was well below the long-term (1979-2007) average, they all also overestimated it. This result suggests that although there is a skill in the seasonal forecasts relative to climatology (albeit a changing climatology), there remains the need for a concerted effort to improve sea ice predictions—the need that motivated the present report.

In the case of seasonal predictions, the existing capabilities in data-model synthesis have yet to be exploited in the Arctic because prediction capabilities at these timescales are relatively new and because the Arctic observational and modeling communities have tended to be distinct. Therefore, as a first step in designing OSEs and OSSEs, there is a particularly urgent need for a coordinated effort by these communities to design a set of experiments that will provide quantitative metrics of the impact of various observation types, locations, and densities on seasonal sea ice forecasts, as well as the accuracy and temporal resolution that are required.

Possible observational variables for inclusion in these experiments are ice thickness distributions, ice extent and concentration, snow on sea ice, and the upper-ocean profiles of temperature, salinity and current velocities. The latter category of observations includes ocean measurements not only from under the ice, but from the surrounding open ocean. In addition to these state variables, consideration should be given to measurements focused on the exchange of energy between the air-ice and ice-ocean boundaries, which drive ice dynamics and thermodynamics (e.g. radiation, sensible heat, moisture, and momentum) The OSEs and OSSEs would address impacts of measurement errors as well as varying distributions of measurements.

These types of experiments may be regarded as prerequisites for the design of an Arctic observing network (NRC, 2006), and seasonal sea ice prediction can provide a compelling focus for such experiments. Further, many of the variables listed above (e.g., ice thickness, snow on sea ice, and under-ice ocean profiles) are observational challenges in their own right. The logistics and expenses involved in obtaining these measurements adds to the urgency of OSEs and OSSEs to justify, for sea ice prediction and for other applications, future investments in the observations.

Key Strategy: Enhanced Numerical Model Capabilities

Enhancement of model-based predictive capabilities will require coordinated experiments to (a) identify which variables and processes are critical to simulating a realistic ice cover, (b) investigate the source of climate model drift, and (c) guide decisions regarding high-priority model development needs and the expansion of models to include additional capabilities and variables of interest to stakeholders.

Model intercomparison projects (MIPs), such as for the Arctic (AMIP2), the Arctic Ocean (AOMIP3), and sea ice (SIMIP4), have played an important role in identifying





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