Subseasonal Prediction of Regional Antarctic Sea Ice by a Deep Learning Model

doi:10.1029/2023GL104347

CORC > 海洋研究所 > 中国科学院海洋研究所 > 海洋环流与波动重点实验室

	Subseasonal Prediction of Regional Antarctic Sea Ice by a Deep Learning Model
	Wang, Yunhe 1; Yuan, Xiaojun 3; Ren, Yibin 1; Bushuk, Mitchell 4; Shu, Qi 2; Li, Cuihua 3; Li, Xiaofeng 1
刊名	GEOPHYSICAL RESEARCH LETTERS
	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.
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关键词	PREDICTABILITY ; FORECAST ; IMPACTS ; TRENDS
WOS研究方向	Geology
语种	英语
出版者	AMER GEOPHYSICAL UNION
WOS记录号	WOS:001058983400001
内容类型	期刊论文
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.Chinese Acad Sci, Inst Oceanol, CAS Key Lab Ocean Circulat & Waves, Qingdao, Peoples R China 2.Minist Nat Resources, Inst Oceanog 1, Qingdao, Peoples R China 3.Columbia Univ, Lamont Doherty Earth Observ, Palisades, NY 10964 USA 4.NOAA, Geophys Fluid Dynam Lab, Princeton, NJ USA
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.