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The convolutional neural network for Pacific decadal oscillation forecast

文献类型：期刊论文

作者Skanupong, Nutta2,3,4; Xu, Yongsheng1,2,3,4,8; Yu, Lejiang7; Wan, Zhang5; Wang, Shuo6
刊名ENVIRONMENTAL RESEARCH LETTERS
出版日期2024-12-01
卷号19期号:12页码:11
关键词convolutional neural network (CNN) machine learning (ML) pacific decadal oscillation (PDO) sea surface temperature (SST)
ISSN号1748-9326
DOI10.1088/1748-9326/ad8be2
通讯作者Xu, Yongsheng(yongsheng.xu@alumni.caltech.edu)
英文摘要The Pacific decadal oscillation (PDO) is often described as a long-lived El Nino-like pattern of Pacific climate variability, and it has widespread climate and ecosystem impacts. PDO forecasts can provide useful information for policymakers on how to handle PDO impacts. Nevertheless, due to the long duration of the PDO cycles and their complex formation mechanisms, it remains a challenge to predict long lead time PDO. In this paper, we propose a transfer-learning-enhanced convolutional neural network (CNN) to tackle complex ocean dynamic forecasting and predict PDO events with up to a one-year lead time. Our method first trains the CNN on historical simulations from Coupled Model Intercomparison Project 6 (CMIP6), covering the period from 1850 to 1972. This prior knowledge is then refined by further training the model with observational data from 1854 to 1972, ensuring robust performance on unseen data. Additionally, k-fold cross-validation is also employed to evaluate the model's performance across diverse subsets of data, enhancing its reliability. Throughout the testing phase from 1983 to 2022, the CNN model consistently outperforms existing dynamical forecast systems, exhibiting superior correlation skills in predicting annual mean PDO indices and PDO phases, including displaying resilience to seasonal variations. The transferred CNN is thus a powerful method to predict PDO events and is potentially valuable for a wide range of applications. This work directly supports the objectives of the World Climate Research Programme Grand Challenge on Climate Prediction.
WOS关键词SEA-SURFACE TEMPERATURE ; NORTH PACIFIC ; MODEL ; VARIABILITY
资助项目Strategic Priority Research Program of the Chinese Academy of Sciences[LSKJ202201406-2] ; Laoshan Laboratory science and technology innovation projects[U22A20587] ; NSFC-Shandong Joint Fund Key Project[41906027] ; National Natural Science Foundation of China
WOS研究方向Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences
语种英语
WOS记录号WOS:001350947800001
出版者IOP Publishing Ltd
源URL[http://ir.qdio.ac.cn/handle/337002/199417]  
专题海洋研究所_海洋环流与波动重点实验室
通讯作者Xu, Yongsheng
作者单位1.Laoshan Lab, Lab Ocean & Climate Dynam, Qingdao, Peoples R China
2.Univ Chinese Acad Sci, Beijing, Peoples R China
3.Chinese Acad Sci, Inst Oceanol, Key Lab Ocean Circulat & Waves, Qingdao, Peoples R China
4.Chinese Acad Sci, Inst Oceanol, Key Lab Ocean Observat & Forecasting, Qingdao, Peoples R China
5.Univ Manchester, Dept Comp Sci, Manchester, England
6.Univ Birmingham, Sch Comp Sci, Birmingham, England
7.Polar Res Inst China, MNR Key Lab Polar Sci, Shanghai, Peoples R China
8.Chinese Acad Sci, Ctr Ocean Megasci, Qingdao, Peoples R China
推荐引用方式
GB/T 7714		 Skanupong, Nutta,Xu, Yongsheng,Yu, Lejiang,et al. The convolutional neural network for Pacific decadal oscillation forecast[J]. ENVIRONMENTAL RESEARCH LETTERS,2024,19(12):11.
APA Skanupong, Nutta,Xu, Yongsheng,Yu, Lejiang,Wan, Zhang,&Wang, Shuo.(2024).The convolutional neural network for Pacific decadal oscillation forecast.ENVIRONMENTAL RESEARCH LETTERS,19(12),11.
MLA Skanupong, Nutta,et al."The convolutional neural network for Pacific decadal oscillation forecast".ENVIRONMENTAL RESEARCH LETTERS 19.12(2024):11.

入库方式： OAI收割

来源：海洋研究所

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