| 基于高效统计模型预测9月北极航道沿线海冰范围 |
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| 引用本文： | 李莎,王牧音,黄文誉,徐世明,王斌,白玉琪.基于高效统计模型预测9月北极航道沿线海冰范围[J].海洋学报(英文版),2020,42(5):11-25. |
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| 作者姓名： | 李莎 王牧音 黄文誉 徐世明 王斌 白玉琪 |
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| 作者单位： | 地球系统数值模拟教育部重点实验室, 清华大学地球系统科学系, 北京, 100084;大气海洋联合研究所, 美国华盛顿大学, 西雅图, WA 98195;太平洋海洋环境实验室, 美国国家海洋与大气管理局, 西雅图, WA 98115;地球系统数值模拟教育部重点实验室, 清华大学地球系统科学系, 北京, 100084;大气科学和地球流体力学数值模拟国家重点实验室, 中国科学院大气物理研究所, 北京, 100029 |
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| 摘 要： | The rapid decrease in Arctic sea ice cover and thickness not only has a linkage with extreme weather in the midlatitudes 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 seaice 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.
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| 关 键 词： | 局地海冰 北极航道 机器学习 统计模型 预测 |
| 收稿时间： | 2019/8/2 0:00:00 |
Using a skillful statistical model to predict September sea ice covering Arctic shipping routes |
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| Li Sh,Wang Muyin,Huang Wenyu,Xu Shiming,Wang Bin,Bai Yuqi.Using a skillful statistical model to predict September sea ice covering Arctic shipping routes[J].Acta Oceanologica Sinica,2020,42(5):11-25. |
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| Authors: | Li Sh Wang Muyin Huang Wenyu Xu Shiming Wang Bin Bai Yuqi |
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| Institution: | Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China;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;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 |
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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. |
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| Keywords: | regional sea ice Arctic shipping routes machine learning statistical model predictions |
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