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Publication Date
1 October 2014

Sea Ice Volume and Age: Sensitivity to physical parameterizations and thickness resolution in the CICE sea ice model

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New dynamics parameterizations in Version 5 of the Los Alamos Sea Ice Model, CICE, feature an anisotropic rheology and variable drag coefficients. This study investigates their effect on Arctic sea ice volume and age simulations, along with the effects of several pre-existing model options: a parameter that represents the mean cumulative area of ice participating in ridging, the resolution of the ice thickness distribution, and the resolution of the vertical temperature and salinity profiles.

By increasing shear stress between floes, the anisotropic rheology slows the ice motion, producing a thicker, older ice pack. The inclusion of variable drag coefficients, which depend on modeled roughness elements such as deformed ice and melt pond edges, leads to thinner ice and a more realistic simulation of sea ice age. Several feedback processes act to enhance differences among the runs. Notably, if less open water is produced mechanically through ice deformational processes, the simulated ice thins relative to runs with more mechanically produced open water. Thermodynamic processes can have opposing effects on ice age and volume; for instance, growth of new ice increases the volume while decreasing the age of the pack. Therefore, age data provides additional information useful for differentiating among process parameterization effects and sensitivities to other model parameters.

Resolution of thicker ice types is crucial for proper modeling of sea ice volume, because the volume of ice in the thicker ice categories determines the total ice volume. Model thickness categories tend to focus resolution for thinner ice; this paper demonstrates that 5 ice thickness categories are not enough to accurately resolve the ice thickness distribution for simulations of ice volume.

“Sea Ice Volume And Age: Sensitivity To Physical Parameterizations And Thickness Resolution In The Cice Sea Ice Model”. 2014. Ocean Modelling. doi:10.1016/j.ocemod.2014.08.001.
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