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Influence of El Niño events on sea surface salinity over the central equatorial Indian Ocean
Environmental Research 182 (2020) 109097
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Influence of El Niño events on sea surface salinity over the central equatorial Indian Ocean
T
Wu Yuea, Liu Linb,c,∗, Zheng Xiaotonga a
Key Laboratory of Physical Oceanography, Institute for Advanced Ocean Studies, Ocean University of China and Qingdao National Laboratory for Marine Science and Technology, Qingdao, 266100, China b Laboratory for Regional Oceanography and Numerical Modeling, Qingdao National Laboratory for Marine Science and Technology, Qingdao, 266100, China c Center for Ocean and Climate Research, First Institute of Oceanography, Ministry of Natural Resources, Qingdao, 266100, China
A R T I C LE I N FO
A B S T R A C T
Keywords: El Niño Yoshida-wyrtki jet Salinity Interannual variability
The El Niño event is a major large-scale air-sea interaction phenomenon over the tropical Pacific region. Previous studies classified El Niño into three types — canonical El Niño, El Niño Modoki I, and El Niño Modoki II. This research reveals that different types of El Niño present dramatic effects on the interannual variability of sea surface salinity over the central equatorial Indian Ocean in the boreal autumn. The decreasing (increasing) sea surface salinity during the canonical El Niño and the EI Niño Modoki I (the EI Niño Modoki II) events is identified. The salinity budget analysis is performed to identify the dominant factors responsible for the variability of sea surface salinity over the central Indian Ocean. The results indicate that the wind-driven anomalous zonal advection plays an important role in sea surface salinity variability during the El Niño events associated with the forcing from the anomalous Walker circulation over the equatorial Indian Ocean.
1. Introduction Ocean salinity is a natural freshwater tracer in the global hydrological cycle (Yu, 2011). It plays an important role in describing the ocean state, ocean circulation, mixed-layer depth (MLD), barrier layer depth, subduction processes and so on (Durack and Wijffels, 2010; Fedorov et al., 2004; Sprintall and Tomczak, 1992; Thompson et al., 2006; Balaguru et al., 2016). Due to sparse in-situ observation, previous studies paid less attention to ocean salinity (e.g., Zhang et al., 2016a,2016b), strongly limiting our understanding of the ocean environment and relevant variability on different timescales. The climatological Indian Ocean (IO) salinity is high in the Arabian Sea (AS) and low in the Bay of Bengal (BOB). The combined effects of local evaporation, precipitation, and upper ocean dynamical and thermodynamical processes are responsible for the spatial pattern of sea surface salinity in the tropical IO. For example, the local precipitation was crucial for the surface salinity stratification over the IO (Qu and Meyers, 2005). Besides, ocean circulation also contributes to the variability of ocean salinity in the IO. The Indonesian throughflow (ITF) transports freshwater from the western Pacific into the eastern IO to change the IO surface salinity (Phillips et al., 2005; Gordon, 2005; Sprintall et al., 2009). The Yoshida-Wyrtki Jet (WJ) (Wyrtki, 1973;
Reppin et al., 1999) is a seasonal dependent current over the tropical IO, influencing the salinity distribution in the equatorial IO region (Nagura and McPhaden, 2010; Zhang et al., 2013). Interannual variabilities of ocean circulation (e.g., WJ) and precipitation, which are related to major ocean-atmosphere coupled modes, influence sea surface salinity. For example, the Indian Ocean Dipole (IOD), an interannual climate mode over the tropical IO involving air-sea interaction in zonal direction (Saji et al., 1999; Webster et al., 1999; Liu et al., 2011, 2014; Zheng et al., 2010, 2013), can significantly influences the surface salinity in the equatorial IO (Zhang et al., 2013, 2017; Qiu et al., 2012; Thompson et al., 2006). The increasing (decreasing) surface salinity in the southeast IO during the positive (negative) IOD is identified. Additionally, El Niño can also remotely influences sea surface salinity in the tropical Indian Ocean (Zhang et al., 2016a,2016b). Recent studies pointed out there are two types of El Niño — the EP El Niño and the CP El Niño (Wang et al., 2000; Trenberth and Stepaniak, 2001; Larkin and Harrison, 2005; Yu et al., 2008; Kao and Yu, 2009), which leads to distinct influences on local and remote climate. Wang and Wang (2013, 2014) further classified the CP type into El Niño Modoki I and II. Wu et al. (2018) revealed that different types of El Niño present distinct effects on the variation of WJ over the
∗ Corresponding author. Laboratory for Regional Oceanography and Numerical Modeling, Qingdao National Laboratory for Marine Science and Technology, Qingdao, 266100, China. E-mail address: liul@fio.org.cn (L. Lin).
https://doi.org/10.1016/j.envres.2019.109097 Received 14 November 2019; Received in revised form 23 December 2019; Accepted 27 December 2019 Available online 28 December 2019 0013-9351/ © 2020 Elsevier Inc. All rights reserved.
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equatorial IO in the boreal autumn. However, the influence from different types of El Niño on sea surface salinity over the central IO was not answered in their research. This work focuses on the variability of sea surface salinity over the central IO. It is found that the influences of the different types of El Niño on sea surface salinity in the central IO are distinct. The decreasing sea surface salinity during the canonical El Niño and the El Niño Modoki I, and the increasing sea surface salinity during the El Niño Modoki II are both associated with the wind-driven anomalous zonal advection. The paper is organized as follows. Section 2 introduces the data sets used in the study. Section 3 shows mixed layer salinity (MLS) spatial distribution characteristics of the different types of El Niño. Section 4 analyzes MLS budget during the different types of El Niño. Section 5 is a summary and discussion.
Table 1 Various groups of El Niño events. EI Niño Modoki I
EI Niño Modoki II
1951 1957 1965 1972 1982 1997 2006
1963 1987 1990 1991 2002 2015
1958 1968 1979 1992 2004 2009
entrainment, in influencing the MLS, the salinity budget is utilized in this paper. The MLD is calculated as the depth where the density changes by a fixed amount, Δσ (Δσ = σmld (T10 + ΔT, S10, P) − σ10 (T10, S10, P), where ΔT = 0.5 °C, P = 0) from the density at the reference depth of 10 m (Sprintall and Tomczak, 1992). Where T10 and S10 is potential temperature and salinity at 10 m, ΔT is potential temperature difference and P is pressure. The equation for MLS tendency is same as that in Feng et al. (1998), which is written as:
2. Data and methodology 2.1. Data The Simple Ocean Data Assimilation (SODA, version 2.2.4, Carton et al., 2005; 2008; Giese and Ray, 2011) is used in this study. SODA is based on Parallel Ocean Program physics with an average 0.25°×0.4°×40-level resolution. Observations include virtually all available hydrographic profile data, as well as ocean station data, moored temperature and salinity time series, surface temperature and salinity observations of various types, and nighttime infrared satellite SST data. The output is monthly averaged and mapped onto a uniform 0.5°×0.5°×40-level grid. The reanalysis provides three types of variables, those well constrained by observations, those partly constrained by dynamical relationships to variables frequently observed, and those poorly constrained such as horizontal current divergence. Several observational and reanalysis datasets are also used in this study. The monthly atmospheric datasets include the NOAA Earth System Research Laboratory (ESRL) 20th Century Reanalysis (20CR) with a resolution of 2.0°×2.0° (Compo et al., 2006) during 1979–2010, and the climate prediction center merged analysis of precipitation (GPCP, Huffman et al., 1997; Adler et al., 2003) and evaporation from the Objective Analysis Flux (OAFLUX, Yu and Weller, 2007; 2008). In order to make sure the results from the 20CR reanalysis are data independent, we also performed the same analysis by NCEP/NCAR reanalysis. Within the period of 1950–2008, the results from two reanalysis datasets are quite similar. Thus, this paper only shows the results from 20CR reanalysis.
S (P − E ) ∂Sm ∂S ∂S S − S−h =− 0 − ⎛um m + vm m ⎞ − we m ∂t h ∂x ∂y ⎠ h ⎝ ⎜
0
where dvm =
Sm = 1 h
⎟
0
∂ ∫−h S′v′ ⎞ ⎞ ⎛ ∂ ∫ S′u′dz 1⎛ F − ⎜ −h + ⎜ ⎟ ⎟⎟ h⎜ ∂x ∂y ⎝ ⎠⎠ ⎝
+
0
1 h
(2)
0
0
−h
−h
∫ Sdz , S′ = S − Sm , um = h1 ∫ udz , u′ = u − um ,an-
∫ vdz ,andv′ = v − vm .Sm, um , and vm is mean MLS, surface −h
zonal current, and meridional current respectively.S′, u′, and v′ is deviation from vertical mean values in the water column. h is the mixed layer depth.So, S−h, P , and E is surface salinity, salinity below the mixed-layer, precipitation, and evaporation, respectively. dh we = dt + w−h is entrainment velocity. F is turbulent salinity flux at depth h, which is same as the turbulent term in the mixed-layer and has little effect on the seasonal variability of salinity. ∂Sm is the MLS tendency. On the right side of equation (2), the first ∂t term is the surface freshwater flux, the second term is the horizontal advection, the third term is the vertical convolution, and the fourth term is the salinity advection perturbation. Previous studies (e.g., Zhang et al., 2013) have shown that the salinity in the central IO is less affected by the vertical convolution and advection perturbations, so these two terms are ignored in our analysis. Each variable can be divided into two parts: the climatological mean and interannual variability u m = u‾ + u′; vm = v‾ + v′; Sm = S‾ + S′; P= p‾ + p′; E= E‾ + E′. To separate the interannual variability from the seasonal cycle, equation (2) can be rewritten as:
2.2. The classification of El Niño Here we divide El Niño events into three types following the classification in Wang and Wang (2013, 2014). Canonical El Niño is defined by the Nino 3 (150°W-90°W, 5°S-5°N) SST anomalies such that the 5month running mean Nino 3 SST anomalies are +0.5 °C. El Niño Modoki is defined by the El Niño Modoki Index (EMI, Ashok et al., 2007).
EMI=[SSTA]C − 0.5 × [SSTA]E − 0.5 × [SSTA]W
Canonical EI Niño
∂S‾ ∂S′ ∂S‾ ∂S′ ∂S‾ ∂S′ ∂S‾ ∂S′ − v′ + = −u‾ − u‾ − v‾ − v‾ − u′ − u′ ∂t ∂t ∂x ∂x ∂y ∂y ∂x ∂x ∂S‾ ∂S′ S − v′ − 0 (P‾ + P′ − E‾ − E′) ∂y ∂x h
(1)
where the brackets with a subscript represent the area-averaged SSTA over the central Pacific region C (165°E−140°W, 10°S-10°N), the eastern Pacific region E (110°W-70°W, 15°S-5°N) and the western Pacific region W (125°E−145°E, 10°S-20°N), respectively. Based on the influence on rainfall, Wang and Wang (2013) further defined two types of El Niño Modoki, which are so-called El Niño Modoki I and II. Table 1 shows various groups of El Niño events.
(3)
Removal of salinity budget in climatology
∂S‾ ∂S‾ ∂S‾ S = −u‾ − v‾ − 0 (P‾ − E‾) ∂t ∂x ∂y h
(4)
In the rest of the paper, the interannual variability is mainly considered. By neglecting the higher order nonlinear terms, equation (3) can be rewritten as
2.3. Salinity budget analysis In order to quantify the roles of each process, such as surface freshwater flux, ocean advection, diffusive processes, and vertical 2
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Fig. 1. Distribution of climatological mixed-layer salinity (g kg−1) (a) and the standard deviation (b) in autumn season (October–November). The central equatorial IO (70°E−80°E, 3°S-5°N) is indicated by the black box.
∂S′ ∂S′ ∂S′ ∂S‾ ∂S′ ∂S‾ ∂S′ S = −u‾ − v‾ − u′ − u′ − v′ − v′ − 0 (p′ − E ′) ∂t ∂x ∂y ∂x ∂x ∂y ∂y h
Niño are found not only in meridional direction but also in zonal direction (Fig. 2g, h, i). During canonical El Niño events, the negative salinity anomalies along the equator prevail from 60°E to 90°E, 100 m to surface. The sea surface salinity is 35.3 g kg−1, which is 0.3 g kg−1 lower than the climatological value. The pattern of El Niño Modoki I events is similar to that of canonical case. Negative salinity anomalies appear in the eastern equatorial region but with weaker magnitude. El Niño Modoki II events displace a distinct vertical structure with positive salinity anomalies in most of the upper equatorial region. The maximum positive salinity concentrates on the central IO from 70°E to 80°E.
(5)
3. Spatial distribution of MLS The climatological MLS is salty (fresh) in the western and northern (eastern and southern) IO with significant salinity front at the central equatorial IO in autumn season (Zhang and Du, 2012). The maximum value of MLS reaches 36.9 g kg−1 in the AS and 34.2 g kg−1 in the BOB (Fig. 1a). During October–November, the high MLS occupies the central equatorial IO and forms a salty tongue penetrating into the eastern IO, leading to the significant salinity gradient along the equator. MLS presents significant interannual variation in the tropical IO (Qiu et al., 2012). The standard deviation of MLS is much larger in the BOB than that in the AS. Additionally, the coastal region around BOB occupies the maximum interannual variation compared to the small variation along the west coastal region of the AS. The central IO is also featured by significant interannual variation (Fig. 1b). Due to the large gradient and the strong interannual variation, this study focuses on the MLS variation in the central equatorial IO (70°E−80°E, 3°S-5°N). Fig. 2 shows the spatial distribution of anomalous MLS in the IO during El Niño developing phase (October–November). During canonical El Niño events, negative MLS anomalies appear in the equatorial IO from 60°E to 100°E (Fig. 2a). The maximum value of negative MLS anomalies is 0.3 g kg−1. The significant positive MLS anomalies are found over the region close to Sumatra-Java islands with maximum value around 0.3 g kg−1. During El Niño Modoki I events, the spatial pattern of MLS anomalies is similar to that of canonical El Niño. Negative MLS anomalies appear in the equatorial region while positive MLS anomalies appear in the southeastern IO (Fig. 2b). In contrast, the distribution is distinct during El Niño Modoki II events. Positive MLS anomalies are located at the central equatorial IO and negative MLS anomalies appear along the coastal region of Sumatra-Java islands (Fig. 2c). Moreover, the strength of anomalous MLS in El Niño Modoki II events is much weaker than that in canonical El Niño events. In addition to the horizontal distribution, the vertical structures of salinity anomalies in the equatorial IO are distinct among different types of El Niño. The negative MLS anomalies appear in the central IO (70°E−80°E) during the canonical El Niño and the El Niño Modoki I. The negative salinity anomalies reach −0.3 g kg−1 during the former, stronger than that during the latter (Fig. 2d and e). In contrast, the positive salinity anomalies occur from the surface to 100 m in the central equatorial IO during the El Niño Modoki II (Fig. 2f). Fig. 2d, e and f also indicate that MLD along the equatorial IO in the El Niño Modoki II event is much deeper than those in other two types of El Niño. The difference patterns of salinity anomalies in different types of El
4. Salinity budget analysis Section 3 depicts the salinity distribution over the equatorial IO corresponding to different types of El Niño, especially in the central equatorial IO. In this section, the salinity budget is utilized to identify the dominant factor influencing the variation of salinity over the central equatorial IO. Fig. 3 shows the results of salinity budget in the central equatorial IO during the boreal autumn. Different types of El Niño correspond to various salinity changes. Consistent with the results in section 3, salinity tendency is negative (positive) during the canonical El Niño and the El Niño Modoki I (the El Niño Modoki II). The dominant tendency term is horizontal advection, while the surface freshwater flux terms play the second role (Fig. 3a–c). Among separate terms responsible for the change of salinity tendency, horizontal advection terms provide the dominant contribution in all El Niño types. During the canonical El Niño and the El Niño Modoki I (the El Niño Modoki II), advection terms are negative (positive), consistent with the salinity tendency result (Fig. 3a–c). Further analysis demonstrates that the different advection terms play different roles in salinity variability during different types of El Niño. During canonical El Niño events and El Niño Modoki II events, the mean salinity advected by anomalous zonal current dominates the total advection term. However, during El Niño Modoki I events, the mean salinity advected by anomalous zonal current, the mean salinity advected by anomalous meridional current, and the anomalous salinity advected by anomalous zonal current nearly equally contributes to the total advection term. During canonical El Niño events, the surface freshwater flux term is positive, leading to a positive salinity tendency. By contrast, during El Niño Modoki I and II events, the surface freshwater flux term is negative. Further analysis reveals that the anomalous precipitation dominates the surface freshwater flux (Fig. 3d–f), while the evaporation term is much weaker, indicating the importance of rainfall shaping the salinity variability on interannual timescale in the central IO. Fig. 4a–c shows the zonal current anomalies during different types of El Niño. The anomalous westward zonal current (u′ < 0) appears along the equator during the canonical El Niño and the El Niño Modoki I. The velocity is stronger in the canonical El Niño, of which the 3
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Fig. 2. Composites of mixed-layer salinity anomalies (shading, g kg−1) and the mixed-layer salinity climatology (contour line, g kg−1) in autumn season for different El Niño events in IO. a, b, c are horizontal spatial distribution in tropical IO. d, e, f are latitude-depth profiles averaged between 70°E and 80°E. g, h, i are longitudedepth profiles averaged between 3°S and 3°N.
maximum velocity reaches 0.2 m s−1. The intensity and influence of anomalous zonal current in El Niño Modoki I events are weaker than those in the canonical El Niño (Fig. 4a and b). However, during the El Niño Modoki II, the eastward zonal current anomalies (u′ > 0) appear in the central equatorial IO (Fig. 4c), meaning stronger WJ current. In terms of climatological salinity distribution (Fig. 1a), the negative zonal gradient ( ∂S‾ < 0) is found over the central IO. Combined with the mean ∂x salinity gradient and anomalous zonal advection, negative (positive) zonal currents induce negative (positive) salinity tendency. During El Niño Modoki II events, the positive zonal current anomalies lead to the ∂S‾ negative u′ ∂x , resulting in the positive salinity anomalies in the central IO (Fig. 4i). During canonical El Niño events, the negative zonal current anomalies lead to the negative salinity anomalies in the central equatorial IO (Fig. 4g). Fig. 4d–f shows that the meridional current anomalies during different types of El Niño events. The anomalous southward currents during El Niño Modoki II events are located in the equatorial region. In contrast, the anomalous northward meridional currents occur during the canonical El Niño and the El Niño Modoki I. Considering the meridional gradient of mean salinity in the central equatorial IO ( ∂S‾ > 0),
between MLS and zonal current during past decades (Fig. 5). Fig. 5a shows the anomalous salinity tendency and negative zonal current anomaly from 1950 to 2010 in the boreal autumn. The correlation coefficient is −0.445 between them, significant at 95% confidence level. The 20-year sliding correlation between the salinity anomaly and negative zonal current anomaly shows the decadal variation of the relationship. There is a modulation around 1990. Before 1990, zonal current and salinity present weak positive correlation and the averaged correlation coefficient is 0.432. After 1990, the averaged correlation coefficient increases to 0.571. The mechanism of this decadal modulation is still not fully understood and needs further investigation. Based on observational and reanalysis datasets, this work reveals the differences of salinity anomaly in boreal autumn over the center equatorial IO during different types of El Niño. During the canonical El Niño and the El Niño Modoki I (the El Niño Modoki II) events, the MLS in the central equatorial IO decreases (increases). Salinity budget results demonstrate that the variations of MLS during different types of El Niño are mainly controlled by the differences of zonal advection terms. ∂S‾ The dominant term is u′ ∂x , which presents the effect of anomalous zonal current shaping the climatological mean salinity gradient. During the canonical El Niño and the El Niño Modoki I the MLS in the central equatorial IO is negative due to the westward zonal current anomalies. During the El Niño Modoki II, anomalous eastward zonal current (i.e., stronger WJ), transports more high-salty water from the AS to the central equatorial IO, resulting in a positive salinity anomaly over the central IO. This study indicates the importance of ocean advection to the salinity variability over the central IO rather than the surface freshwater flux on interannual timescale.
∂y
the negative meridional current anomalies induce the positive salinity tendency. During the canonical El Niño and the El Niño Modoki I, po∂S‾ sitive meridional current anomalies result in the positive v′ ∂y , which further decreases salinity. 5. Discussion and summary Since the salinity over the central IO is strongly linked with the zonal current on interannual timescale, we examine the relationship 4
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Fig. 3. Mixed-layer salinity anomaly budget in central IO (units: g kg−1) in difference El Niño events. A, b, c are overall salinity tendency terms (St, black), salinity advection term (ADV, red), E-P (E-P, blue), and residuals (res, pink); D, e, f show the surface freshwater term, E-P (E-P, blue), evaporation (E, black), and negative ∂S′ ∂S precipitation (-P, deep sky blue). g, h, i are salinity advection (ADV, red), u‾ ∂S′ (u*sa, royal blue), − u′ ‾ (-ua*s, steel blue), − u′ ∂S′ (ua*sa, sea green), v‾ (v*sa, ∂x
∂x
∂S
∂x
∂y
∂S′
green), − v′ ∂y‾ (-va*s, orange), − v′ ∂y (-va*sa, yellow). Upper, middle, and lower rows are canonical, Modoki I, and Modoki II cases. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Acknowledgements
27NSY-Liu, Basic Scientific Fund for National Public Research Institutes of China (2017S02), Taishan Scholars Programs of Shandong Province (No. tsqn201909165),Global Change and Air-Sea Interaction Program (GASI-IPOVAI-03, GASI-IPOVAI-02, GASI-02-IND-STSaut, and GASI-02IND-STSwin), Natural Science Foundation of China grants (41876028), Indian Ocean Ninety-east Ridge Ecosystem and Marine Environment
This study has been conducted from the project ‘The Impact of Global Warming on Ocean-Atmosphere Feedback Strength at the Tropical Indian Ocean’ funded by the Asia-Pacific Network for Global Change Research (APN) with the project reference number: ARCP2013-
Fig. 4. Composites of zonal current anomalies (left), meridional current anomalies (middle) and salinity anomalies (right) in different El Niño events. The shadow areas are above 95% confidence. 5
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Fig. 5. Salinity tendency and zonal current anomaly in central IO (a) and 20-year sliding correlation coefficients between salinity anomaly and zonal current anomaly (b). The blue line is the 95% confidence level. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Monitoring and Protection, the China Ocean Mineral Resources R&D Association (contact DY135-E2-4), and NSFC-Shandong Joint Fund for Marine Science Research Centers (U1606405). This work is supported by Southern Marine Science and Engineering Guangdong Laboratory, Zhuahai, China.
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4911–4923. Zhang, Y.H., Du, Y., Qu, T., 2016a. A sea surface salinity dipole mode in the tropical Indian Ocean. Clim. Dyn. 47, 2573–2585. Zhang, N.N., Feng, M., Du, Y., et al., 2016b. Seasonal and interannual variations of mixed layer salinity in the southeast tropical Indian Ocean. J. Geophys Res Oceans 121. https://doi.org/10.1002/2016JC011854. Zhang, Y.H., Du, Y., Feng, M., 2017. Multi-time scale variability of the sea surface salinity dipole mode in the tropical Indian Ocean. J. Clim. 31 10.1175. Zheng, X.T., Xie, S.P., Vecchi, G.A., et al., 2010. Indian Ocean dipole response to global warming: analysis of ocean–atmospheric feedbacks in a coupled model. J. Clim. 23, 1240–1253. Zheng, X.T., Xie, S.P., Du, Y., et al., 2013. Indian Ocean Dipole response to global warming in the CMIP5 multimodel ensemble. J. Clim. 15, 6067–6080.
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Influence of El Niño events on sea surface salinity over the central equatorial Indian Ocean
T
Wu Yuea, Liu Linb,c,∗, Zheng Xiaotonga a
Key Laboratory of Physical Oceanography, Institute for Advanced Ocean Studies, Ocean University of China and Qingdao National Laboratory for Marine Science and Technology, Qingdao, 266100, China b Laboratory for Regional Oceanography and Numerical Modeling, Qingdao National Laboratory for Marine Science and Technology, Qingdao, 266100, China c Center for Ocean and Climate Research, First Institute of Oceanography, Ministry of Natural Resources, Qingdao, 266100, China
A R T I C LE I N FO
A B S T R A C T
Keywords: El Niño Yoshida-wyrtki jet Salinity Interannual variability
The El Niño event is a major large-scale air-sea interaction phenomenon over the tropical Pacific region. Previous studies classified El Niño into three types — canonical El Niño, El Niño Modoki I, and El Niño Modoki II. This research reveals that different types of El Niño present dramatic effects on the interannual variability of sea surface salinity over the central equatorial Indian Ocean in the boreal autumn. The decreasing (increasing) sea surface salinity during the canonical El Niño and the EI Niño Modoki I (the EI Niño Modoki II) events is identified. The salinity budget analysis is performed to identify the dominant factors responsible for the variability of sea surface salinity over the central Indian Ocean. The results indicate that the wind-driven anomalous zonal advection plays an important role in sea surface salinity variability during the El Niño events associated with the forcing from the anomalous Walker circulation over the equatorial Indian Ocean.
1. Introduction Ocean salinity is a natural freshwater tracer in the global hydrological cycle (Yu, 2011). It plays an important role in describing the ocean state, ocean circulation, mixed-layer depth (MLD), barrier layer depth, subduction processes and so on (Durack and Wijffels, 2010; Fedorov et al., 2004; Sprintall and Tomczak, 1992; Thompson et al., 2006; Balaguru et al., 2016). Due to sparse in-situ observation, previous studies paid less attention to ocean salinity (e.g., Zhang et al., 2016a,2016b), strongly limiting our understanding of the ocean environment and relevant variability on different timescales. The climatological Indian Ocean (IO) salinity is high in the Arabian Sea (AS) and low in the Bay of Bengal (BOB). The combined effects of local evaporation, precipitation, and upper ocean dynamical and thermodynamical processes are responsible for the spatial pattern of sea surface salinity in the tropical IO. For example, the local precipitation was crucial for the surface salinity stratification over the IO (Qu and Meyers, 2005). Besides, ocean circulation also contributes to the variability of ocean salinity in the IO. The Indonesian throughflow (ITF) transports freshwater from the western Pacific into the eastern IO to change the IO surface salinity (Phillips et al., 2005; Gordon, 2005; Sprintall et al., 2009). The Yoshida-Wyrtki Jet (WJ) (Wyrtki, 1973;
Reppin et al., 1999) is a seasonal dependent current over the tropical IO, influencing the salinity distribution in the equatorial IO region (Nagura and McPhaden, 2010; Zhang et al., 2013). Interannual variabilities of ocean circulation (e.g., WJ) and precipitation, which are related to major ocean-atmosphere coupled modes, influence sea surface salinity. For example, the Indian Ocean Dipole (IOD), an interannual climate mode over the tropical IO involving air-sea interaction in zonal direction (Saji et al., 1999; Webster et al., 1999; Liu et al., 2011, 2014; Zheng et al., 2010, 2013), can significantly influences the surface salinity in the equatorial IO (Zhang et al., 2013, 2017; Qiu et al., 2012; Thompson et al., 2006). The increasing (decreasing) surface salinity in the southeast IO during the positive (negative) IOD is identified. Additionally, El Niño can also remotely influences sea surface salinity in the tropical Indian Ocean (Zhang et al., 2016a,2016b). Recent studies pointed out there are two types of El Niño — the EP El Niño and the CP El Niño (Wang et al., 2000; Trenberth and Stepaniak, 2001; Larkin and Harrison, 2005; Yu et al., 2008; Kao and Yu, 2009), which leads to distinct influences on local and remote climate. Wang and Wang (2013, 2014) further classified the CP type into El Niño Modoki I and II. Wu et al. (2018) revealed that different types of El Niño present distinct effects on the variation of WJ over the
∗ Corresponding author. Laboratory for Regional Oceanography and Numerical Modeling, Qingdao National Laboratory for Marine Science and Technology, Qingdao, 266100, China. E-mail address: liul@fio.org.cn (L. Lin).
https://doi.org/10.1016/j.envres.2019.109097 Received 14 November 2019; Received in revised form 23 December 2019; Accepted 27 December 2019 Available online 28 December 2019 0013-9351/ © 2020 Elsevier Inc. All rights reserved.
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equatorial IO in the boreal autumn. However, the influence from different types of El Niño on sea surface salinity over the central IO was not answered in their research. This work focuses on the variability of sea surface salinity over the central IO. It is found that the influences of the different types of El Niño on sea surface salinity in the central IO are distinct. The decreasing sea surface salinity during the canonical El Niño and the El Niño Modoki I, and the increasing sea surface salinity during the El Niño Modoki II are both associated with the wind-driven anomalous zonal advection. The paper is organized as follows. Section 2 introduces the data sets used in the study. Section 3 shows mixed layer salinity (MLS) spatial distribution characteristics of the different types of El Niño. Section 4 analyzes MLS budget during the different types of El Niño. Section 5 is a summary and discussion.
Table 1 Various groups of El Niño events. EI Niño Modoki I
EI Niño Modoki II
1951 1957 1965 1972 1982 1997 2006
1963 1987 1990 1991 2002 2015
1958 1968 1979 1992 2004 2009
entrainment, in influencing the MLS, the salinity budget is utilized in this paper. The MLD is calculated as the depth where the density changes by a fixed amount, Δσ (Δσ = σmld (T10 + ΔT, S10, P) − σ10 (T10, S10, P), where ΔT = 0.5 °C, P = 0) from the density at the reference depth of 10 m (Sprintall and Tomczak, 1992). Where T10 and S10 is potential temperature and salinity at 10 m, ΔT is potential temperature difference and P is pressure. The equation for MLS tendency is same as that in Feng et al. (1998), which is written as:
2. Data and methodology 2.1. Data The Simple Ocean Data Assimilation (SODA, version 2.2.4, Carton et al., 2005; 2008; Giese and Ray, 2011) is used in this study. SODA is based on Parallel Ocean Program physics with an average 0.25°×0.4°×40-level resolution. Observations include virtually all available hydrographic profile data, as well as ocean station data, moored temperature and salinity time series, surface temperature and salinity observations of various types, and nighttime infrared satellite SST data. The output is monthly averaged and mapped onto a uniform 0.5°×0.5°×40-level grid. The reanalysis provides three types of variables, those well constrained by observations, those partly constrained by dynamical relationships to variables frequently observed, and those poorly constrained such as horizontal current divergence. Several observational and reanalysis datasets are also used in this study. The monthly atmospheric datasets include the NOAA Earth System Research Laboratory (ESRL) 20th Century Reanalysis (20CR) with a resolution of 2.0°×2.0° (Compo et al., 2006) during 1979–2010, and the climate prediction center merged analysis of precipitation (GPCP, Huffman et al., 1997; Adler et al., 2003) and evaporation from the Objective Analysis Flux (OAFLUX, Yu and Weller, 2007; 2008). In order to make sure the results from the 20CR reanalysis are data independent, we also performed the same analysis by NCEP/NCAR reanalysis. Within the period of 1950–2008, the results from two reanalysis datasets are quite similar. Thus, this paper only shows the results from 20CR reanalysis.
S (P − E ) ∂Sm ∂S ∂S S − S−h =− 0 − ⎛um m + vm m ⎞ − we m ∂t h ∂x ∂y ⎠ h ⎝ ⎜
0
where dvm =
Sm = 1 h
⎟
0
∂ ∫−h S′v′ ⎞ ⎞ ⎛ ∂ ∫ S′u′dz 1⎛ F − ⎜ −h + ⎜ ⎟ ⎟⎟ h⎜ ∂x ∂y ⎝ ⎠⎠ ⎝
+
0
1 h
(2)
0
0
−h
−h
∫ Sdz , S′ = S − Sm , um = h1 ∫ udz , u′ = u − um ,an-
∫ vdz ,andv′ = v − vm .Sm, um , and vm is mean MLS, surface −h
zonal current, and meridional current respectively.S′, u′, and v′ is deviation from vertical mean values in the water column. h is the mixed layer depth.So, S−h, P , and E is surface salinity, salinity below the mixed-layer, precipitation, and evaporation, respectively. dh we = dt + w−h is entrainment velocity. F is turbulent salinity flux at depth h, which is same as the turbulent term in the mixed-layer and has little effect on the seasonal variability of salinity. ∂Sm is the MLS tendency. On the right side of equation (2), the first ∂t term is the surface freshwater flux, the second term is the horizontal advection, the third term is the vertical convolution, and the fourth term is the salinity advection perturbation. Previous studies (e.g., Zhang et al., 2013) have shown that the salinity in the central IO is less affected by the vertical convolution and advection perturbations, so these two terms are ignored in our analysis. Each variable can be divided into two parts: the climatological mean and interannual variability u m = u‾ + u′; vm = v‾ + v′; Sm = S‾ + S′; P= p‾ + p′; E= E‾ + E′. To separate the interannual variability from the seasonal cycle, equation (2) can be rewritten as:
2.2. The classification of El Niño Here we divide El Niño events into three types following the classification in Wang and Wang (2013, 2014). Canonical El Niño is defined by the Nino 3 (150°W-90°W, 5°S-5°N) SST anomalies such that the 5month running mean Nino 3 SST anomalies are +0.5 °C. El Niño Modoki is defined by the El Niño Modoki Index (EMI, Ashok et al., 2007).
EMI=[SSTA]C − 0.5 × [SSTA]E − 0.5 × [SSTA]W
Canonical EI Niño
∂S‾ ∂S′ ∂S‾ ∂S′ ∂S‾ ∂S′ ∂S‾ ∂S′ − v′ + = −u‾ − u‾ − v‾ − v‾ − u′ − u′ ∂t ∂t ∂x ∂x ∂y ∂y ∂x ∂x ∂S‾ ∂S′ S − v′ − 0 (P‾ + P′ − E‾ − E′) ∂y ∂x h
(1)
where the brackets with a subscript represent the area-averaged SSTA over the central Pacific region C (165°E−140°W, 10°S-10°N), the eastern Pacific region E (110°W-70°W, 15°S-5°N) and the western Pacific region W (125°E−145°E, 10°S-20°N), respectively. Based on the influence on rainfall, Wang and Wang (2013) further defined two types of El Niño Modoki, which are so-called El Niño Modoki I and II. Table 1 shows various groups of El Niño events.
(3)
Removal of salinity budget in climatology
∂S‾ ∂S‾ ∂S‾ S = −u‾ − v‾ − 0 (P‾ − E‾) ∂t ∂x ∂y h
(4)
In the rest of the paper, the interannual variability is mainly considered. By neglecting the higher order nonlinear terms, equation (3) can be rewritten as
2.3. Salinity budget analysis In order to quantify the roles of each process, such as surface freshwater flux, ocean advection, diffusive processes, and vertical 2
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Fig. 1. Distribution of climatological mixed-layer salinity (g kg−1) (a) and the standard deviation (b) in autumn season (October–November). The central equatorial IO (70°E−80°E, 3°S-5°N) is indicated by the black box.
∂S′ ∂S′ ∂S′ ∂S‾ ∂S′ ∂S‾ ∂S′ S = −u‾ − v‾ − u′ − u′ − v′ − v′ − 0 (p′ − E ′) ∂t ∂x ∂y ∂x ∂x ∂y ∂y h
Niño are found not only in meridional direction but also in zonal direction (Fig. 2g, h, i). During canonical El Niño events, the negative salinity anomalies along the equator prevail from 60°E to 90°E, 100 m to surface. The sea surface salinity is 35.3 g kg−1, which is 0.3 g kg−1 lower than the climatological value. The pattern of El Niño Modoki I events is similar to that of canonical case. Negative salinity anomalies appear in the eastern equatorial region but with weaker magnitude. El Niño Modoki II events displace a distinct vertical structure with positive salinity anomalies in most of the upper equatorial region. The maximum positive salinity concentrates on the central IO from 70°E to 80°E.
(5)
3. Spatial distribution of MLS The climatological MLS is salty (fresh) in the western and northern (eastern and southern) IO with significant salinity front at the central equatorial IO in autumn season (Zhang and Du, 2012). The maximum value of MLS reaches 36.9 g kg−1 in the AS and 34.2 g kg−1 in the BOB (Fig. 1a). During October–November, the high MLS occupies the central equatorial IO and forms a salty tongue penetrating into the eastern IO, leading to the significant salinity gradient along the equator. MLS presents significant interannual variation in the tropical IO (Qiu et al., 2012). The standard deviation of MLS is much larger in the BOB than that in the AS. Additionally, the coastal region around BOB occupies the maximum interannual variation compared to the small variation along the west coastal region of the AS. The central IO is also featured by significant interannual variation (Fig. 1b). Due to the large gradient and the strong interannual variation, this study focuses on the MLS variation in the central equatorial IO (70°E−80°E, 3°S-5°N). Fig. 2 shows the spatial distribution of anomalous MLS in the IO during El Niño developing phase (October–November). During canonical El Niño events, negative MLS anomalies appear in the equatorial IO from 60°E to 100°E (Fig. 2a). The maximum value of negative MLS anomalies is 0.3 g kg−1. The significant positive MLS anomalies are found over the region close to Sumatra-Java islands with maximum value around 0.3 g kg−1. During El Niño Modoki I events, the spatial pattern of MLS anomalies is similar to that of canonical El Niño. Negative MLS anomalies appear in the equatorial region while positive MLS anomalies appear in the southeastern IO (Fig. 2b). In contrast, the distribution is distinct during El Niño Modoki II events. Positive MLS anomalies are located at the central equatorial IO and negative MLS anomalies appear along the coastal region of Sumatra-Java islands (Fig. 2c). Moreover, the strength of anomalous MLS in El Niño Modoki II events is much weaker than that in canonical El Niño events. In addition to the horizontal distribution, the vertical structures of salinity anomalies in the equatorial IO are distinct among different types of El Niño. The negative MLS anomalies appear in the central IO (70°E−80°E) during the canonical El Niño and the El Niño Modoki I. The negative salinity anomalies reach −0.3 g kg−1 during the former, stronger than that during the latter (Fig. 2d and e). In contrast, the positive salinity anomalies occur from the surface to 100 m in the central equatorial IO during the El Niño Modoki II (Fig. 2f). Fig. 2d, e and f also indicate that MLD along the equatorial IO in the El Niño Modoki II event is much deeper than those in other two types of El Niño. The difference patterns of salinity anomalies in different types of El
4. Salinity budget analysis Section 3 depicts the salinity distribution over the equatorial IO corresponding to different types of El Niño, especially in the central equatorial IO. In this section, the salinity budget is utilized to identify the dominant factor influencing the variation of salinity over the central equatorial IO. Fig. 3 shows the results of salinity budget in the central equatorial IO during the boreal autumn. Different types of El Niño correspond to various salinity changes. Consistent with the results in section 3, salinity tendency is negative (positive) during the canonical El Niño and the El Niño Modoki I (the El Niño Modoki II). The dominant tendency term is horizontal advection, while the surface freshwater flux terms play the second role (Fig. 3a–c). Among separate terms responsible for the change of salinity tendency, horizontal advection terms provide the dominant contribution in all El Niño types. During the canonical El Niño and the El Niño Modoki I (the El Niño Modoki II), advection terms are negative (positive), consistent with the salinity tendency result (Fig. 3a–c). Further analysis demonstrates that the different advection terms play different roles in salinity variability during different types of El Niño. During canonical El Niño events and El Niño Modoki II events, the mean salinity advected by anomalous zonal current dominates the total advection term. However, during El Niño Modoki I events, the mean salinity advected by anomalous zonal current, the mean salinity advected by anomalous meridional current, and the anomalous salinity advected by anomalous zonal current nearly equally contributes to the total advection term. During canonical El Niño events, the surface freshwater flux term is positive, leading to a positive salinity tendency. By contrast, during El Niño Modoki I and II events, the surface freshwater flux term is negative. Further analysis reveals that the anomalous precipitation dominates the surface freshwater flux (Fig. 3d–f), while the evaporation term is much weaker, indicating the importance of rainfall shaping the salinity variability on interannual timescale in the central IO. Fig. 4a–c shows the zonal current anomalies during different types of El Niño. The anomalous westward zonal current (u′ < 0) appears along the equator during the canonical El Niño and the El Niño Modoki I. The velocity is stronger in the canonical El Niño, of which the 3
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Fig. 2. Composites of mixed-layer salinity anomalies (shading, g kg−1) and the mixed-layer salinity climatology (contour line, g kg−1) in autumn season for different El Niño events in IO. a, b, c are horizontal spatial distribution in tropical IO. d, e, f are latitude-depth profiles averaged between 70°E and 80°E. g, h, i are longitudedepth profiles averaged between 3°S and 3°N.
maximum velocity reaches 0.2 m s−1. The intensity and influence of anomalous zonal current in El Niño Modoki I events are weaker than those in the canonical El Niño (Fig. 4a and b). However, during the El Niño Modoki II, the eastward zonal current anomalies (u′ > 0) appear in the central equatorial IO (Fig. 4c), meaning stronger WJ current. In terms of climatological salinity distribution (Fig. 1a), the negative zonal gradient ( ∂S‾ < 0) is found over the central IO. Combined with the mean ∂x salinity gradient and anomalous zonal advection, negative (positive) zonal currents induce negative (positive) salinity tendency. During El Niño Modoki II events, the positive zonal current anomalies lead to the ∂S‾ negative u′ ∂x , resulting in the positive salinity anomalies in the central IO (Fig. 4i). During canonical El Niño events, the negative zonal current anomalies lead to the negative salinity anomalies in the central equatorial IO (Fig. 4g). Fig. 4d–f shows that the meridional current anomalies during different types of El Niño events. The anomalous southward currents during El Niño Modoki II events are located in the equatorial region. In contrast, the anomalous northward meridional currents occur during the canonical El Niño and the El Niño Modoki I. Considering the meridional gradient of mean salinity in the central equatorial IO ( ∂S‾ > 0),
between MLS and zonal current during past decades (Fig. 5). Fig. 5a shows the anomalous salinity tendency and negative zonal current anomaly from 1950 to 2010 in the boreal autumn. The correlation coefficient is −0.445 between them, significant at 95% confidence level. The 20-year sliding correlation between the salinity anomaly and negative zonal current anomaly shows the decadal variation of the relationship. There is a modulation around 1990. Before 1990, zonal current and salinity present weak positive correlation and the averaged correlation coefficient is 0.432. After 1990, the averaged correlation coefficient increases to 0.571. The mechanism of this decadal modulation is still not fully understood and needs further investigation. Based on observational and reanalysis datasets, this work reveals the differences of salinity anomaly in boreal autumn over the center equatorial IO during different types of El Niño. During the canonical El Niño and the El Niño Modoki I (the El Niño Modoki II) events, the MLS in the central equatorial IO decreases (increases). Salinity budget results demonstrate that the variations of MLS during different types of El Niño are mainly controlled by the differences of zonal advection terms. ∂S‾ The dominant term is u′ ∂x , which presents the effect of anomalous zonal current shaping the climatological mean salinity gradient. During the canonical El Niño and the El Niño Modoki I the MLS in the central equatorial IO is negative due to the westward zonal current anomalies. During the El Niño Modoki II, anomalous eastward zonal current (i.e., stronger WJ), transports more high-salty water from the AS to the central equatorial IO, resulting in a positive salinity anomaly over the central IO. This study indicates the importance of ocean advection to the salinity variability over the central IO rather than the surface freshwater flux on interannual timescale.
∂y
the negative meridional current anomalies induce the positive salinity tendency. During the canonical El Niño and the El Niño Modoki I, po∂S‾ sitive meridional current anomalies result in the positive v′ ∂y , which further decreases salinity. 5. Discussion and summary Since the salinity over the central IO is strongly linked with the zonal current on interannual timescale, we examine the relationship 4
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Fig. 3. Mixed-layer salinity anomaly budget in central IO (units: g kg−1) in difference El Niño events. A, b, c are overall salinity tendency terms (St, black), salinity advection term (ADV, red), E-P (E-P, blue), and residuals (res, pink); D, e, f show the surface freshwater term, E-P (E-P, blue), evaporation (E, black), and negative ∂S′ ∂S precipitation (-P, deep sky blue). g, h, i are salinity advection (ADV, red), u‾ ∂S′ (u*sa, royal blue), − u′ ‾ (-ua*s, steel blue), − u′ ∂S′ (ua*sa, sea green), v‾ (v*sa, ∂x
∂x
∂S
∂x
∂y
∂S′
green), − v′ ∂y‾ (-va*s, orange), − v′ ∂y (-va*sa, yellow). Upper, middle, and lower rows are canonical, Modoki I, and Modoki II cases. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Acknowledgements
27NSY-Liu, Basic Scientific Fund for National Public Research Institutes of China (2017S02), Taishan Scholars Programs of Shandong Province (No. tsqn201909165),Global Change and Air-Sea Interaction Program (GASI-IPOVAI-03, GASI-IPOVAI-02, GASI-02-IND-STSaut, and GASI-02IND-STSwin), Natural Science Foundation of China grants (41876028), Indian Ocean Ninety-east Ridge Ecosystem and Marine Environment
This study has been conducted from the project ‘The Impact of Global Warming on Ocean-Atmosphere Feedback Strength at the Tropical Indian Ocean’ funded by the Asia-Pacific Network for Global Change Research (APN) with the project reference number: ARCP2013-
Fig. 4. Composites of zonal current anomalies (left), meridional current anomalies (middle) and salinity anomalies (right) in different El Niño events. The shadow areas are above 95% confidence. 5
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Fig. 5. Salinity tendency and zonal current anomaly in central IO (a) and 20-year sliding correlation coefficients between salinity anomaly and zonal current anomaly (b). The blue line is the 95% confidence level. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Monitoring and Protection, the China Ocean Mineral Resources R&D Association (contact DY135-E2-4), and NSFC-Shandong Joint Fund for Marine Science Research Centers (U1606405). This work is supported by Southern Marine Science and Engineering Guangdong Laboratory, Zhuahai, China.
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