. The findings serve as evidence against the existence of threshold behavior in the summer or winter ice cover in either hemisphere. Citation: Armour, K. C., I. Eisenman, E. Blanchard-Wrigglesworth, K. E. McCusker, and C. M. Bitz (2011), The reversibility of sea ice loss in a stateof-the-art climate model, Geophys. Res. Lett., 38, L16705,