| Li Sha,Wang Muyin,Huang Wenyu,Xu Shiming,Wang Bin,Bai Yuqi. 2020. Using a skillful statistical model to predict September sea ice covering Arctic shipping routes. Acta Oceanologica Sinica, 42(5):11－25 |
| Using a skillful statistical model to predict September sea ice covering Arctic shipping routes |
| 基于高效统计模型预测9月北极航道沿线海冰范围 |
| Received:August 02, 2019 |
| DOI：10.1007/s13131-020-1595-z |
| Key words:regional sea ice Arctic shipping routes machine learning statistical model predictions |
| 中文关键词: 局地海冰 北极航道 机器学习 统计模型 预测 |
| 基金项目: |
| Author Name | Affiliation | E-mail | | Li Sha | Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China | | | Wang Muyin | Joint Institute for the Study of the Atmosphere and Ocean, University of Washington, Seattle WA, 98195, USA
Pacific Marine Environmental Laboratory, National Oceanic and Atmospheric Administration, Seattle WA, 98115, USA | | | Huang Wenyu | Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China | | | Xu Shiming | Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China | | | Wang Bin | Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China | | | Bai Yuqi | Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China | yuqibai@tsinghua.edu.cn |
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| Abstract: |
| The rapid decrease in Arctic sea ice cover and thickness not only has a linkage with extreme weather in the mid-latitudes but also brings more opportunities for Arctic shipping routes and polar resource exploration, both of which motivate us to further understand causes of sea-ice variations and to obtain more accurate estimates of sea-ice cover in the future. Here, a novel data-driven method, the causal effect networks algorithm, is applied to identify the direct precursors of September sea-ice extent covering the Northern Sea Route and Transpolar Sea Route at different lead times so that statistical models can be constructed for sea-ice prediction. The whole study area was also divided into two parts: the northern region covered by multiyear ice and the southern region covered by seasonal ice. The forecast models of September sea-ice extent in the whole study area (TSIE) and southern region (SSIE) at lead times of 1-4 months can explain over 65% and 79% of the variances, respectively, but the forecast skill of sea-ice extent in the northern region (NSIE) is limited at a lead time of 1 month. At lead times of 1-4 months, local sea-ice concentration and sea-ice thickness have a larger influence on September TSIE and SSIE than other teleconnection factors. When the lead time is more than 4 months, the surface meridional wind anomaly from northern Europe in the preceding autumn or early winter is dominant for September TSIE variations but is comparable to thermodynamic factors for NSIE and SSIE. We suggest that this study provides a complementary approach for predicting regional sea ice and is helpful in evaluating and improving climate models. |
| 中文摘要: |
| 北极海冰覆盖范围及海冰厚度的快速衰减不仅与中纬度极端天气事件密切相关，而且也为北极航道的开通和极地资源的探测提供了更多机遇，这驱使我们进一步了解海冰变化的成因，并更准确地预测未来海冰的变化。本研究采用了一种新颖的数据驱动方法，即因果影响网络算法，以识别出覆盖于东北航道和中央航道部分地区的9月北极海冰范围的直接影响因子。基于这些影响因子可构建不同超前时间情境下的海冰统计预测模型。整个研究区域也被划分为两部分：以常年冰覆盖的北部地区以及以季节性海冰覆盖的南部地区。超前1~4个月时，统计模型分别可以解释9月整个研究区域和南部地区海冰变化方差的65%和79%，但模型对北部地区海冰的有效预测能力仅限制在超前1个月。超前1~4个月时，局地海冰密集度和海冰厚度因子对9月整个研究区域和南部地区海冰范围变化的作用大于其他遥相关因子。当超前时间大于4个月时，前年秋季或早冬时期来自北欧的近地面经向风异常对9月整个研究区域的海冰范围变化起主导作用，但对于北部地区和南部地区而言，该因子与其他热动力因子的作用基本相当。本文认为该研究结果可作为局地北极海冰预测的一种补充性方法，同时也有助于气候模式的评估与改进。 |
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