Abstract

In the eastern tropical Indian Ocean, intraseasonal variability (ISV) affects the regional oceanography and marine ecosystems. Mooring and satellite observations documented two periods of unusually weak ISV during the past two decades, associated with suppressed baroclinic instability of the South Equatorial Current. Regression analysis and model simulations suggest that the exceptionally weak ISVs were caused primarily by the extreme El Niño events and modulated to a lesser extent by the Indian Ocean dipole. Additional observations confirm that the circulation balance in the Indo-Pacific Ocean was disrupted during the extreme El Niño events, impacting the Indonesian Throughflow Indian Ocean dynamics. This research provides substantial evidence for large-scale modes modulating ISV and the abnormal Indo-Pacific dynamical connection during extreme climate modes.

1. Introduction

Oceanic intraseasonal variability (ISV; variability of <90 days) associated with mesoscale processes regulates global climate change, thermohaline circulation, and marine material distribution (Thompson et al. 2014; Zhang et al. 2014). The southeastern tropical Indian Ocean (SETIO) is connected to the western Pacific Ocean via the Indonesian Throughflow (ITF; Fig. 1a). Mass and heat transports accomplished by the ITF, the South Equatorial Current (SEC), and eddy affect regional ocean and climate systems and are a significant component of the global thermohaline circulation (Feng et al. 2018; Gordon and Fine 1996; Lee et al. 2019; Sprintall et al. 2014). The SETIO is a region with strong ISV, primarily during the second half of the year (Fig. 1a; Feng and Wijffels 2002; Qiu et al. 1999; Quadfasel and Cresswell 1992). The SETIO ISV (Fig. 1a) is mainly associated with the current baroclinic instability related to the SEC/ITF system (Feng and Wijffels 2002; Ogata and Masumoto 2011; Trenary and Han 2012; Yu and Potemra 2006). Energy budget analysis demonstrates that the barotropic contribution is an order of magnitude smaller than the baroclinic one (Ogata and Masumoto 2011). The ISV, accompanied by changes of sea surface temperature (SST) and thermocline depth, was suggested to modulate regional upwelling and ecosystems (Chen et al. 2015), as well as Madden–Julian oscillations (Zhou and Murtugudde 2020) that interact with El Niño–Southern Oscillation (ENSO) (McPhaden 1999; Peng et al. 2019; Roxy et al. 2019).

Fig. 1.

(a) The standard deviation (STD) of intraseasonal (30–90-day bandpass filtered) SSHA from AVISO (×10−2 m s−1). The red star denotes the mooring site, 10°S, 100°E. The black rectangle marks the region of 10°–15°S, 100°–115°E. Abbreviations: SEC, South Equatorial Current; ITF, Indonesian throughflow; SJC, South Java Current; EGC, Eastern Gyral Current; LC, Leeuwin Current. (b) Mooring-observed daily zonal current u (×10−2 m s−1) over 50–300-m interval from April 2016 through April 2018. The solid and dashed black lines mark 10 and −10 cm s−1 contours, respectively. Positive u is toward the east. (c) As in (b), but for meridional current υ. Positive υ is toward the north. (d) Variance spectra of the averaged u (blue solid line) and υ (red solid line), and the 95% confidence curves (blue/red dashed lines). (e) EKE derived from the mooring observation (×10−4 m2 s−2). The dashed boxed area marks the unusually weak period of ISV activities.

Fig. 1.

(a) The standard deviation (STD) of intraseasonal (30–90-day bandpass filtered) SSHA from AVISO (×10−2 m s−1). The red star denotes the mooring site, 10°S, 100°E. The black rectangle marks the region of 10°–15°S, 100°–115°E. Abbreviations: SEC, South Equatorial Current; ITF, Indonesian throughflow; SJC, South Java Current; EGC, Eastern Gyral Current; LC, Leeuwin Current. (b) Mooring-observed daily zonal current u (×10−2 m s−1) over 50–300-m interval from April 2016 through April 2018. The solid and dashed black lines mark 10 and −10 cm s−1 contours, respectively. Positive u is toward the east. (c) As in (b), but for meridional current υ. Positive υ is toward the north. (d) Variance spectra of the averaged u (blue solid line) and υ (red solid line), and the 95% confidence curves (blue/red dashed lines). (e) EKE derived from the mooring observation (×10−4 m2 s−2). The dashed boxed area marks the unusually weak period of ISV activities.

The SETIO ISV displays significant interannual variability, modulated by the Indian Ocean dipole (IOD) events (Ogata and Masumoto 2010, 2011). During positive IOD events, negative SST anomalies and low sea surface height anomalies (SSHAs) off the southern coast of Sumatra–Java, creating larger meridional temperature gradient and associated vertical shear in the SEC, increasing baroclinic instability, which induce stronger ISV.

As the ENSO is the dominant climate mode affecting the ITF magnitude at interannual time scale (Ffield et al. 2000; Gordon et al. 2019; Meyers 1996; Potemra et al. 2002), it is reasonable to expect that ENSO also plays an important role in affecting the ISV in the SETIO. During El Niño events, for example, the westerly wind bursts intensify the North Equatorial Current (NEC) of the Pacific Ocean and induce a northward shift of its bifurcation point (Kim et al. 2004; Qiu and Lukas 1996). As a result, the Mindanao Current strengthens and the ITF weakens (Feng et al. 2018; Liu et al. 2006; Masumoto and Yamagata 1991; Meyers 1996; Tozuka et al. 2002; Wang et al. 2004), and the intrusion of the Kuroshio, via the Luzon Strait, into the South China Sea (SCS) strengthen (Qu et al. 2004; Sheremet 2001; Wang and Yuan 2014; Yaremchuk and Qu 2004; Yuan and Wang 2011) affecting the SCS circulation (Chu et al. 2017; Zhao and Zhu 2016; Zhu et al. 2015), as well as the surface layer of the ITF (Gordon et al. 2012; Tozuka et al. 2007). However, synchronous observations of the ISV in the SETIO and circulation in the Indo-Pacific Ocean are limited, and the impact of the ENSO on the ISV of the SETIO has not been investigated.

Since 2015, a subsurface mooring system was deployed at 10°S, 100°E by the Chinese Academy of Sciences to continuously monitor the circulation and ISV within the SETIO, with 16 moorings deployed in the SCS and the western Pacific Ocean (Song et al. 2018; Zhang et al. 2017). In this paper, we first analyze the mooring data in the SETIO identifying the two extremely weak ISV periods within the latest two decades. We conclude that these abnormally weak ISV periods are mainly caused by the extreme El Niño events, rather than the IOD events as might have been expected. Based on the mooring data in the SCS and western Pacific Ocean, we confirm the Indo-Pacific dynamical connection during the extreme El Niño events, including the northward shift of the NEC bifurcation, increased Luzon Strait transport, the strengthened Pacific North Equatorial Countercurrent (NECC), and weakened ITF. During the extreme ENSO events and changes in the ITF, the dynamic balance in the Indo-Pacific Ocean is disturbed, causing significant anomalies not only on interannual time scale but also on intraseasonal time scale. A detailed understanding of ENSO’s role in affecting the SETIO ISV will provide a new perspective on understanding the ENSO impact, allow a more complete assessment of the Indo-Pacific connection, and contribute to our understanding of the effects of large-scale climate modes on ISV within the Indian Ocean.

2. Data and methods

a. Observational and reanalysis data

Current measurements from the mooring deployed at 10°S, 100°E (star in Fig. 1a) are used to investigate ISV of currents in the SETIO. The mooring provides data from March 2016 to March 2018 with vertical resolution of 8 m and sampling time frequency of 1 h. In this study, the observed current velocities in the effective measurement range of 50–300 m are linearly interpolated onto uniform 5-m intervals, and hourly measurements are averaged into daily data. Another 16 moorings are deployed to monitor circulation in the SCS and western Pacific Ocean, and their locations and observed periods are summarized in Table 1. Monthly data averaged in the upper 200 m from the moorings are used to examine the circulation change associated with the ENSO events in the Indo-Pacific Ocean.

Table 1.

Mooring locations and observation periods.

Mooring locations and observation periods.
Mooring locations and observation periods.

To assess the mooring data in a basin-scale perspective over the SETIO, we will also analyze the satellite observed daily sea surface height (SSH) and geostrophic currents anomalies, distributed by the Archiving, Validation, and Interpretation of Satellite Oceanographic (AVISO; Le Traon et al. 1998), and the total ocean surface current (both geostrophic and Ekman components) from the Ocean Surface Current Analysis–Real Time (OSCAR) product with 5-day intervals (Bonjean and Lagerloef 2002). Daily cross-calibrated multiplatform (CCMP) surface wind vectors V2.0 (Atlas et al. 2011) from 1994 to 2017 are used to investigate the relationship between wind stress anomaly and the ISV. Monthly sea surface temperature from Optimum Interpolation Sea Surface Temperature (OISST; Banzon et al. 2016; Reynolds et al. 2007) during 1983–2017 and monthly precipitation from Tropical Rainfall Measuring Mission (TRMM; Kummerow et al. 2000) during 1998–2016 are examined to understand variation of the stratification in the SETIO. Furthermore, temperature, salinity, and current data from the Bluelink Reanalysis (BRAN; Ogata and Masumoto 2010, 2011) are used to examine the underlying mechanisms of the extremely weak ISV periods. The eddy-resolving reanalysis is available beginning January 1994, with 1/10° horizontal grid spacing, and 51 vertical levels. The daily BRAN data from January 1994–December 2017 are used in our analysis. Monthly currents from the ECMWF Ocean Reanalysis System, version 4(ORAS4) (Balmaseda et al. 2013) with a horizontal resolution of 1° and 42 vertical levels during January 1994–December 2016 are analyzed to verify our model performance (see section 2c).

b. A 2.5-layer reduced-gravity model

We use a 2.5-layer reduced-gravity model to investigate baroclinic instability (Qiu 1999). The model can effectively evaluate the intensity of the baroclinic instability determined by stratification and vertical velocity shear by seeking the normal mode solution. Details of the model can be found in the supplemental material.

c. OGCM experiments

The ocean general circulation model (OGCM) used in this study is the Hybrid Coordinate Ocean Model (HYCOM; Halliwell 2004), version 2.2.18, which is configured to the Indo-Pacific Ocean basin (19°E–68°W and 55°S–50°N). The resolution is 1/3° between 70° and 170°E in zonal direction and 25°S–25°N in meridional direction, and gradually changes to 1° to the model boundaries. The model has 35 hybrid vertical layers, with a top layer thickness of 5 m. Details of the model can be found in Li et al. (2018).

The model was spun up for 30 years, using monthly climatologic fields of ERA-20C as surface forcing. Then HYCOM was integrated forward from 1940 to 2016 with daily ERA-20C fields for 1940–2010 and daily ERA-Interim fields for 2011–16, which is referred to as the HYCOM Control Run (CR). The second experiment is the “Pacific run” (PAC), in which daily forcing is retained in the Pacific (the same as in CR), whereas monthly climatologic forcing (retaining only seasonal cycle) is imposed on the Indian Ocean. There is a 5° transition zone, where the daily wind stress gradually changes to climatologic forcing. Thus, the Indian Ocean variability in the PAC is primarily induced by the Pacific Ocean variability through the ITF. Their difference can be roughly attributable to local atmospheric forcing within the Indian Ocean, including both atmospheric teleconnections from other basins and local climate variability. It should be stated that the Pacific forcing effect and local forcing effect may not be clearly separable in such linear sense. Outputs of the two experiments are stored as monthly mean data for the analysis period of 1994–2016.

3. Results

a. Two periods of unusually weak ISV in the SETIO

Figures 1b and 1c show the daily time series of zonal current u and meridional current υ recorded by the mooring at 10°S, 100°E (marked by the star in Fig. 1a) from April 2016 to April 2018 over the 50–300-m-depth interval. Both u and υ exhibit strong intraseasonal variations. The power spectra of υ (Fig. 1d) shows strong spectral amplitude at intraseasonal periods, with distinct peaks at 32 day and within the 52–71-day range (red line in Fig. 1d). The spectra of u (blue line) also has significant intraseasonal peaks at 52 and 83 days.

Guided by the variance spectra (Fig. 1d), we investigate eddy kinetic energy (EKE; Fig. 1e) of the 30–90-day period as representative of the ISV signal. In oceanographic studies, EKE is widely used to represent the activity of ocean internal variability, such as mesoscale eddies, tropical instability waves, and submesoscale filaments. EKE is calculated using the formula EKE = (1/2)(u2 + υ2), where u′and υ′ are intraseasonal zonal and meridional velocities, respectively. The mooring reveals a weak EKE period in the boreal fall of 2016 (marked by the dashed box in Fig. 1e), when large intraseasonal variations typically occur (discussed below).

Considering the limited temporal and spatial coverage of mooring observation, we further investigate the ISV using the AVISO and OSCAR data. AVISO SSH and OSCAR current data are suitable for exploring the spatial structure and temporal evolution of the ISV detected by the mooring (Fig. S1 in the online supplemental material).

A composite analysis of current from 5-day OSCAR and intraseasonal SSHA from AVISO during March 2016–March 2018 is conducted (Fig. 2). The +1 standard deviation (STD) of SSHA at the mooring site is used to identify the strong SSHA events. The composite results based on seven positive SSHA events show that a series of eddies propagate westward, passing the mooring. The ISVs along the Sumatra–Java coast are weaker than in the 10°–15°S latitude band, suggesting that the remotely forced signals from the equatorial Indian Ocean affecting the mooring site are not the dominating factor governing the ISV in the SETIO. Feng and Wijffels (2002) examined the regional intraseasonal Ekman pumping and concluded that the direct wind-driven effect can be excluded as a primary generation mechanism of the ISV since it can account for only ~1-cm sea level change over 30 days. The eddies are located along the SEC axis (vectors in Fig. 2a), confirming that instability of the background current is responsible for the strong SETIO ISV. Specifically, the eddy activity is mainly modulated through the baroclinic energy conversion (Feng and Wijffels 2002; Ogata and Masumoto 2011), which is the primary source of eddy energy over most of the World Ocean (Stammer 1997).

Fig. 2.

Composite OSCAR currents (vectors; m s−1) and intraseasonal AVISO SSHA (color; cm) corresponding to the high SSHA events during January 1994–March 2018, with a 30-day interval. The high SSHA events are identified with a criterion of SSHA at 10°S, 100°E exceeding one STD value. The 0 day is defined by the SSHA maximum at 10°S, 100°E.

Fig. 2.

Composite OSCAR currents (vectors; m s−1) and intraseasonal AVISO SSHA (color; cm) corresponding to the high SSHA events during January 1994–March 2018, with a 30-day interval. The high SSHA events are identified with a criterion of SSHA at 10°S, 100°E exceeding one STD value. The 0 day is defined by the SSHA maximum at 10°S, 100°E.

The AVSIO data further verify the weak EKE during boreal fall season of 2016. The EKE averaged at 10°–15°S, 100°–115°E, the site of strong ISV(marked by the black box in Fig. 1a), shows obvious interannual variations (black line in Fig. 3a) and significant seasonality (gray line in Fig. 3a), with large EKE peaks occurring during September–November (the gray shadings). Consistent with the mooring data, the EKE from AVISO reveals the unusually weak EKE in September–November 2016, with an amplitude of approximately half of the 1994–2017 climatological mean for all season and 32% for the 1994–2017 mean for the September–November season (Fig. 3a). Over the last two decades, there was only one other similar unusual interannual event that occurred in boreal fall 1998.

Fig. 3.

(a) Time series of EKE averaged over (10°–15°S, 100°–115°E; marked by black rectangle in Fig. 1a) from AVISO (black line) and BRAN (red line) from January 1994–March 2018. Monthly climatological EKE and the mean EKE over this region are also shown by gray and blue dashed lines, respectively. (b) The DMI and Niño-3.4 for each day (color) and 330-day low-pass filtered (blue and red lines) from January 1994–March 2018. The horizontal dashed lines show ±1.5 STD.

Fig. 3.

(a) Time series of EKE averaged over (10°–15°S, 100°–115°E; marked by black rectangle in Fig. 1a) from AVISO (black line) and BRAN (red line) from January 1994–March 2018. Monthly climatological EKE and the mean EKE over this region are also shown by gray and blue dashed lines, respectively. (b) The DMI and Niño-3.4 for each day (color) and 330-day low-pass filtered (blue and red lines) from January 1994–March 2018. The horizontal dashed lines show ±1.5 STD.

b. Modulation by large-scale climate modes

We further examine instability of the SEC in the SETIO to determine the likely cause for the weak EKE in the boreal fall seasons of 2016 and 1998. Following Qiu (1999) and based on a 2.5-layer reduced-gravity model (see section 2 and supplemental material), we examine contributions of vertical velocity shear and stratification to the baroclinic instability. Since it takes months to develop unstable waves (elucidated in the following text) and the geostrophic component of the SEC would be strongest in July to September (Meyers et al. 1995), we choose the parameter values appropriate for the SETIO in August to investigate the instability. The reference latitudes for f0 and β are defined at 11.2°S according the SEC axis (see Fig. 4a). As the mean mixed layer depth is 45 m and the thermocline is mainly located above 200 m in the SEC region (Fig. S2), we set the first and second layers as 50 and 150 m, respectively. Other parameters are estimated along 110°E from the BRAN data and are listed in Table 2.

Fig. 4.

Causes for the unusually weak ISVs. (a) The climatological zonal velocity along 110°E in August from BRAN over 1994–2017 (m s−1). (b) As in (a), but in August 2016. (c) The climatological N2 (10−4 s−2) along 110°E in August from BRAN over 1994–2017. N is the buoyancy frequency. (d) As in (c), but in August 2016. (e) Growth rate kci as a function of zonal wavenumber in August. (f) Baroclinic instability analysis in August for the climatological situation, 2016, and 1998 (m s−1). See text for definition.

Fig. 4.

Causes for the unusually weak ISVs. (a) The climatological zonal velocity along 110°E in August from BRAN over 1994–2017 (m s−1). (b) As in (a), but in August 2016. (c) The climatological N2 (10−4 s−2) along 110°E in August from BRAN over 1994–2017. N is the buoyancy frequency. (d) As in (c), but in August 2016. (e) Growth rate kci as a function of zonal wavenumber in August. (f) Baroclinic instability analysis in August for the climatological situation, 2016, and 1998 (m s−1). See text for definition.

Table 2.

Parameter values appropriate for the SEC region in August.

Parameter values appropriate for the SEC region in August.
Parameter values appropriate for the SEC region in August.

The necessary condition for the instability of the south Indian Ocean is BC > 0 (Qiu 1999), where BC = |U1U2| − δ1γ2λ2β = shear − stratification. Figures 4a and 4c show the climatological zonal geostrophic velocity and N2 along 110°E in August, where N is the buoyancy frequency. We observe a strong SEC with a maximum westward velocity of 0.38 m s−1, and a weak vertical density gradient related to a large heat loss at the sea surface and strong mixing in this season. The strong vertical velocity shear and the weak stratification are favorable for satisfying the necessary condition leading to baroclinic instability (gray bar in Fig. 4f). The most unstable wave in August has kci = 0.027 day−1, corresponding to a time scale of ~40 days (Fig. 4e). The time scale explains that the SEC is strongest from July to September (Meyers et al. 1995), whereas the EKE peak appears from September to November (Fig. 3a).

In comparison with climatology, August 2016 has larger N2 and thus stronger stratification (Fig. 4d), which is less likely to generate baroclinic instability, and subsequent ISV. Furthermore, the vertical velocity shear sharply weakens to half of the climatological value (Fig. 4f) because of the weak SEC (Fig. 4b). As a result, the baroclinic instability is abnormally weak with BC value of 0.88 cm s−1, which is only 11% of the climatology (7.8 cm s−1; Fig. 4f). Similar situation appears in 1998 with BC of 0.11 cm s−1 (Fig. 4f). Thus, the weak EKEs in boreal falls 2016 and 1998 shown in Fig. 3a are attributed to the weak baroclinic instability, primarily due to the weaker SEC and its vertical shear during these two years.

The dipole mode index (DMI; an indicator of the phase of IOD) and Niño-3.4 indicate that 2016 and 1998 were anomalous. Figure 3b presents the IOD and ENSO evolutions, indicating extreme climate events based on a threshold of ±1.5 STD of the DMI and Niño-3.4 index. The years 2016 and 1998 were extreme negative IOD years, following the 1997 and 2015 extreme El Niño events. We propose that the combined effect of El Niño and negative IOD led to the extreme weakest ISVs in 2016 and 1998: The SEC and vertical velocity shear are weakened (e.g., Fig. 4b) by the anomalous northward sea level and temperature gradients (Figs. S3a–d) related to the negative IOD and the weaker ITF associated with a delayed response to El Niño. In addition, the warming and increased precipitation anomalies (Figs. S3e and S3f) related to the negative IOD events strengthen the stratification in the SETIO. The mean N2 averaged in the upper 200 m in August 2016 (1998) is 24.6% (18.0%) larger than the climatological result in August. By using the climatological temperature and salinity to calculate the N2 for August 2016 (1998), we estimate that temperature and salinity variations strengthen the N2 by roughly 17.4% and 7.0% (13.6% and 4.5%) in August 2016 (1998) (Fig. S4). As a result of weakened vertical shear and enhanced stratification, the baroclinic instabilities in 2016 and 1998 were suppressed.

To support the above argument, we examine partial correlations of the SEC transport along section 110°E with the DMI and Niño-3.4 index. The SEC transport is defined as the westward transport in the upper 400 m, which can affect both the vertical shear and stratification. Based on the BRAN data, we obtain the time series of transport anomaly (Fig. 5b) and BC anomaly (Fig. S5) during 1994–2017. The interannual anomalies of SEC transport are highly correlated with the BC anomalies with a correlation coefficient of 0.61 for the 1994–2016 period (Fig. S5), suggesting that the SEC transport is a good index for examining the baroclinicity.

Fig. 5.

Modulation by large-scale climate modes. (a) Lagged partial correlations between the interannual SEC transport anomaly with the DMI and Niño-3.4 index when the indices lead by 0–11 months. (b) Time series of the interannual SEC transport anomaly from BRAN (gray line), and its simulation using ENSO and IOD indices. (c) The interannual SEC transport anomalies from HYCOM CR (blue bars) and HYCOM PAC (red bars).

Fig. 5.

Modulation by large-scale climate modes. (a) Lagged partial correlations between the interannual SEC transport anomaly with the DMI and Niño-3.4 index when the indices lead by 0–11 months. (b) Time series of the interannual SEC transport anomaly from BRAN (gray line), and its simulation using ENSO and IOD indices. (c) The interannual SEC transport anomalies from HYCOM CR (blue bars) and HYCOM PAC (red bars).

The DMI is significantly correlated with the interannual SEC transport anomaly with the correlation coefficient of 0.42 (0.41) when the DMI leads by 1 (0) month (Fig. 5a), suggesting that the negative IOD events weaken the SEC with minimal lag. In comparison, the transport/Niño-3.4 correlation reaches the maximum of −0.63 when Niño-3.4 leads by 7 months (Fig. 5a), demonstrating that the El Niño events effectively weaken the SEC in the following year. Lagged correlation analyses between SEC transport and SSH further support the above assessment. Figure 6 shows the lagged correlation analyses between the interannual SEC transport anomaly along section 110°E and the interannual SSH anomaly at each grid point from BRAN, HYCOM CR, and HYCOM PAC (see section 2c), when SSH leads transport by 6, 4, 2, and 0 months. In BRAN and HYCOM CR, the positive correlation in the western Pacific Ocean demonstrates that the SEC transport is effectively increased by the high SSH anomaly there when SSH leads by 6 and 4 months. The negative correlation appears along the equator and Sumatra and Java coasts in the eastern Indian Ocean in 0 month because low SSH anomaly along the coast of Sumatra–Java leads to larger meridional pressure gradient and associated stronger SEC. In comparison, in HYCOM PAC, high correlation, exceeding +0.70, appears in the western Pacific Ocean and to the north of Australia, verifying that the Indian Ocean variability can be induced by the Pacific Ocean variability through the ITF.

Fig. 6.

Correlation coefficients between interannual SEC transport anomaly along section 110°E and the interannual SSH anomaly at each grid point from (a) BRAN, (b) HYCOM CR, and (c) HYCOM PAC, when SSH leads transport by 6, 4, 2, and 0 months.

Fig. 6.

Correlation coefficients between interannual SEC transport anomaly along section 110°E and the interannual SSH anomaly at each grid point from (a) BRAN, (b) HYCOM CR, and (c) HYCOM PAC, when SSH leads transport by 6, 4, 2, and 0 months.

The regression analysis with ENSO and IOD indices, Tfit(t) = −2.01 × IOD(t − 1) + 2.74 × ENSO(t − 7) + 0.35, reasonably predicts interannual variation of the SEC transport (Fig. 5b), with the prediction-data correlation of r = 0.69 and the STD ratio of STDprediction/STDTransport = 0.69. We predict the SEC transport variation based on ENSO and IOD indices (Fig. 5b). Overall, the SEC transport responds predominantly to ENSO. The STD of transport prediction is 2.53 Sv (106 m3 s−1) using ENSO indices [2.73 × ENSO(t − 7) + 0.18] and 1.42 Sv using IOD indices [−2.93 × IOD(t − 1) + 0.21], compared to 2.84 Sv using both ENSO and IOD indices. Correlation coefficient between prediction using ENSO (IOD) indices and transport from BRAN is 0.62 (0.35). The weaker SEC transports in 1998 and 2016 are mainly dominated by the extreme El Niño events with secondary impact of the negative IOD events (Fig. 5b).

The SEC transport variability modulated by the extreme El Niño events can be further verified by the HYCOM experiments. Time series of the interannual SEC transport anomaly along section 110°E from HYCOM CR agree well with those from BRAN and ORAS4 (Fig. S6). The correlation coefficients between HYCOM CR and BRAN (ORAS4) are 0.59 (0.71). The STDs are 4.11, 3.30, and 2.30 Sv for BRAN, HYCOM CR, and ORAS4, respectively. The weak BC anomalies in 2016 and 1998 are also successfully captured by HYCOM CR (figure not shown). HYCOM thus can be used to shed light on the roles played by the extreme El Niño events in determining the Indian Ocean SEC variability. HYCOM CR successfully captures the weaker SEC in 1998 and 2016 (blue bars in Fig. 5c). Overall, the interannual SEC anomaly in HYCOM PAC (red bars in Fig. 5c), which is primarily induced by the ITF, could not capture the SEC variability in HYCOM CR. The correlation coefficient of the interannual SEC transport anomaly between HYCOM CR and HYCOM PAC (CR PAC) is 0.42 (0.65). These results suggest that atmospheric forcing anomalies within the Indian Ocean, including local climate variability associated with the IOD and remote atmospheric forcing from the ENSO, play a larger role than the ITF in causing the interannual SEC anomaly. However, effects from the Pacific Ocean variability through the ITF are not negligible, since STD of the interannual SEC transport anomaly in HYCOM PAC reaches 2.74 Sv in comparison to 3.30 Sv in HYCOM CR and 3.28 Sv in their difference, HYCOM CR − HYCOM_PAC. Particularly, significant negative transport anomalies occur in 1997/98 and 2015/16 in HYCOM PAC, suggesting that the extreme El Niño events effectively change the SEC transport through the ITF.

c. The observed interbasin connection in the tropical Indo-Pacific Ocean

The circulation in the eastern Indian Ocean, the SCS, and the western Pacific Ocean, generally maintain a dynamic balance, with each ocean basin being capable of generating its own climate mode of variability (e.g., ENSO and IOD). These climate modes, however, are not completely independent (Cai et al. 2012; Chakravorty et al. 2014; Stuecker et al. 2017), and they can interact via both atmospheric and oceanic connections across the Maritime Continent. The unusually weak ISV in the SETIO in 2016 and 1998 is mainly attributable to the extreme El Niño events partly related to the weakened SEC/ITF transport, which is associated with changes of the ocean circulations in the western Pacific and the SCS.

Geostrophic currents anomalies from AVISO during October 2015–March 2016 verify the above inference (Fig. 7). The westward current anomaly along ~16.7°N in the western Pacific Ocean is induced by the northward shift of the NEC. The bifurcation of the NEC, defined as the latitude at which the meridional velocity averaged within a 5° band off the east coast of the Philippines (Qu and Lukas 2003), shifts northward from 13.4°N at the surface during the climatological October–March to 15.3°N during October 2015–March 2016. The Luzon Strait transport into the SCS thus increases, as so does the Luzon Strait throughflow in the eastern basin (Liu et al. 2006), which affects the northern Makassar Strait (Gordon et al. 2012). In contrast, because of an anomalous negative wind stress curl associated with the El Niño event (Zhao and Zhu 2016), the equatorward western boundary current in the SCS weakens significantly with an anticyclonic gyre appearing in the western SCS (Fig. 7). The SCS water into the southern Makassar Strait through Karimata Strait displays a weak relationship to ENSO (Gordon et al. 2012). The Mindanao Current (MC) strengthens substantially with the maximal υ velocity of −0.74 m s−1at 6°N during October 2015–March 2016 compared with the value of −0.51 m s−1 for the climatological situation. The NECC u velocity at 140°E reaches 0.69 m s−1, which is 92% higher than the climatological value. The changing current pattern can also be captured by BRAN and HYCOM CR (figure not shown).

Fig. 7.

Geostrophic current anomalies and schematic of the currents change. Geostrophic current anomalies during October 2015–March 2016 from AVISO over 1994–2017. Gray arrows show current anomalies with amplitudes less than 0.10 m s−1. Stars, squares, and circles denote locations of moorings used in this study. Moorings in Luzon Strait and Xisha area are marked with “LZ”, “XS01,” and “XS02.” Triangle denotes the moorings used in Gordon et al. (2019) to monitor the ITF transport. Schematic showing the currents strengthening in red and weakening in blue during the extreme El Niño events. Abbreviations: NEC, North Equatorial Current; NECC, North Equatorial Countercurrent; ITF, Indonesian throughflow; MC, Mindanao Current; SEC, South Equatorial Current; SCSWBC, western boundary current in the SCS; LSTF, Luzon Strait throughflow.

Fig. 7.

Geostrophic current anomalies and schematic of the currents change. Geostrophic current anomalies during October 2015–March 2016 from AVISO over 1994–2017. Gray arrows show current anomalies with amplitudes less than 0.10 m s−1. Stars, squares, and circles denote locations of moorings used in this study. Moorings in Luzon Strait and Xisha area are marked with “LZ”, “XS01,” and “XS02.” Triangle denotes the moorings used in Gordon et al. (2019) to monitor the ITF transport. Schematic showing the currents strengthening in red and weakening in blue during the extreme El Niño events. Abbreviations: NEC, North Equatorial Current; NECC, North Equatorial Countercurrent; ITF, Indonesian throughflow; MC, Mindanao Current; SEC, South Equatorial Current; SCSWBC, western boundary current in the SCS; LSTF, Luzon Strait throughflow.

The mooring observations in the SCS and western Pacific further reveal evolution of the circulation. Figure 8a shows monthly currents in the upper 200 m observed by the moorings in the western Pacific Ocean (red squares in Fig. 7). From December 2015, the current with no-uniform direction near 18°N shifts to a stable westward current, which lasts until September 2016, with a large u velocity of ~0.31 m s−1 in February 2016 (Fig. 8a). This westward current is induced by the northward-shift NEC, which can be captured by the moorings at 10.5°N, 13° and 15.5°N along 130°E before August 2015 (black vectors in Fig. 8a), and the moorings at 11.6°N, 139°E and 11°N, 130°E after September 2016 (red vectors and black vectors in Fig. 8a). The NEC does not exist at 8°N. The meandering NECC can also be identified in the mooring arrays, and the amplitude during October 2015–January 2016 reaches 0.36 m s−1 at 4.7°N, 140°E in compared to 0.24 and 0.10 m s−1 during the same period in the previous and last years, respectively (Fig. 8b). The stronger NECC in winter 2015 is also captured by the mooring at 3°N, 143.6°E (cyan vectors in Fig. 8b).

Fig. 8.

Mooring observed currents. (a) Monthly currents averaged in the upper 200 m from the moorings labeled by red squares in Fig. 7. Black and red vectors represent the observed currents along 130° and 139°E, respectively. Red dashed box highlights the currents during October 2015–March 2016. (b) As in (a), but for the moorings labeled by red circles in Fig. 7. (c) Times series of monthly currents averaged in the upper 200 m from the three moorings labeled by red stars in Fig. 7. The unit vectors and bars represent directions and magnitudes of the currents. Red lines mark the three winters during the observational periods: November 2014–January 2015, November 2015–January 2016, and November 2016–January 2017.

Fig. 8.

Mooring observed currents. (a) Monthly currents averaged in the upper 200 m from the moorings labeled by red squares in Fig. 7. Black and red vectors represent the observed currents along 130° and 139°E, respectively. Red dashed box highlights the currents during October 2015–March 2016. (b) As in (a), but for the moorings labeled by red circles in Fig. 7. (c) Times series of monthly currents averaged in the upper 200 m from the three moorings labeled by red stars in Fig. 7. The unit vectors and bars represent directions and magnitudes of the currents. Red lines mark the three winters during the observational periods: November 2014–January 2015, November 2015–January 2016, and November 2016–January 2017.

Moorings located in the western SCS and the Luzon Strait (red stars in Fig. 7) record significantly altered circulation. Unit vectors and bars shown in Fig. 8c represent the direction and magnitude of the observed currents in the upper 200 m, respectively. The northwestward current at the Luzon Strait (20°N, 120.5°E) increases from December 2015 and the larger current lasts for half a year (LZ in Fig. 8c). This phenomenon is contrary to our perception that intrusion of the Kuroshio into the SCS mainly occurs in winter (Qu et al. 2000; Yuan et al. 2006). The mean velocity amplitude in winter 2015/16 (red lines in Fig. 8c) reaches 0.27 m s−1, which is more than twice of the value in winter 2016/17 as a normal year. The SCS western boundary current weakens significantly due to the extreme 2015 El Niño event. The southwestward currents revealed by the two moorings in the western SCS (XS01, XS02 in Fig. 8c) are only 0.04 and 0.05 m s−1 in winter 2015/16, which are significantly smaller than that of 0.06 and 0.14 m s−1 in winter 2014/15 and 0.15 and 0.15 m s−1 in winter 2016–17. Based on five pressure-recording inverted echo sounders and satellite altimeter data, the SCS western boundary current during the winter of 2015–16 exhibit the lowest transport, 3.7 Sv, since 1993 (Zhao and Zhu 2016; Zhu et al. 2015), caused by the extreme 2015 El Niño event through an atmosphere teleconnection. In addition, 13.3 years of mooring records in the Makassar Strait, the primary path of ITF, exposed the weakened southward flow in 2014 and more so in 2015/16 (Gordon et al. 2019).

The dramatic change of circulation revealed by mooring observations associated with AVISO data verify that the balance of the Indo-Pacific Ocean circulation was altered during the extreme ENSO events, causing the unusually weak ISVs in the SETIO. The results suggest that interbasin interactions can play important roles in causing variations of ocean circulations, and thus need to be considered when examining ocean dynamics.

4. Discussion and conclusions

The ISV within the SETIO has been found to be modulated by the IOD, but the impact of ENSO is ambiguous. Based on in situ and satellite observations and ocean reanalysis data, this study investigates features of the ISV in the SETIO, and reports two of the weakest ISV periods during the latest two decades. The two unusually weak ISVs occur in the boreal falls 2016 and 1998, when the EKE is only 32% of the climatological mean for the season. The extreme El Niño events in 2015/16 and 1997/98 substantially weaken the SEC transport and the related baroclinic instability of the SEC, and depress the ISV. It takes ~9 months for ENSO to modulate the ISV in the SETIO. The abnormal ISV is expected to modulate the equatorial upwelling in the SETIO, and Madden–Julian oscillation that interacts with ENSO.

Although the weaker ISVs in 1998 and 2016 are mainly influenced by the extreme El Niño events in 2015/16 and 1997/98, a contribution from the negative IOD events in 2016 and 1998 is evident. High EKE (larger than 1.5 STD above the mean) occurs in 1994, 1999–2000, 2006–08, 2011–12, 2015, and 2017 (Fig. 3a), corresponding to the positive IOD years or the following years of La Niña events (Fig. 3b). A special event is that the EKE in 1997 is not strong although 1997 is an extreme positive IOD year. Compared with 1994, Ogata and Masumoto (2010) pointed out that the mismatch in the phase between the seasonal and interannual temperature evolutions result in the weaker baroclinicity in 1997. In addition, the wind stress anomalies in 1997 are not conducive for EKE generation in the SETIO (Fig. S7).

The mooring observing system in the Indo-Pacific Ocean effectively monitored the complicated circulation changes, and confirmed the Indo-Pacific dynamical connection during the extreme ENSO/IOD events. Figure 7 is a schematic diagram summarizing the changing current pattern (strengthening in red and weakening in blue). Mass and heat transports in the Indo-Pacific Ocean (i.e., the ITF transport and eddy transport) affect the heat and freshwater balances in the Indian Ocean, the meridional overturning circulation in the Atlantic Ocean, and the global thermohaline circulation. The abnormal interbasin mass and heat exchanges and unusually weak ISV during climate modes are thus expected to have an important impact on ocean dynamics and a substantial feedback to climate variability, a topic that deserves to be further investigated.

The 2.5-layer reduced-gravity model reveals features of the baroclinic instability in the SEC region. Herein, we examine instability sensitivity to the parameters. Figure 9a shows the growth rate dependence when the meridional wavenumber l is altered for the SEC system. The zonal wavenumber k of the most unstable mode decreases when the assumed l value increases. The maximal kci is about 0.04 day−1, corresponding to the minimal time scale of ~25 day. If l is set as 1.6 × 10−5 m−1, the isotropic assumption is satisfied approximately that the most unstable mode has the zonal wavenumber k of 1.4 × 10−5 m−1 as shown in Fig. 4e. As the weak baroclinic instability in August 1998 and 2016 results mainly from the weaker vertical shear, we examine the maximum growth rates as a function of U1 and U2 for the parameter values listed in Table 2 to test the sensitivity of baroclinic instability to the vertical velocity shear. The stippled blue areas correspond to the possible value for the baroclinic instability in August. Since δ1γ2λ2β equals 0.03, the vertical velocity shear only needed to exceed the critical value 0.03 m s−1. This is why there existed a large area in the (U1U2) space of Fig. 9b where instability could happen. As shown in Fig. 9, for climatological values of U1 and U2 in August (Table 2), instabilities can occur; when U1 weakens to ~ −0.1 m s−1, the system becomes stable, supporting our argument that weakened SEC transport and vertical shear reduce instabilities and thus EKE.

Fig. 9.

(a) Growth rate dependence on the meridional wavenumber l for the SEC system. (b) Maximum growth rate dependence on U1 and U2 for the parameter values listed in Table 2. The stippled gray areas are dynamically stable. The stippled blue areas correspond to the possible value for the baroclinic instability.

Fig. 9.

(a) Growth rate dependence on the meridional wavenumber l for the SEC system. (b) Maximum growth rate dependence on U1 and U2 for the parameter values listed in Table 2. The stippled gray areas are dynamically stable. The stippled blue areas correspond to the possible value for the baroclinic instability.

The ITF geostrophic transport has been revealed to present a decadal change with a strengthening trend of about 1 Sv every 10 years during 1984–2013 (Liu et al. 2015). Decadal changes of the SEC and the ISV in the SETIO are thus expected. We utilize the zonal velocity along 110°E from BRAN to examine the SEC change. The transport of the SEC in the upper 400 m increases 1.7 Sv from 23.9 Sv during 1994–2005 to 25.6 Sv during 2006–17, which is comparable with the ITF strengthening trend. The EKE at 10°–15°S, 100°–115°E during 2006–17 is 13% correspondingly larger than that during 1994–2005.

Acknowledgments

AVISO data were obtained at http://marine.copernicus.eu/, the OSCAR data at ftp://podaac-ftp.jpl.nasa.gov/allData/oscar/preview/L4/oscar_third_deg, the CCMP data at http://www.remss.com/measurements/ccmp/, BRAN at https://wp.csiro.au/bluelink/global/bran/, and OISST at https://www.ncdc.noaa.gov/oisst. We thank Dr. Qiang Wang and Jianing Wang for processing the mooring data in the SCS and the western Pacific Ocean. This work is supported by the Strategic Priority Research Program of CAS (XDB42000000); NSFC 41822602; Key Special Project for Introduced Talents Team of Southern Marine Science and Engineering Guangdong Laboratory (GML2019ZD0306 and GML2019ZD0302); XDA20060502; NSFC 41976016 and 41676010; 2017YFB0502700; the Pearl River S&T Nova Program of Guangzhou (201806010105); Youth Innovation Promotion Association CAS (2017397), LTOZZ2002 and NHXX2018WL0101. Lamont-Doherty Earth Observatory Contribution Number 8426. The authors declare no competing financial interests.

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