requirements. Likewise, many of the needs and challenges associated with sea ice prediction depend on the timescales of interest. At shorter timescales (seasonal to interannual), predictive capability 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.” Challenges on the seasonal timescales include:
• Understanding the relative strengths and weaknesses of the different existing approaches used to generate seasonal ice forecasts (statistical algorithms, coupled ice-ocean models driven by prescribed atmospheric forcing, and coupled atmosphere-ocean-ice models);
• Establishing specific key observational data requirements necessary for defining the initial ice-ocean state for seasonal sea ice predictions; and
• Providing access to observational data at fast turnaround times.
At longer (decadal and greater) timescales, the role of trends in external forcings (e.g., increasing greenhouse gases) and of factors that control the forcings is likely to provide some predictive potential because they account for increasingly large fractions of the change from present sea ice conditions. A critical point of uncertainty remains regarding the timescale at which a transition occurs between these two regimes, and there is likely to be an intermediate timescale for which the potential predictability is low. The primary challenge at these longer timescales is to improve the ability to simulate realistic forcings by the atmosphere and ocean using coupled climate models at decadal timescales, and to identify the model variable and/or processes that contribute to unrealistic simulations.
In light of these challenges and while recognizing that there are limitations in current modeling and observational techniques, the committee offers possible strategies to significantly enhance our understanding and predictions of the Arctic sea ice cover over seasonal to decadal timescales:
• A systematic evaluation of the existing seasonal prediction capabilities to establish baseline expectations for predictive power and to set the stage for advances in predictive capability;
• A highly coordinated and integrated process-based study of seasonal sea ice focused on understanding the impact of the increasing predominance of younger, first-year ice on sea ice predictions and offering an opportunity to identify, develop, and test instruments and observational platforms;
• Inform research investments related to observational needs (e.g., observation types, locations, and coverage) in support of sea ice modeling and prediction by conducting an organized set of model sensitivity studies.
• Enhance the capabilities of numerical models through a coordinated experiment with multiple models to (a)