Using a skillful statistical model to predict September sea ice covering Arctic shipping routes

Sha Li; Muyin Wang; Wenyu Huang; Shiming Xu; Bin Wang; Yuqi Bai

doi:10.1007/s13131-020-1595-z

Volume 39 Issue 5

May 2020

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Sha Li, Muyin Wang, Wenyu Huang, Shiming Xu, Bin Wang, Yuqi Bai. Using a skillful statistical model to predict September sea ice covering Arctic shipping routes[J]. Acta Oceanologica Sinica, 2020, 39(5): 11-25. doi: 10.1007/s13131-020-1595-z

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Sha Li, Muyin Wang, Wenyu Huang, Shiming Xu, Bin Wang, Yuqi Bai. Using a skillful statistical model to predict September sea ice covering Arctic shipping routes[J]. Acta Oceanologica Sinica, 2020, 39(5): 11-25. doi: 10.1007/s13131-020-1595-z

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Using a skillful statistical model to predict September sea ice covering Arctic shipping routes

doi: 10.1007/s13131-020-1595-z

Sha Li¹,
Muyin Wang^{2, 3},
Wenyu Huang¹,
Shiming Xu¹,
Bin Wang^{1, 4},
Yuqi Bai^{1
,
,}

1.
Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
2.
Joint Institute for the Study of the Atmosphere and Ocean, University of Washington, Seattle WA, 98195, USA
3.
Pacific Marine Environmental Laboratory, National Oceanic and Atmospheric Administration, Seattle WA, 98115, USA
4.
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

Funds: The National Key Research and Development Program of China under contract Nos 2016YFF0202705 and 2018YFA0605904; the Joint Institute for the Study of the Atmosphere and Ocean (JISAO) under contract NOAA Cooperative Agreement NA15OAR4320063, contribution No. 2019-1044, and PMEL contribution No. 5052.

More Information

Corresponding author: Email: yuqibai@tsinghua.edu.cn
Received Date: 2019-08-02
Accepted Date: 2019-12-25

Available Online: 2020-12-28

Publish Date: 2020-05-25

Abstract

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.
- regional sea ice,
- Arctic shipping routes,
- machine learning,
- statistical model,
- predictions

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References(71)

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