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.

中文翻译：

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替代海冰模型可实现高效调整

预测海冰覆盖的变化对于航运、生态系统监测和气候建模至关重要。然而，目前的海冰模型预测的冰比北极多，南极少。改善这些基于物理的模型对观测的拟合具有挑战性，因为这些模型运行成本高，因此优化成本高。在这里，我们构建了一个机器学习代理，它模拟了模型物理变化对洛斯阿拉莫斯海冰模型 (CICE) 海冰面积预测的影响。我们使用代理模型来研究 CICE 对控制参数变化的敏感性：冰的脊和反照率；雪的反照率、老化和热导率；融水对反照率的影响；以及池塘对反照率的影响。我们发现 CICE' 不同半球对这些模型参数的敏感性不同。我们建议未来的海冰建模者在季节性和半球间的基础上分离雪电导率和雪粒度分布，我们建议这些参数的最佳值。这将使制作更接近北极和南极海冰观测的模型成为可能。这些结果表明，领先的海冰模型行为的重要方面可以通过相对简单的支持向量回归替代模型来捕获，并且这种替代模型极大地增加了调整完整模拟的难度。我们推荐这些参数的最佳值。这将使制作更接近北极和南极海冰观测的模型成为可能。这些结果表明，领先的海冰模型行为的重要方面可以通过相对简单的支持向量回归替代模型来捕获，并且这种替代模型极大地增加了调整完整模拟的难度。我们推荐这些参数的最佳值。这将使制作更接近北极和南极海冰观测的模型成为可能。这些结果表明，领先的海冰模型行为的重要方面可以通过相对简单的支持向量回归替代模型来捕获，并且这种替代模型极大地增加了调整完整模拟的难度。