Over the last five decades, Arctic sea ice has been shrinking in area and thickness. As a result, increased marine traffic has created a need for improved sea ice forecasting on seasonal to annual time-scales. In this study, we introduce a novel UNET-based deep learning model to forecast sea ice concentration up to 12 months. Based on yearly hindcast validation, the UNET 3-, 6-, 9-, and 12-month predictions provided more accurate and stable predictions than did the four baseline models: the Copernicus Climate Change Service (C3S), the damped anomaly persistence (DP) forecast, and two deep learning approach, the Convolutional Neural Network (CNN) models and Convolutional Long Short-Term Memory (ConvLSTM). During years with large departures from the long-term trend, the proposed UNET model exhibited promising SIC prediction results with root-mean-square errors (RMSEs), which were reduced from 17.35 to 7.07 % compared to the four baseline models. Our findings also confirmed the relative importance of each predictor variable (temperature, incoming solar radiation, wind speed and direction) in long-term prediction. Past SIC conditions, together with surface temperature emerged as the most important factors for SIC prediction, especially in the marginal ice zone. Incoming solar radiation and wind speed and direction showed greater sensitivity in predicting SICs in areas with thin ice. This model offers the potential to shape Arctic development and management plans and strategies, ensuring extended forecasting periods and enhanced prediction accuracy.

中文翻译：

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使用深度学习长期预测北极海冰浓度：表面温度、辐射和风况的影响


在过去的五十年里，北极海冰的面积和厚度一直在缩小。因此，海上交通量的增加产生了在季节性到年度时间尺度上改进海冰预报的需求。在这项研究中，我们引入了一种基于 UNET 的新型深度学习模型来预测长达 12 个月的海冰浓度。基于年度后报验证，UNET 3 个月、6 个月、9 个月和 12 个月的预测提供了比四个基线模型更准确和稳定的预测：哥白尼气候变化服务 （C3S）、阻尼异常持续性 （DP） 预测和两种深度学习方法，即卷积神经网络 （CNN） 模型和卷积长短期记忆 （ConvLSTM）。在与长期趋势大相径庭的年份中，所提出的 UNET 模型表现出有希望的 SIC 预测结果，均方根误差 （RMSEs），与四个基线模型相比，误差从 17.35% 降低到 7.07%。我们的研究结果还证实了每个预测变量（温度、入射太阳辐射、风速和风向）在长期预测中的相对重要性。过去的 SIC 条件以及地表温度成为预测 SIC 的最重要因素，尤其是在边缘冰区。入射太阳辐射以及风速和风向在预测薄冰地区的 SIC 方面表现出更高的敏感性。该模型为塑造北极开发和管理计划及战略提供了潜力，确保延长预报期并提高预测准确性。