{"title":"欧亚北极沿岸海冰消失加剧了与印度洋变暖相关的梅雨-白雨季强降雨","authors":"Xiaodan Chen, Zhiping Wen, Jiping Liu, Wei Mei, Ruonan Zhang, Sihua Huang, Yuanyuan Guo, Juncong Li","doi":"10.1038/s41612-024-00770-7","DOIUrl":null,"url":null,"abstract":"Heavy Meiyu-Baiu rainfall can pose threat to the dense population in East Asia by catastrophic flooding. Although previous studies have identified Indian Ocean (IO) warming as the major cause of heavy Meiyu-Baiu rainfall, it failed to predict the record-breaking rainfall in July 2020. Synthesizing observational analysis, large-ensemble climate simulations, and atmospheric simulations, we show that sea-ice loss in the Kara Sea in May can intensify the IO warming-induced heavy Meiyu-Baiu rainfall and well explains the record-breaking rainfall in July 2020. In the precondition of IO warming, sea-ice loss tends to prolong Meiyu-Baiu season and strengthen convective activity over the Meiyu-Baiu region, thereby enhancing the IO warming-induced heavy Meiyu-Baiu rainfall by ~50% and doubling the risk of extreme events comparable to or greater than the one in 2020. A statistical model is further constructed to demonstrate that taking Arctic sea ice into consideration can significantly improve the seasonal prediction of extreme Meiyu-Baiu rainfall.","PeriodicalId":19438,"journal":{"name":"npj Climate and Atmospheric Science","volume":" ","pages":"1-12"},"PeriodicalIF":8.5000,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41612-024-00770-7.pdf","citationCount":"0","resultStr":"{\"title\":\"Sea-ice loss in Eurasian Arctic coast intensifies heavy Meiyu-Baiu rainfall associated with Indian Ocean warming\",\"authors\":\"Xiaodan Chen, Zhiping Wen, Jiping Liu, Wei Mei, Ruonan Zhang, Sihua Huang, Yuanyuan Guo, Juncong Li\",\"doi\":\"10.1038/s41612-024-00770-7\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Heavy Meiyu-Baiu rainfall can pose threat to the dense population in East Asia by catastrophic flooding. Although previous studies have identified Indian Ocean (IO) warming as the major cause of heavy Meiyu-Baiu rainfall, it failed to predict the record-breaking rainfall in July 2020. Synthesizing observational analysis, large-ensemble climate simulations, and atmospheric simulations, we show that sea-ice loss in the Kara Sea in May can intensify the IO warming-induced heavy Meiyu-Baiu rainfall and well explains the record-breaking rainfall in July 2020. In the precondition of IO warming, sea-ice loss tends to prolong Meiyu-Baiu season and strengthen convective activity over the Meiyu-Baiu region, thereby enhancing the IO warming-induced heavy Meiyu-Baiu rainfall by ~50% and doubling the risk of extreme events comparable to or greater than the one in 2020. A statistical model is further constructed to demonstrate that taking Arctic sea ice into consideration can significantly improve the seasonal prediction of extreme Meiyu-Baiu rainfall.\",\"PeriodicalId\":19438,\"journal\":{\"name\":\"npj Climate and Atmospheric Science\",\"volume\":\" \",\"pages\":\"1-12\"},\"PeriodicalIF\":8.5000,\"publicationDate\":\"2024-09-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.nature.com/articles/s41612-024-00770-7.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"npj Climate and Atmospheric Science\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://www.nature.com/articles/s41612-024-00770-7\",\"RegionNum\":1,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"METEOROLOGY & ATMOSPHERIC SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"npj Climate and Atmospheric Science","FirstCategoryId":"89","ListUrlMain":"https://www.nature.com/articles/s41612-024-00770-7","RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"METEOROLOGY & ATMOSPHERIC SCIENCES","Score":null,"Total":0}
Sea-ice loss in Eurasian Arctic coast intensifies heavy Meiyu-Baiu rainfall associated with Indian Ocean warming
Heavy Meiyu-Baiu rainfall can pose threat to the dense population in East Asia by catastrophic flooding. Although previous studies have identified Indian Ocean (IO) warming as the major cause of heavy Meiyu-Baiu rainfall, it failed to predict the record-breaking rainfall in July 2020. Synthesizing observational analysis, large-ensemble climate simulations, and atmospheric simulations, we show that sea-ice loss in the Kara Sea in May can intensify the IO warming-induced heavy Meiyu-Baiu rainfall and well explains the record-breaking rainfall in July 2020. In the precondition of IO warming, sea-ice loss tends to prolong Meiyu-Baiu season and strengthen convective activity over the Meiyu-Baiu region, thereby enhancing the IO warming-induced heavy Meiyu-Baiu rainfall by ~50% and doubling the risk of extreme events comparable to or greater than the one in 2020. A statistical model is further constructed to demonstrate that taking Arctic sea ice into consideration can significantly improve the seasonal prediction of extreme Meiyu-Baiu rainfall.
期刊介绍:
npj Climate and Atmospheric Science is an open-access journal encompassing the relevant physical, chemical, and biological aspects of atmospheric and climate science. The journal places particular emphasis on regional studies that unveil new insights into specific localities, including examinations of local atmospheric composition, such as aerosols.
The range of topics covered by the journal includes climate dynamics, climate variability, weather and climate prediction, climate change, ocean dynamics, weather extremes, air pollution, atmospheric chemistry (including aerosols), the hydrological cycle, and atmosphere–ocean and atmosphere–land interactions. The journal welcomes studies employing a diverse array of methods, including numerical and statistical modeling, the development and application of in situ observational techniques, remote sensing, and the development or evaluation of new reanalyses.