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