Surrogate sea ice model enables efficient tuning

Abstract

Predicting changes in sea ice cover is critical for shipping, ecosystem monitoring, and climate modeling. Current sea ice models, however, predict more ice than is observed in the Arctic, and less in the Antarctic. Improving the fit of these physics-based models to observations is challenging because the models are expensive to run, and therefore expensive to optimize. Here, we construct a machine learning surrogate that emulates the effect of changing model 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 to changes in the parameters governing: ice's ridging and albedo; snow's albedo, aging, and thermal conductivity; the effect of meltwater on albedo; and the effect of ponds on albedo. We find that CICE's sensitivity to these model parameters differs between hemispheres. We propose that future sea ice modelers separate the snow conductivity and snow grain size distributions on a seasonal and inter-hemispheric basis, and we recommend optimal values of these parameters. This will make it possible to make models that fit observations of both Arctic and Antarctic sea ice more closely. These results demonstrate that important aspects of the behavior of a leading sea ice model can be captured by a relatively simple support vector regression surrogate model, and that this surrogate dramatically increases the ease of tuning the full simulation.