In recent decades, warming temperatures have caused sharp reductions in the volume of sea ice in the Arctic Ocean. Predicting changes in Arctic sea ice thickness is vital in a changing Arctic for making decisions about shipping and resource management in the region. We propose a statistical spatio-temporal two-stage model for sea ice thickness and use it to generate probabilistic forecasts up to three months into the future. Our approach combines a contour model to predict the ice-covered region with a Gaussian random field to model ice thickness conditional on the ice-covered region. Using the most complete estimates of sea ice thickness currently available, we apply our method to forecast Arctic sea ice thickness. Point predictions and prediction intervals from our model offer comparable accuracy and improved calibration compared with existing forecasts. We show that existing forecasts produced by ensembles of deterministic dynamic models can have large errors and poor calibration. We also show that our statistical model can generate good forecasts of aggregate quantities such as overall and regional sea ice volume. Supplementary materials accompanying this paper appear on-line.

近几十年来，气温升高导致北冰洋海冰体积急剧减少。在不断变化的北极中，预测北极海冰厚度的变化对于制定该地区的航运和资源管理至关重要。我们提出了海冰厚度的统计时空两阶段模型，并使用它来生成未来三个月的概率预测。我们的方法结合了一个轮廓模型来预测冰覆盖区域和一个高斯随机场来模拟冰覆盖区域的冰厚度条件。使用目前可用的最完整的海冰厚度估计，我们应用我们的方法来预测北极海冰厚度。与现有预测相比，我们模型的点预测和预测区间提供了相当的准确性和改进的校准。我们表明，由确定性动态模型的集合产生的现有预测可能具有较大的误差和较差的校准。我们还表明，我们的统计模型可以很好地预测总量，例如整体和区域海冰量。本文随附的补充材料已在线发布。