Abstract
In polar regions, the exchange of heat, fresh water and salt water, and momentum between ocean and atmosphere is strongly affected by the presence of sea-ice cover. As a growing number of climate models include a dynamic-thermodynamic sea-ice component to take these effects into account, it might be asked whether sea ice is adequately represented in these simulations, and how far these simulations fit with physical observations.Sea ice in the classical models (Hibler, 1979; Parkinson and Washington, 1979) that have been available for two decades, is regarded as a two-dimensional (2-D) continuum covering the ocean surface. The prognostic variables describing the ice pack are horizontal ice velocity, areal coverage (ice concentration), and ice thickness. In numerical models, these variables and their evolution in space and time are solved on an Enlerian grid.A number of observational data are available to verify the model results. Sea-ice drift is observed from drifting buoys deployed on ice floes. Areal sea-ice coverage can be observed with satellite-borne passive-microwave sensors (SMMR, SSM/I). For ice thickness, which cannot be observed with remote-sensing techniques, rather few, scattered observations from upward-looking sonars on submarines and moorings are available.This article gives an overview of three additional variables representing sea ice in large-scale climate models. These are (1) roughness, (2) age of the ice. introduced as two prognostic variables, and (3) simulated trajectories of ice motion, which are diagnosed from the Enlerian velocity grid. The new variables enable a more detailed look at sea ice in models, helping to understand better the coupled dynami-thermodynamic processes determining the polar ice cover. Further, the new variables offer important, additional possibilities for comparing the simulated sea-ice properties with available observations.
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