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    Lag correlations between SSTA in the southeastern tropical Indian Ocean in fall and tropical SSTA in different seasons over the period of 1990–2009: (a) winter (December–February of the following year), (b) spring (March–May), (c) summer (June–August), and (d) fall (September–November) of the following year. The contour interval is 0.3. Shading indicates positive and negative correlations above the 95% significance level.

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    Lag correlations between SSTA in the southeastern tropical Indian Ocean in fall and Indo-Pacific non-ENSO SSTA in different seasons over the period of 1990–2009: (a) winter, (b) spring, (c) summer, and (d) fall. Shading indicates positive and negative correlations above the 95% significance level.

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    Lag correlations between warm pool SSTA in fall and Indo-Pacific SSTA in different seasons over the period of 1990–2009: (a) winter, (b) spring, (c) summer, and (d) fall. Shading indicates positive and negative correlations above the 95% significance level.

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    Lag correlations between SSHA in the southeastern tropical Indian Ocean in fall and tropical SSHA in different seasons over the period of 1993–2009: (a) winter, (b) spring, (c) summer, and (d) fall of the following year. The contour interval is 0.2. Shading indicates positive and negative correlations above the 95% significance level.

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    Lag correlations between SSHA in the southeastern tropical Indian Ocean in fall and Indo-Pacific non-ENSO SSHA in different seasons over the period of 1993–2009: (a) winter, (b) spring, (c) summer, and (d) fall. Shading indicates positive and negative correlations above the 95% significance level.

  • View in gallery

    Lag correlations between SSTA in the southeastern tropical Indian Ocean in fall and temperature anomalies in the Pacific equatorial vertical section in different seasons over the period of 1990–2003: (a) winter, (b) spring, (c) summer, and (d) fall of the following year. The contour interval is 0.2. The dark (light) shading indicates positive and negative correlations above the 95% (90%) significance level.

  • View in gallery

    Lag correlations between SSTA in the southeastern tropical Indian Ocean in fall and non-ENSO temperature anomalies in the Pacific equatorial vertical section in different seasons over the period of 1990–2003: (a) winter, (b) spring, (c) summer, and (d) fall of the following year. The contour interval is 0.2. The dark (light) shading indicates positive and negative correlations above the 95% (90%) significance level.

  • View in gallery

    Lag correlations between SSTA in the southeastern tropical Indian Ocean in fall and temperature anomalies in the vertical section along 6°N in different seasons over the period of 1990–2003: (a) winter, (b) spring, (c) summer, and (d) fall of the following year. The contour interval is 0.2. The dark (light) shading indicates positive and negative correlations above the 95% (90%) significance level.

  • View in gallery

    Lag correlations between SSTA in the southeastern tropical Indian Ocean in fall and non-ENSO temperature anomalies in the vertical section along 6°N in different seasons over the period of 1990–2003: (a) winter, (b) spring, (c) summer, and (d) fall of the following year. The contour interval is 0.2. The dark (light) shading indicates positive and negative correlations above the 95% (90%) significance level.

  • View in gallery

    ITF transport anomalies and the IOD, Niño-3.4 indices. (top) Low-pass-filtered time series of the monthly geostrophic transport anomalies of the ITF (black, solid) and of the South Java Current (black, dashed) across the IX1 section in the eastern Indian Ocean. The Ekman transport anomalies (grey, dotted) are drawn for comparison. The cutoff period of the filter is 13 months. (bottom) The Niño-3.4 SSTA (black, dashed) and DMI (black, solid) indices are shown.

  • View in gallery

    Lag correlations between ITF transport anomalies at the IX1 section and the Niño-3.4 index over the period of 1990–2008. Positive months indicate that ITF lags the Niño-3.4 index. Solid and dashed horizontal lines stand for the 95% and 99% significance levels, respectively.

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    Lag correlations between ITF transport anomalies at the IX1 section and DMI over the period of 1990–2008. Positive months indicate that ITF lags DMI. Solid and dashed horizontal lines stand for the 95% and 99% significance levels, respectively.

  • View in gallery

    Lag correlations between western Pacific SZWA in fall and Indo-Pacific SZWA in different seasons over the period of 1990–2009: (a) winter, (b) spring, (c) summer, and (d) fall. Shading indicates positive and negative correlations above the 95% significance level.

  • View in gallery

    Lag correlations between western Pacific SZWA in fall and Indo-Pacific SSTA in different seasons over the period of 1990–2009: (a) winter, (b) spring, (c) summer, and (d) fall. Shading indicates positive and negative correlations above the 95% significance level.

  • View in gallery

    Lag correlations between western Pacific SZWA in fall and Indo-Pacific subsurface temperature anomalies at 120-m depth in different seasons over the period of 1990–2003: (a) winter, (b) spring, (c) summer, and (d) fall. Shading indicates positive and negative correlations above the 95% significance level.

  • View in gallery

    Hovmöller plot of monthly SZWA of NCEP–NCAR reanalysis data averaged between 5°S and 5°N. The contour interval is 1 m s−1, with the zero-value contour omitted. Shading indicates positive anomalies greater than 1 m s−1. The domain of 130°–150°E is marked with two vertical lines.

  • View in gallery

    Lag correlations between western Pacific SZWA in fall and temperature anomalies in the Pacific equatorial vertical section in different seasons over the period of 1990–2003: (a) winter, (b) spring, (c) summer, and (d) fall of the following year. The contour interval is 0.2. The dark (light) shading indicates positive and negative correlations above the 95% (90%) significance level.

  • View in gallery

    Lag correlations between western Pacific SZWA in fall and Indo-Pacific SSHA in different seasons over the period of 1993–2009: (a) winter, (b) spring, (c) summer, and (d) fall. Shading indicates positive and negative correlations above the 95% significance level.

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    Schematic pathway of the Indo-Pacific sea level anomaly propagation.

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Interannual Climate Variability over the Tropical Pacific Ocean Induced by the Indian Ocean Dipole through the Indonesian Throughflow

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  • 1 Key Laboratory of Ocean Circulation and Waves, and Institute of Oceanology, Chinese Academy of Sciences, Qingdao, China
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Abstract

The authors’ previous dynamical study has suggested a link between the Indian and Pacific Ocean interannual climate variations through the transport variations of the Indonesian Throughflow. In this study, the consistency of this oceanic channel link with observations is investigated using correlation analyses of observed ocean temperature, sea surface height, and surface wind data. The analyses show significant lag correlations between the sea surface temperature anomalies (SSTA) in the southeastern tropical Indian Ocean in fall and those in the eastern Pacific cold tongue in the following summer through fall seasons, suggesting potential predictability of ENSO events beyond the period of 1 yr. The dynamics of this teleconnection seem not through the atmospheric bridge, because the wind anomalies in the far western equatorial Pacific in fall have insignificant correlations with the cold tongue anomalies at time lags beyond one season. Correlation analyses between the sea surface height anomalies (SSHA) in the southeastern tropical Indian Ocean and those over the Indo-Pacific basin suggest eastward propagation of the upwelling anomalies from the Indian Ocean into the equatorial Pacific Ocean through the Indonesian Seas. Correlations in the subsurface temperature in the equatorial vertical section of the Pacific Ocean confirm the propagation. In spite of the limitation of the short time series of observations available, the study seems to suggest that the ocean channel connection between the two basins is important for the evolution and predictability of ENSO.

Corresponding author address: Dongliang Yuan, Institute of Oceanology, Chinese Academy of Sciences CAS Key Lab of Ocean Circulation and Waves, 7 Nanhai Road, Qingdao 266071, China. E-mail: dyuan@qdio.ac.cn

Abstract

The authors’ previous dynamical study has suggested a link between the Indian and Pacific Ocean interannual climate variations through the transport variations of the Indonesian Throughflow. In this study, the consistency of this oceanic channel link with observations is investigated using correlation analyses of observed ocean temperature, sea surface height, and surface wind data. The analyses show significant lag correlations between the sea surface temperature anomalies (SSTA) in the southeastern tropical Indian Ocean in fall and those in the eastern Pacific cold tongue in the following summer through fall seasons, suggesting potential predictability of ENSO events beyond the period of 1 yr. The dynamics of this teleconnection seem not through the atmospheric bridge, because the wind anomalies in the far western equatorial Pacific in fall have insignificant correlations with the cold tongue anomalies at time lags beyond one season. Correlation analyses between the sea surface height anomalies (SSHA) in the southeastern tropical Indian Ocean and those over the Indo-Pacific basin suggest eastward propagation of the upwelling anomalies from the Indian Ocean into the equatorial Pacific Ocean through the Indonesian Seas. Correlations in the subsurface temperature in the equatorial vertical section of the Pacific Ocean confirm the propagation. In spite of the limitation of the short time series of observations available, the study seems to suggest that the ocean channel connection between the two basins is important for the evolution and predictability of ENSO.

Corresponding author address: Dongliang Yuan, Institute of Oceanology, Chinese Academy of Sciences CAS Key Lab of Ocean Circulation and Waves, 7 Nanhai Road, Qingdao 266071, China. E-mail: dyuan@qdio.ac.cn

1. Introduction

Recently, Yuan et al. (2011) used numerical experiments to demonstrate that tropical Indian Ocean interannual variations force significant coupled variability in the tropical Pacific Ocean through the heat transport variability of the Indonesian Throughflow (ITF). In this study, we use observational data to examine the consistency of the dynamics with observations.

The El Niño–South Oscillation (ENSO) phenomenon refers to the interannual irregular episodes of anomalous warming and cooling in the eastern equatorial Pacific, which are called El Niño and La Niña events, respectively, and the associated atmosphere surface pressure differences between the western and the eastern equatorial Pacific Ocean. Indian Ocean dipole (IOD) events are the interannual out-of-phase variability between the western and eastern equatorial Indian Ocean sea surface temperature anomalies (SSTA) (Webster et al. 1999; Saji et al. 1999). It is widely recognized that SSTA over the tropical Indian Ocean and over the tropical Pacific Ocean influence each other through the atmospheric Walker circulation (Wu and Meng 1998; Lau and Nath 2000, 2003; Alexander et al. 2002; Lau et al. 2005; Wu and Kirtman 2004, Annamalai et al. 2005; Behera et al. 2006). The role of the oceanic dynamics associated with the variability of the ITF, however, has been largely overlooked in the published literature in the past.

Existing studies have suggested a significant influence of Indian Ocean variations on ENSO predictability (Yamagata and Masumoto 1989; Clarke and Van Gorder 2003; Behera and Yamagata 2003; Kug et al. 2006). Recent studies have shown that ENSO can be predicted beyond the spring predictability barrier if IOD is used as a precursor or a driving force (e.g., Luo et al. 2010; Izumo et al. 2010). The dynamics of the enhanced predictability have been attributed to the atmospheric bridge in the past. The hypothesis suggests that increased convection in the eastern tropical Indian Ocean during a negative IOD event speeds up the Walker circulation (easterly anomalies over the equatorial Pacific and westerly anomaly over the Indian Ocean) in fall, which generates anomalous warming in the eastern Pacific 1 yr later through the advective–reflective mechanism of Picaut et al. (1997) and vice versa during a positive IOD event. Lately, based on numerical experiments, Yuan et al. (2011) have suggested that the ITF variability plays an important role in the forcing of the interannual variations of the tropical Pacific Ocean by IOD.

Using a hierarchy of numerical models, Yuan et al. (2011) have demonstrated that the upwelling anomalies in the tropical eastern Indian Ocean during IOD events are able to penetrate into the equatorial Pacific Ocean through the Indonesian Seas. Numerical experiments using a 1.5-layer, reduced-gravity model with very high resolution in the Indonesian Seas area to resolve all the channels of the Maritime Continent have indicated clearly that the Indian Ocean’s equatorial Kelvin waves can reach the equatorial Pacific through the Indonesian Seas’ channels. Similar penetrations of the Indian Ocean interannual circulation signals into the Pacific Ocean through the Indonesian Seas have also been verified using an ocean general circulation model in that study. Experiments using a coupled general circulation model have shown that the ITF variabilities driven by both ENSO and IOD force thermocline depth anomalies in the western Pacific warm pool, which influence the SSTA in the eastern Pacific cold tongue in the next summer through fall following the IOD event.

The ITF refers to the oceanic transport from the western Pacific Ocean to the southeastern Indian Ocean through the porous and irregular Indonesian Seas. The estimated total and partial ITF transports from channel measurements and from repeated expendable bathythermograph (XBT) measurements along a line between western Australia and the Java island (the so-called IX1 line) range from below 0 (from the Indian Ocean toward the Pacific) to over 20 Sv (1 Sv ≡ 106 m3 s−1) into the Indian Ocean, with a mean ITF transport of about 10 Sv (MacDonald 1998; Wijffels et al. 2008). The heat transport of the ITF is estimated between 0.5 PW (1 PW = 1015 W) (Vranes et al. 2002) and 1.4 PW (Ganachaud and Wunsch 2000), which is comparable to the total surface net heat flux over the northern Indian Ocean and into the western Pacific warm pool (Webster et al. 1998). These large transports and variability suggest the important role of the ITF in the heat budget of the western Pacific warm pool.

Wyrtki (1987) proposed that the ITF is driven by the pressure gradient between the western Pacific and the eastern Indian Ocean across the Indonesian Seas. Nof (1996) presented an analytic solution showing that the ITF is driven by the pressure head in the western Pacific Ocean generated by the nonlinear collision of the western boundary currents. The ITF transport is observed to decreases during El Niño and increases during La Niña (Meyers 1996; Gordon et al. 1999; Fieux et al. 1996), the dynamics of which are believed to be related to the leaky reflection of the equatorial Rossby waves at the Pacific western boundary (Clarke and Liu 1994; Wijffels and Meyers 2004). There are also studies suggesting significant non-ENSO signals in the ITF transport originating from the tropical Indian Ocean (Murtugudde et al. 1998; Qiu et al. 1999; Sprintall et al. 2000; Molcard et al. 2001).

So far, most of the flow measurements made in the major channels of the Indonesian Seas for ITF to enter the eastern Indian Ocean are of short duration (Gordon et al. 1999, 2008; Cresswell and Luick 2001; Luick and Cresswell 2001; Molcard et al. 1994, 1996, 2001). Repeated XBT measurements along the IX1 line made since 1987, however, have provided long time series of the variations of the ITF transport on the eastern Indian Ocean side based on the geostrophic balance (Meyer 1996; Wijffels et al. 2008). These time series, together with the long time series of the sea level, wind, and surface and subsurface temperature observations over the Indo-Pacific basin, will be used to investigate the role of the ITF in connecting IOD with ENSO in this study. The importance of the oceanic dynamics linking IOD to the Pacific Ocean climate variations is underlined by the significant enhancement of the ENSO predictability beyond the leading time of 1 yr as the Indian Ocean variability is included in the coupled climate model forecast.

The next section describes the data used in this study. Section 3 presents the results of the lag correlation analyses based on the observational and reanalysis data. Section 4 contains the discussion and summary of this study.

2. Data

The sea surface temperature (SST) data used in this study are the Hadley Centre Sea Ice and Sea Surface Temperature (HADISST; Rayner et al. 2003) dataset compiled on a 1° latitude × 1° longitude grid for the period of 1990–2009 based on in situ and satellite observations. The subsurface temperature data are obtained from the Joint Environmental Data Analysis Center of the Scripps Institution of Oceanography, which cover the period of 1990–2003 (White 1995). This archive contains temperature at 11 levels (0, 20, 40, 60, 80, 120, 160, 200, 240, 300, and 400 m) on a 2° latitude × 5° longitude grid. The sea level observations are the merged sea surface height anomalies (SSHA) measured by the satellite altimeter onboard of the Ocean Topography Experiment (TOPEX)/Poseidon satellite, European Remote Sensing Satellite (ERS), and Jason-1 since 1993 and are calibrated, merged, and archived by the Archiving, Validation, and Interpretation of Satellite Oceanographic data (AVISO) project (ftp://ftp.aviso.oceanobs.com). For consistency, we will focus on the data during the period of 1993–2010, when all of the satellite SST, sea level, and measurements of the subsurface temperature of the equatorial Pacific Ocean by the Tropical Atmosphere Ocean array are available.

The XBT data along the IX1 section have been combined with a statistical temperature/salinity relation based on historical hydrographic data to estimate the geostrophic transport of the ITF (Meyers 1996; Wijffels et al. 2008). The XBT data cover the domain from 35° to 5°S, from 100° to 117°E, and from 1987 to 2008. Interannual monthly anomalies of the geostrophic transport in reference to the 700-m level of no motion across the IX1 section are calculated based on the monthly climatology for the period of 1987–2008. The basic characteristics of the interannual anomalies and the climatology remain the same, even if the strong 1997/98 El Niño and the 1994 IOD are excluded in the calculation of the climatology. The South Java Current refers to the transport through the IX1 section north of 10°S.

In addition, atmosphere reanalysis data are used to examine the atmospheric bridge process. The surface zonal wind data is obtained from the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis data (Kalnay et al. 1996) for the period of 1990–2009 on a 2.5° × 2.5° grid. The European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis for the period of 1990–2001 (Uppala et al. 2005) is also used to examine the results.

The IOD mode index (DMI) is calculated as the difference of SSTA between the western (10°S–10°N, 50°–70°E) and eastern (10°S–0°, 90°–110°E) equatorial Indian Ocean defined by Saji et al. (1999). The Niño-3.4 index is calculated as the average SSTA in the area of (5°S–5°N, 170°–120°W). The warm pool SSTA is averaged over (130°E–120°W and (SST above 28.5°C). The surface zonal wind anomalies (SZWA) over the western Pacific are averaged in the area of (5°S–5°N, 130°–150°E). The boreal spring is defined as being from March to May, summer is defined as being from June to August, fall is defined as being from September to November, and winter is defined as being from December to the next February. Boreal seasons are used throughout the text of this paper.

The interannual anomalies of the subsurface temperature are calculated with their seasonal cycle of 1990–2003 removed. The SSTA and SZWA are interannual anomalies with the monthly climatologies of 1990–2009 removed. The SSHA are interannual anomalies with the monthly climatology of 1993–2009 removed. The use of common period time series of 1993–2003, with only 10 yr of data, results in essentially the same lag correlations, except that the SSTA correlations are not as high above the levels of significance because of the short time series (figures not shown).

The lag correlation is calculated as the correlation between the interannual anomalies of fall and the interannual anomalies of other seasons (the following winter, spring, summer, and fall). The significance levels are computed based on the Student’s t test. The signal associated with ENSO is calculated based on a regression against the Niño-3.4 index. A Gaussian filter with a cutoff period of 13 months was used to smooth the monthly transport anomalies of ITF, South Java Current, the surface Ekman flow, the DMI, and Niño-3.4 index if necessary.

3. Results

a. Lag correlation of SSTA

The correlations between the area-averaged SSTA in boreal fall (September through November) in the southeastern tropical Indian Ocean (0°–10°S, 90°–110°E) and the SSTA over the Indo-Pacific basin in the following winter through fall seasons are calculated based on the Hadley Center SST data for the period of 1990–2009. The fall SSTA in the southeastern tropical Indian Ocean are used to represent the eastern pole of the IOD at its peak. A significant ENSO-type teleconnection above the 95% significance level is indicated by the negative correlation in the eastern Pacific cold tongue and by the positive correlation in the western Pacific warm pool and in the subtropical northern and southern Pacific in winter (Fig. 1a). In addition, in the western and central Indian Ocean, the correlation is negative and above the 95% significance level, reflecting the influence of the peak IOD phase in the late fall season.

Fig. 1.
Fig. 1.

Lag correlations between SSTA in the southeastern tropical Indian Ocean in fall and tropical SSTA in different seasons over the period of 1990–2009: (a) winter (December–February of the following year), (b) spring (March–May), (c) summer (June–August), and (d) fall (September–November) of the following year. The contour interval is 0.3. Shading indicates positive and negative correlations above the 95% significance level.

Citation: Journal of Climate 26, 9; 10.1175/JCLI-D-12-00117.1

The significant teleconnection in winter, however, does not persist beyond the coming spring season. The correlation between the SSTA in the southeastern tropical Indian Ocean in fall and the SSTA over the Pacific basin in the next spring is weak (Fig. 1b), except for a belt of positive correlation in the central subtropical southern Pacific Ocean, which diminishes quickly within the next month or so. The weak correlation in spring suggests that the atmospheric bridge process between IOD and ENSO in winter is short lived.

Nevertheless, significant correlation between the SSTA in the southeastern tropical Indian Ocean in fall and the cold tongue SSTA reappears in the following summer and fall seasons (Figs. 1c,d), which is above the 95% significance level and is very similar to the structure of the ENSO–IOD teleconnection in winter, except for an opposite sign. The significant correlation suggests that subsurface oceanic processes carry the IOD signals into the equatorial Pacific Ocean.

Lag correlations between the SSTA in the southeastern tropical Indian Ocean in fall and those in the Indo-Pacific basin beyond the time lag of 1 yr are generally weak and insignificant everywhere, suggesting that the memory of the IOD event in the tropical Pacific and Indian Oceans are generally no more than 1 yr. Those correlation results are not discussed further here (figure not shown). Whether this indicates some kind of damped biennial oscillations is an open question beyond the scope of this paper.

Analyses suggest that the lag correlation between the SSTA in the southeastern tropical Indian Ocean in fall and the cold tongue SSTA at the 1-yr time lag is still significant even if the ENSO signal is removed (Fig. 2), suggesting that the teleconnection between the eastern equatorial Indian and Pacific Oceans is not dependent on ENSO. Here, the ENSO signal is defined as a regression of the anomaly time series on the Niño-3.4 index. The lag correlation with the ENSO signal removed is a rigorous test of the teleconnection mechanism, because IOD and ENSO are highly correlated so that the removal of the ENSO signal has inevitably removed some of the IOD signal. Yet, the teleconnection is still significant in the non-ENSO SSTA fields.

Fig. 2.
Fig. 2.

Lag correlations between SSTA in the southeastern tropical Indian Ocean in fall and Indo-Pacific non-ENSO SSTA in different seasons over the period of 1990–2009: (a) winter, (b) spring, (c) summer, and (d) fall. Shading indicates positive and negative correlations above the 95% significance level.

Citation: Journal of Climate 26, 9; 10.1175/JCLI-D-12-00117.1

The lag correlation between the warm pool (130°E–120°W; SST above 28.5°C) SSTA in fall and the SSTA in the cold tongue in the summer and fall of the next year is found insignificant (Fig. 3). This suggests that the SSTA in the cold tongue in the following year are not started from the warm pool in the previous fall season. In fact, the warm pool SSTA at any season are found not in strong correlation with the cold tongue SSTA beyond the time lag of a season or two (not shown). These results suggest that the Walker circulation over the tropical Pacific Ocean is probably of short memory.

Fig. 3.
Fig. 3.

Lag correlations between warm pool SSTA in fall and Indo-Pacific SSTA in different seasons over the period of 1990–2009: (a) winter, (b) spring, (c) summer, and (d) fall. Shading indicates positive and negative correlations above the 95% significance level.

Citation: Journal of Climate 26, 9; 10.1175/JCLI-D-12-00117.1

b. Lag correlation of sea level anomalies

The results of the above correlation analyses of the SSTA are confirmed by the correlations of the satellite altimeter data of sea level. The lag correlations between the SSHA in the southeastern tropical Indian Ocean in fall and the SSHA over the Indo-Pacific basin are reminiscent of the SSTA correlations (Fig. 4). The lag correlation with the Indo-Pacific SSHA in the immediate following winter shows the typical ENSO–IOD teleconnection patterns, with the SSHA over the western Pacific and eastern Indian Ocean in opposite sign with those in the eastern Pacific cold tongue and in the western Indian Ocean (Fig. 4a). The high correlation with the cold tongue SSHA disappears in the spring of the next year, while the high correlation in the western Indian Ocean persists, consistent with the westward propagation of the equatorial and off-equatorial Rossby waves (Masumoto and Meyers 1998; Jury and Huang 2004; Yuan and Liu 2009). The significant lag correlation in the narrow equatorial zone in the western equatorial Pacific Ocean and the Indonesian Seas suggests the influence from the Indian Ocean (Fig. 4b). Some influence from the off-equatorial Rossby waves in the Pacific Ocean is also indicated by the lag correlation. The significant lag correlation in the equatorial western Pacific and the Indonesian Seas in spring eventually leads to the significant lag correlation in the eastern Pacific cold tongue in the following summer and fall seasons (Figs. 4c,d), which is in agreement with the SSTA analyses. The lag correlations thus suggest strongly that the oceanic channel (i.e., the ITF) plays an important role in connecting the IOD forcing with the Pacific ENSO events.

Fig. 4.
Fig. 4.

Lag correlations between SSHA in the southeastern tropical Indian Ocean in fall and tropical SSHA in different seasons over the period of 1993–2009: (a) winter, (b) spring, (c) summer, and (d) fall of the following year. The contour interval is 0.2. Shading indicates positive and negative correlations above the 95% significance level.

Citation: Journal of Climate 26, 9; 10.1175/JCLI-D-12-00117.1

It is worth mentioning that the delayed oscillator theory of ENSO dynamics suggests that western boundary reflections of negative feedback play an important role in the cycling of ENSO (Schopf and Suarez 1988; Battisti 1988). McPhaden and Yu (1999), Delcroix et al. (2000), and Yuan et al. (2004) have shown that upwelling Rossby wave anomalies dominated the western Pacific Ocean in the summer through winter seasons of 1997 and were reflected into the equatorial Kelvin waves to terminate the 1997/98 El Niño in the coming spring. However, these Rossby waves are generally not linked to the oceanic anomalies in the eastern equatorial Indian Ocean, because the waveguide from the western Pacific Ocean to the eastern Indian Ocean is through the Indonesian Seas and along the western coasts of New Guinea and Australia (Clarke and Liu 1994; Wijffels and Meyers 2004; McClean et al. 2005) and the ocean thermocline anomalies are generally not in strong coupling with the atmosphere over the western Pacific Ocean (Lukas and Lindstrom 1991; Wang and McPhaden 2001; Yuan 2009). Therefore, the SSHA in the southeastern tropical Indian Ocean in fall are generally not in strong correlation with the western Pacific reflection anomalies in the off-equatorial areas.

The lag correlation between the SSHA in the southeastern tropical Indian Ocean in fall and the SSHA over the equatorial Pacific throughout the following year is still significant, even if the signal associated with ENSO is removed from the Indo-Pacific SSHA fields. Figure 5 shows the lag correlation with the non-ENSO SSHA over the Indo-Pacific basin in different seasons over the period of 1993–2009. The non-ENSO SSHA are obtained by subtracting the anomalies regressed on the Niño-3.4 SSTA index from the total anomalies. The significant lag correlation in the cold tongue in the central and eastern equatorial Pacific Ocean throughout the following year suggests the origin of equatorial Kelvin waves from the eastern equatorial Indian Ocean to the eastern equatorial Pacific Ocean. This process is independent of ENSO.

Fig. 5.
Fig. 5.

Lag correlations between SSHA in the southeastern tropical Indian Ocean in fall and Indo-Pacific non-ENSO SSHA in different seasons over the period of 1993–2009: (a) winter, (b) spring, (c) summer, and (d) fall. Shading indicates positive and negative correlations above the 95% significance level.

Citation: Journal of Climate 26, 9; 10.1175/JCLI-D-12-00117.1

c. Subsurface correlation

The forcing of IOD on ENSO through the ITF variability is also suggested by the subsurface temperature anomalies. The correlation between the SSTA in the southeastern tropical Indian Ocean in fall and the subsurface temperature anomalies in the equatorial Pacific vertical section shows significant positive subsurface correlation in the warm pool in winter, juxtaposing with significant negative correlation in the cold tongue in the east (Fig. 6). The correlation is above the 95% significant level and is consistent with the SSTA and SSHA lag correlation of IOD and ENSO teleconnection in winter shown in Figs. 1 and 4. The significant lag correlation in the subsurface in the western Pacific warm pool indicates eastward propagation into the central and eastern equatorial Pacific Ocean in the following spring and summer. The subsurface signal explains the ENSO predictability beyond the spring barrier. By fall, the lag correlation indicates that the subsurface temperature anomalies have surfaced in the area east of the date line along the equator, which explains the significant correlation between the SSTA in the southeastern tropical Indian Ocean in fall and the cold tongue SSTA at the 1-yr time lag in Fig. 1d. The propagation is also consistent with the lag correlation between the SSHA in the southeastern tropical Indian Ocean and those in the cold tongue at the 1-yr lag. The subsurface temperature data are based primarily on the Tropical Atmosphere Ocean array observations, which do not cover the ocean deeper than 250 m in the central and eastern equatorial Pacific. The downward propagation of the equatorial Kelvin waves is indicated but not fully resolved by the observations.

Fig. 6.
Fig. 6.

Lag correlations between SSTA in the southeastern tropical Indian Ocean in fall and temperature anomalies in the Pacific equatorial vertical section in different seasons over the period of 1990–2003: (a) winter, (b) spring, (c) summer, and (d) fall of the following year. The contour interval is 0.2. The dark (light) shading indicates positive and negative correlations above the 95% (90%) significance level.

Citation: Journal of Climate 26, 9; 10.1175/JCLI-D-12-00117.1

The subsurface correlation in spring following an IOD event is not dependent on ENSO. In fact, the subsurface correlation is significant even if the ENSO signal in the subsurface temperature is removed (Fig. 7), which is consistent with the significant lag correlation between the SSHA in the southeastern tropical Indian Ocean and those in the cold tongue with the ENSO signals removed in the altimeter data (Fig. 5). In comparison, the lag correlations between the SSTA in the southeastern tropical Indian Ocean and the temperature anomalies in the vertical section along 6°N of the Pacific Ocean generally show weak propagation of oceanic signals associated with the IOD events in the off-equatorial Pacific Ocean (Fig. 8). Significant lag correlation is present in the western and eastern Pacific in the winters following the IOD events because ENSO and IOD are frequently coincident. This significant lag correlation disappears if the temperature anomalies associated with the Niño-3.4 SST index are removed (Fig. 9). The situation is about the same along 6°S (not shown). The lack of significant propagating signals in the off-equatorial Pacific Ocean associated with IOD suggests that the eastward-propagating subsurface non-ENSO signals in the equatorial vertical section are not associated with the Rossby wave reflection at the Pacific’s western boundary. Rather, the teleconnection between the eastern equatorial Indian and Pacific Oceans at the 1-yr time lag is most likely induced by the ITF variability.

Fig. 7.
Fig. 7.

Lag correlations between SSTA in the southeastern tropical Indian Ocean in fall and non-ENSO temperature anomalies in the Pacific equatorial vertical section in different seasons over the period of 1990–2003: (a) winter, (b) spring, (c) summer, and (d) fall of the following year. The contour interval is 0.2. The dark (light) shading indicates positive and negative correlations above the 95% (90%) significance level.

Citation: Journal of Climate 26, 9; 10.1175/JCLI-D-12-00117.1

Fig. 8.
Fig. 8.

Lag correlations between SSTA in the southeastern tropical Indian Ocean in fall and temperature anomalies in the vertical section along 6°N in different seasons over the period of 1990–2003: (a) winter, (b) spring, (c) summer, and (d) fall of the following year. The contour interval is 0.2. The dark (light) shading indicates positive and negative correlations above the 95% (90%) significance level.

Citation: Journal of Climate 26, 9; 10.1175/JCLI-D-12-00117.1

Fig. 9.
Fig. 9.

Lag correlations between SSTA in the southeastern tropical Indian Ocean in fall and non-ENSO temperature anomalies in the vertical section along 6°N in different seasons over the period of 1990–2003: (a) winter, (b) spring, (c) summer, and (d) fall of the following year. The contour interval is 0.2. The dark (light) shading indicates positive and negative correlations above the 95% (90%) significance level.

Citation: Journal of Climate 26, 9; 10.1175/JCLI-D-12-00117.1

d. Variability of the ITF

The interannual anomalies of the ITF volume transport is calculated from the geostrophic currents in reference to the 700-m level of no motion in the IX1 section based on the XBT data (Fig. 10). In addition, the transport anomalies of the South Java Current flowing along the Sumatra–Java coast north of 10°S through the IX1 section and the surface Ekman transport based on the NCEP–NCAR reanalysis wind are calculated for comparison. The time series have been filtered by a Gaussian filter with a cutoff period at 13 months. The filtered time series of DMI and the Niño-3.4 index are shown in the bottom panel for reference.

Fig. 10.
Fig. 10.

ITF transport anomalies and the IOD, Niño-3.4 indices. (top) Low-pass-filtered time series of the monthly geostrophic transport anomalies of the ITF (black, solid) and of the South Java Current (black, dashed) across the IX1 section in the eastern Indian Ocean. The Ekman transport anomalies (grey, dotted) are drawn for comparison. The cutoff period of the filter is 13 months. (bottom) The Niño-3.4 SSTA (black, dashed) and DMI (black, solid) indices are shown.

Citation: Journal of Climate 26, 9; 10.1175/JCLI-D-12-00117.1

The ITF transport anomalies show major signals associated with the ENSO and IOD events. The effects of the IOD forcing on ITF variability are clearly evident in the time series. The correlation between the filtered DMI and the filtered monthly transport anomalies of the ITF is −0.35, above the 99% significance level. The correlation between the filtered Niño-3.4 SST index and the filtered monthly transport anomalies of the ITF is, however, only −0.05, below the 95% significance level.

Further calculations indicate that the correlations between ITF and Niño-3.4 index are positive above the 99% significance level if the former lags the latter by 3–11 months (Fig. 11). The maximum correlation between the filtered Niño-3.4 SST index and ITF anomalies occurs at 0.33, with the former leading the latter by 7 months. This phenomenon can be explained by the propagation of the equatorial Rossby waves from the central–eastern equatorial Pacific to the western equatorial Pacific Ocean. The correlations between ITF and Niño-3.4 index are negative above the 99% significance level if the former leads the latter by 3–6 months, which can be explained by the fact that the IOD anomalies peak in fall before the ENSO anomalies over the Pacific Ocean peak in the coming winter through spring seasons.

Fig. 11.
Fig. 11.

Lag correlations between ITF transport anomalies at the IX1 section and the Niño-3.4 index over the period of 1990–2008. Positive months indicate that ITF lags the Niño-3.4 index. Solid and dashed horizontal lines stand for the 95% and 99% significance levels, respectively.

Citation: Journal of Climate 26, 9; 10.1175/JCLI-D-12-00117.1

In comparison, the correlations between the ITF transport anomalies and the DMI are negative above the 99% significance level if the former leads the latter by −1 through −5 months (Fig. 12). The maximum correlation between the DMI and the ITF occurs at a near-zero time lag and is above the 99% significance level. The lead time of a few months is trivial and can be explained by the fact that the IX1 section is located very close to the eastern pole of the DMI calculation. The correlations between the ITF transport anomalies and the DMI are positive above the 99% significance level if the former lags the latter by 6 through 11 months. This lag can be explained by the fact that the IOD and ENSO are closely correlated and the latter impact the ITF through the propagation of the equatorial Rossby waves to the western equatorial Pacific Ocean and into the Indonesian Seas. These analyses suggest strongly that the ITF variability is subject to the influence of both IOD and ENSO. The IOD-forced ITF variability implies warm pool heat content variability associated with IOD.

Fig. 12.
Fig. 12.

Lag correlations between ITF transport anomalies at the IX1 section and DMI over the period of 1990–2008. Positive months indicate that ITF lags DMI. Solid and dashed horizontal lines stand for the 95% and 99% significance levels, respectively.

Citation: Journal of Climate 26, 9; 10.1175/JCLI-D-12-00117.1

A significant part of the ITF transport variability is associated with the transport anomalies of the South Java Current flowing along the Sumatra–Java coast north of 10°S through the IX1 section (Fig. 10). The surface Ekman transport of the winds has a smaller amplitude on average, suggesting the dominance of oceanic thermocline processes in the ITF transport variations. The correlation between the DMI and the transport anomalies of the South Java Current is −0.19, above the 98% significant level. In comparison, the correlation between the Niño-3.4 SST index and the transport anomalies of the South Java Current is 0.02, way below the 95% significance level.

e. Effects of the atmospheric bridge

The atmospheric bridge process of IOD forcing on ENSO suggests that variations of the Walker circulation over the equatorial Pacific Ocean are forced by the Indian Ocean SSTA through the western Pacific wind anomalies, which in turn drive the ENSO variability in the equatorial Pacific Ocean (Izumo et al. 2010). However, the SZWA in the western equatorial Pacific (5°S–5°N, 130°–150°E) in fall are in poor correlation with the SZWA over the equatorial Pacific Ocean beyond one season (Fig. 13). The weak correlation suggests that the previous hypothesis of the Walker cell interaction is not supported by the NCEP–NCAR reanalysis wind product. Additional analyses using the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) winds show essentially the same weak correlations between the far western equatorial Pacific SZWA in fall and the SZWA over the central and eastern equatorial Pacific at time lags beyond a season (not shown). These results seriously challenge the role of the atmospheric bridge process.

Fig. 13.
Fig. 13.

Lag correlations between western Pacific SZWA in fall and Indo-Pacific SZWA in different seasons over the period of 1990–2009: (a) winter, (b) spring, (c) summer, and (d) fall. Shading indicates positive and negative correlations above the 95% significance level.

Citation: Journal of Climate 26, 9; 10.1175/JCLI-D-12-00117.1

In addition, the lag correlations between the SZWA over the far western Pacific in fall with the SSTA in the eastern equatorial Pacific cold tongue in the next summer through fall seasons are all weak and insignificant (Fig. 14), suggesting that the strong teleconnection between the eastern equatorial Indian and Pacific Oceans at the 1-yr time lag seen in the SSTA and SSHA correlations is unlikely induced by the atmospheric bridge process. The lack of persistent atmospheric bridge connection across the Indian and Pacific basins is also reflected in the insignificant lag correlations between the SZWA over the far western Pacific in fall and the subsurface temperature anomalies in the cold tongue thermocline at the 1-yr time lag (Fig. 15).

Fig. 14.
Fig. 14.

Lag correlations between western Pacific SZWA in fall and Indo-Pacific SSTA in different seasons over the period of 1990–2009: (a) winter, (b) spring, (c) summer, and (d) fall. Shading indicates positive and negative correlations above the 95% significance level.

Citation: Journal of Climate 26, 9; 10.1175/JCLI-D-12-00117.1

Fig. 15.
Fig. 15.

Lag correlations between western Pacific SZWA in fall and Indo-Pacific subsurface temperature anomalies at 120-m depth in different seasons over the period of 1990–2003: (a) winter, (b) spring, (c) summer, and (d) fall. Shading indicates positive and negative correlations above the 95% significance level.

Citation: Journal of Climate 26, 9; 10.1175/JCLI-D-12-00117.1

The longitude–time plot of the SZWA averaged between 5°S and 5°N over the equatorial Indo-Pacific basin shows clearly that the connections over the Indonesian Seas between the Walker cells in the two basins are weak and insignificant (Fig. 16). The SZWA over the far western equatorial Pacific are in general not in significant correlation with the SZWA in the eastern equatorial Pacific 1 yr later, except during the 1997/98 El Niño. However, this single event does not produce statistically significant lag correlations between the SZWA in the far western equatorial Pacific and the atmospheric and oceanic anomalies over the eastern equatorial Pacific Ocean.

Fig. 16.
Fig. 16.

Hovmöller plot of monthly SZWA of NCEP–NCAR reanalysis data averaged between 5°S and 5°N. The contour interval is 1 m s−1, with the zero-value contour omitted. Shading indicates positive anomalies greater than 1 m s−1. The domain of 130°–150°E is marked with two vertical lines.

Citation: Journal of Climate 26, 9; 10.1175/JCLI-D-12-00117.1

The lag correlations between the SZWA over the far western equatorial Pacific in fall and the subsurface temperature anomalies in the eastern equatorial Pacific cold tongue in the next winter through spring seasons are strong and significant (Figs. 17a,b), which is also reflected in the correlations of SZWA with surface and subsurface temperature anomalies in Figs. 14 and 15. These strong correlations can be explained by the persistence of the ENSO events in the ocean’s main thermocline, which is highly correlated with the IOD events in general. However, the lag correlations between the far western equatorial Pacific SZWA in fall and the subsurface temperature anomalies in the next summer through fall seasons in the cold tongue are weak and insignificant (Figs. 17c,d), which underlines the dominating effects of the oceanic channel dynamics over the atmospheric bridge process in forcing the cold tongue anomalies at the 1-yr lag.

Fig. 17.
Fig. 17.

Lag correlations between western Pacific SZWA in fall and temperature anomalies in the Pacific equatorial vertical section in different seasons over the period of 1990–2003: (a) winter, (b) spring, (c) summer, and (d) fall of the following year. The contour interval is 0.2. The dark (light) shading indicates positive and negative correlations above the 95% (90%) significance level.

Citation: Journal of Climate 26, 9; 10.1175/JCLI-D-12-00117.1

One may argue that the signal of the far western Pacific SZWA in fall could be first buried into the western Pacific thermocline, which then drives the ensuing coupled evolution of the tropical Pacific Ocean and atmosphere through more complicated processes. However, the lag correlation between the western Pacific SZWA in fall and the SSHA near the western boundary of the Pacific Ocean is weak and insignificant throughout the following year (Fig. 18). The weak correlation of the SZWA with the thermocline depth variations in the far western Pacific has been discussed by Yuan et al. (2004), which has been attributed to the short wind fetch and the western boundary condition of the ocean currents. These facts suggest that the wind anomalies in the far western equatorial Pacific in fall have not generated significant propagating equatorial Kelvin waves to the eastern Pacific Ocean. Furthermore, the correlation between the western Pacific SZWA and the eastern Pacific cold tongue SSHA beyond one season lags is weak and insignificant, suggesting that the significant correlation between the SSHA in the southeastern tropical Indian Ocean in fall and those in the cold tongue at the 1-yr time lag is unlikely induced by the atmospheric bridge process.

Fig. 18.
Fig. 18.

Lag correlations between western Pacific SZWA in fall and Indo-Pacific SSHA in different seasons over the period of 1993–2009: (a) winter, (b) spring, (c) summer, and (d) fall. Shading indicates positive and negative correlations above the 95% significance level.

Citation: Journal of Climate 26, 9; 10.1175/JCLI-D-12-00117.1

4. Discussion and summary

Stimulated by the dynamics study of Yuan et al. (2011) using a hierarchy of numerical models to demonstrate that tropical Indian Ocean interannual variations force significant coupled variability in the tropical Pacific Ocean through the heat transport variability of the ITF observational data are used in this study to detect the dynamics in the real ocean uncovered by that study and to examine the consistency of the model simulations with observations. Significant lag correlations between the anomalies of SST or sea surface height in an area in the southeastern tropical Indian Ocean in fall and the anomalies in the cold tongue in the eastern equatorial Pacific Ocean at a 1-yr time lag are identified based on the Hadley Center SST and the satellite altimeter data. The teleconnection is further shown to propagate from the eastern Indian Ocean to the western and farther to the eastern equatorial Pacific Ocean through the Indonesian Seas in the main ocean thermocline (Figs. 47), consistent with the model experiment results. It is therefore suggested that the oceanic channel dynamics (i.e., the ITF) play an important role in the forcing of the IOD on the interannual climate variations over the tropical Pacific Ocean 1 yr later (Fig. 19).

Fig. 19.
Fig. 19.

Schematic pathway of the Indo-Pacific sea level anomaly propagation.

Citation: Journal of Climate 26, 9; 10.1175/JCLI-D-12-00117.1

In comparison, the lag correlations between the surface zonal wind anomalies over the far western equatorial Pacific in fall and the oceanic anomalies in the western equatorial Pacific in the next year and in the cold tongue in the eastern equatorial Pacific Ocean 1 yr later are all small and insignificant, which are in contrast to the significant teleconnection in the ocean between the eastern Indian and Pacific Ocean. The results suggest that the atmospheric bridge processes are not the main reason of the teleconnection at the 1-yr time lag.

The propagation of the Indian Ocean equatorial Kelvin waves along the Sumatra–Java island chain and into the Indonesian Seas is in agreement with the latest observations at the Lombok and Ombai Straits at intraseasonal time scales (Sprintall et al. 2000; Molcard et al. 2001; Wijffels and Meyers 2004; Kandaga et al. 2009; Drushka et al. 2010). The propagation of the interannual Kelvin waves into the western Pacific Ocean has not been observed so far because of the short time series of the strait measurements. However, the simple model experiments of Yuan et al. (2011) have shown that the penetration of Kelvin waves into the western Pacific at the interannual time scales is much stronger than that at the intraseasonal time scales (cf. Qiu et al. 1999). Thus, it is possible that the ITF play a role in the forcing of IOD on the interannual climate variability over the tropical Pacific. Upon reaching the western Pacific Ocean, the anomalies are organized into the equatorial Kelvin waves to propagate to the eastern equatorial Pacific and influence the cold tongue SSTA through upwelling anomalies.

It is worth mentioning that existing studies of the atmospheric bridge process are based primarily on correlations of the atmospheric anomalies with DMI. Since the calculation of DMI uses the SSTA in the eastern equatorial Indian Ocean, the effects of the ocean channel dynamics have been incorporated into the correlation analyses. In comparison, the examination of the correlations based on the wind anomalies in the far western equatorial Pacific in this study is a rigorous test of the atmospheric bridge dynamics connecting the climate variations over the two basins. The results of this study suggest that the effects of the atmospheric bridge are weak since the 1990s compared with the ocean channel dynamics at time lags beyond a season.

In summary, the analyses of the surface and subsurface correlations, although limited by the short time series of the observations available, suggest that the ITF play an important role in connecting the IOD with the Pacific interannual climate variations at the time lag of 1 yr. This oceanic channel mechanism is important for enhanced predictability of ENSO beyond the spring barrier. The disclosed dynamics and structure of the correlations suggest that models of the Indo-Pacific basin are of better prediction skills than those of the Pacific basin only.

Acknowledgments

We thank Gary Meyers for sharing the XBT data along the IX1 section. Discussions with B. Qiu and W. Cai were valuable. Support from the China 973 Project 2012CB956000, NSF grants (41176019, 40888001, 40806010, and 41005042) of China, SFC grant (ZR2010DM007) of Shandong Province, and an open fund of LTO (1101), are gratefully acknowledged. H. Zhou was supported by a Fund of GCMAC, SOA (1102).

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