Article(id=1212062424463700707, tenantId=1146029695717560320, journalId=1149651085930835976, issueId=1212062420277792984, articleNumber=null, orderNo=null, doi=10.12284/hyxb2023100, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1667923200000, receivedDateStr=2022-11-09, revisedDate=1680192000000, revisedDateStr=2023-03-31, acceptedDate=null, acceptedDateStr=null, onlineDate=1766907801023, onlineDateStr=2025-12-28, pubDate=1693411200000, pubDateStr=2023-08-31, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1766907801023, onlineIssueDateStr=2025-12-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1766907801023, creator=13701087609, updateTime=1766907801023, updator=13701087609, issue=Issue{id=1212062420277792984, tenantId=1146029695717560320, journalId=1149651085930835976, year='2023', volume='45', issue='8', pageStart='1', pageEnd='190', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1766907800024, creator=13701087609, updateTime=1766924671641, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1212133185010398004, tenantId=1146029695717560320, journalId=1149651085930835976, issueId=1212062420277792984, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1212133185010398005, tenantId=1146029695717560320, journalId=1149651085930835976, issueId=1212062420277792984, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=11, endPage=23, ext={EN=ArticleExt(id=1212062425201898258, articleId=1212062424463700707, tenantId=1146029695717560320, journalId=1149651085930835976, language=EN, title=Interannual variability of winter water in the Indian Ocean Sector of the Southern Ocean and its causes during 2011−2020, columnId=1194652705852465724, journalTitle=Haiyang Xuebao, columnName=Article, runingTitle=null, highlight=null, articleAbstract=

Multiple studies have confirmed the long-term property modification of water columns from the bottom to the sea surface at the Southern Ocean and pointed out that it is closely related to the changes of large-scale external forcing. However, the higher frequency interannual variability of the water masses and its causes are still poorly understood, including the winter water (WW), which is the most vulnerable to external forcing near the sea surface. Based on repeated hydrographic observations along 110°E in Januaries 2011 to 2020 and meteorological reanalysis datasets, this study estimated interannual variability of the WW layer in the seasonal ice zone (SIZ) and its possible causes over ten years. Results show that WW properties have significant temporal and spatial variability in this region. A significant positive correlation between the WW core temperature anomaly and the previous-year Antarctic Oscillation (AAO) index anomaly (R = 0.69) and a negative correlation between the AAO index and the turning latitude of the local zonal wind component (R = −0.61), indicate that a larger (smaller) AAO index corresponds to a southward (northward) shift of the divergence zone, and the increase (decrease) of the WW core temperature in the SIZ. A negative correspondence between the local net precipitation anomaly and the WW core salinity anomaly indicates the negative net precipitation anomaly (less freshwater transport to the ocean) after 2016 contributes to an increase in the WW core salinity anomaly. Meanwhile, the local eddy kinetic energy anomaly is negatively correlated with the WW thickness anomaly (R = −0.70), which supports the idea that the enhancement (decrease) in the intensity of persistent cyclonic eddies in this region may strengthen (weaken) the upward pumping to shoal the depth of the circumpolar deep water, and further affect the WW thickness. This study contributes to an in-depth understanding of the specific response of water columns in the Southern Ocean to the high-frequency variability of external forcing.

, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=Copyright © 2023 Pratacultural Science. All rights reserved., copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, authorCompany=null, fund=null, authors=null, authorsList=Lingqiao Cheng, Junjie Meng, Deng Li, Yujiro Kitade, Chunling Zhang, Juncheng Zuo), CN=ArticleExt(id=1212062428322460569, articleId=1212062424463700707, tenantId=1146029695717560320, journalId=1149651085930835976, language=CN, title=2011−2020年间南大洋印度洋扇区冬季水的年际变化特征及其成因, columnId=1149698756456657529, journalTitle=海洋学报, columnName=论文, runingTitle=null, highlight=null, articleAbstract=

已有多个研究证实南大洋自海表到底层的海水特性存在长期变化特征,并指出其与大尺度外部强迫的改变紧密相关。然而,截至目前海洋学界对各水团的更高频年际变化特征及其影响原因依然了解甚少,其中包括了近海表最易受外部强迫影响的冬季水(Winter Water, WW)。本文结合2011−2020年间每年1月沿110°E断面采集的重复观测资料和再分析气象资料,研究季节性冰区内WW层10年间的年际变化。结果发现,该海域WW特性具有显著的时空变化特征。WW核心温度距平与前一年的南极涛动(Antarctic Oscillation, AAO)指数距平具有显著正相关关系(R = 0.69),而AAO指数与局地纬向风转向所在纬度呈负相关关系(R = −0.61),说明AAO指数越大(小)时,辐散带会向南(北)移动,季节性冰区的WW核心温度升高(降低)。局地净降水量距平变化与WW核心盐度距平的变化相反,2016年之后负的净降水量距平(大气向海洋输送淡水减少)促使WW核心盐度距平增大。另外,局地涡动能距平与WW厚度距平呈负相关关系(R = −0.70),据此推测该海域持续存在的气旋式涡旋的强度增强(减弱),引发向上抽吸作用增强(减弱),导致绕极深层水的深度变浅(加深),进而引起其上层WW层厚度的变化。通过本研究工作,有助于深入理解南大洋海洋水柱对外部强迫高频变化的具体响应。

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程灵巧(1985-), 女, 浙江省台州市人,副教授,主要从事海洋微结构混合、极地海洋学研究。E-mail:

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2 自然资源部海洋生态监测与修复技术重点实验室,上海 201306
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程灵巧(1985-), 女, 浙江省台州市人,副教授,主要从事海洋微结构混合、极地海洋学研究。E-mail:

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程灵巧(1985-), 女, 浙江省台州市人,副教授,主要从事海洋微结构混合、极地海洋学研究。E-mail:

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Geophysical Research Letters, 2017, 44(17): 9008−9019., articleTitle=null, refAbstract=null)], funds=[Fund(id=1215323589268652033, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1212062424463700707, awardId=null, language=CN, fundingSource=自然资源部海洋生态监测与修复技术重点实验室开放研究基金(MEMRT202211);国家自然科学基金(42130402, 42176012)。, fundOrder=null, country=null)], companyList=[AuthorCompany(id=1215323582880727744, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1212062424463700707, xref=1, ext=[AuthorCompanyExt(id=1215323582889116353, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1212062424463700707, companyId=1215323582880727744, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1 上海海洋大学 海洋科学学院,上海 201306)]), AuthorCompany(id=1215323582952030916, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1212062424463700707, xref=1, ext=[AuthorCompanyExt(id=1215323582956225222, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1212062424463700707, companyId=1215323582952030916, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China)]), AuthorCompany(id=1215323583044305610, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1212062424463700707, xref=2, ext=[AuthorCompanyExt(id=1215323583048499915, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1212062424463700707, companyId=1215323583044305610, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2 自然资源部海洋生态监测与修复技术重点实验室,上海 201306)]), AuthorCompany(id=1215323583144968913, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1212062424463700707, xref=2, ext=[AuthorCompanyExt(id=1215323583153357522, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1212062424463700707, companyId=1215323583144968913, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2Key Laboratory of Marine Ecological Monitoring and Restoration Technologies, Ministry of Natural Resources, Shanghai 201306, China)]), AuthorCompany(id=1215323583228854997, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1212062424463700707, xref=3, ext=[AuthorCompanyExt(id=1215323583237243605, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1212062424463700707, companyId=1215323583228854997, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=3 上海海洋大学 极地研究中心,上海 201306)]), AuthorCompany(id=1215323583337906906, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1212062424463700707, xref=3, ext=[AuthorCompanyExt(id=1215323583342101211, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1212062424463700707, companyId=1215323583337906906, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=3Center for Polar Research, Shanghai Ocean University, Shanghai 201306, China)]), AuthorCompany(id=1215323583438570207, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1212062424463700707, xref=4, ext=[AuthorCompanyExt(id=1215323583446958816, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1212062424463700707, companyId=1215323583438570207, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=4 日本东京海洋大学 海洋科学系,东京 108-8477)]), AuthorCompany(id=1215323583539233507, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1212062424463700707, xref=4, ext=[AuthorCompanyExt(id=1215323583547622117, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1212062424463700707, companyId=1215323583539233507, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=4Department of Ocean Sciences, Tokyo University of Marine Science and Technology, Tokyo 108-8477, Japan)])], figs=[ArticleFig(id=1215323587364438925, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1212062424463700707, language=EN, label=Fig. 1, caption=Distribution of observation sites

Numbers represent the number of repeated observations at the corresponding stations; the light green line and red line represent the winter ice edge and the summer ice edge, respectively, calculated from the mean 15% sea ice concentration lines in Januaries and Julies over 2011−2020; bottom topography is drawn according to the ETOPO1 dataset[45]

, figureFileSmall=4QBq8lPQ3twxj8zKx0iuUQ==, figureFileBig=dDFXfMnRKVahs2wT2/z4Ww==, tableContent=null), ArticleFig(id=1215323587460907922, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1212062424463700707, language=CN, label=图1, caption=观测站点分布

各站点对应数字代表重复观测次数;浅绿色线和红色线分别代表冬季冰缘线和夏季冰缘线,分别为2011−2020年10年平均的1月和7月的月平均15%海冰密集度线;海底地形基于ETOPO1数据[45]绘制

, figureFileSmall=4QBq8lPQ3twxj8zKx0iuUQ==, figureFileBig=dDFXfMnRKVahs2wT2/z4Ww==, tableContent=null), ArticleFig(id=1215323587578348443, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1212062424463700707, language=EN, label=Fig. 2, caption=Cross sections of potential temperature (θ) and salinity obtained along 110°E in Januaries 2011−2020

Contours indicate salinity; black dashed lines show the θ = −0.5℃ isotherms, being the upper and lower boundaries of the WW layer; white dot represents the depth where the cold core temperature is observed at each station

, figureFileSmall=u9JChlGNIcNB4Fv624Nvww==, figureFileBig=xRTNeVNm2FlvTKw0rvZ22A==, tableContent=null), ArticleFig(id=1215323587649651617, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1212062424463700707, language=CN, label=图2, caption=2011−2020年1月沿110°E获得的位温(θ)、盐度断面图

等值线表示盐度;黑色虚线为θ = −0.5℃等温线,代表WW层的上下界;白点代表每个站点冷核最低温度所在深度

, figureFileSmall=u9JChlGNIcNB4Fv624Nvww==, figureFileBig=xRTNeVNm2FlvTKw0rvZ22A==, tableContent=null), ArticleFig(id=1215323587725149091, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1212062424463700707, language=EN, label=Fig. 3, caption=Distributions of potential temperature and salinity for WW cold core at all stations during 2011−2020

Different colors distinguish the years of observation; black triangles represent the mean values at the same integer latitude stations; error bars represent one standard deviation

, figureFileSmall=ULfG7AhAmVXAk/5lkSpB3w==, figureFileBig=JT0/uOPhQ8ybhLxUXBLlUg==, tableContent=null), ArticleFig(id=1215323587800646568, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1212062424463700707, language=CN, label=图3, caption=2011−2020年间所有观测站点上WW冷核对应的位温和盐度分布

不同颜色区分观测的年份;黑色三角代表同一整数纬度站点上的平均值;误差条代表1个标准差

, figureFileSmall=ULfG7AhAmVXAk/5lkSpB3w==, figureFileBig=JT0/uOPhQ8ybhLxUXBLlUg==, tableContent=null), ArticleFig(id=1215323587888726958, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1212062424463700707, language=EN, label=Fig. 4, caption=Time series of anomalies of core temperature (a), salinity (b), neutral density (c), dissolved oxygen concentration (d) at cold core of WW, thickness anomalies of WW (e), and depth anomalies of the cold core of WW (f) during 2011−2020

Error bars denote one standard deviation of the results between different grids for the same year, as the spatial variability components

, figureFileSmall=p8PgaOu4kQtk+F27MYGBaQ==, figureFileBig=EqAnl8c3/8Kp8J7AtKhXbQ==, tableContent=null), ArticleFig(id=1215323587985195954, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1212062424463700707, language=CN, label=图4, caption=2011−2020年间WW核心温度距平(a)、盐度距平(b)、中性密度距平(c)、溶解氧浓度距平(d)、厚度距平(e)和冷核所在深度距平(f)的时间序列

误差条代表相同年份不同网格之间结果的一个标准差,即空间变化部分

, figureFileSmall=p8PgaOu4kQtk+F27MYGBaQ==, figureFileBig=EqAnl8c3/8Kp8J7AtKhXbQ==, tableContent=null), ArticleFig(id=1215323588073276342, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1212062424463700707, language=EN, label=Fig. 5, caption=Time series of AAO index (a), Lt (b), and their correlation (c)

In figure a and figure b, gray circles and dashed lines show the monthly results; black lines are results after 3-month moving average; the dotted lines with black dots are annual averages, respectively

, figureFileSmall=/K1mXFWSXw/uzgHchRXh/Q==, figureFileBig=5aL9BG/Pk/ZjdqcjMFvt+w==, tableContent=null), ArticleFig(id=1215323588152968122, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1212062424463700707, language=CN, label=图5, caption=AAO指数(a)、Lt(b)的时间序列及两者相关关系(c)

图a和图b中灰色空心圆圈及虚线代表月平均结果;黑线代表每3个月滑动平均后结果;点线及黑点代表年平均结果

, figureFileSmall=/K1mXFWSXw/uzgHchRXh/Q==, figureFileBig=5aL9BG/Pk/ZjdqcjMFvt+w==, tableContent=null), ArticleFig(id=1215323588262020031, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1212062424463700707, language=EN, label=Fig. 6, caption=Time series of anomalies of eddy kinetic energy (a), net precipitation (b) and sea ice concentration (c) based on 10-year averages over the study area for the period of 2010−2019

Grey line in figure a represents the daily average and the black line represents the results after one-month moving average; grey lines in figure b and figure c represent monthly averages and the black line represents the result after 3-month moving average; red lines represent the annual averages, 10-year averages are marked at the right bottom corner of each subplot

, figureFileSmall=JpFp/mTWyeeMiVZNhtQdMA==, figureFileBig=iF1az2fJQGHOqVZFN8CVhA==, tableContent=null), ArticleFig(id=1215323588329128899, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1212062424463700707, language=CN, label=图6, caption=研究海域2010−2019年间基于10年平均的涡动能(EKE)距平(a),净降水量(P−E)距平(b)和海冰密集度(SIC)距平(c)时间序列

图a中灰色线代表日平均,黑色线代表以1个月为跨度实现的滑动平均结果;图b和图c中灰色线代表月平均,黑色线代表每3个月一次滑动平均的结果;红色线代表年平均,10年平均值标注于各分图右下角

, figureFileSmall=JpFp/mTWyeeMiVZNhtQdMA==, figureFileBig=iF1az2fJQGHOqVZFN8CVhA==, tableContent=null), ArticleFig(id=1215323588421403594, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1212062424463700707, language=EN, label=Fig. 7, caption=Comparison of time series of annual external forcing anomalies and temperature anomalies of WW core

The left axis, the internal solid line and dots represent external forcing anomalies; the right axis, the dashed line and the triangle represent WW core temperature anomalies

, figureFileSmall=mhXK9+CVvOreg/FiGnSOAw==, figureFileBig=oWyM0jPhEH/pSaCoTbKjSg==, tableContent=null), ArticleFig(id=1215323588484318160, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1212062424463700707, language=CN, label=图7, caption=外部强迫年平均距平和WW核心温度距平的时间序列比较

纵坐标左侧刻度、内部实线和圆点代表外部强迫距平;纵坐标右侧刻度、虚线和三角代表WW核心温度距平

, figureFileSmall=mhXK9+CVvOreg/FiGnSOAw==, figureFileBig=oWyM0jPhEH/pSaCoTbKjSg==, tableContent=null), ArticleFig(id=1215323588589175765, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1212062424463700707, language=EN, label=Fig. 8, caption=Comparison of time series of annual external forcing anomalies and salinity anomalies of WW core

The left axis, the internal solid line and dots represent external forcing anomalies; the right axis, the dashed line and the triangle represent WW core salinity anomalies

, figureFileSmall=S1A9PaKkscBLsTvz+/fmcw==, figureFileBig=IbrxnXLLnvT4u5iSDvmUCA==, tableContent=null), ArticleFig(id=1215323588677256155, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1212062424463700707, language=CN, label=图8, caption=外部强迫年平均距平和WW核心盐度距平的时间序列比较

纵坐标左侧刻度、内部实线和圆点代表外部强迫距平;纵坐标右侧刻度、虚线和三角代表WW核心盐度距平

, figureFileSmall=S1A9PaKkscBLsTvz+/fmcw==, figureFileBig=IbrxnXLLnvT4u5iSDvmUCA==, tableContent=null), ArticleFig(id=1215323588752753633, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1212062424463700707, language=EN, label=Fig. 9, caption=Comparison of time series of annual external forcing anomalies and WW thickness anomalies

The left axis, the internal solid line and dots represent external forcing anomalies; the right axis, the dashed line and the triangle represent WW thickness anomalies

, figureFileSmall=TSWBtOHGDf4ZnL5czl1uRQ==, figureFileBig=7bQnqLZWnJMVOwwT2X0Klg==, tableContent=null), ArticleFig(id=1215323588870194155, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1212062424463700707, language=CN, label=图9, caption=外部强迫年平均距平和WW厚度距平的时间序列比较

纵坐标左侧刻度、内部实线和圆点代表外部强迫距平;纵坐标右侧刻度、虚线和三角代表WW厚度距平

, figureFileSmall=TSWBtOHGDf4ZnL5czl1uRQ==, figureFileBig=7bQnqLZWnJMVOwwT2X0Klg==, tableContent=null), ArticleFig(id=1215323588949885936, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1212062424463700707, language=EN, label=Table 1, caption=

Date information of observation stations

, figureFileSmall=null, figureFileBig=null, tableContent=
纬度2011年2012年2013年2014年2015年2016年2017年2018年2019年2020年
注:2011年在60°S站点的观测日期为2010年12月31日,用下划线标注。
60°S${\underline {12月31日} }$1月3日1月6日1月19日1月18日1月23日1月7日1月8日1月9日1月15日
61°S1月3日1月7日1月20日1月18日1月23日1月8日1月8日1月10日1月15日
61.5°S1月17日
62°S1月1日1月8日1月22日1月20日1月25日1月9日1月9日1月11日1月17日
62.5°S1月8日1月20日1月18日
63°S1月1日1月5日1月8日1月23日1月28日1月25日1月15日1月10日1月12日1月18日
63.45°S1月17日
63.5°S1月6日1月9日1月21日1月17日1月20日
64°S1月1日1月6日1月9日1月23日1月21日1月26日1月10日1月16日1月20日
64.24°S1月10日
64.30°S1月9日
64.5°S1月6日1月12日1月22日1月24日
64.68°S1月12日
65°S1月2日1月9日1月24日1月22日1月27日1月15日1月23日
65.28°S1月11日
), ArticleFig(id=1215323589037966325, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1212062424463700707, language=CN, label=表1, caption=

观测站位日期信息

, figureFileSmall=null, figureFileBig=null, tableContent=
纬度2011年2012年2013年2014年2015年2016年2017年2018年2019年2020年
注:2011年在60°S站点的观测日期为2010年12月31日,用下划线标注。
60°S${\underline {12月31日} }$1月3日1月6日1月19日1月18日1月23日1月7日1月8日1月9日1月15日
61°S1月3日1月7日1月20日1月18日1月23日1月8日1月8日1月10日1月15日
61.5°S1月17日
62°S1月1日1月8日1月22日1月20日1月25日1月9日1月9日1月11日1月17日
62.5°S1月8日1月20日1月18日
63°S1月1日1月5日1月8日1月23日1月28日1月25日1月15日1月10日1月12日1月18日
63.45°S1月17日
63.5°S1月6日1月9日1月21日1月17日1月20日
64°S1月1日1月6日1月9日1月23日1月21日1月26日1月10日1月16日1月20日
64.24°S1月10日
64.30°S1月9日
64.5°S1月6日1月12日1月22日1月24日
64.68°S1月12日
65°S1月2日1月9日1月24日1月22日1月27日1月15日1月23日
65.28°S1月11日
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2011−2020年间南大洋印度洋扇区冬季水的年际变化特征及其成因
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程灵巧 1, 2, 3 , 孟俊杰 1 , 李等 1 , 北出裕二郎 4 , 张春玲 1, 2 , 左军成 1, 2
海洋学报 | 论文 2023,45(8): 11-23
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海洋学报 | 论文 2023, 45(8): 11-23
2011−2020年间南大洋印度洋扇区冬季水的年际变化特征及其成因
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程灵巧1, 2, 3 , 孟俊杰1, 李等1, 北出裕二郎4, 张春玲1, 2, 左军成1, 2
作者信息
  • 1 上海海洋大学 海洋科学学院,上海 201306
  • 2 自然资源部海洋生态监测与修复技术重点实验室,上海 201306
  • 3 上海海洋大学 极地研究中心,上海 201306
  • 4 日本东京海洋大学 海洋科学系,东京 108-8477
  • 程灵巧(1985-), 女, 浙江省台州市人,副教授,主要从事海洋微结构混合、极地海洋学研究。E-mail:

Interannual variability of winter water in the Indian Ocean Sector of the Southern Ocean and its causes during 2011−2020
Lingqiao Cheng1, 2, 3 , Junjie Meng1, Deng Li1, Yujiro Kitade4, Chunling Zhang1, 2, Juncheng Zuo1, 2
Affiliations
  • 1College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China
  • 2Key Laboratory of Marine Ecological Monitoring and Restoration Technologies, Ministry of Natural Resources, Shanghai 201306, China
  • 3Center for Polar Research, Shanghai Ocean University, Shanghai 201306, China
  • 4Department of Ocean Sciences, Tokyo University of Marine Science and Technology, Tokyo 108-8477, Japan
出版时间: 2023-08-31 doi: 10.12284/hyxb2023100
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已有多个研究证实南大洋自海表到底层的海水特性存在长期变化特征,并指出其与大尺度外部强迫的改变紧密相关。然而,截至目前海洋学界对各水团的更高频年际变化特征及其影响原因依然了解甚少,其中包括了近海表最易受外部强迫影响的冬季水(Winter Water, WW)。本文结合2011−2020年间每年1月沿110°E断面采集的重复观测资料和再分析气象资料,研究季节性冰区内WW层10年间的年际变化。结果发现,该海域WW特性具有显著的时空变化特征。WW核心温度距平与前一年的南极涛动(Antarctic Oscillation, AAO)指数距平具有显著正相关关系(R = 0.69),而AAO指数与局地纬向风转向所在纬度呈负相关关系(R = −0.61),说明AAO指数越大(小)时,辐散带会向南(北)移动,季节性冰区的WW核心温度升高(降低)。局地净降水量距平变化与WW核心盐度距平的变化相反,2016年之后负的净降水量距平(大气向海洋输送淡水减少)促使WW核心盐度距平增大。另外,局地涡动能距平与WW厚度距平呈负相关关系(R = −0.70),据此推测该海域持续存在的气旋式涡旋的强度增强(减弱),引发向上抽吸作用增强(减弱),导致绕极深层水的深度变浅(加深),进而引起其上层WW层厚度的变化。通过本研究工作,有助于深入理解南大洋海洋水柱对外部强迫高频变化的具体响应。

南大洋印度洋扇区  /  冬季水  /  年际变化  /  外部强迫

Multiple studies have confirmed the long-term property modification of water columns from the bottom to the sea surface at the Southern Ocean and pointed out that it is closely related to the changes of large-scale external forcing. However, the higher frequency interannual variability of the water masses and its causes are still poorly understood, including the winter water (WW), which is the most vulnerable to external forcing near the sea surface. Based on repeated hydrographic observations along 110°E in Januaries 2011 to 2020 and meteorological reanalysis datasets, this study estimated interannual variability of the WW layer in the seasonal ice zone (SIZ) and its possible causes over ten years. Results show that WW properties have significant temporal and spatial variability in this region. A significant positive correlation between the WW core temperature anomaly and the previous-year Antarctic Oscillation (AAO) index anomaly (R = 0.69) and a negative correlation between the AAO index and the turning latitude of the local zonal wind component (R = −0.61), indicate that a larger (smaller) AAO index corresponds to a southward (northward) shift of the divergence zone, and the increase (decrease) of the WW core temperature in the SIZ. A negative correspondence between the local net precipitation anomaly and the WW core salinity anomaly indicates the negative net precipitation anomaly (less freshwater transport to the ocean) after 2016 contributes to an increase in the WW core salinity anomaly. Meanwhile, the local eddy kinetic energy anomaly is negatively correlated with the WW thickness anomaly (R = −0.70), which supports the idea that the enhancement (decrease) in the intensity of persistent cyclonic eddies in this region may strengthen (weaken) the upward pumping to shoal the depth of the circumpolar deep water, and further affect the WW thickness. This study contributes to an in-depth understanding of the specific response of water columns in the Southern Ocean to the high-frequency variability of external forcing.

the Indian Ocean Sector of the Southern Ocean  /  winter water  /  interannual variability  /  external forcing
程灵巧, 孟俊杰, 李等, 北出裕二郎, 张春玲, 左军成. 2011−2020年间南大洋印度洋扇区冬季水的年际变化特征及其成因. 海洋学报, 2023 , 45 (8) : 11 -23 . DOI: 10.12284/hyxb2023100
Lingqiao Cheng, Junjie Meng, Deng Li, Yujiro Kitade, Chunling Zhang, Juncheng Zuo. Interannual variability of winter water in the Indian Ocean Sector of the Southern Ocean and its causes during 2011−2020[J]. Haiyang Xuebao, 2023 , 45 (8) : 11 -23 . DOI: 10.12284/hyxb2023100
南大洋的海洋环流体系主要由自西向东流动的南极绕极流(Antarctic Circumpolar, ACC)和横跨ACC的经向翻转环流构成。ACC作为热量和淡水通量跨洋输送的主要通道,在太平洋、大西洋和印度洋之间传播气候信号[1-2]。另一方面,南大洋经向翻转环流实现了浅层至底层之间的物质与能量交换,对热量、碳和其他气候影响要素的运输和储存具有重要贡献[3-6]。该海域各层水团作为海洋热量和物质的重要载体,直接或间接参与到环流体系中,其中的温盐等特性能反映海洋热量和物质分布状态和变化。
近几十年来,多个研究证实了南大洋水柱自海表到底层存在温盐变化现象,并认为与极地冰川融化、大气强迫变化、大陆架海域水体结构改变等相关[7-17]。Gille[18]对比分析了20世纪90年代与20世纪30年代以来的50~70年间南半球1 000 m以浅水层的热含量长期变化趋势,发现该水深范围大幅升温,且暖化集中在ACC流域;南大洋的暖化趋势与长期的ACC南移存在较高一致性。Böning等[19]探测到南大洋海表至1 000 m以深的暖化、淡化趋势,并指出该变化的一部分与ACC流域水团特性改变有关。Aoki等[20-21]报道了南大洋印度洋扇区表层至深层(0~900 m)水团特性的多年暖化特征。Aoki等[8]记录了140°E上季节性冰区内1994−2012年间全水柱的淡化趋势,发现冬季水(Winter Water, WW)淡化速率为0.03 (10 a)−1,并伴有显著年际变化特征,全球水循环增强导致的极地净降水量增多被认为是主要原因[22-24]。此外,全球气候变化背景下风驱动南极周边海冰北向运动,带来的淡水对南大洋的淡化也有贡献作用[25]。最新模型研究显示,厄尔尼诺和拉尼娜事件会造成南大洋500 m以浅水层温度变化[26]。Auger等[27]基于在南大洋140°E线上连续25年的重复观测资料,分析了800 m以浅水层温度的长期变化趋势及年际变化,指出亚南极水、近海表亚极地水及次表层亚极地深层水的长期变暖趋势具有主导性,明确了南大洋的显著暖化和上绕极深层水的变浅。另外,在南大洋底层,南极底层水(Antarctic Bottom Water, AABW)同样存在长期变化特征。在大西洋扇区的威德尔海,AABW具有0.025℃/(10 a)的暖化趋势,但没有明显的淡化趋势[28-31];而在印度洋扇区的澳大利亚−南极海盆中,AABW被发现存在显著的淡化趋势 [7-8, 10-11, 32-35]。Shimada等[36]指出去除了ACC南移影响后,澳大利亚−南极海盆底层的暖化依然存在,最终导致该海盆深海通风变浅。
如上述所示,前人研究对南大洋水柱的温盐特性长期变化已经有了较好把握,然而在机制分析方面依然存在不足。最近研究表明,探究较高频的年际变化及其成因可以提高对南大洋水柱特性变化机制的相关认识:近几年澳大利亚−南极海盆内的AABW盐度出现明显逆转[9],被归因于2014年后罗斯海底层水的盐度、密度和厚度恢复[37-38],且进一步追溯其具体机制为:正相位的南极涛动(Antarctic Oscillation, AAO)和厄尔尼诺现象共同导致南极大陆边缘东风减弱,致使从阿蒙森海向罗斯海大陆架输入的海冰减少,并造成罗斯海海冰生成量增加[38]。由此可知,南大洋海洋水柱对外部强迫的变化具有灵敏响应,并伴随着显著高频变化特征。
因此,为了完善对南大洋水柱特性变化的认识,有必要基于长期连续观测资料开展水团的年际尺度变化特征及其影响因素的精细研究。本文聚焦到印度洋扇区内季节性冰区的WW所在层。WW是南极表层水的重要组成部分,它的产生具有季节性,是前一个冬季混合层的残余,被夏季更暖的表层水所覆盖[39-40],因此WW核心特性能够反映大气−海洋−海冰的综合变化[8]。WW与海表和深层水团的相互作用可以改变热量收支和碳循环[41]。本文基于2011年以来在澳大利亚−南极海盆沿着110°E经度线获取的连续10年的南半球夏季现场观测资料,解析WW特性变化;对应分析大气降水、风场、海冰等强迫的变化,旨在揭示外部强迫变化对WW特性的影响。
本文主要基于日本东京海洋大学观测实习船“海鹰丸”于2011−2020年间每年1月在该断面采集的温度、盐度和溶解氧浓度数据,聚焦南大洋110°E经度线上60°S至南极陆坡前缘季节性冰区内WW所在层的多年变化,其中作者所在团队共参与了6次现场观测。图1显示了各站点位置及重复观测次数。表1汇总了各站点的观测日期。2016年1月由于海冰覆盖的影响,本该在(65°S,110°E)的站点往西移了1°,但是基于Shimada等[42]关于地形限制水柱分布的结论,实测对比(65°S,109°E)和(65°S,110°E)两点的水深,发现两者差异小于32 m,因此认为此处偏离引起的水团空间变化有限,故在下文进行数据分析时统一归于110°E上的水文数据。温盐深仪(SBE911plus)观测获得温度和盐度剖面,两者精度分别达到0.002℃和0.002;关于溶解氧浓度的观测,2011−2014年间使用了海鸟公司SBE43传感器,自2015年开始改为JFE Alec公司的 Rinko Optode传感器,数据精度均达到1%[43]
以往研究对5次中国南极科考的实测数据进行统计,在开阔大洋中将次表层水温小于−0.5℃作为鉴定WW的标准[44]。本文采用该标准定义WW层,判定位温(θ)为−0.5℃的等温线所在上下深度作为WW的上下界面,两者之差为该水层厚度。2017年和2019年在60°S站点上观测到的最低温度高于−0.5℃,因此认为这两个航次中未在该站点观察到WW。在计算年际变化时,由于不同年份的外部观测条件限制,导致各航次观测站点位置未能完全重复(表1),因此为了尽量消除空间变化影响,本文以相邻两个整数纬度之间为一个网格对WW冷核温度、盐度、中性密度、溶解氧浓度及WW厚度进行网格化处理。在单个网格内以10年平均值为基础计算该网格的各年特性距平。最终,对同一航次所有网格的距平求平均值和标准差,该平均值在所有年份上的线性回归表征10年整体变化,标准差代表空间差异。最南端65°S站点受陆坡锋南北振荡影响,不同年份WW层厚度差异达到100 m量级,因此为了消除该部分影响,在计算年际变化时未考虑65°S站点的水文结果。
除现场观测数据外,关于大气强迫相关要素,本文扩大范围到56°~68°S之间以110°E为中心的100°~120°E分析区域。数据来源为哥白尼气候变化服务中心提供的ERA5全球再分析产品和AVISO卫星高度计数据。本文主要使用海表10 m风场的纬向分量、净降水量(降水量−蒸发量,Precipitation−Evaporation, P−E),海冰密集度(Sea Ice Concentration, SIC)和海表温度(Sea Surface Temperature, SST)的月平均数据。数据空间分辨率为0.25° × 0.25°,时间范围为2010年1月至2019年12月。同时利用每日的海表地转流速距平数据,计算了涡动能(Eddy Kinetic Energy, EKE),公式为
$ {\rm{EKE}} = \frac{1}{2}(u{'^2} + v{'^2}) \text{,} $
式中,$u' $$v' $分别为纬向和经向的地转流速距平。此外,月平均的AAO指数由美国国家海洋和大气管理局下设的气候预测中心提供。
2011−2020年连续10年的观测结果显示,在整个季节性冰区内,WW普遍存在于较温暖的夏季表层水和相对高温高盐的南极绕极深层水之间,具有显著的冷核特征,且从南往北延伸(图2)。WW层分布具有明显的空间差异,在靠近夏季冰缘附近的最南端65°S站点,WW层厚度最大,2014年该站点WW下界最深到达420 × 104 Pa ,上界最浅延伸到海表面(图2d)。而在64°S以北站点上,海洋表层被暖水覆盖,WW分布于次表层,厚度在100 m左右变化,所在深度具有越往北越深的趋势。另外,对比不同年份的断面图可知,WW层的这种空间分布具有年际变化特征,主要体现在各年WW层厚度、所在深度及北向延伸程度各有不同:在2014年,WW层整体最厚,而在接下来的2015年、2016年除南端站点外,WW层最薄;WW上界面在2011年和2014年最浅达到20 × 104 Pa 、下界面在2018年达到最深的140 ×104 Pa 附近;除了2017年和2019年,其他大部分年份−0.5℃以下的WW都到达了最北的60°S站点。
为进一步研究WW冷核特性及其时空变化,绘制了经向上冷核的位温(θ)和盐度分布(图3)。对比不同站点的θ可知,10年间冷核温度呈现出从南往北升高的趋势,同时伴随着对应盐度的递减(除60°S站点外)。在最南端的65°S站点,冷核位温最低值(θmin)达到接近海表冰点的−1.81℃,对应盐度大于34.35;而在最北端的60°S站点,θmin为−1.43℃,对应盐度为34.02。另外,同一站点不同年份的冷核特性也表现出显著的年际变化。如在60°S站点,冷核位温在2019年最高达到0.31℃,比2018年观测到的最低位温高出1.74℃。同时,不同年份冷核的盐度值也有明显差异,其中在62°S站点上,10年间的冷核盐度的变化区间达到0.2。综上,在10年期间内,沿着110°E的季节性冰区内WW层的厚度和温盐特性都具有显著的时空变化特征,反映了大气−海洋−海冰相互作用的年际变化[8]
接着对WW特性的整体变化进行归纳(图4)。首先,基于10个航次表征WW特性的各要素距平,在时间域上获得线性回归来表征整体变化趋势。正如第2节所述,该结果排除了65°S站位上的水文数据。在2011−2020年间WW冷核位温以0.098℃/(10 a)的速率升高,对应盐度以0.017 (10 a)−1的速率增大,中性密度以0.012 kg/(m3·(10 a))的速率增大,溶解氧浓度增加速率为4.8 μmol/(kg·(10 a)),WW厚度减小速率为13.9 m/(10 a),同时WW冷核所在深度以17.3 m/(10 a)的速率变浅。相对于显著的年际变化,这样的整体变化趋势比较微弱,且除了WW冷核所在深度外,其他要素的整体趋势未通过显著性检验。另外参考文献[27],计算图4中所示各要素的长期趋势与年际变化之比,获得主导性关系:
$ S _{i}=\frac{n y \times a_{i}}{{\rm{S T D}}\left(a_{i} x_{i}-y_{i}\right)} \text{,} $
式中,ny = 10是重复观测年数;a为线性回归线的斜率(如图4各子图右下角所示);x为年份;y为各单一年份距平的平均值;下标i为1、2、3、4、5、6,代表不同的6个要素;STD为标准差。Si绝对值大于1表示长期趋势为主导,反之年际变化为主导。结果显示,WW冷核温度、盐度、中性密度、溶解氧浓度这4个要素的距平主要以年际变化为主导,而WW厚度和冷核所在深度距平以长期变化趋势为主导。基于以上显著性检验和主导性检验结果,本文关注WW特性的年际变化的主旨符合该水团大部分要素在这10年期间的高频变化特征。
图4中可以看出,WW的不同要素变化之间具有部分对应性。WW中性密度距平的变化特征基本与盐度距平一致(图4b图4c),验证了极地环境下密度主要由盐度决定的特点。WW核心温度距平在2014年达到第一个极小值,对应核心盐度、厚度和深度距平的极大值,以及溶解氧浓度距平的极小值。这种WW核心温度距平变化与核心盐度、厚度和深度距平变化相反,与溶解氧浓度距平变化相同的特点,也出现在其他年份的观测结果中,其中包括最近的2018−2020年。此外,核心温度在2016年达到最大正距平;反之,核心盐度和厚度在2015年、2016年出现显著负距平。
考虑到WW由上一个冬季混合层演变而来,因此本节向前推一年,分析2010−2019年期间的AAO指数、风场、净降水量、海表涡动能和海冰密集度,探究这些外部强迫的变化特征。AAO表征南半球高纬度地区大气变化模态,正相位的AAO会导致西风带增强且南移[46],因此AAO模态变化可以作用局地风场,从而改变海冰分布和海洋内部温盐结构[38, 47]。同时,位于南ACC锋附近的南极辐散带对应海水的涌升区,由其北侧ACC上方的西风带和南侧极地东风带之间的转向所驱动。根据辐散带定义,本研究将纬向风转向时所在纬度(记为Lt)作为其中心位置,以此确定辐散带乃至南ACC锋的南北振荡特征。图5显示了2010−2019年10年间AAO指数、研究海域100°~120°E之间平均Lt,及两者之间的相关关系。在这10年期间,正相位AAO占主导地位:在2010年和2015−2018年的5年内主要呈现正相位;2011−2014年间AAO指数在正负间振荡,具有接近0值的年平均;仅在2019年表现出较明显的负相位。AAO指数与局地Lt之间具有显著负相关关系(R = −0.61,p < 0.01)),即AAO指数越大(越小)Lt越往南(往北)移动,与Thompson等[46]和Silvano等[38]关于AAO相位与风场关系的结果一致。因此,2011−2014年间持续相对较弱的AAO可能促使辐散带北移,对应2014年Lt到达最北(年平均Lt = 61.8°S),2014年1月观测到最发达的WW层。相反的,2014年11月开始明显增强且持续到2016年的正相位AAO导致2015年和2016年上半年Lt往南偏移,对应2015年、2016年最薄的WW厚度、最低的WW核心盐度(图2图4)及2016年最暖的WW核心温度(图4)。
此外,基于10年平均结果计算研究海域的EKE、P−E和SIC距平,并进行时间序列分析(图6)。受季节性海冰覆盖影响,相较于ACC流轴海域较高的EKE分布[48-49],研究海域内EKE相对较弱,10年平均EKE仅有49.78 cm2/s2图6a)。在观测年份期间,2012年至2014年6月间大部分EKE处于负距平状态,是低值保持最长的一段时间,对应图5中较弱的AAO时期(AAO指数年平均接近0)。而从2014年7月开始,尽管AAO未有明显增大,EKE距平出现大幅升高,并在接下来的3个月间保持在最大的20.0 cm2/s2左右,导致2014年的年平均EKE值回升。其次,10年期间研究海域的净降水量均值为1.47 mm/d,同样伴有高频振荡(图6b)。从年平均来看,截至2015年,P−E距平在0值上下波动,而从2016年开始P−E持续处于负距平状态,即对应年份内大气向海洋输送淡水减少。与上两个变量较高频的变化特征不同,SIC以年周期变化为主,伴随年际间显著差异(图6c)。年平均SIC距平变化特征与年平均EKE距平结果基本呈现相反趋势,即研究海域内SIC大(小)的年份海表EKE较弱(强)。
AAO指数、Lt、P−E、EKE及SIC都具有显著的年际变化特征。接下来将这些外部强迫的年平均距平与WW层特性时间序列进行对比分析。图7显示每年1月份观测所得的WW核心温度距平与前一年的年平均外部强迫变化之间的对应关系。首先,WW核心温度距平与AAO指数距平具有基本一致的变化模式(相关系数R = 0.69,p < 0.05),即AAO指数增强(减弱),WW核心温度升高(降低)。相反的,Lt与AAO指数具有负相关关系(图5c),因而除个别年份(2017年、2018年)外,Lt往南(北)偏移对应WW核心温度变大(变小)。Sabu等[41]基于历史数据分析了AAO指数对WW层的影响,指出在AAO正相位年份,WW核心会变弱且不连续,而AAO负相位对应连续且稳定的WW核心。本文中AAO指数正距平对应相对较高的WW核心温度(2016年1月结果),AAO指数负距平对应相对较低的WW核心温度(2014年1月结果)的结果支持了他们的观点,且从统计学上明确了两者的显著关联性。其次,虽然在统计学上未表现出显著相关关系,但是除了2012年和2019年,EKE距平与WW核心温度的年际变化趋于一致,而SIC距平与WW核心温度距平的年际变化则趋于相反。此外,结果显示P−E和SST距平年际变化对WW核心温度并无明显影响(图7e图7f)。
图8显示WW核心盐度距平与前一年的年平均外部强迫变化之间的对应关系。相较之下,与WW核心盐度距平最具相关性可能的是P−E距平(R =−0.61,p = 0.06)。在统计学上并未发现其他外部强迫与WW核心盐度具有显著相关关系。P−E距平与WW核心盐度距平之间相反的对应关系,说明海表淡水通量年际变化信号可能被WW层及时记录下来。2016年开始连续负的海表净降水量距平(大气向海洋的淡水输送减弱),导致WW核心盐度增加,使其密度升高(图4)。Aoki等[8]曾指出,全球水循环增强背景下极地降水增多,导致南大洋印度洋扇区水柱出现长期淡化趋势。本文结果在更高频的年际变化中发现了WW核心盐度与降水量的关系,对该观点进行了补充。
在WW核心温盐特性之外,继续分析外部强迫对WW厚度的影响(图9)。结果显示,EKE距平与WW厚度距平之间具有显著负相关关系(R = −0.70,p < 0.05)。此外,从统计学上并未发现其他外部强迫对WW厚度有明显影响作用。WW厚度能够反映冬季混合层在垂向上的扩张能力,而EKE体现涡旋活动强度,两者的相关关系说明了涡旋活动影响研究海域上层海水的垂向再分配。由于EKE增强(减弱)对应WW厚度减小(增大),同时根据前人研究已知,该海域被多个持续存在的气旋式涡旋主导[50-51],由此推测气旋式涡旋强度增强会引起向上抽吸效应增强,WW下方的绕极深层水被带到更浅深度,最终导致WW厚度变薄。
需要注意的是,正如图4所示,WW核心盐度距平与WW厚度距平之间也存在显著相关关系(R = 0.71,p < 0.05),且两者的年际变化与WW核心温度距平变化在部分年份相反。因此,虽然图7图9中分别获得WW核心温度变化与AAO指数、盐度变化与净降水量变化、WW厚度变化与EKE强度有关的结果,但是这些外部强迫之间本就存在着复杂的关联性,且AAO变化作为主要原因影响着局地大气场和海表动力场[38, 46-47],因此影响WW层各要素变化的外部强迫原因必然是综合的。同时前人研究指出,南大洋SIC和SST之间具有强的耦合关系,且两者受AAO变化和厄尔尼诺/拉尼娜事件影响[52]。本文基于统计学结果未发现这两者变化与WW层温盐特性和厚度变化有显著关系,但是时间序列分布显示,WW核心温度距平和SIC、SST距平变化呈相反趋势,而WW核心盐度和WW厚度距平与SIC和SST距平具有较一致的变化特征。
基于2011−2020年间每年夏季(1月)沿着110°E采集的连续重复观测资料,本文分析了南大洋印度洋扇区内季节性冰区的WW层时空变化特征,并初步探讨了AAO、纬向风转向点、P−E、EKE、SIC及SST变化对WW层特性及其厚度的可能影响。主要结果如下:
(1)这10年期间,WW冷核特性及WW厚度的年际变化显著。其中,WW核心温度变化与核心盐度、中性密度、WW核心所在深度及WW层厚度的变化在部分年份相反,而与溶解氧浓度变化一致,主要体现在具有显著极值的2014年及2018−2020年的结果中。WW核心盐度与WW厚度之间具有显著正相关关系(R = 0.71)。
(2)AAO作用于研究海域,AAO指数与纬向风转向所在纬度(辐散带中心)呈负相关关系(R = −0.61)。同时,年平均AAO距平与WW核心温度距平呈显著正相关关系(R = 0.69)。可知,正(负)相位AAO促使西风带最南端往南(北)移动,南ACC锋相应南(北)移,对应WW核心温度升高(降低)。因此,WW核心温度的高频变化一定程度上反映了辐散带乃至ACC的南北振荡。
(3)WW核心盐度受净降水量影响,2016−2019年连续的负降水量距平对应WW核心盐度的正距平,对2010年代后期整体盐度增加有贡献作用;EKE距平与WW层厚度呈显著负相关关系(R = −0.70),即EKE增大对应WW层厚度变薄。结合前人研究结果,推测该海域气旋式涡旋强度增强(减弱)导致向上抽吸效应增强(减弱),可能致使绕极深层水的深度变浅(加深),进而影响其上层WW层厚度。
本文聚焦到WW层特性的年际变化并初步探讨了外部强迫对该变化的影响。然而,由于WW层所在深度较浅,且其特性在经向上差异较大,季节尺度甚至更高频的海表强迫变动及空间上非区域平均的更小尺度变化都可能引起该层特性的变化,可是受限于实测数据的1年期间隔,本文未能去除这些高频信号的混叠效应。因而在今后工作中有必要缩短观测间隔,比如开展锚碇观测,并且分离出不同时空尺度的外部强迫影响,从而获得进一步的精细化结果。
  • 自然资源部海洋生态监测与修复技术重点实验室开放研究基金(MEMRT202211);国家自然科学基金(42130402, 42176012)。
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2023年第45卷第8期
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文章信息
doi: 10.12284/hyxb2023100
  • 接收时间:2022-11-09
  • 首发时间:2025-12-28
  • 出版时间:2023-08-31
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  • 收稿日期:2022-11-09
  • 修回日期:2023-03-31
基金
自然资源部海洋生态监测与修复技术重点实验室开放研究基金(MEMRT202211);国家自然科学基金(42130402, 42176012)。
作者信息
    1 上海海洋大学 海洋科学学院,上海 201306
    2 自然资源部海洋生态监测与修复技术重点实验室,上海 201306
    3 上海海洋大学 极地研究中心,上海 201306
    4 日本东京海洋大学 海洋科学系,东京 108-8477
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2种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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