{"title":"解读马登-朱利安振荡中的混沌现象","authors":"Guosen Chen","doi":"10.1038/s41612-024-00870-4","DOIUrl":null,"url":null,"abstract":"The Madden-Julian Oscillation (MJO), having far-reaching impact on Earth’s climate and human society, is an important tropical phenomenon characterized by a typical 30–90-day period in phase evolution. The MJO was hardly being connected to chaos, which is featured with aperiodicity. However, unlike the quasi-periodic phase evolution, the event-to-event changes of MJO, to some extent represented by its amplitude evolution, is irregular. By presenting multiple evidence, we demonstrate that the MJO’s amplitude evolution is deterministically chaotic. Combining eigen-time-delay embedding and Koopman operator into a regression model, we further reveal the cause of chaos through a data-drive approach. We show that the dynamics of MJO amplitude can be decomposed into multiple periodic obits and nonlinear interaction between them. The aperiodic motion resulted from these nonlinear interactions disturbs the periodic obits asynchronously, leading to chaotic evolution of MJO amplitude. The findings here unveil the strong nonlinear nature of the MJO, explaining the difficulties in the long-term MJO prediction and providing new insight into MJO’s complexity.","PeriodicalId":19438,"journal":{"name":"npj Climate and Atmospheric Science","volume":" ","pages":"1-10"},"PeriodicalIF":8.5000,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41612-024-00870-4.pdf","citationCount":"0","resultStr":"{\"title\":\"Deciphering chaos in the Madden-Julian oscillation\",\"authors\":\"Guosen Chen\",\"doi\":\"10.1038/s41612-024-00870-4\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The Madden-Julian Oscillation (MJO), having far-reaching impact on Earth’s climate and human society, is an important tropical phenomenon characterized by a typical 30–90-day period in phase evolution. The MJO was hardly being connected to chaos, which is featured with aperiodicity. However, unlike the quasi-periodic phase evolution, the event-to-event changes of MJO, to some extent represented by its amplitude evolution, is irregular. By presenting multiple evidence, we demonstrate that the MJO’s amplitude evolution is deterministically chaotic. Combining eigen-time-delay embedding and Koopman operator into a regression model, we further reveal the cause of chaos through a data-drive approach. We show that the dynamics of MJO amplitude can be decomposed into multiple periodic obits and nonlinear interaction between them. The aperiodic motion resulted from these nonlinear interactions disturbs the periodic obits asynchronously, leading to chaotic evolution of MJO amplitude. The findings here unveil the strong nonlinear nature of the MJO, explaining the difficulties in the long-term MJO prediction and providing new insight into MJO’s complexity.\",\"PeriodicalId\":19438,\"journal\":{\"name\":\"npj Climate and Atmospheric Science\",\"volume\":\" \",\"pages\":\"1-10\"},\"PeriodicalIF\":8.5000,\"publicationDate\":\"2024-12-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.nature.com/articles/s41612-024-00870-4.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"npj Climate and Atmospheric Science\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://www.nature.com/articles/s41612-024-00870-4\",\"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/s41612-024-00870-4","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}
Deciphering chaos in the Madden-Julian oscillation
The Madden-Julian Oscillation (MJO), having far-reaching impact on Earth’s climate and human society, is an important tropical phenomenon characterized by a typical 30–90-day period in phase evolution. The MJO was hardly being connected to chaos, which is featured with aperiodicity. However, unlike the quasi-periodic phase evolution, the event-to-event changes of MJO, to some extent represented by its amplitude evolution, is irregular. By presenting multiple evidence, we demonstrate that the MJO’s amplitude evolution is deterministically chaotic. Combining eigen-time-delay embedding and Koopman operator into a regression model, we further reveal the cause of chaos through a data-drive approach. We show that the dynamics of MJO amplitude can be decomposed into multiple periodic obits and nonlinear interaction between them. The aperiodic motion resulted from these nonlinear interactions disturbs the periodic obits asynchronously, leading to chaotic evolution of MJO amplitude. The findings here unveil the strong nonlinear nature of the MJO, explaining the difficulties in the long-term MJO prediction and providing new insight into MJO’s complexity.
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.