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Real-time predictions of the 2023-2024 climate conditions in the tropical Pacific using a purely data-driven Transformer model

文献类型：期刊论文

作者Zhang, Rong-Hua1,3; Zhou, Lu2,4; Gao, Chuan2,3; Tao, Lingjiang1
刊名SCIENCE CHINA-EARTH SCIENCES
出版日期2024-10-09
页码18
关键词Transformer model 3D-Geoformer Coupling representation The 2023-2024 El Ni & ntilde Real-time prediction Performance and evaluation o
ISSN号1674-7313
DOI10.1007/s11430-024-1396-x
通讯作者Zhang, Rong-Hua(rzhang@nuist.edu.cn) ; Gao, Chuan(gaochuan@qdio.ac.cn)
英文摘要Following triple La Ni & ntilde;a events during 2020-2022, the future evolution of climate conditions over the tropical Pacific has been a focused interest in ENSO-related communities. Observations and modeling studies indicate that an El Ni & ntilde;o event is occurring in 2023; however, large uncertainties remain in terms of its detailed evolution, and the factors affecting its resultant amplitude remain to be understood. Here, a novel deep learning-based Transformer model is adopted to make real-time predictions for the 2023-2024 climate conditions in the tropical Pacific. Several key fields vital to the El Ni & ntilde;o and Southern Oscillation (ENSO) in the tropical Pacific are collectively and simultaneously utilized in model training and in making predictions; therefore, this purely data-driven model is configured in both training and predicting procedures such that the coupled ocean-atmosphere interactions are adequately represented. Also similar to dynamic models, the prediction procedure is executed in a rolling manner to allow ocean-atmosphere anomaly exchanges month by month; the related key fields during multi-month time intervals (TIs) prior to prediction target months are taken as input predictors, serving as initial conditions to precondition the future evolution more effectively. Real-time predictions indicate that the climate conditions in the tropical Pacific are surely to develop into an El Ni & ntilde;o state in late 2023. Furthermore, sensitivity experiments are conducted to examine how prediction skills are affected by the input predictor specifications, including TIs during which information on initial conditions is retained for making predictions. A comparison with other dynamic coupled models is also made to demonstrate the prediction performance for the 2023-2024 El Ni & ntilde;o event.
WOS关键词INTERMEDIATE COUPLED MODEL ; EL-NINO ; EQUATORIAL PACIFIC ; ENSO ; OCEAN ; PREDICTABILITY ; REANALYSIS ; FORECASTS
资助项目Laoshan Laboratory[LSKJ202202402] ; National Natural Science Foundation of China[42030410] ; National Natural Science Foundation of China[42176032] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDB40000000] ; Startup Foundation for Introducing Talent of NUIST ; Jiangsu Innovation Research Group[JSSCTD202346]
WOS研究方向Geology
语种英语
WOS记录号WOS:001335893900003
出版者SCIENCE PRESS
源URL[http://ir.qdio.ac.cn/handle/337002/199492]  
专题海洋研究所_海洋环流与波动重点实验室
通讯作者Zhang, Rong-Hua; Gao, Chuan
作者单位1.Nanjing Univ Informat Sci & Technol, Sch Marine Sci, Nanjing 210044, Peoples R China
2.Chinese Acad Sci, Inst Oceanol, Key Lab Ocean Observat & Forecasting, Key Lab Ocean Circulat & Waves, Qingdao 266071, Peoples R China
3.Laoshan Lab, Qingdao 266237, Peoples R China
4.Univ Chinese Acad Sci, Beijing 101408, Peoples R China
推荐引用方式
GB/T 7714		 Zhang, Rong-Hua,Zhou, Lu,Gao, Chuan,et al. Real-time predictions of the 2023-2024 climate conditions in the tropical Pacific using a purely data-driven Transformer model[J]. SCIENCE CHINA-EARTH SCIENCES,2024:18.
APA Zhang, Rong-Hua,Zhou, Lu,Gao, Chuan,&Tao, Lingjiang.(2024).Real-time predictions of the 2023-2024 climate conditions in the tropical Pacific using a purely data-driven Transformer model.SCIENCE CHINA-EARTH SCIENCES,18.
MLA Zhang, Rong-Hua,et al."Real-time predictions of the 2023-2024 climate conditions in the tropical Pacific using a purely data-driven Transformer model".SCIENCE CHINA-EARTH SCIENCES (2024):18.

入库方式： OAI收割

来源：海洋研究所

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