3.2 Improve models for understanding sea ice processes and for enhanced forecasting and prediction of sea ice behavior at a range of spatial and temporal scales.
Numerical models are essential tools that complement observations for understanding sea ice processes (e.g., motion and deformation of the ice cover, ice topography and snow depth, and melt ponds that influence the ice thickness distribution). Process models and understanding, in turn, inform the representation of sea ice processes and air-ice-ocean interactions: in large-scale coupled models such as operational models that focus on providing forecasts at short time scales (hourly, daily, weekly); and in Arctic System models used for research to predict the state of the ice over long time scales (seasonal, annual, decadal). No single agency is responsible for sea ice process modeling, operational forecasting, and Arctic System modeling, so Collaborations offers a forum for bringing together multiple agencies and the sea ice research community. ’s implementation structure supports cooperation in improving sea ice process models and large-scale model physics to quantify uncertainty and enhance prediction capability at a range of spatial and temporal scales.
Performance elements from the Arctic research plan
3.2.1 Support investigator-driven modeling studies designed to understand and parameterize key sea ice properties and processes, including ice thickness distribution, topography, and strength; ice motion, deformation and mechanics; snow depth distribution and melt pond characteristics; surface albedo and energy balance; and biogeochemistry.
3.2.2 Enhance operational sea ice forecasting and research-oriented prediction capabilities through improvements to model physics (explicit and parameterized); initialization techniques; assimilation of observations, model evaluation and verification; evaluation of model skill, post-processing techniques and forecast guidance tools used in operational forecasts and decision support.