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Journal of Tropical Oceanography >

2019 , Vol. 38 >Issue 2: 32 - 42

DOI: https://doi.org/10.11978/2018061

Marine Meteorology

Distinguishing two types of El Niño in the Tropical Pacific using key region sea surface salinity index

  • FANG Zhujun , 1 ,
  • ZHI Hai 1 ,
  • LIN Pengfei , 2, 3 ,
  • WEI Xiang 1
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  • 1. College of Atmospheric Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China
  • 2. State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics (IAP), Chinese Academy of Sciences, Beijing 100049, China
  • 3. University of Chinese Academy of Sciences, Beijing 100049, China;
Corresponding author: LIN Pengfei. E-mail:

Received date: 2018-06-04

  Request revised date: 2018-07-28

  Online published: 2019-04-15

Supported by

National Key Research and Development Program of China (2016YFC1401601)

National Natural Science Foundation of China (41690122, 41690120)

Chinese Academy of Sciences Strategic Priority Project (XDA11010105, XDA11020306, XDA11010304)

Copyright

热带海洋学报编辑部
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Abstract

In this paper, the sea surface salinity anomaly (SSSA) index from the EN4 reanalysis data during 1980-2016 is used to distinguish two types of El Niño. We identify the salinity variation region which has a close contact with two types of El Niño in the tropical Pacific. We demonstrate that salinity time series in different boxes are related sea surface temperature variation, and can indicate two types of El Niño. Depending on the key salinity field contrast of their correlation coefficient of temporal and horizontal distribution. Evidently, the region of SSS field is mainly located in the equatorial region with symmetric distribution in the western tropical Pacific during the eastern Pacific (EP) El Niño. One significant region of SSS field is migrated westward, another skewing southward east of the dateline as asymmetric distribution during the central Pacific El Niño, which is different from the EP El Niño. Based on these characteristics, we find key areas of an index that represents two types of El Niño and distinguishes them. Finally, we use this index to reproduce recent El Niño events.

Key words： two type of El Niño;; sea surface salinity anomaly; index; Tropical Pacific

Cite this article

FANG Zhujun , ZHI Hai , LIN Pengfei , WEI Xiang . Distinguishing two types of El Niño in the Tropical Pacific using key region sea surface salinity index[J]. Journal of Tropical Oceanography, 2019 , 38(2) : 32 -42 . DOI: 10.11978/2018061

厄尔尼诺—南方涛动(El Niño-Southern Oscillation, 简称ENSO)是一个重要的气候现象, 发生在赤道太平洋海域, 是地球上最重要、最强的年际变化信号, 局地海洋和大气环流均出现明显的异常, 影响着全球的气候变化(Bjerknes, 1969)。观测资料已经证明在全球气候系统中, 不同的物理场之间存在着相互作用, 这些相互作用可以对海表面温度进行调制, 进而对厄尔尼诺的性质产生重要影响(Kim et al, 2009; Weng et al, 2009)。这些相互作用中包括盐度异常间接调制海表面温度异常(SSTA)作用(Zheng et al, 2015)。海洋的盐度和温度一样, 是海洋水循环中的一种极为重要的物理量(Lagerloef, 2002), 盐度与海洋的密度、水平压力梯度、海洋稳定层结、赤道温跃层厚度等存在密切联系(Fedorov et al, 2004), 海洋盐度在热带气候动力过程中可以起到重要作用(Huang et al, 2008)。因此, 研究海表盐度异常可以从更多、更广的角度扩展对ENSO的研究和预测。
厄尔尼诺事件作为热带太平洋中最为重要的年际变化特征, 其水平分布特征具有多样性。针对传统的厄尔尼诺事件, 其代表性特征为在南美秘鲁沿岸地区爆发SSTA异常偏高的现象, 影响贯穿了整个热带太平洋(Rasmusson et al, 1982; Wang, 1995)。因此, 也称这种厄尔尼诺现象为东部太平洋型厄尔尼诺(以下简称EP型)。近年来的观测发现, 在热带太平洋地区还存在着一种特殊类型的厄尔尼诺现象, 它的正SSTA最大值主要集中在太平洋中部地区, 因此也称其为中部型厄尔尼诺现象(以下简称CP型)(Yu et al, 2007, 2010)。自20世纪90年代以来, CP型厄尔尼诺的频率和强度较过去均有显著增强(Lee et al, 2010; Newman et al, 2011; Xu et al, 2012)。近年来对厄尔尼诺的多样性特征已经有了很多的研究(Wittenberg et al, 2006; Weng et al, 2009; Kim et al, 2009; Ashok et al, 2009; Singh et al, 2011; Menkes et al, 2014; McPhaden, 2015)。许多学者发现CP型与EP型厄尔尼诺彼此具有独立性, 可以尝试通过一种或几种物理量场将其区分(Larkin et al, 2005; Ashok et al, 2007; Guan et al, 2008; Kao et al, 2009)。
随着观测资料和观测手段的增加, 过去只能依赖观测船等有限的手段获得海洋数据的方式, 如今已经逐渐被海洋实时观测系统(array for real-time geostrophic oceanography, 简称ARGO)工作的开展所代替。ARGO可以提供包括海洋盐度资料在内的多种海洋物理场数据, 这些更精确, 分辨率更高, 深度更深的海洋数据, 不仅可以检验理论的正确性, 还能够用于更深入的研究。因此, 研究海表面盐度异常(SSSA)与两类厄尔尼诺的关系也成为研究热点。由于海洋盐度是海洋变化中的重要物理量, 一方面, 与温度相比盐度具有良好的保守性, 因此经常在环流的研究中扮演有效示踪物的角色; 另一方面, 盐度通过调节密度变化影响海洋中热量的垂直输送(Picaut et al, 1996), 参与海洋的动力过程(杜岩 等, 2004; Yu et al, 2007; Ashok et al, 2007; Li et al, 2013)。盐度本身又受控于淡水通量(Cravatte et al, 2009)、海洋平流、海洋垂直混合卷夹, 以及地表径流等多重因素的作用。
观测发现在厄尔尼诺爆发时, 首先由于SSTA导致降水产生异常, 而降水的异常又导致了淡水通量(蒸发-降水)的异常, 进一步影响了海表面盐度(SSS)的变化, SSS的变化又作用于海洋的密度, 导致海洋密度场发生变化, 进而对海洋中混合层厚度(MLD)和障碍层厚度(BLT)产生影响(Galperin et al, 1988; Lukas et al, 1991; Zeng et al, 2009), 而MLD和BLT的变化, 对海洋热量的垂直传递同样产生影响, 最终作用于海表面温度(SST), 以上过程完整的形成了一个正反馈作用(Fedorov et al, 2004; Maes et al, 2005)。在CP型厄尔尼诺中, 由于淡水通量的负异常所引起的负盐度异常向西偏移(Kug et al, 2009; Singh et al, 2011), 因此此时MLD和BLT同样也发生向西偏移(Zheng et al, 2012)。
在海洋盐度研究中, SSS指数化的方法近年来被广泛使用。由于SSS锋与西太平洋暖池东部边缘的关系密切(Delcroix, 1998; Bosc et al, 2009), 并且SSS锋的位置十分接近日界线(Eldin et al, 1997), 因此可以使用34.8‰等盐度线沿赤道的纬向位置作为指数, 即Niño-34.8指数, 用于表征SSS锋和西太暖池东边缘的纬向分布特征。还有一些学者也提出了许多不同的SSS指数, 这些SSS指数与包括南方涛动指数(SOI指数)、Niño-3区、Niño-3.4区、Niño-4区指数在内的诸多温度指数, 均具有具相当的一致性(Rasmusson et al, 1982; Trenberth, 1997)。也有学者选择位于南太平洋(0—10°S, 150°—90°W)的SSS指数, 又称为西南太平洋指数(SEPSI)进行研究, 同样发现SEPSI与厄尔尼诺的新型厄尔尼诺现象(Modoki)和贯穿型厄尔尼诺现象(Trans-Niño)指数高度相关(Qu et al, 2014)。当发生CP型El Niño时期或者Modoki El Niño时期(Harrison et al, 1998), 该指数具有正异常, 进一步导致了纬向SST梯度在中东热带太平洋之间增加, 它可以被用来描述厄尔尼诺的类型(Yu et al, 2007; Ashok et al, 2007)。
综合以往的研究发现, SSS的指数化工作具有十分重要的意义。然而盐度指数与El Niño多样性之间究竟是存在个别区域的线性相关还是多区域之间线性相关, 以及其在时间场中的超前滞后关系、作用机制都有待进一步研究。本文的主要目的是基于热带太平洋的关键区域的SSSA的变化与两类El Niño的关系, 找出能够区分两类El Niño的SSSA盐度指数。

1 资料和方法

1.1 资料

本文使用的温度和盐度资料来自英国气象局哈德莱中心(Met Office Hadley Center)提供的强化海洋数据(enhance ocean data, 简称EN.4.1.1f)的逐月再分析海表盐度资料(Ingleby et al, 2007; Giese et al, 2016), 数据全部时段从1900年1月至今, 本文选取其中1980年1月至2016年12月。空间分辨率为1°×1°, 垂直共42层。验证所用盐度和温度资料来自ARGO数据(http://apdrc.soest.hawaii.edu/projects/argo/), 该数据的空间分辨率为1°×1°, 可获得逐月数据以及气候态尺度的空间平均场, 其垂直共27层, 从5m至2000m, 数据时段从2005年1月至今。

1.2 方法

根据Xiang等(2013)的方法, 两类El Niño事件定义为: 从海洋尼诺指数(Oceanic Niño index, 简称ONI指数)中以每一年ONI指数超过0.5℃的月份为起始时间, 至次年的1月为结束时间, 连续三个月即可认为发生一次El Niño。例如, 1997/1998年厄尔尼诺事件中, 可以认为是从5月份为起始, 到次年1月。以此为根据分别选取5次EP El Niño事件(1982/1983、1986/1987、1997/1998、2006/2007和2015/2016)和5次CP El Niño事件(1991/1992、1994/1995、2002/2003、2004/2005和2009/2010)过程, 并通过5次特殊事件的合成场, 代表EP和CP型El Niño的水平分布特征。该方案的优点为: 1) 所选取的CP型El Niño事件的暖中心持续存在于中太平洋地区; 2) 暖中心在减弱的过程中向东太平洋偏移十分有限。
同样还需要使用两个指数分别代表CP型和EP型El Niño的时间序列。选用Niño3指数表征EP型El Niño, 其中Niño3指数为(5°S—5°N, 150°—90°W)海温距平的区域平均。使用Niño3指数的优势在于, Niño3区是EP型El Niño事件发生时海表温度异常最主要的发生发展区域。
本文采用新型厄尔尼诺现象指数(El Niño Modoki index, 简称EMI指数)代表CP型El Niño指数(Harrison et al, 1998; Ashok et al, 2007), EMI指数的定义为:
$EMI=[SSTA]_C-0.5[SSTA]_E-0.5[SSTA]_W$
式中: [SSTA]C、[SSTA]E、[SSTA]W分别代表太平洋中部(10°S—10°N, 165°E—140°W)、太平洋东部(15°S—5°N, 110°—70°W)和太平洋西部(10°S—20°N, 125°—145°E)海表面温度距平的区域平均。
选取EMI指数的优势在于, 通过相关性分析结果表明, EMI同经验正交函数(empirical orthogonal function, 简称EOF)第二模的时间序列第二主成分(the second principal component, 简称PC2)有很高的相关性, 说明EMI在很大程度上可以捕捉到赤道太平洋SSTA进行EOF分解得到的第二个模态的分布型, 可以恰当的描述El Niño Modoki事件(Harrison et al, 1998; Weng et al, 2009)。

2 热带地区盐度表征并区分两类El Niño的关键区

2.1 温度异常在两类El Niño时期的主要特征和差异

为了验证本文所选用的5次EP型和5次CP型El Niño事件的合理性。首先选择了SSTA的水平分布(图1a、1c、1e)来对所用方法进行验证。合成的CP型El Niño的SSTA水平分布(图1a), 其中心区域位于赤道中太平洋(180—160°W, 5°S—5°N); 典型的EP型El Niño的SSTA水平分布(图1c)则位于传统的赤道中东太平洋。两类El Niño事件的SSTA的差异主要出现在135°W以东区域(图1e), 呈现出CP型SSTA低于EP型SSTA的特征。因此可以认为本文的方法所选El Niño特殊合成事件的正确。
Fig. 1 Horizontal distribution of SSTA and SSSA and the difference between two types of El Niño events. (a, c, e) Sea surface temperature; (b, d, f) sea surface salinity; (a, b) CP type; (c, d) EP type; (e, f) CP minus EP

图1 两类El Niño事件期间SSTA (单位: ℃)和SSSA (单位: ‰)的水平分布及两类事件之间对应的差值
a、c、e为海表温度; b、d、f为海表盐度; a、b为CP型; c、d为EP型; e、f为差值(CP-EP)

2.2 盐度异常在两类El Niño时期的主要特征和差异

使用与SSTA类似的方法, 从两类El Niño发展时期的SSSA差异中, 寻找关键区域。在CP型和EP型赤道区域, 其SSSA分布大体相似, 都呈现了西负东正的特征(图1b、1d)。两类El Niño时期SSSA在150°E—150°W的暖池东边缘为负异常, 而其两侧(150°E以西和150°W以东)均为正异常。但是两者在南北纬的赤道外呈现了非常强的差异(图1f)。在中西太平洋地区CP型的负异常在分布范围和强度上均强于EP型的负异常, 而东太平洋在南北纬10°附近, CP型的盐度异常也要强于EP时期, 在海洋大陆地区EP型El Niño的正异常要强于CP时期。因此有理由认为两类El Niño期间存在多个对两类El Niño产生影响的SSSA关键区。

2.3 热带地区SSSA与两类El Niño的相关关系

对明显的SSSA差异区域进行分析, 注意到在图1f中SSSA差异场的分布主要呈西北—东南走向或者东北—西南走向的水平分布结构, 很难用一个完整的矩形框将其囊括。因此使用热带太平洋地区逐点的SSSA时间序列分别与代表CP型的EMI指数和EP型的Niño3指数进行时间序列的相关分析, 得到SSSA与CP和EP的时间相关系数水平分布图(图2)。从图2中可以明显看到, CP型与EP型El Niño的相关系数水平分布存在显著的差异。在CP型El Niño时期, 相关系数的水平分布呈现出了西北—东南走向, 其正相关系数最大值分布主要位于热带东南太平洋副热带区域和澳大利亚以东区域; 负相关系数最大值则位于日界线以西, 并且在赤道日界线附近呈现出了南北的非对称性。在EP型El Niño时期, 其水平分布则为东—西走向或西南—东北走向, 最大正相关系数则主要出现在了日界线以西的菲律宾以东洋面和澳大利亚以东地区; 负相关系数最大值区域则以日界线和赤道为轴呈现中心对称分布, 强度强于CP型El Niño时期同区域的强度。为了更好地区分CP和EP型El Niño, 本文集中在二者差异较大的区域, 即南太平洋区域、菲律宾以东区域和日界线附近这三个区域, 并以此为根据进行区域划分, 进而构建SSSA指数。
Fig. 2 Horizontal distributions of correlation coefficient. (a) CP type (EMI index), and (b) EP type (Niño3 Index)

图2 区域中逐点的盐度分别与采用SSTA为指标(EMI或者Nino3)的时间序列求时间相关, 逐点相关系数构成相关系数水平分布图
a. CP型; b. EP型

2.4 超前滞后时间相关系数水平分布

在以往的研究中发现盐度变化与SST变化之间存在明显的超前滞后相关关系(Huang et al, 2008)。在确定指数的关键区之前, 先要确定在两类El Niño事件期间SSSA与SSTA二者的超前滞后关系, 并选择出二者相关关系最为密切的时间。所以逐点的SSSA与CP和EP型El Niño的指数分别进行SSSA超前指数4个月变化至SSSA滞后指数4个月的时间相关系数水平分布的分析。
由CP型(图3)和EP型(图4)分析结果可以看到, 在CP型El Niño中, 位于日界线以西的相关系数负值区域最大值, 在SSSA超前4个月至超前1个月过程中递增, 且在超前1个月时达到最大负相关系数-0.66。而在东南太平洋的正相关系数区域, 同样由SSSA超前4个月时期开始递增, 至同期达到正相关系数的最大值, 此后正相关系数在滞后过程继续递增, 至滞后4个月达到最大值0.54, 但正相关区域范围开始缩小。同样地, 在EP型El Niño过程中, SSSA主要的相关系数大值区中, 自SSSA超前4个月起始开始增加, 至超前1个月时, 其作用范围达到最大, 而相关系数负相关中心最大值可达-0.62, 而正相关系数达到0.42左右, 与CP型El Niño相同, 在达到同期相关后, 当SSSA滞后于SSTA变化时, 其正相关系数仍然增加达到最大的0.59, 但范围开始减小; 负相关系数则几乎不变, 范围同样开始减小。值得注意的是, 当盐度滞后于温度变化达到第四个月时, CP型与EP型的相关系数水平分布十分相似, 可以认为, 其受到El Niño信号的影响基本衰退, 开始呈现正常平均态分布。
Fig. 3 Horizontal distributions of lead and lag correlation coefficients between the time series of SSSA on each grid and EMI index. SSSA leading SSTA by four months to one month is shown in (a, b, c, d); the zero lead month of SSSA and SSTA is shown in (e); SSSA lagging SSTA by 1~4 months is shown in (f, g, h, i)

图3 区域内逐点的SSSA时间序列与采用CP型SSTA为指标(EMI指数)的时间序列进行时间相关, 逐点相关系数的相关系数水平分布图
a、b、c、d依次为盐度超前温度4个月至1个月; e为同期相关; f、g、h、i分别为盐度滞后于温度1个月至4个月

Fig. 4 Horizontal distributions of lead and lag correlation coefficients between the time series of SSSA on each grid and Niño3 index. SSSA leading SSTA by four months to one month is shown in (a, b, c, d); the zero lead month of SSSA and SSTA is shown in (e); SSSA lagging SSTA by 1-4 months is shown in (f, g, h, i)

图4 区域内逐点的SSSA时间序列与采用EP型SSTA为指标(Niño3指数)的时间序列进行时间相关, 逐点相关系数的相关系数水平分布图
a、b、c、d依次为盐度超前温度4个月至1个月; e为同期相关; f、g、h、i分别为盐度滞后于温度1个月至4个月

根据上述超前滞后性分析, 虽然在滞后时间段中EP型El Niño的相关系数大于超前时期。但是其一, 由于我们的目的是为了更好利用指数对SSTA进行预测, 因此滞后相关系数的意义远没有超前相关系数大; 其二, 当SSSA滞后于SSTA时, 对于区分CP和EP型El Niño也显得较为困难。综上两点原因, 选取SSSA超前SSTA一个月的时间为构建盐度指数的最佳时间。

2.5 时间相关系数验证

为了进一步验证所选区域的合理性, 使用SSSA在热带太平洋的水平分布场回归到代表EP型El Niño的Niño3指数和代表CP型El Niño的EMI指数(图5)。SSSA分布特征在EP和CP时期具有显著的不同, 在CP型(图5a)中回归出的负SSSA区域主要集中在180°日界线以西出现, 负中心区域位于(0°, 160°E), 而正中心区域则沿180°以东的赤道向东分布, 并且出现了南北向的非对称分布特征。SSSA在北太平洋的正异常区域的范围和强度要大于同经度的南太平洋。在EP型(图5b)中回归出的SSSA分布, 其正负大值中心在180°以西的西太平洋更加显著, SSSA的分布表现出自北向南正负正的水平分布特征。因此, 图5的SSSA异常区域分布与图2中的相关系数水平分布特征十分相似, 可以认为所选区域合理。
Fig. 5 Regressions of SSSA on each grid to EMI index (a) and Nino3 index (b)

图5 分别为SSSA场回归到代表CP的EMI指数(a)和代表EP的Niño3指数(b)的水平分布图

3 盐度中太平洋指数/东太平洋盐度指数(SCPI/SEPI)特征

3.1 SCPI/SPEI指数的构建

通过上述筛选, 关键区若要能区分并代表两类El Niño事件, 则需要满足两个条件: 1) 该关键区与它代表的El Niño类型具有很强的相关性; 2) 该关键区与另一种El Niño的相关性很差。因此, 选择位于东南太平洋的D区和日界线以西的B区(即图6a、6b中的红框区)的两个关键区进行组合,共同作为CP型El Niño的关键区; 同理, 选择位于菲律宾以东洋面的A关键区和赤道中太平洋的C关键区(图6a、6b中的蓝框区)进行组合,共同作为EP型El Niño的关键区。通过对上述4个区域进行组合, 构造出一组以SSSA为变量的指数对两类El Niño进行分类, SCPI/SEPI指数的定义如下所示:
$\text{SCPI}=0.7{{\left[ \text{SSSA} \right]}_{\text{D}}}-0.3{{[\text{SSSA}]}_{\text{B}}}$
$\text{SCPI}=0.3{{\left[ \text{SSSA} \right]}_{\text{A}}}-0.7{{[\text{SSSA}]}_{\text{C}}}$
式中: [SSSA]B和[SSSA]D分别代表西太平洋(5°S—5°N, 150°—165°E)和东南太平洋(5°—20°S, 90°—135°W)的CP型的关键区内海表面盐度距平的区域平均; 而[SSSA]A和[SSSA]C则分别代表菲律宾以东洋面(0°—10°N, 130°—150°E)和日界线附近中太平洋(5°S—5°N, 175°E—160°W)的EP型的关键区内海表面盐度距平的区域平均。区域前的权重系数则是根据各个关键区的面积权重乘以相关系数强度权重的组合所得。CP型以指数大于1.0倍标准差, EP型以指数大于1.5倍标准差, 为标准选取异常月, 且至少连续3个月以上达到异常即可视为一次El Niño过程。
Fig. 6 Salinity correlated with the representing time series of CP El Niño (EMI index) (a) and EP El Niño (Niño3 Index) (b). The horizontal distribution of the correlation coefficient is shown. The red and blue boxes represent the characteristic areas of CP El Niño and EP El Niño, respectively

图6 盐度逐点的时间序列与代表CP的时间序列(EMI指数)和EP的时间序列(Niño3指数)做超前1个月的时间相关的相关系数关键区。
a. CP型; b. EP型。图中红框和蓝框分别代表CP和EP型的特征区

3.2 SPCI/SPEI与SSTA的空间回归

为了进一步研究及检验SPEI/SPCI指数的适用性, 利用2005—2016年ARGO数据中的SSTA回归到SPEI/SPCI指数上。在SPCI的回归结果中(图7a), SPCI很好地回归出CP型El Niño事件, 回归场的正值中心位于日界线附近。而SPEI的回归结果(图7b)也能够回归出EP型El Niño事件, 回归正值中心位于传统的Niño3、Niño4区。因此进一步验证了SPCI和SPEI的合理性, 图7为SPEI/SPCI与热带地区EOF的特征场的时间序列的优劣对比。
Fig. 7 Regressions of SSTA on each grid to SCPI index (a) and SEPI index (b) by ARGO data

图7 ARGO数据中SSTA场回归到代表CP的SCPI指数(a)和代表EP的SEPI指数(b)的水平分布图

3.3 SCPI/SPEI指数的相关性

由于SCPI/SEPI指数是基于CP和EP多区域的组合而成, 因此其理应对CP和EP型El Niño的空间时间相关系数具有很好的响应, 表1中显示了在选取的37a的时间段中, SCPI/SPEI的时间空间相关系数。
Tab. 1 The temporal and spatial correlation coefficients between the CP/EP El Niño and SCPI/SEPI index, and the four EOF modes in the tropical Pacific

表1 SCPI/SEPI指数以及热带地区EOF前四模态的时间系数与CP、EP型El Niño的时间和空间相关系数

类型 空间相关 时间相关
CP EP CP EP
SCPI 0.73 0.15 0.70 0.17
SEPI 0.15 0.50 0.11 0.69
PC1 0.30 0.56 0.31 0.31
PC2 0.08 0.76 0.22 0.43
PC3 0.69 0.34 0.58 0.09
PC4 0.29 -0.10 0.17 -0.14

注: PC表示EOF分析的各个主成分

在表1中, 可以看到SCPI指数与CP型的空间相关系数达到了0.73, 时间相关系数到达了0.7, 二者均远超过了信度为0.001的显著性检验。而SEPI的空间和时间相关系数同样的达到了0.5和0.69也均超过了信度为0.001的显著性检验。同时可以看到在SCPI与EP型El Niño的空间相关系数中, 相关系数没有超过0.15, 所以可以认为两者的相关关系很弱。同样, SEPI与CP型El Niño的空间时间相关系数均不超过0.2, 因此有理由认为SCPI/SEPI既可以很好地表征CP和EP型El Niño事件, 又可以很好地区分彼此。
同样, 对热带太平洋地区海表面盐度的EOF分析的前四模态(图8)对比分析显示: 在EOF的前四模态中, 第一模态(PC1)与第四模态(PC4)的时间序列与CP和EP的相关系数都相对较低, 仅为0.30左右, 而且不能够很好地区分CP和EP型El Niño; 但是第二模态(PC2)对于EP型的时间空间相关系数都很高, 分别达到了0.76和0.43, 超过了信度为0.001的显著性检验; 第三模态(PC3)对于CP型的时间和空间相关同样达到了0.695和0.58, 同样超过了显著性检验。然而PC2与CP型的时间相关系数以及PC3与EP的空间相关系数与SCPI/SPEI对另一类El Niño的低相关性(0.15, 0.11)而言, 都相对偏大, 分别达到了0.22和0.34均超过了信度为0.01的显著性检验。可以认为PC2和PC3二者在区分CP和EP型El Niño时不如SCPI和SEPI效果好。同样, EOF的前四模态还有一个显著的缺陷, 其前四模态的解释方差都偏低, 与CP和EP有关的第二、三模态的解释方差仅为12.8%和6.5%。综上分析, 选择区域化的SCPI和SEPI更能够代表和区分两类El Niño。
Fig. 8 The first four EOF modes of SSSA (left panels) and the corresponding time coefficients (right panels) in the tropical Pacific

图8 热带地区SSSA的EOF分析前四模态水平分布图(左)及其时间系数图(右)
分图右上角的数字表示各模态的解释方差

3.4 SEPI/SCPI个例验证

为了对上节中所建立指数的适用性进行个例验证, 采取ARGO观测资料进行个例验证。根据先前定义的SCPI/SEPI指数, 在CP型El Niño中SCPI在2005年10月、2009年12月和2012年11月时超过1.0倍标准差(图9a), 而在EP型El Niño中, SEPI只有2015/2016年超过了1.5倍标准差(图9b)。因此我们以CP型和EP型的几处最大值作SSTA的平均合成(图9c、9d)。SEPI所选择的峰值点合成出的SSTA呈现出了2015/2016年El Niño的水平分布特征, 尽管由SCPI所选择的峰值点同样合成出了正SSTA位于赤道中太平洋地区的CP型El Niño的水平分布, 但其SSTA相对于EP型较低。
Fig. 9 The time series of SCPI/EMI index (a), SEPI/ Niño3 index (b) and the compound horizontal distribution by SSTA extreme points when reaching the standard of SCPI/SEPI index, (c) CP, and (d) EP

图9 指数的时间序列
a．SCPI/EMI指数; b. SEPI/Niño3指数达到SCPI/SPEI指数标准时, 极值点合成SSTA水平分布图; c. CP型; d. EP型

4 讨论和结论

自Harrison等(1998)发现并提出赤道中太平洋的Modoki型的El Niño, 即CP型El Niño开始, 对于CP型和EP型El Niño的研究开展了广泛的讨论, 包括两类El Niño的生成、发展和对应相关物理场的差异。本文研究了SSSA在两类El Niño中的时空分布特征, 分析了两类El Niño的可区别性和可测性。分析发现: 利用相关系数水平分布图和SSTA回归出的SSSA异常分布, 体现了在EP型El Niño时期, 日界线以西的西太平洋暖池区域呈现了南北沿赤道非对称分布特征; 在CP型时期, 用SSTA回归出的SSSA负值中心区域在赤道太平洋向西偏移, 并且在南太平洋存在显著正异常区, 这与EP存在着明显的差异。这些异同特征为验证我们研究设想提供了必要条件。
部分SSSA的水平分布特征的差异, 在过去已经有一些研究对其形成的物理机制做出过解释。EP型El Niño时期, 在赤道太平洋地区(2°S—2°N), EP现象的SSSA最大淡化发生在日界线附近, 表层平流作用和降水变化是SSSA在ENSO时期产生响应的主要机制(Singh et al, 2011)。EP型El Niño向东的洋流异常主要从热带西太平洋到中东太平洋, 与暖池低盐度水的东偏保持一致, 进而导致了重要的南太平洋复合带(SPCZ)向东北偏移以及SPCZ区域中降水的减小。而当降水异常发生变化, 进入到SSSA中, 会直接导致SSS的降低。而在CP时期, 对于赤道地区的SSSA变化, 存在一个日界线以西的纬向平流辐合, 导致中太平洋暖池低盐度淡水无法有效传播, SPCZ向赤道方向偏移(Hasson et al, 2013)。在降水场中, CP型El Niño降水正异常中心向西偏移(Kug et al, 2009), SSSA收支在表面平流和降水双重作用下, 西太暖池地区的SSS有细微增加, 因此SSSA的最大淡化区域与EP时期对比其向西偏移约15个经度, 而34.8‰的等盐度线的偏移则只有EP型的一半。Zheng等(2012)研究结果显示, CP型的El Niño过程中, SSS对于密度层结和混合层起到了重要的作用, 而且SSS的贡献程度甚至要超越温度。此外, 另一个关键点是, 在此前关注较少的南太平洋海洋荒漠区域出现了经向非对称的SSSA正异常区域。南太平洋SSSA区域在EP型El Niño时期很弱而且不具有经向非对称的特征, CP型时却异常强大。
通过以上多种因素以SSSA为基础, 定义一组能够对两类El Niño进行区分和预测的SSSA指数。试图以SSSA指数从另一个角度对近年来发生的两类El Niño进行研究和预测。但是, 对于文中所提到的多个SSSA关键区, 尤其是南太平洋关键区的盐度收支及其与周边区域的响应和物理机制在本文中没有进行研究与探索, 次表层海水作为影响盐度的重要区域也并未考虑。此外由于盐度对SSTA是间接影响, SSSA作为指数相较于SSTA指数, 在实时性、可靠性上都略有欠缺, 以上几点将在以后进行深入研究。

The authors have declared that no competing interests exist.

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DOI

[12]
GUAN BIN, NIGAM S, 2008. Pacific sea surface temperatures in the twentieth century: an evolution-centric analysis of variability and trend[J]. Journal of Climate, 21(12): 2790-2809.A consistent analysis of natural variability and secular trend in Pacific SSTs in the 20th century is presented. By focusing on spatial and temporal recurrence, but without imposition of periodicity constraints, this single analysis discriminates between biennial, ENSO and decadal variabilities, leading to refined evolutionary descriptions; and between these natural variability modes and secular trend; all without advance filtering (and potential aliasing) of the SST record. SST anomalies of all four seasons are analyzed together using the extended-EOF technique. Canonical ENSO variability is encapsulated in two modes that depict the growth (east-to-west along the equator) and decay (near-simultaneous amplitude loss across the basin) phases. Another interannual mode, energetic in recent decades, is shown linked to the west-to-east SST development seen in post climate-shift ENSOs; the non- canonical ESNO mode. The mode is closely related to Chiang and Vimont's meridional mode, and leads to some reduction in canonical ENSO's oscillatory tendency. Pacific decadal variability is characterized by two modes: The Pan-Pacific mode has a horse-shoe structure with the closed end skirting the North American coast; and a quiescent eastern equatorial Pacific. The mode exhibits surprising connections to the tropical/subtropical Atlantic, with correlations there resembling the Atlantic Multidecadal Oscillation. The second decadal mode---the North Pacific mode---captures the 1976/77 climate shift and is closer to Mantua's Pacific Decadal Oscillation. Our analysis shows, perhaps, for the first time, the striking links of the North Pacific mode to the western tropical Pacific and Indian Ocean SSTs. The physicality of both modes is assessed from correlations with the Pacific biological time series. Finally, the secular trend is characterized: Implicit accommodation of natural variability leads to a non-stationary SST trend, including mid-century cooling. The SST trend is remarkably similar to the global surface air temperature trend. Geographically, a sliver of cooling in found in the central equatorial Pacific in the midst of wide- spread but non-uniform warming in all basins. An extensive suite of sensitivity tests, including counts of the number of observational analogs of the modes in test analyses, support the robustness of this analysis.

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[13]
HARRISON D E, LARKIN N K, 1998. El Niño - Southern Oscillation sea surface temperature and wind anomalies, 1946-1993[J]. Reviews of Geophysics, 36(3): 353-399.El Ni o-Southern Oscillation (ENSO) periods, which occur irregularly every few years, are a major perturbation of the Earth's climate system that involves large-scale changes in winds, rainfall, sea surface temperature (SST), and surface pressure. In some areas of the world there are disastrous droughts, and in others there is serious flooding. North American weather patterns are also affected. It is important to develop skillful forecasts for ENSO periods. Our goal here is to provide an overview of the global ocean and atmosphere surface changes that typically occur during ENSO periods. Knowledge of these anomaly patterns is needed in order to improve our understanding and forecasts of ENSO. With a global surface data set we describe the statistically significant patterns of SST, surface wind, and surface pressure changes that on average are associated with the 10 post-World War II ENSO periods. We present these average anomaly results as an "ENSO composite." It is useful to identify phases of the typical ENSO and examine the statistically significant elements phase by phase. An ENSO by ENSO period search indicates that about two thirds of these elements occur in 90% or more of the ENSO periods: we define a "Robust ENSO Composite" from these frequently occurring elements and find it to be an Indo-Pacific phenomenon. Limitations in the surface data set make it possible that this study has not identified all the important aspects of ENSO periods; data are very sparse in both space and time over much of the tropics and the southern hemisphere. However, we suggest that any theory or model of ENSO should at least exhibit the features of this robust composite, and is unlikely to able to represent adequately the large-scale environmental impacts of ENSO unless it does so.

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[14]
HASSON A E A, DELCROIX T, DUSSIN R, 2013. An assessment of the mixed layer salinity budget in the tropical Pacific Ocean. Observations and modelling (1990-2009)[J]. Ocean Dynamics, 63(2-3): 179-194.This paper investigates mechanisms controlling the mixed-layer salinity (MLS) in the tropical Pacific during 1990-2009. We use monthly 1A degrees aEuro parts per thousand x 1A degrees gridded observations of salinity, horizontal current and fresh water flux, and a validated ocean general circulation model with no direct MLS relaxation in both its full resolution (0.25A degrees and 5 days) and re-sampled as the observation time/space grid resolution. The present study shows that the mean spatial distribution of MLS results from a subtle balance between surface forcing (E -aEuro parts per thousand P, evaporation minus precipitation), horizontal advection (at low and high frequencies) and subsurface forcing (entrainment and mixing), all terms being of analogous importance. Large-scale seasonal MLS variability is found mainly in the Intertropical and South Pacific Convergence Zones due to changes in their meridional location (and related heavy P), in the North Equatorial Counter Currents, and partly in the subsurface forcing. Maximum interannual variability is found in the western Pacific warm pool and in both convergence zones, in relation to El Nio Southern Oscillation (ENSO) events. In the equatorial band, this later variability is due chiefly to the horizontal advection of low salinity waters from the western to the central-eastern basin during El Nio (and vice versa during La Nia), with contrasted evolution for the Eastern and Central Pacific ENSO types. Our findings reveal that all terms of the MLS equation, including high-frequency (< 1 month) salinity advection, have to be considered to close the salinity budget, ruling out the use of MLS (or sea surface salinity) only to directly infer the mean, seasonal and/or interannual fresh water fluxes.

DOI

[15]
HUANG BOYIN, XUE YAN, BEHRINGER D W, 2008. Impacts of Argo salinity in NCEP global ocean data assimilation system: the tropical Indian Ocean[J]. Journal of Geophysical Research: Oceans, 113(C8): C08002, doi: 10.1029/2007JC004388.

[16]
INGLEBY B, HUDDLESTON M, 2007. Quality control of ocean temperature and salinity profiles — Historical and real-time data[J]. Journal of Marine Systems, 65(1-4): 158-175.

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[17]
KAO H-Y, YU JINYI, 2009. Contrasting eastern-Pacific and central-Pacific types of ENSO[J]. Journal of Climate, 22(3): 615-632.

DOI

[18]
KIM H-M, WEBSTER P J, CURRY J A, 2009. Impact of shifting patterns of Pacific Ocean warming on North Atlantic tropical cyclones[J]. Science, 325(5936): 77-80.Two distinctly different forms of tropical Pacific Ocean warming are shown to have substantially different impacts on the frequency and tracks of North Atlantic tropical cyclones. The eastern Pacific warming (EPW) is identical to that of the conventional El Ni o, whereas the central Pacific warming (CPW) has maximum temperature anomalies located near the dateline. In contrast to EPW events, CPW episodes are associated with a greater-than-average frequency and increasing landfall potential along the Gulf of Mexico coast and Central America. Differences are shown to be associated with the modulation of vertical wind shear in the main development region forced by differential teleconnection patterns emanating from the Pacific. The CPW is more predictable than the EPW, potentially increasing the predictability of cyclones on seasonal time scales.

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[19]
KUG J-S, JIN FEIFEI, AN S-I, 2009. Two types of El Niño events: cold tongue El Niño and warm pool El Niño[J]. Journal of Climate, 22(6): 1499-1515.

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[20]
LAGERLOEF G S E, 2002. Introduction to the special section: The role of surface salinity on upper ocean dynamics, air-sea interaction and climate[J]. Journal of Geophysical Research: Oceans, 107(C12): doi: 10.1029/2002JC001669.http://www.agu.org/pubs/crossref/2002/2002JC001669.shtml

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[21]
LARKIN N K, HARRISON D E, 2005. On the definition of El Niño and associated seasonal average U.S. weather anomalies[J]. Geophysical Research Letters, 32(13): L13705.A new NOAA definition of El Ni09o identifies a number of additional El Ni09o seasons beyond those conventionally agreed. These additional seasons are characterized by SST anomalies primarily in the western central equatorial Pacific. We show here that the seasonal weather anomalies over the U.S. associated with these additional Dateline El Ni09o seasons are substantially different from those associated with conventional El Ni09o seasons. Although some regions have similar associated anomalies, most of the major regional anomalies are quite different. Treating the two as a single phenomenon yields weaker overall seasonal weather associations and does not take advantage of the stronger associations available when the two are treated separately.

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[22]
LEE T, MCPHADEN M J, 2010. Increasing intensity of El Niño in the central-equatorial Pacific[J]. Geophysical Research Letters, 37(14): doi: 10.1029/2010GL044007.http://www.agu.org/pubs/crossref/2010/2010GL044007.shtml

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[23]
LI YUANLONG, WANG FAN, HAN WEIQING, 2013. Interannual sea surface salinity variations observed in the tropical North Pacific Ocean[J]. Geophysical Research Letters, 40(10): 2194-2199.Analysis of observational data sets reveals pronounced interannual variations of sea surface salinity (SSS) in the tropical North Pacific (7 degrees N-15 degrees N) during 2000-2012. SSS anomalies with maximum magnitudes >0.2 occur in the central Pacific and translate westward at a speed of 15-20 cm s(-1). The signals are weakened during their westward movement but reinforced in the Philippine Sea. Budget analysis for the mixed layer salinity suggests that in the central Pacific, El Nino-Southern Oscillation-related atmospheric freshwater forcing and ocean advection changes are both important in generating and maintaining these large SSS anomalies. In the advection term, the most contributing component is the meridional Ekman advection induced by trade winds. These SSS anomalies are subsequently carried westward by the North Equatorial Current, which is the primary cause of SSS variations in the Philippine Sea. Freshwater forcing is also at work in the Philippine Sea, but its role is generally secondary.

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[24]
LUKAS R, LINDSTROM E, 1991. The mixed layer of the western equatorial Pacific Ocean[J]. Journal of Geophysical Research: Oceans, 96(S01): 3343-3357.The mixed layer of the western equatorial Pacific and its thermodynamics are poorly known because of a general lack of data. Conductivity-temperature-depth (CTD) profiles from the recent Western Equatorial Pacific Ocean Circulation Study (WEPOCS) cruises have been analyzed for various measures of the upper layer and mixed layer thickness, using criteria which depend on vertical gradients of temperature, salinity, and density. From 434 profiles, the average mixed layer depth in the western equatorial Pacific during the two WEPOCS cruises was 29 m, which is about a factor of 3 shallower than had previously been thought. The mean depth of the top of the thermocline was found to be 64 m, so there is a nearly isothermal layer that is deeper than the mixed layer. This discrepancy is attributable to salinity stratification. It is hypothesized that the waters in this 090008barrier090009 layer between the bottom of the mixed layer and the top of the thermocline are formed to the east of the WEPOCS region, and subducted below the shallow and lighter mixed layer waters found in the west. Under light wind conditions, there was a tendency for warm and thin layers to form at the sea surface as a result of diurnal heating; however, there did not appear to be any nighttime maximum to the mixed layer depth associated with convective overturn due to cooling. This contrast with the central Pacific may be caused by the influence of salinity on the thermodynamics of the mixed layer. A strong westerly wind burst was observed during WEPOCS II, and apparently the mixed layer nearly doubled in depth while cooling by more than 100°C. Evidence of downwelling near the equator, and upwelling off the equator, was seen in the distribution of temperature, salinity, and density in the meridional section along 14300°E, which was occupied immediately following the wind event. This event was apparently strong enough to erode through the salinity-stratified layer and into the thermocline, resulting in the observed cooling. The results of this study suggest that except during strong wind events, entrainment cooling may not be an important component of the heat budget of the western Pacific warm pool. This has potentially important implications for the El Ni01±o/Southern Oscillation (ENSO) phenomenon.

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[25]
MAES C, PICAUT J, BELAMARI S, 2005. Importance of the salinity barrier layer for the buildup of El Niño[J]. Journal of Climate, 18(1): 104-118.

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[26]
MCPHADEN M J, 2015. Playing hide and seek with El Niño[J]. Nature Climate Change, 5(9): 791-795.A much-anticipated 'monster' El Nino failed to materialize in 2014, whereas an unforeseen strong El Nino is developing in 2015. El Nino continues to surprise us, despite decades of research into its causes. Natural variations most probably account for recent events, but climate change may also have played a role.

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[27]
MENKES C E, LENGAIGNE M, VIALARD J, et al, 2014. About the role of westerly wind events in the possible development of an El Niño in 2014[J]. Geophysical Research Letters, 41(18): 6476-6483.Abstract Similarities between early 1997 and 2014 has prompted climate scientists to wonder if an El Ni09o matching the 1997 “El Ni09o of the century” could develop in 2014. Until April 2014, the equatorial Pacific exhibited positive heat content anomalies along with an eastward warm pool displacement similar to those found during the onset of strong El Ni09o events. Yet in July 2014, the warm pool had retreated back to its climatological positions and equatorial temperature anomalies were much weaker than in mid-1997. Dedicated oceanic simulations reveal that these weak interannual anomalies can be attributed to differences in Westerly Wind Event (WWE) sequences. In contrast with 1997, the lack of WWEs from April to June significantly limited the growth of eastern Pacific anomalies and the eastward warm pool displacement in 2014. With the absence of additional WWE activity, prospects for a mature El Ni09o in late 2014 are fading.

DOI

[28]
NEWMAN M, SHIN S-I, ALEXANDER M A, 2011. Natural variation in ENSO flavors[J]. Geophysical Research Letters, 38(14): doi: 10.1029/2011GL047658.Using a multivariate, “patterns-based”, red noise approach to 42 years of observed tropical SST, thermocline depth, and zonal wind stress seasonal anomalies, it is shown that natural random variations can account for the observed variability of Central Pacific (CP) and Eastern Pacific (EP) ENSO events. The recent multidecadal increase in the number of CP events relative to EP events, which has been hypothesized to be connected to anthropogenic change in the state of the ocean, is also found to be consistent with multivariate red noise and hence with stationary statistics. ENSO “flavors” are the consequence of differing combinations of two initially orthogonal spatial patterns that are precursors to CP or EP events of both signs. These precursors can be excited by random weather forcing and subsequently result in SST anomaly amplification primarily through surface or thermocline feedbacks, respectively.

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[29]
PICAUT J, IOUALALEN M, MENKES C, et al, 1996. Mechanism of the zonal displacements of the Pacific warm pool: implications for ENSO[J]. Science, 274(5292): 1486-1489.The western equatorial Pacific warm pool is subject to strong east-west migrations on interannual time scales in phase with the Southern Oscillation Index. The dominance of surface zonal advection in this migration is demonstrated with four different current data sets and three ocean models. The eastward advection of warm and less saline water from the western Pacific together with the westward advection of cold and more saline water from the central-eastern Pacific induces a convergence of water masses at the eastern edge of the warm pool and a well-defined salinity front. The location of this convergence is zonally displaced in association with El Nino-La Nina wind-driven surface current variations. These advective processes and water-mass convergences have significant implications for understanding and simulating coupled ocean-atmosphere interactions associated with El Nino-Southern Oscillation (ENSO).

DOI PMID

[30]
QU TANGDONG, YU JINYI, 2014. ENSO indices from sea surface salinity observed by Aquarius and Argo[J]. Journal of Oceanography, 70(4): 367-375.Analysis of the first 2602months of data from the Aquarius satellite confirms the existence of a sharp sea surface salinity (SSS) front along the equator in the western equatorial Pacific. Following several earlier studies, we use the longitudinal location of the 34.8-psu isohaline as an index, termed Ni09o-S34.8, to measure the zonal displacement of the SSS front and consequently the eastern edge of the western Pacific warm pool. The on-going collection of the Array for Real-time Geostrophic Oceanography (ARGO) program data shows high correlations between Ni09o-S34.8 and the existing indices of El Ni09o, suggesting its potential important role in ENSO evolution. Further analysis of the ARGO data reveals that SSS variability in the southeastern tropical Pacific is crucial to identify the type of El Ni09o. A new SSS index, termed the southeastern Pacific SSS index (SEPSI), is defined based on the SSS variability in the region (0°–10°S, 150°–90°W). The SEPSI is highly correlated with the El Ni09o Modoki index, as well as the Trans-Ni09o index, introduced by previous studies. It has large positive anomalies during central Pacific El Ni09o or El Ni09o Modoki events, as a result of enhanced zonal sea surface temperature gradients between the central and eastern tropical Pacific, and can be used to characterize the type of El Ni09o. The processes that possibly control these SSS indices are also discussed.

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[31]
RASMUSSON E M, CARPENTER T H, 1982. Variations in tropical sea surface temperature and surface wind fields associated with the Southern Oscillation/El Niño[J]. Monthly Weather Review, 110(5): 354-384.

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[32]
SINGH A, DELCROIX T, CRAVATTE S, 2011. Contrasting the flavors of El Niño-southern oscillation using sea surface salinity observations[J]. Journal of Geophysical Research: Oceans, 116(C6): doi: 10.1029/2010JC006862.

[33]
TRENBERTH K E, 1997. The definition of El Niño[J]. Bulletin of the American Meteorological Society, 78(12): 2771-2778.

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[34]
WANG BIN, 1995. Interdecadal changes in El Niño onset in the last four decades[J]. Journal of Climate, 8(2): 267-285.

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[35]
WENG HENGYI, BEHERA S K, YAMAGATA T, 2009. Anomalous winter climate conditions in the Pacific Rim during recent El Niño Modoki and El Niño events[J]. Climate Dynamics, 32(5): 663-674.Present work compares impacts of El Ni09o Modoki and El Ni09o on anomalous climate in the Pacific rim during boreal winters of 1979–2005. El Ni09o Modoki (El Ni09o) is associated with tripole (dipole) patterns in anomalies of sea-surface temperature, precipitation, and upper-level divergent wind in the tropical Pacific, which are related to multiple “boomerangs” of ocean-atmosphere conditions in the Pacific. Zonal and meridional extents of those “boomerangs” reflect their independent influences, which are seen from lower latitudes in the west to higher latitudes in the east. In the central Pacific, more moisture is transported from the tropics to higher latitudes during El Ni09o Modoki owing to displacement of the wet “boomerang” arms more poleward toward east. Discontinuities at outer “boomerang” arms manifest intense interactions between tropical and subtropical/extratropical systems. The Pacific/North American pattern and related climate anomalies in North America found in earlier studies are modified in very different ways by the two phenomena. The seesaw with the dry north and the wet south in the western USA is more likely to occur during El Ni09o Modoki, while much of the western USA is wet during El Ni09o. The moisture to the southwestern USA is transported from the northward shifted ITCZ during El Ni09o Modoki, while it is carried by the storms traveling along the southerly shifted polar front jet during El Ni09o. The East Asian winter monsoon related anticyclone is over the South China Sea during El Ni09o Modoki as compared to its position over the Philippine Sea during El Ni09o, causing opposite precipitation anomalies in the southern East Asia between the two phenomena.

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[36]
WITTENBERG A T, ROSATI A, LAU N-C, et al, 2006. GFDL’s CM2 global coupled climate models. Part III: tropical Pacific climate and ENSO[J]. Journal of Climate, 19(5): 698-722.

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[37]
XIANG BAOQIANG, WANG BIN, LI T, 2013. A new paradigm for the predominance of standing central Pacific Warming after the late 1990s[J]. Climate Dynamics, 41(2): 327-340.Canonical El Ni01±o has a warming center in the eastern Pacific (EP), but in recent decades, El Ni01±o warming center tends to occur more frequently in the central Pacific (CP). The definitions and names of this new type of El Ni01±o, however, have been notoriously diverse, which makes it difficult to understand why the warming center shifts. Here, we show that the new type of El Ni01±o events is characterized by: 1) the maximum warming standing and persisting in the CP and 2) the warming extending to the EP only briefly during its peak phase. For this reason, we refer to it as standing CP warming (CPW). Global warming has been blamed for the westward shift of maximum warming as well as more frequent occurrence of CPW. However, we find that since the late 1990s the standing CPW becomes a dominant mode in the Pacific; meanwhile, the epochal mean trade winds have strengthened and the equatorial thermocline slope has increased, contrary to the global warming-induced weakening trades and flattening thermocline. We propose that the recent predominance of standing CPW arises from a dramatic decadal change characterized by a grand La Ni01±a-like background pattern and strong divergence in the CP atmospheric boundary layer. After the late 1990s, the anomalous mean CP wind divergence tends to weaken the anomalous convection and shift it westward from the underlying SST warming due to the suppressed low- level convergence feedback. This leads to a westward shift of anomalous westerly response and thus a zonally in-phase SST tendency, preventing eastward propagation of the SST anomaly. We anticipate more CPW events will occur in the coming decade provided the grand La Ni01±a-like background state persists.

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[38]
XU KANG, ZHU CONGWEN, HE JINHAI, 2012. Linkage between the dominant modes in Pacific subsurface ocean temperature and the two type ENSO events[J]. Chinese Science Bulletin, 57(26): 3491-3496.We investigate the variations of subsurface ocean temperature (SOT) based on the monthly-Simple Ocean Data Assimilation (SODA) during 1958-2007, and discuss the linkage between the variations of SOT and the eastern and central Pacific ENSO (EP and CP-ENSO) events. The wavelet analyses suggest that the variation of the EP and CP-ENSO events shows the 2-7 and the 10-15 years oscillation in the tropical sea surface temperature (SST), and coupled with a zonal dipole mode and a tripole mode in the SOT anomalous field reveled by the singular value decomposition (SVD) analysis. During the mature phase of CP-ENSO, the positive center of SOT at the subsurface layer locates in the west of dateline, which results in the increase of SOT in the Nio4 region and causes the CP-ENSO event. Statistical analysis implies that, the eastern and central Pacific subsurface indices which are defined by the expansion coefficients of the first and third SVD mode for SOT have shown the capabilities in disguising the EP and CP-ENSO events, respectively. In addition, corresponding to the increase of the SOT amplitude on the 10-15 years time scale, we found that the frequency and intensity of CP-El Nio events has exhibited an upward trend after the 1980s, which suggests that the CP-ENSO event has shown an enhanced impact on the global climate in the past decades.

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[39]
YU JINYI, KAO H-Y, 2007. Decadal changes of ENSO persistence barrier in SST and ocean heat content indices: 1958-2001[J]. Journal of Geophysical Research: Atmospheres, 112(D13): doi: 10.1029/2006JD007654.http://www.agu.org/pubs/crossref/2007/2006JD007654.shtml

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[40]
YU JINYI, KAO H-Y, LEE T, 2010. Subtropics-related interannual sea surface temperature variability in the central equatorial Pacific[J]. Journal of Climate, 23(11): 2869-2884.Interannual sea surface temperature (SST) variability in the central equatorial Pacific consists of a component related to eastern Pacific SST variations (called Type-1 SST variability) and a component not related to them (called Type-2 SST variability). Lead–lagged regression and ocean surface-layer temperature balance analyses were performed to contrast their control mechanisms. Type-1 variability is part of the canonical, which is characterized by SST anomalies extending from the South American coast to the central Pacific, is coupled with the Southern Oscillation, and is associated with basinwide subsurface ocean variations. This type of variability is dominated by a major 4–5-yr periodicity and a minor biennial (2–2.5 yr) periodicity. In contrast, Type-2 variability is dominated by a biennial periodicity, is associated with local air–sea interactions, and lacks a basinwide anomaly structure. In addition, Type-2 SST variability exhibits a strong connection to the subtropics of both hemispheres, particularly the Northern Hemisphere. Type-2 SST anomalies appear first in the northeastern subtropical Pacific and later spread toward the central equatorial Pacific, being generated in both regions by anomalous surface heat flux forcing associated with wind anomalies. The SST anomalies undergo rapid intensification in the central equatorial Pacific through ocean advection processes, and eventually decay as a result of surface heat flux damping and zonal advection. The southward spreading of trade wind anomalies within the northeastern subtropics-to-central tropics pathway of Type-2 variability is associated with intensity variations of the subtropical high. Type-2 variability is found to become stronger after 1990, associated with a concurrent increase in the subtropical variability. It is concluded that Type-2 interannual variability represents a subtropical-excited phenomenon that is different from the conventional ENSO Type-1 variability.

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[41]
ZENG LILI, DU YAN, XIE SHANGPING, 2009. Barrier layer in the South China Sea during summer 2000[J]. Dynamics of Atmospheres and Oceans, 47(1-3): 38-54.

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[42]
ZHENG FEI, WAN LIYING, WANG HUI, 2012. Distinguished effects of interannual salinity variability on the development of the central-Pacific El Niño events[J]. Atmospheric and Oceanic Science Letters, 5(2): 123-127.

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[43]
ZHENG FEI, ZHANG RONGHUA, 2015. Interannually varying salinity effects on ENSO in the tropical Pacific: a diagnostic analysis from Argo[J]. Ocean Dynamics, 65(5): 691-705.In this paper, three-dimensional temperature and salinity fields from Argo profiles are used to diagnose the interannual variations of some related upper oceanic fields in the tropical Pacific, with a focus on interannually varying salinity effects on the El Ni09o-Southern Oscillation (ENSO) events. It is clearly demonstrated that the salinity field plays a significantly large role in modulating the density and mixed layer (ML) over the western-central tropical Pacific. In particular, the contribution of interannually varying salinity to the interannual variations in density, ML, and stratification is surprisingly larger than that of interannually varying temperature. Over the entire region west of the dateline, the salinity effects are not limited to the surface but are clearly seen below the ML as represented in density and stratification fields. Furthermore, the mechanism for how the anomalous salinity field is modulating the ENSO cycle is investigated and explained through the El Ni09o (2009–2010) and La Ni09a (2010–2011) cases. Evidently, salinity field is shown to exert a significant influence on interannual variability as it directly affects the vertical mixing and entrainment at the base of the ML, the processes important to sea surface temperature (SST) in the equatorial regions.

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