improvement in our ability to predict characteristics of the sea ice cover. At the foundation of this challenge lies a set of specific questions related to sea ice prediction common among the broad stakeholder community: (1) Where is the ice at any given time (extent and concentration)? (2) What is it like (thickness distribution, multiyear ice versus first-year ice, packed or loose)? (3) What is its movement? (4) How and why has it changed? A major complicating factor in devising a strategy to more effectively address these questions is associated with the detailed and differing needs of the stakeholders, which shapes temporal and spatial requirements. For instance, while decadal projections of pan-Arctic ice extent and composition may serve the needs of decision makers who determine classification of endangered species or of planners who decide whether to build an ice-worthy ship, the long-range predictions will be less useful to marine operators whose main concern is the position of the ice edge. Springtime whaling and walrus hunting by coastal communities is dependent on over-ice transportation, requiring seasonal projections for the stability of the ice cover, its thickness and roughness, and the presence of open water close to shore. Detailed local information of this kind is not presently captured by monthly-to-seasonal ice forecasts and is only beginning to be targeted by experimental sea ice forecasts (e.g., Sea Ice Walrus Outlook1

Many of the needs and challenges associated with sea ice prediction depend on the timescales of interest. At shorter timescales (seasonal to interannual), internal variability will severely limit sea ice predictability. For example, summertime wind patterns are known to impact summer and autumn ice extent, yet weather patterns are essentially unpredictable beyond about 2 weeks. Additional atmospheric observations are not going to change this fundamental prediction barrier. Rather, predictive capability for sea ice on these timescales is thought to reside primarily in an adequate knowledge of the initial ice-ocean state, although admittedly little information exists on what constitutes an “adequate knowledge.” At longer (interannual to decadal and beyond) timescales, the role of trends in forcing (e.g., rising concentrations of greenhouse gases, changes in ocean mixing, increases in river discharge) is likely to provide some predictive potential, as it accounts for increasingly larger fractions of the change from present sea ice conditions.

A critical gap of uncertainty remains regarding the timescale between these two regimes, in which the potential predictability is low. Blanchard-Wrigglesworth et al. (2011) addressed these various prediction timescales in a numerical modeling study and suggested that a time frame of minimal predictability (for total Arctic Basin ice area) occurs from about 2 to 4r years. Although this study represents a state-of-the-art assessment of sea ice predictability, the results are based on one model (Community Climate System Model [CCSM4]) and are contingent on the ability of that model to capture the spectrum of oceanic, atmospheric, and terrestrial variability that affects sea ice. Hence the robustness of this



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