Subseasonal Prediction of Regional Antarctic Sea Ice by a Deep Learning Model
文献类型：期刊论文
| 作者 | Wang, Yunhe4 ; Yuan, Xiaojun1; Ren, Yibin4; Bushuk, Mitchell3; Shu, Qi2; Li, Cuihua1; Li, Xiaofeng4
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| 刊名 | GEOPHYSICAL RESEARCH LETTERS
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| 出版日期 | 2023-09-16 |
| 卷号 | 50期号:17页码:10 |
| 关键词 | Antarctic sea ice prediction |
| ISSN号 | 0094-8276 |
| DOI | 10.1029/2023GL104347 |
| 通讯作者 | Yuan, Xiaojun(xyuan@ldeo.columbia.edu) ; Li, Xiaofeng(lixf@qdio.ac.cn) |
| 英文摘要 | Antarctic sea ice concentration (SIC) prediction at seasonal scale has been documented, but a gap remains at subseasonal scale (1-8 weeks) due to limited understanding of ice-related physical mechanisms. To overcome this limitation, we developed a deep learning model named Sea Ice Prediction Network (SIPNet) that can predict SIC without the need to account for complex physical processes. Compared to mainstream dynamical models like European Centre for Medium-Range Weather Forecasts, National Centers for Environmental Prediction, and Seamless System for Prediction and Earth System Research developed at Geophysical Fluid Dynamics Laboratory, as well as a relatively advanced statistical model like the linear Markov model, SIPNet outperforms them all, effectively filling the gap in subseasonal Antarctic SIC prediction capability. SIPNet results indicate that autumn SIC variability contributes the most to sea ice predictability, whereas spring contributes the least. In addition, the Weddell Sea displays the highest sea ice predictability, while predictability is low in the West Pacific. SIPNet can also capture the signal of ENSO and SAM on sea ice. |
| WOS关键词 | PREDICTABILITY ; FORECAST ; IMPACTS ; TRENDS |
| 资助项目 | This work is supported by the Natural Science Foundation of Shandong Province, China (ZR2021QD059); National Natural Science Foundation of China (42106223 and 42206202); China Postdoctoral Science Foundation (2020TQ0322); and Strategic Priority Research Pr[42106223] ; Natural Science Foundation of Shandong Province, China[42206202] ; Natural Science Foundation of Shandong Province, China[2020TQ0322] ; National Natural Science Foundation of China[XDB42000000] ; China Postdoctoral Science Foundation ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Lamont-Doherty Earth Observatory of Columbia University ; [ZR2021QD059] |
| WOS研究方向 | Geology |
| 语种 | 英语 |
| WOS记录号 | WOS:001058983400001 |
| 出版者 | AMER GEOPHYSICAL UNION |
| 源URL | [http://ir.qdio.ac.cn/handle/337002/182936]  |
| 专题 | 海洋研究所_海洋环流与波动重点实验室 |
| 通讯作者 | Yuan, Xiaojun; Li, Xiaofeng |
| 作者单位 | 1.Columbia Univ, Lamont Doherty Earth Observ, Palisades, NY 10964 USA 2.Minist Nat Resources, Inst Oceanog 1, Qingdao, Peoples R China 3.NOAA, Geophys Fluid Dynam Lab, Princeton, NJ USA 4.Chinese Acad Sci, Inst Oceanol, CAS Key Lab Ocean Circulat & Waves, Qingdao, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Yunhe,Yuan, Xiaojun,Ren, Yibin,et al. Subseasonal Prediction of Regional Antarctic Sea Ice by a Deep Learning Model[J]. GEOPHYSICAL RESEARCH LETTERS,2023,50(17):10. |
| APA | Wang, Yunhe.,Yuan, Xiaojun.,Ren, Yibin.,Bushuk, Mitchell.,Shu, Qi.,...&Li, Xiaofeng.(2023).Subseasonal Prediction of Regional Antarctic Sea Ice by a Deep Learning Model.GEOPHYSICAL RESEARCH LETTERS,50(17),10. |
| MLA | Wang, Yunhe,et al."Subseasonal Prediction of Regional Antarctic Sea Ice by a Deep Learning Model".GEOPHYSICAL RESEARCH LETTERS 50.17(2023):10. |
入库方式： OAI收割
来源：海洋研究所
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