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  • View in gallery

    Indian Ocean temperature (color shaded; °C) and currents averaged in upper 100 m [vectors; 0.5 (m s−1)1/2; presented as (m s−1)1/2 for better vector visibility] from the OFAM3 model simulation (averaged during 1979–2014). Average temperature transports from the ITF (115°E) and across 32°S are indicated.

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    Latitude–time evolution of low-pass-filtered (with period > 3 years) Indian Ocean meridional temperature transport anomalies [PW (= 1015 W)] for the (a) total, (b) overturning, and (c) gyre component.

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    Latitude–time diagrams of the meridional temperature transport: (a) external mode, (b) Ekman, (c) vertical shear, and (d) the sum of the three components.

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    (a) Correlation coefficients between total temperature transport anomaly and the external mode (red line), Ekman (blue dash-dotted line), and vertical shear (black dashed line) component. (b) Standard deviation of the temperature transport anomalies of the external mode (red line), Ekman (blue dash-dotted line), and vertical shear (black dashed line) components. The components are calculated using monthly data fields and are 3-yr low-pass filtered. The correlations significant above 95% confidence level are denoted with thick lines.

  • View in gallery

    (a) EOF1 (PW) and (b) the normalized PC1 time series (red line) of low-frequency Indian Ocean meridional temperature transport. The low-frequency Niño-3.4 index (blue line) is superimposed in (b).

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    El Niño composites of (a) wind stress (vectors; dyn m−2) and temperature anomalies (0–2000 m averaged; shaded); (c) total meridional temperature transport anomaly (black line) and its decomposition into external mode (yellow line), Ekman (blue line), and vertical shear (red line) components; and (e) cumulative heat balance contributions from meridional temperature transport (black line), net surface heat flux (yellow line), ITF heat transport (red line), and the rate of change in heat storage (blue dashed line). (b),(d),(f) As in (a), (c), and (e), respectively, but for La Niña composites. Regions enclosed by black contours in (a) and (b) denote temperature anomalies significant at the 95% level.

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    A schematic diagram of Indian Ocean temperature transport anomalies during El Niño. The background shows the regression patterns of wind stress (vectors) and sea surface height anomalies (shaded) against the normalized PC1 of Indian Ocean meridional temperature transport at zero lag.

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    El Niño composites of (a) meridional overturning streamfunction anomalies and contributions from (b) external mode , (c) Ekman , and (d) vertical shear . (e)–(h) As in (a)–(d), but for La Niña composites. The overturning streamfunction anomalies significant at the 95% confidence level are hatched in (a) and (e).

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    (a) Correlation coefficient between low-frequency and . Shaded contours with labeled white contours show regions of negative correlation. (b) Correlation between low-frequency vertical shear and external components of meridional temperature transport.

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    (a) January, (b) July, and (c) annual mean streamfunctions (Sv) of meridional overturning transport (1979–2014) for the Indian Ocean.

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    Seasonal cycle of Indian Ocean meridional temperature transport (1979–2014).

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Pacific Influences on the Meridional Temperature Transport of the Indian Ocean

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  • 1 Physical Oceanography Laboratory/Qingdao Collaborative Innovation Centre of Marine Science and Technology, Ocean University of China, and College of Oceanic and Atmospheric Sciences, Ocean University of China, Qingdao, China, and CSIRO Oceans and Atmosphere, Crawley, Western Australia, Australia
  • | 2 CSIRO Oceans and Atmosphere, Crawley, Western Australia, and Centre for Southern Hemisphere Oceans Research, CSIRO Oceans and Atmosphere, and CSIRO Oceans and Atmosphere, Hobart, Tasmania, Australia
  • | 3 Centre for Southern Hemisphere Oceans Research, CSIRO Oceans and Atmosphere, CSIRO Oceans and Atmosphere, and CSIRO Oceans and Atmosphere, Hobart, Tasmania, Australia
  • | 4 Physical Oceanography Laboratory/Qingdao Collaborative Innovation Centre of Marine Science and Technology, Ocean University of China, and College of Oceanic and Atmospheric Sciences, Ocean University of China, Qingdao, China
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Abstract

In this study, low-frequency variability of the meridional temperature transport in the Indian Ocean is examined using a mesoscale-eddy-resolving global ocean circulation model for the period 1979–2014. The dominant empirical orthogonal function (EOF) of the meridional temperature transport is found to be highly influenced by Pacific El Niño–Southern Oscillation (ENSO) through both oceanic and atmospheric waveguides, with the southward temperature transport being stronger during La Niña and weaker during El Niño. A dynamical decomposition of the meridional streamfunction and temperature transport shows that the relative importance of different dynamic modes varies with latitude; these modes act together to contribute to the coherent ENSO response. The Ekman mode explains a larger part of low-frequency variability in overturning and temperature transport north of the equator. Between 25° and 3°S, variations associated with vertical shear mode are of greater importance. The external mode has an important contribution between 30° and 25°S where the western boundary currents impinge on topography. South of 25°S, the variability of the external mode contribution has significant negative correlations with the vertical shear mode, suggesting that the large variability of external mode depends on the joint effects of baroclinicity and topography, such that hydrographic sections alone may not be suitable for deducing changes in the meridional temperature transport at these latitudes.

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Corresponding author: Ming Feng, ming.feng@csiro.au

Abstract

In this study, low-frequency variability of the meridional temperature transport in the Indian Ocean is examined using a mesoscale-eddy-resolving global ocean circulation model for the period 1979–2014. The dominant empirical orthogonal function (EOF) of the meridional temperature transport is found to be highly influenced by Pacific El Niño–Southern Oscillation (ENSO) through both oceanic and atmospheric waveguides, with the southward temperature transport being stronger during La Niña and weaker during El Niño. A dynamical decomposition of the meridional streamfunction and temperature transport shows that the relative importance of different dynamic modes varies with latitude; these modes act together to contribute to the coherent ENSO response. The Ekman mode explains a larger part of low-frequency variability in overturning and temperature transport north of the equator. Between 25° and 3°S, variations associated with vertical shear mode are of greater importance. The external mode has an important contribution between 30° and 25°S where the western boundary currents impinge on topography. South of 25°S, the variability of the external mode contribution has significant negative correlations with the vertical shear mode, suggesting that the large variability of external mode depends on the joint effects of baroclinicity and topography, such that hydrographic sections alone may not be suitable for deducing changes in the meridional temperature transport at these latitudes.

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Corresponding author: Ming Feng, ming.feng@csiro.au

1. Introduction

Poleward heat transport from the low to high latitudes is crucial to balance the global radiation and heat balances (Bjerknes et al. 1933). Previous studies suggested that the atmosphere and the ocean are of equal importance in transporting heat meridionally (Carissimo et al. 1985; Trenberth and Solomon 1994; Ganachaud and Wunsch 2000). In the Southern Hemisphere, the southward heat flux across 32°S of the Indian Ocean is estimated to play a dominant role in balancing heat loss in the Southern Ocean and the Atlantic (Talley 2013).

Estimates of the time-mean southward temperature transport in the subtropical Indian Ocean, using the transoceanic hydrographic sections at 32°S, vary considerably in the literature, mostly due to choices of the vertical extent of the deep overturning: −1.67 PW (1 PW = 1015 W) in Toole and Warren (1993), −1.30 ± 0.28 PW in Macdonald (1998), and −0.87 ± 0.06 PW in Sloyan and Rintoul (2001). More recently, Hernández-Guerra and Talley (2016) found that the southward temperature transports increased by approximately 0.6 PW from 2002 to 2009. The Indian Ocean overturning structure at 32°S has been described previously using hydrographic data (Bryden and Beal 2001; Sloyan and Rintoul 2001; Ganachaud 2003; McDonagh et al. 2008; Hernández-Guerra and Talley 2016) and model simulations (Garternicht and Schott 1997; Zhang and Marotzke 1999; Ferron and Marotzke 2003). Previous estimates of the time-mean deep meridional overturning circulation (MOC) strength vary between 2 and 27 Sv (1 Sv ≡ 106 m3 s−1), with the overturning depth varying between 1400 and 3700 m.

North of 10°S, the meridional heat transport of the Indian Ocean displays seasonal variations in response to the Ekman transport driven by monsoonal winds (Wacongne and Pacanowski 1996, Garternicht and Schott 1997, Lee and Marotzke 1998). At 20°S, Hsiung et al. (1989) found large southward temperature transport occurring most of the year, except in boreal winter, with a peak in May (2.5 PW).

The meridional temperature transport of the Indian Ocean also exhibits pronounced interannual variability. The importance of the Ekman heat transport anomalies has been identified (Jayne and Marotzke 2001; Chirokova and Webster 2006). However, the influences from geostrophic transport anomalies, forced by Pacific climate variability, as well as a comprehensive decomposition analysis of the meridional heat transport, have not been conducted.

On interannual time scale, large sea level and thermocline variability observed in the south Indian Ocean (SIO; 32°–10°S) arise primarily due to El Niño–Southern Oscillation (ENSO) teleconnection via an atmospheric bridge (Masumoto and Meyers 1998; Wang et al. 2001; Rao and Behera 2005; Perigaud and Delecluse 1993; Chambers et al. 1999). During El Niño an anomalous anticyclonic wind stress curl develops over the east Indian Ocean, resulting in the westward propagation of downwelling off-equatorial Rossby waves in the southeast Indian Ocean (Masumoto and Meyers 1998), which deepens the thermocline in the southwestern Indian Ocean (Xie et al. 2002). In addition to the atmospheric teleconnections, thermocline and sea level variability off the western Australian coast are influenced by ENSO mainly through the equatorial and coastal ocean waveguides, via the Indonesian Archipelago (Potemra 2001; Feng et al. 2003; Wijffels and Meyers 2004; Feng et al. 2010).

As the low-latitude exchange route connecting the Indian and the Pacific Oceans, the Indonesian Throughflow (ITF; Godfrey 1996; Wijffels et al. 2008) carries warm tropical Pacific Ocean water into the Indian Ocean and influences the regional ocean circulation (Domingues et al. 2007) and the transport of heat (Sprintall et al. 2009) in the Indian Ocean. The ITF transport has pronounced variability at interannual and decadal time scales (Meyers 1996; Wijffels and Meyers 2004; Tillinger and Gordon 2009; Liu et al. 2015). Wainwright et al. (2008) found that the ITF transport had weakened after the Pacific climate regime shift in mid-1970s, based on expendable bathythermograph (XBT) data. Since the mid-1990s, in response to the strengthened Pacific Walker circulation in the negative phase of the interdecadal Pacific oscillation (IPO), a rebound of the ITF has been documented (Feng et al. 2011).

In this study, we investigated low-frequency (3-yr low-pass filtered) variations of the Indian Ocean meridional temperature transport using a mesoscale-eddy-resolving ocean model, based on the Ocean Forecasting Australia Model version 3 (OFAM3). We identified the role of ENSO and the relationship between the Indian Ocean meridional temperature transport and the meridional overturning circulation. The paper is organized as follows. Section 2 briefly describes the ocean model and analysis methodology. In section 3, we examine the low-frequency variations of the Indian Ocean meridional temperature transport and their mechanisms. We further explore the link between the Indian Ocean meridional temperature transport and the Indian Ocean meridional overturning streamfunction in section 4. The summary and discussion are provided in section 5.

2. Data and method

a. The OFAM3 model data

OFAM3, based on version 4p1d of the Geophysical Fluid Dynamics Laboratory (GFDL) Modular Ocean Model (Griffies et al. 2004), is a near-global mesoscale-eddy-resolving ocean model with a horizontal resolution of 1/10° from 75°S to 75°N. There are 51 vertical levels with thickness ranging from 5 m in the upper 40 m to about 1000 m near the bottom. Partial grid cells are used to improve the representation of bottom topography.

In this study, we use an OFAM3 simulation, forced with ECMWF interim reanalysis 3-hourly surface heat, freshwater, and momentum fluxes (Dee and Uppala 2009) from 1979 to 2014 (Feng et al. 2016). In this version, bulk formulas (Large and Yeager 2004) are used in the calculation of evaporation and turbulent sensible and latent heat flux, and updated at each model time step (Zhang et al. 2016). Sea surface salinity (SSS) is restored to monthly SSS from CSIRO Atlas of Regional Seas (CARS) climatology (released in 2009; Ridgway and Dunn 2003) with a restoring time scale of 180 days. In addition, in order to minimize the drift in deep ocean fields, a stationary correction is applied to the model temperature and salinity tendency terms below 2000-m depth, derived from a spinup run by restoring the model to CARS climatology with a restoring time scale of 1 year (Zhang et al. 2016; Feng et al. 2016). In the present work, the analyses are based on the monthly mean temperature, salinity, and velocity fields during 1979–2014.

OFAM3 and earlier versions of OFAM models have been used for studying eddy characteristics and ocean circulation in the Indian and Pacific Oceans (Schiller et al. 2008; Matear et al. 2013; Oke et al. 2013; Qin et al. 2015; Rykova et al. 2017). Zhang et al. (2016) reported that OFAM3 was able to capture the observed large-scale circulation and eddy characteristics as compared to the satellite and in situ observations. Using OFAM3, Feng et al. (2016) investigated the ocean boundary currents around Australia, including the ITF and the Leeuwin Current, and found that the mean structure and variability of the ocean boundary currents from the model are consistent with existing observations. The estimated seasonal cycles of the meridional overturning streamfunction and oceanic temperature transport are in relatively good agreement with earlier studies (see details in appendix A). Thus, it is appropriate to use the model simulation to investigate the low-frequency temperature transport of the Indian Ocean.

b. Temperature transport

Using the meridional velocity and potential temperature the meridional temperature transport is given by
e1
where is the potential density of seawater and is the specific heat capacity of seawater at constant pressure.
The temperature transport can be decomposed into the overturning QOT and gyre QGYRE components, which are given by (Bryan 1982; Böning and Herrmann 1994)
e2
e3
e4
where square brackets represent the zonal averages of velocity or potential temperature, and asterisks denote the deviations from these zonal averages.
The meridional Ekman temperature transport is given by Kraus and Levitus (1986) and Levitus (1987):
e5
where is the Coriolis parameter, is the zonal wind stress, θek is the sea surface temperature, and is the vertically averaged potential temperature at a given latitude. In this paper positive values represent northward or eastward fluxes.

c. Dynamical decomposition of the meridional streamfunction

Following, Lee and Marotzke (1998) and Hirschi and Marotzke (2007), the meridional streamfunction [Eq. (6)] is decomposed into three components [Eqs. (7), (9), and (11)]. The first component is an external mode that is associated with the vertically averaged meridional flow over the zonally nonuniform topography. As suggested by Lee and Marotzke (1998), the external mode represents the influences of topography, frictional effects, and the wind stress curl. In the Indian Ocean, this would also include the net ITF transport. The second component can be described as the surface Ekman flow forced by wind stress and the compensating depth-averaged barotropic flow. The third component describes the shear mode that is due to the zonal density difference, and therefore it reflects the vertical shear of meridional flow. Cabanes et al. (2008) pointed out that the zonal density gradient varies in response to variability of the wind stress curl or buoyancy forcing.

The meridional streamfunction in the Indian Ocean can be decomposed as
e6
e7
e8
e9
e10
e11
e12
e13
where H is the depth of the ocean, and −Hz′ ≤ 0; and are the western and eastern limits of the Indian Ocean basin, respectively; is the reference density, f is the Coriolis parameter, L is the width of basin at the surface, is the thickness for the Ekman layer (assumed to be 50 m in this study), and A is the area of the cross section (longitude–depth section).

The Ekman velocity is calculated using the zonal wind stress over the depth of Ekman layer. The geostrophic velocity is computed using the thermal wind approximation with a level of no motion at the ocean sea floor. For a flat basin, the zonally integrated shear is simply proportional to the seawater densities at the eastern and western boundary, according to Eq. (12). Note that the vertically integrated shear component should be zero because the depth-averaged transport is included in the external mode. To ensure the volume balance for , its depth average is subtracted in calculating .

3. Low-frequency variability of the meridional temperature transport

a. Climatological mean SST and upper ocean currents

The annual mean structure of the SST averaged over 1979–2014 shows warm pools in eastern and northern Indian Ocean (surface temperatures > 29°C; Fig. 1). SST decreases gradually from the tropical Indian Ocean to the south. North of 10°S, the SST has a strong zonal gradient in the western Indian Ocean, and a more homogeneous SST distribution over the eastern Indian Ocean. In contrast, south of 10°S there is only a weak zonal temperature gradient that increases at the western boundary associated with the Agulhas Current. The upper ocean circulation of the southern Indian Ocean (upper 100-m current vectors) is characterized by the westward flowing South Equatorial Current (SEC) north of 20°S, which is partly supplied by the ITF (Fig. 1). At the east coast of Madagascar, the SEC splits into two branches, the Northeast and Southeast Madagascar Currents (NEMC and SEMC), respectively. Between 20° and 30°S a permanent eastward flow, the South Indian Countercurrent (SICC) is found. At 115°E, the westward ITF carries warm water from the Pacific into the Indian Ocean, resulting in a heat input of −1.08 ± 0.34 PW into the subtropical Indian Ocean. At 32°S, the southward temperature flux is −1.19 ± 0.24 PW. The difference is the net heat gain in the Indian Ocean through air–sea fluxes.

Fig. 1.
Fig. 1.

Indian Ocean temperature (color shaded; °C) and currents averaged in upper 100 m [vectors; 0.5 (m s−1)1/2; presented as (m s−1)1/2 for better vector visibility] from the OFAM3 model simulation (averaged during 1979–2014). Average temperature transports from the ITF (115°E) and across 32°S are indicated.

Citation: Journal of Climate 32, 4; 10.1175/JCLI-D-18-0349.1

b. Variations of low-frequency meridional temperature transport

The low-frequency variations (3-yr low-pass filtered) of the meridional temperature transport in the Indian Ocean for the period 1979–2014 (low-frequency ENSO variability is retained) show large anomalies in the SIO (Fig. 2a). The meridional temperature transport anomalies have the largest variations of greater than 0.2 PW near 12°S. The low-frequency temperature transport variability in the SIO (32°–10°S) is significantly correlated with the low-pass-filtered Niño-3.4 index at 0.84 (95% confidence level; see Figs. 2a and 5b). Thus, ENSO plays an important role in the Indian Ocean temperature transport variability, consistent with previous studies (e.g., Klein et al. 1999; Xie et al. 2002). While the temperature transport variability is weaker north of 10°S, the variability is generally in phase with the SIO, except between 2008 and 2012. South of 20°N, most of the temperature transport variability is found in the overturning component (Fig. 2).

Fig. 2.
Fig. 2.

Latitude–time evolution of low-pass-filtered (with period > 3 years) Indian Ocean meridional temperature transport anomalies [PW (= 1015 W)] for the (a) total, (b) overturning, and (c) gyre component.

Citation: Journal of Climate 32, 4; 10.1175/JCLI-D-18-0349.1

We decompose the Indian Ocean meridional temperature transport into contributions from external, Ekman, and vertical shear modes, similar to the approach by Hall and Bryden (1982). The consistency between the total meridional temperature transport variability and the sum of the three modes suggest negligible errors in our decomposition (Figs. 2a and 3d).

Fig. 3.
Fig. 3.

Latitude–time diagrams of the meridional temperature transport: (a) external mode, (b) Ekman, (c) vertical shear, and (d) the sum of the three components.

Citation: Journal of Climate 32, 4; 10.1175/JCLI-D-18-0349.1

Between 3° and 10°N, the Ekman and shear modes are large but mostly compensate each other (Figs. 3 and 4). The vertical shear mode plays a dominant role in capturing variations of the total temperature transport between 25° and 3°S (Figs. 3c and 4). The contribution of the external mode has a complex pattern (Fig. 3a). There is no significant correlation between external mode and total temperature transport (Fig. 4a); however, low-frequency variations of this component have comparable magnitudes with the vertical shear mode south of 25°S (Fig. 4b). South of 10°S, the Ekman component only plays a minor role.

Fig. 4.
Fig. 4.

(a) Correlation coefficients between total temperature transport anomaly and the external mode (red line), Ekman (blue dash-dotted line), and vertical shear (black dashed line) component. (b) Standard deviation of the temperature transport anomalies of the external mode (red line), Ekman (blue dash-dotted line), and vertical shear (black dashed line) components. The components are calculated using monthly data fields and are 3-yr low-pass filtered. The correlations significant above 95% confidence level are denoted with thick lines.

Citation: Journal of Climate 32, 4; 10.1175/JCLI-D-18-0349.1

c. Mechanisms of low-frequency variability in Indian Ocean meridional temperature transport

An EOF analysis is employed to identify the dominant modes of the Indian Ocean low-frequency meridional temperature transport (32°S–30°N) during 1979–2014. The EOF1 mode accounts for 76% of the total variance and has the same sign over the entire Indian Ocean north of 32°S [Fig. 5; we investigate the sensitivity of the EOF analysis by changing the southern boundary from 30° to 34°S or the northern boundary from 30°N to the equator, and the EOF1 pattern and the associated PC1 are not sensitive to the choices of the boundaries (not shown)]. A broad maximum amplitude is found between 12° and 20°S, with a rapid decrease north of 12°S and decaying gradually south of 20°S.

Fig. 5.
Fig. 5.

(a) EOF1 (PW) and (b) the normalized PC1 time series (red line) of low-frequency Indian Ocean meridional temperature transport. The low-frequency Niño-3.4 index (blue line) is superimposed in (b).

Citation: Journal of Climate 32, 4; 10.1175/JCLI-D-18-0349.1

The time series of the EOF1 is significantly correlated with the low-frequency Niño-3.4 index at 0.83 (above 95% confidence level; Fig. 5b; less association exists after 2004). Therefore, EOF1 represents a relationship between the low-frequency meridional temperature transport variability and ENSO. To examine the physical connection between EOF1 and ENSO, we composite the model temperature, wind, and temperature transport anomalies (1979–2014) into El Niño and La Niña based on one standard deviation of low-frequency Niño-3.4. During this period we have four El Niño events (1982, 1987, 1991, and 1997) and seven La Niña events (1984, 1985, 1989, 1996, 1999, 2000, and 2011).

The spatial patterns displayed in composite maps of temperature (averaged between 0 and 2000 m) and wind anomalies are consistent with characteristics of ENSO influences on the Indian Ocean (e.g., Xie et al. 2002; Schott et al. 2009; Figs. 6a,b). During El Niño, cold temperature anomalies are found east of 90°E and warm temperature anomalies in the tropical western Indian Ocean. Anomalous southeasterly-easterly winds exist off the Sumatra–Java coast and along the equator, and northwesterly wind anomalies exist over the central south Indian Ocean. The wind anomalies result in anomalous anticyclonic wind stress curls that can drive downwelling Rossby waves (Masumoto and Meyers 1998; Chambers et al. 1999), leading to the warm temperature anomalies in the tropical western Indian Ocean (Xie et al. 2002). For La Niña, the signs of the anomalous temperature and wind are reversed.

Fig. 6.
Fig. 6.

El Niño composites of (a) wind stress (vectors; dyn m−2) and temperature anomalies (0–2000 m averaged; shaded); (c) total meridional temperature transport anomaly (black line) and its decomposition into external mode (yellow line), Ekman (blue line), and vertical shear (red line) components; and (e) cumulative heat balance contributions from meridional temperature transport (black line), net surface heat flux (yellow line), ITF heat transport (red line), and the rate of change in heat storage (blue dashed line). (b),(d),(f) As in (a), (c), and (e), respectively, but for La Niña composites. Regions enclosed by black contours in (a) and (b) denote temperature anomalies significant at the 95% level.

Citation: Journal of Climate 32, 4; 10.1175/JCLI-D-18-0349.1

During El Niño (Fig. 6c), positive (northward) temperature transport anomalies are found over the entire basin, with a maximum value of 0.18 PW at 12°S. North of the equator, the easterly wind anomalies over the equatorial Indian Ocean drive a significant northward Ekman temperature transport anomaly, overcoming the southward geostrophic temperature transport anomaly in response to the east–west temperature gradient, resulting in small northward temperature transport anomalies (<0.03 PW) (Fig. 6c). Between 10° and 3°S, the shear component dominates over the Ekman component, resulting in a small net northward temperature transport anomaly. South of 10°S, the Ekman, shear, and external mode contributions to the net temperature transport variability have comparable magnitude, all contributing to the ~0.1 PW northward temperature transport anomaly. During La Niña events, coherent southward total temperature transport anomalies are seen from 32°S to the northern Indian Ocean, with ~0.1 PW magnitude south of 12°S. The contributions from the three components are opposite to those during El Niño, except that the shear component plays a more significant role south of 20°S.

El Niño is characterized by a weakening of the atmospheric Walker circulation and decreased atmospheric convection over the western tropical Pacific, which is associated with the easterly anomalies over the equatorial Indian Ocean and anticyclonic wind stress curl anomalies over the southeast tropical Indian Ocean (Fig. 7). The wind anomalies drive the positive steric height anomalies in the southwest tropical Indian Ocean. In the meantime, negative steric height anomalies occur along the Australian coastal waveguide in the southeast Indian Ocean, which is associated with the remote forcing through the oceanic pathway from the Pacific via the ITF. Thus, the oceanic and atmospheric teleconnections act together to drive the meridional temperature transport variations of the Indian Ocean during El Niño: in the tropical north Indian Ocean (0°–10°N), surface Ekman transport associated with the anomalous easterlies dominates the temperature transport. In the tropical south Indian Ocean between the equator and 10°S, the northward geostrophic transport anomalies are mostly due to low sea levels off the Java and Sumatra coasts driven by the wind stress anomalies, dominating over the Ekman transport anomalies; farther south, the teleconnected sea level anomalies across the basin induce the northward temperature transport anomalies. The opposite mechanisms would occur during La Niña.

Fig. 7.
Fig. 7.

A schematic diagram of Indian Ocean temperature transport anomalies during El Niño. The background shows the regression patterns of wind stress (vectors) and sea surface height anomalies (shaded) against the normalized PC1 of Indian Ocean meridional temperature transport at zero lag.

Citation: Journal of Climate 32, 4; 10.1175/JCLI-D-18-0349.1

We further investigate the ENSO influence on the meridional temperature transport anomaly by assessing the Indian Ocean heat balance (appendix B). During El Niño, the heat storage increase in the northern Indian Ocean is balanced by the temperature transport anomalies, as well as positive net surface heat flux anomalies (Fig. 6e), associated with the suppressed convection and the resultant increase in solar radiation (Klein et al. 1999). In the SIO, the weakened ITF heat advection is the only contributor to the cooling (Table 1), due to negative temperature transport anomalies in response to El Niño (Clarke and Liu 1994; Liu et al. 2015; Meyers 1996). The enhanced ITF heat advection is the largest contributor to warm the SIO during La Niña; the effect of the net surface heat flux is small (Table 1 and Fig. 6f). Overall, most of the anomalous ITF heat advection is compensated by the meridional temperature transport anomalies across the southern boundary of the SIO during both El Niño and La Niña.

Table 1.

Composite analysis of the SIO (32°–10°S) heat balance (PW): ITF temperature transport, temperature transport and its dynamical components (i.e., external mode, Ekman, and vertical shear components), net air–sea surface heat fluxes, and the rate of change in heat storage.

Table 1.

4. Meridional temperature transport and meridional overturning streamfunction

a. Variations of the meridional overturning streamfunction

Mean and seasonal variations of the meridional overturning streamfunction are consistent with early studies (appendix A). On interannual time scales, the streamfunction anomaly is characterized by an anomalous northward transport in the upper ocean south of 10°S and a subsurface clockwise pattern in the subsurface centered at 10°S during El Niño. This northward transport is mainly contributed by the external mode and vertical shear mode (Figs. 8b,d). The Ekman component is important in the equatorial region (Fig. 8c). The streamfunction anomalies during La Niña (Fig. 8e) are almost reversed from El Niño. As the meridional temperature transport is strongly weighted by the transport anomalies in the upper ocean, there is a close connection between the meridional temperature transport and the streamfunction anomalies.

Fig. 8.
Fig. 8.

El Niño composites of (a) meridional overturning streamfunction anomalies and contributions from (b) external mode , (c) Ekman , and (d) vertical shear . (e)–(h) As in (a)–(d), but for La Niña composites. The overturning streamfunction anomalies significant at the 95% confidence level are hatched in (a) and (e).

Citation: Journal of Climate 32, 4; 10.1175/JCLI-D-18-0349.1

b. The external mode

The external mode of meridional overturning is associated with zonally nonuniform topography and the horizontal barotropic gyre that flows over the topography. On interannual time scales, there are significant negative correlations between low-frequency and south of 15°S and north of the equator during 1979–2014 (Fig. 9a), likely dominated by the joint effects of baroclinicity, topography, and wind stress curl [see Eq. (14) in Sime et al. 2006].

Fig. 9.
Fig. 9.

(a) Correlation coefficient between low-frequency and . Shaded contours with labeled white contours show regions of negative correlation. (b) Correlation between low-frequency vertical shear and external components of meridional temperature transport.

Citation: Journal of Climate 32, 4; 10.1175/JCLI-D-18-0349.1

Large variability of meridional temperature transport from the external mode, however, only occurs in the regions between 30° and 25°S, where the Agulhas Current impinges on western boundary topography (Figs. 3a and 4b). North of 10°N, high correlations between the external mode and vertical shear for temperature transport are found (Fig. 9b), but with only small magnitude (Figs. 3a and 4b). In other regions (e.g., 25°–15°S and 3°–10°N), strong negative correlations between and are primarily in the deep ocean (below 1000 m), resulting in weak correlation between vertical shear and external mode contributions to the meridional temperature transport (Fig. 9b).

5. Summary and discussion

In this paper, we have examined the low-frequency variability (3-yr low-pass filtered) of the Indian Ocean meridional temperature transport using a mesoscale eddy-resolving near-global ocean circulation model for the period of 1979–2014. The first EOF mode of the meridional temperature transport across the entire Indian Ocean is strongly correlated with the Niño-3.4 index. Both the ENSO-related oceanic and atmospheric teleconnections play a significant role in influencing the low-frequency Indian Ocean variability, with stronger southward temperature transport during La Niña and weaker transport during El Niño.

A dynamical decomposition of the temperature transport and meridional overturning streamfunction shows that the low-frequency variability of Ekman mode is more important than the shear mode north of the equator. In the southern Indian Ocean between 10°N and 25°S and away from the equator, the temperature transport variability is primarily due to the shear mode. The variability of the temperature transport associated with the external mode component is particularly large in the latitudes of 30°–25°S, where western boundary currents pass over the zonally nonuniform topography and there are strong negative correlations between the external mode and vertical shear . This could pose a challenge in estimating meridional temperature transport variability from hydrographic surveys alone.

a. Variation in the transmission of ENSO signals into the Indian Ocean

Since the early 2000s, the relationship between the variations of temperature transport and ENSO has weakened (Fig. 5b), likely related to the reduction of ENSO strength. Shi et al. (2007) found that the signal-to-noise ratio is low and the meridional extent of subtropical North Pacific Rossby wave pathway into the Indian Ocean is narrow during weak ENSO periods.

b. Comparing with the Atlantic MOC study

The close relationships between the Atlantic meridional heat transport (MHT) and MOC are identified from models (e.g., Jia 2003) and observational data (Johns et al. 2011; Msadek et al. 2013). These suggest that understanding the mechanism of the overturning variability is important to better identify the temperature transport variability in the Indian Ocean. The interannual variability in Atlantic MOC is largely caused by Ekman transport (Cabanes et al. 2008; Roberts et al. 2013), whereas the variability in the Indian Ocean transport has been shown to be determined by several mechanisms. The long-term observations (RAPID-MOCHA) along 26.5°N in the North Atlantic offer excellent opportunities to study the variability of the MOC and the associated MHT (Johns et al. 2011; Rayner et al. 2011). The observations of the Atlantic MOC and MHT have been used to explain and improve the performance of the climate models (e.g., Msadek et al. 2013). In this study, OFAM3 model results indicate that the importance of the low-frequency variations of the external mode in SIO is a striking difference from the North Atlantic. The thermal wind relationship and the Ekman contributions are insufficient to estimate the low-frequency variations of Indian Ocean meridional temperature transport. Thus, a combination of boundary current monitoring arrays and interior bottom pressure measurement is required across SIO basin.

c. Role of Indian Ocean deep overturning

OFAM3 is primarily designed to study upper ocean dynamics and ocean variability, and has a coarse vertical resolution in the deep ocean (five layers between 2000 m and the bottom). Lee and Marotzke (1998) suggested that the very weak seasonal variation of Indian Ocean temperature transport at 10°N reported by McCreary et al. (1993, using a 2.5-layer model) may be a result of the absence of the intermediate and deep overturning. Based on WOCE hydrographic data, the deep overturning has been shown to carry 10% of the total southward energy flux at 32°S (Ferron and Marotzke 2003). Variations of Indian Ocean temperature transport associated with the change of deep overturning vertical structure (e.g., Sloyan and Rintoul 2001) may not be well simulated in this model. To accurately capture the variability of the Indian Ocean meridional overturning and temperature transport, it is imperative for models to better resolve the bottom and deep circulation.

Acknowledgments

MF and BMS was supported by Centre for Southern Hemisphere Oceans Research (CSHOR), which is a joint initiative between the Qingdao National Laboratory for Marine Science and Technology (QNLM), CSIRO, University of New South Wales, and University of Tasmania. BMS was supported by the CSIRO Oceans and Atmosphere Decadal Forecasting Project. JL was supported by National Natural Science Foundation of China (41676001, 41521091). JM would like to acknowledge the support from the China Scholarship Council (201606330014) for his visit to CSIRO. This work is also supported by the NSFC international collaboration research funds (41628601). The OFAM3 model experiments are supported by the Ocean Downscaling Strategic Project, funded by CSIRO Oceans and Atmosphere, in close collaboration with the Bluelink team (http://wp.csiro.au/bluelink/global/). All modeling experiments were performed on the Australian National Computing Infrastructure (NCI; http://nci.org.au/). The Niño-3.4 index is provided by the NOAA Earth System Research Laboratory (https://www.esrl.noaa.gov/psd/data/climateindices/list/). The ERA-interim forcing data are provided by ECMWF (http://apps.ecmwf.int/datasets/data/interim-full-mnth/).

APPENDIX A

OFAM3 Model Evaluation

The OFAM3 model has been demonstrated to be able to capture the large-scale circulation as evaluated against satellite and in situ observations (Zhang et al. 2016). The estimated meridional transport streamfunctions for January and July are shown in Fig. A1 and are compared to those of Garternicht and Schott (1997) and Lee and Marotzke (1998). To summarize, many of the characteristics of our zonally integrated seasonal meridional overturning estimates are in fairly good agreement with these two studies. Additionally, the deep inflow at southern boundary (32°S) is present in all three models. However, the strength of the cells in OFAM3 (roughly ±16 Sv) appears to be weaker than that in other two studies (about ±20 Sv). The overturning cell near 10°N for July reported by Lee and Marotzke (1998) using a regional model is much stronger than our estimation, while the cell is not seen in Garternicht and Schott (1997). There are other noted model differences in the overturning for different model estimates. For instance, OFAM3 has weaker shallow cross-equatorial cells.

Fig. A1.
Fig. A1.

(a) January, (b) July, and (c) annual mean streamfunctions (Sv) of meridional overturning transport (1979–2014) for the Indian Ocean.

Citation: Journal of Climate 32, 4; 10.1175/JCLI-D-18-0349.1

Latitudinal distribution of the annual cycle of meridional temperature transport for the Indian Ocean (Fig. A2) is compared with results from hydrographic data (Hastenrath and Greischar 1993) and from models (Wacongne and Pacanowski 1996; Chirokova and Webster 2006). All distributions show a common feature in seasonal variations of the temperature transport, namely a southward transport from boreal spring to fall and northward transport in the rest of year. The magnitude and phase of OFAM3 temperature transports are broadly consistent with these studies. In OFAM3, the maximum southward transport is about 2 PW, larger than the result of Chirokova and Webster (2006; about 1.5 PW). Overall, the seasonal cycles of the oceanic temperature transport and overturning from OFAM3 are in good agreement with other studies.

Fig. A2.
Fig. A2.

Seasonal cycle of Indian Ocean meridional temperature transport (1979–2014).

Citation: Journal of Climate 32, 4; 10.1175/JCLI-D-18-0349.1

APPENDIX B

Heat Budget

The model heat budget for the Indian Ocean can be written as in Eq. (B1) (following Loschnigg and Webster 2000) using 1) the change in heat storage , 2) heat advection terms Adv, 3) the net surface heat flux , and 4) the residual term Res:
eb1
eb2
eb3
eb4
where T is potential temperature, t is time, is the density, is the heat capacity of seawater, u is the zonal and υ the meridional velocity, and Adv is the integral of the westward temperature transport from ITF across 115°E [first term of Eq. (B3)] and northward temperature transport across a given latitude [second term of Eq. (B3)]. The four terms on the right-hand side of Eq. (B4) correspond to net surface heat flux due to contributions from the solar radiation , net longwave radiation , latent heat flux , and sensible heat flux .

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