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Predicting changes in sea ice cover is critical for shipping, ecosystemmonitoring, and climate modeling. Current sea ice models, however, predict moreice than is observed in the Arctic, and less in the Antarctic. Improving thefit of these physics-based models to observations is challenging because themodels are expensive to run, and therefore expensive to optimize. Here, weconstruct a machine learning surrogate that emulates the effect of changingmodel physics on forecasts of sea ice area from the Los Alamos Sea Ice Model(CICE). We use the surrogate model to investigate the sensitivity of CICE tochanges in the parameters governing: ice's ridging and albedo; snow's albedo,aging, and thermal conductivity; the effect of meltwater on albedo; and theeffect of ponds on albedo. We find that CICE's sensitivity to these modelparameters differs between hemispheres. We propose that future sea ice modelersseparate the snow conductivity and snow grain size distributions on a seasonaland inter-hemispheric basis, and we recommend optimal values of theseparameters. This will make it possible to make models that fit observations ofboth Arctic and Antarctic sea ice more closely. These results demonstrate thatimportant aspects of the behavior of a leading sea ice model can be captured bya relatively simple support vector regression surrogate model, and that thissurrogate dramatically increases the ease of tuning the full simulation.

Surrogate sea ice model enables efficient tuning