Yi Zheng , Bo Sun , Wanling Li , Siyu Zhou , Jiarui Cai , Huixin Li , Shengping He
{"title":"Attribution of regional Hadley circulation intensity changes in the Northern Hemisphere","authors":"Yi Zheng , Bo Sun , Wanling Li , Siyu Zhou , Jiarui Cai , Huixin Li , Shengping He","doi":"10.1016/j.aosl.2025.100613","DOIUrl":null,"url":null,"abstract":"<div><div>The discrepancy in the trends of the global zonal mean (GZM) intensity of the Hadley circulation (HCI) between reanalysis data and model simulations has been a problem for understanding the changes in HCI and the influence of external forcings. To understand the reason for this discrepancy, this study investigates the trends of intensity of regional HCI of the Northern Hemisphere over the eastern Pacific (EPA), western Pacific (WPA), Atlantic (ATL), Africa (AFR), the Indian Ocean (IDO), and residual area (RA), based on six reanalysis datasets and 13 CMIP6 models. In reanalysis data, the trends in regional HCI over EPA and ATL (WPA and AFR) contribute to (partially offset) the increasing trend in GZM HCI, while the trends in regional HCI over IDO are different in different reanalysis data. The CMIP6 models skillfully reproduce the trends in regional HCI over EPA, ATL, WPA, and AFR, but simulate notable decreasing trends in regional HCI over IDO, which is a key reason for the opposite trends in GZM HCI between reanalysis data and models. The discrepancy in IDO can be attributed to differences in the simulation of diabatic heating and zonal friction between reanalysis data and models. Optimal fingerprint analysis indicates that anthropogenic (ANT) and non-greenhouse gas (NOGHG) forcings are the dominant drivers of the HCI trends in the EPA and ATL regions. In the WPA (AFR) region, NOGHG (ANT) forcing serves as the primary driver. The findings contribute to improving the representation of regional HCI trends in models and improving the attribution of external forcings.</div><div>摘要</div><div>基于6套再分析资料和13个CMIP6模式, 研究发现模式能够较好地再现北半球东太平洋, 西太平洋, 大西洋和非洲的哈德来环流强度变化趋势. 但在印度洋区域, 再分析数据与模式模拟的趋势存在较大差异. 这一差异主要归因于模式与再分析数据在非绝热加热和纬向摩擦力模拟上的不同表现. 最优指纹法分析表明, 人为强迫和非温室气体强迫是北半球局地哈德来环流强度变化的主要驱动因素. 本研究揭示了人类活动对北半球不同区域哈德来环流变化的重要影响, 并阐明了再分析资料与CMIP6模式中北半球哈德来环流变化差异的原因.</div></div>","PeriodicalId":47210,"journal":{"name":"Atmospheric and Oceanic Science Letters","volume":"18 6","pages":"Article 100613"},"PeriodicalIF":3.2000,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Atmospheric and Oceanic Science Letters","FirstCategoryId":"89","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S167428342500025X","RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"METEOROLOGY & ATMOSPHERIC SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
The discrepancy in the trends of the global zonal mean (GZM) intensity of the Hadley circulation (HCI) between reanalysis data and model simulations has been a problem for understanding the changes in HCI and the influence of external forcings. To understand the reason for this discrepancy, this study investigates the trends of intensity of regional HCI of the Northern Hemisphere over the eastern Pacific (EPA), western Pacific (WPA), Atlantic (ATL), Africa (AFR), the Indian Ocean (IDO), and residual area (RA), based on six reanalysis datasets and 13 CMIP6 models. In reanalysis data, the trends in regional HCI over EPA and ATL (WPA and AFR) contribute to (partially offset) the increasing trend in GZM HCI, while the trends in regional HCI over IDO are different in different reanalysis data. The CMIP6 models skillfully reproduce the trends in regional HCI over EPA, ATL, WPA, and AFR, but simulate notable decreasing trends in regional HCI over IDO, which is a key reason for the opposite trends in GZM HCI between reanalysis data and models. The discrepancy in IDO can be attributed to differences in the simulation of diabatic heating and zonal friction between reanalysis data and models. Optimal fingerprint analysis indicates that anthropogenic (ANT) and non-greenhouse gas (NOGHG) forcings are the dominant drivers of the HCI trends in the EPA and ATL regions. In the WPA (AFR) region, NOGHG (ANT) forcing serves as the primary driver. The findings contribute to improving the representation of regional HCI trends in models and improving the attribution of external forcings.