中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 海洋研究所
  3. 中国科学院海洋研究所
  4. 海洋环流与波动重点实验室
SICNetseason V1.0: a transformer-based deep learning model for seasonal Arctic sea ice prediction by incorporating sea ice thickness data

文献类型：期刊论文

作者Ren, Yibin1,2,3; Li, Xiaofeng1,2,3; Wang, Yunhe1,2,3
刊名GEOSCIENTIFIC MODEL DEVELOPMENT
出版日期2025-05-14
卷号18期号:9页码:2665-2678
ISSN号1991-959X
DOI10.5194/gmd-18-2665-2025
通讯作者Li, Xiaofeng(lixf@qdio.ac.cn)
英文摘要The Arctic sea ice suffers dramatic retreat in summer and fall, which has far-reaching consequences for the global climate and commercial activities. Accurate seasonal sea ice predictions significantly infer climate change and are crucial for planning commercial activities. However, seasonal prediction of the summer sea ice encounters a significant obstacle known as the spring predictability barrier (SPB): predictions made later than the date of melt onset (roughly May) demonstrate good skill in predicting summer sea ice, while predictions made during or earlier than May exhibit considerably lower skill. This study develops a transformer-based deep learning model, SICNet(season) (V1.0), to predict the Arctic sea ice concentration on a seasonal scale. Including spring sea ice thickness (SIT) data in the model significantly improves the prediction skill at the SPB point. A 20-year (2000-2019) test demonstrates that the detrended anomaly correlation coefficient (ACC) of September sea ice extent (sea ice concentration >15 %) predicted by our model during May and April is improved by 7.7 % and 10.61 %, respectively, compared to the ACC predicted by the state-of-the-art dynamic model SEAS5 from the European Centre for Medium-Range Weather Forecasts (ECMWF). Compared with the anomaly persistence benchmark, the mentioned improvement is 41.02 % and 36.33 %. Our deep learning model significantly reduces prediction errors in terms of September's sea ice concentration on seasonal scales compared to SEAS5 and the anomaly persistence model (Persistence). The spring SIT data are key in optimizing the predictions around the SPB, contributing to an enhancement in ACC of more than 20 % in September's sea ice extent (SIE) for 4- to 5-month-lead predictions. Our model achieves good generalizability in predicting the September SIE of 2020-2023.
WOS关键词INITIAL CONDITIONS ; FORECAST SKILL ; PREDICTABILITY ; EXTENT ; AMPLIFICATION ; VARIABILITY ; SATELLITE ; ENSEMBLE ; SYSTEM ; DRIVEN
资助项目National Science Foundation of China[42206202] ; National Science Foundation of China[42221005] ; Laoshan Laboratory Innovation Project[LSKJ202202302] ; China-Portugal Xinghai Beltand Road[2022YFE0204600]
WOS研究方向Geology
语种英语
WOS记录号WOS:001487622400001
出版者COPERNICUS GESELLSCHAFT MBH
源URL[http://ir.qdio.ac.cn/handle/337002/202043]  
专题海洋研究所_海洋环流与波动重点实验室
通讯作者Li, Xiaofeng
作者单位1.Chinese Acad Sci, Inst Oceanol, Key Lab Ocean Circulat & Waves, Qingdao, Peoples R China
2.Qingdao Key Lab Artificial Intelligence Oceanog, Qingdao, Peoples R China
3.Chinese Acad Sci, Inst Oceanol, Key Lab Ocean Observat & Forecasting, Qingdao, Peoples R China
推荐引用方式
GB/T 7714		 Ren, Yibin,Li, Xiaofeng,Wang, Yunhe. SICNetseason V1.0: a transformer-based deep learning model for seasonal Arctic sea ice prediction by incorporating sea ice thickness data[J]. GEOSCIENTIFIC MODEL DEVELOPMENT,2025,18(9):2665-2678.
APA Ren, Yibin,Li, Xiaofeng,&Wang, Yunhe.(2025).SICNetseason V1.0: a transformer-based deep learning model for seasonal Arctic sea ice prediction by incorporating sea ice thickness data.GEOSCIENTIFIC MODEL DEVELOPMENT,18(9),2665-2678.
MLA Ren, Yibin,et al."SICNetseason V1.0: a transformer-based deep learning model for seasonal Arctic sea ice prediction by incorporating sea ice thickness data".GEOSCIENTIFIC MODEL DEVELOPMENT 18.9(2025):2665-2678.

入库方式： OAI收割

来源：海洋研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。