Real-time predictions of the 2023–2024 climate conditions in the tropical Pacific using a purely data-driven Transformer model
作者机构：School of Marine Sciences Nanjing University of Information Science and Technology Laoshan Laboratory Key Laboratory of Ocean Observation and Forecasting Key Laboratory of Ocean Circulation and Waves Institute of OceanologyChinese Academy of Sciences University of Chinese Academy of Sciences
出 版 物：《Science China Earth Sciences》 (中国科学:地球科学(英文版))
年 卷 期：2024年
核心收录：
学科分类：07[理学] 070601[理学-气象学] 0706[理学-大气科学]
基 金：supported by the Laoshan Laboratory (Grant No. LSKJ202202402) the National Natural Science Foundation of China (Grant Nos. 42030410 & 42176032) the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDB40000000) the Startup Foundation for Introducing Talent of NUIST the Jiangsu Innovation Research Group (Grant No. JSSCTD202346)
摘 要：Following triple La Ni?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?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?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?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?o event.
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