文献类型：期刊文献

英文题名：Improved seasonal predictions of the Western Pacific subtropical high by incorporating tropical Atlantic influences

作者：Gan, Qiuying[1,2,3];Leung, Jeremy Cheuk-Hin[3];Wang, Lei[4];Xu, Daosheng[3];Li, Weijing[5];Qian, Weihong[6];Dong, Wenjie[1,2];Zhang, Banglin[3,7,8]

机构：[1]Sun Yat Sen Univ, Sch Atmospher Sci, Zhuhai, Peoples R China;[2]Sun Yat Sen Univ, Key Lab Trop Atmosphere Ocean Syst, Southern Marine Sci & Engn Guangdong Lab Zhuhai, Minist Educ, Zhuhai, Peoples R China;[3]Hunan Inst Adv Technol, Changsha, Peoples R China;[4]Guangdong Ocean Univ, Coll Ocean & Meteorol, Lab Coastal Ocean Variat & Disaster Predict, Zhanjiang, Peoples R China;[5]China Meteorol Adm, Natl Climate Ctr, Beijing, Peoples R China;[6]Peking Univ, Dept Atmospher & Ocean Sci, Beijing, Peoples R China;[7]Lanzhou Univ, Coll Atmospher Sci, Lanzhou, Peoples R China;[8]China Meteorol Adm, Key Lab High Impact Weather Special, Changsha, Peoples R China

年份：2026

卷号：8

期号：1

外文期刊名：ENVIRONMENTAL RESEARCH COMMUNICATIONS

收录：SCI-EXPANDED(收录号：WOS:001661461100001)、、Scopus(收录号：2-s2.0-105027687061)、WOS

基金：This work is funded by the Project supported by Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) (311024009), National Natural Science Foundation of China (42261144687,U21A6001, 42175173, 42405038, and 42575021), the Innovative Team Plan for the Department of Education of Guangdong Province (2023KCXTD015), Guangdong Basic and Applied Basic Research Foundation (2023A1515240036), and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) (SML2024SP011).

语种：英文

外文关键词：Western Pacific subtropical high; seasonal prediction; tropical Atlantic influences; springtime tropical Atlantic SSTA

外文摘要：The Western Pacific Subtropical High (WPSH) significantly influences East Asian weather and air pollution. Previous studies have indicated that tropical sea surface temperature anomalies (SSTAs) are important indicators of WPSH intensity. While current seasonal predictions rely on spring predictors from the Indo-Pacific and North Atlantic, the growing influence of the tropical Atlantic (TA) has been overlooked. This study introduces springtime TA SSTA as a new predictor into a Long Short-Term Memory (LSTM) model, alongside established predictors, for seasonal prediction of WPSH. Across the WPSH prediction models using 8-predictor combinations, incorporating TA SSTA consistently improved WPSH prediction accuracy. The optimal prediction model, which combines TA SSTA with three established predictors, exhibits stable and high predictive skill and achieves an 18.0% increase in explained variance (R2). Our results highlight the critical role of springtime TA SSTA for predicting summertime WPSH intensity, establishing a promising way for extreme weather and pollution event predictions.