Interannual Climate Variability over the Tropical Pacific Ocean Induced by the Indian Ocean Dipole...

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

DONGLIANG YUAN, HUI ZHOU, AND XIA ZHAO

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

(Manuscript received 17 February 2012, in final form 29 October 2012)

ABSTRACT

The authors’ previous dynamical study has suggested a link between the Indian and Pacific Ocean in-

terannual 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 ob-

served ocean temperature, sea surface height, and surface wind data. The analyses show significant lag cor-

relations between the sea surface temperature anomalies (SSTA) in the southeastern tropical IndianOcean 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 tem-

perature in the equatorial vertical section of the Pacific Ocean confirm the propagation. In spite of the lim-

itation 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.

1. Introduction

Recently, Yuan et al. (2011) used numerical experi-

ments to demonstrate that tropical Indian Ocean in-

terannual 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 con-

sistency of the dynamics with observations.

The El Nino–South Oscillation (ENSO) phenomenon

refers to the interannual irregular episodes of anoma-

lous warming and cooling in the eastern equatorial Pa-

cific, which are called El Nino and La Nina events,

respectively, and the associated atmosphere surface

pressure differences between the western and the east-

ern equatorial Pacific Ocean. Indian Ocean dipole (IOD)

events are the interannual out-of-phase variability be-

tween 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 andKirtman 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 en-

hanced predictability have been attributed to the at-

mospheric bridge in the past. The hypothesis suggests

that increased convection in the eastern tropical Indian

Ocean during a negative IOD event speeds up the

Corresponding author address: Dongliang Yuan, Institute of

Oceanology, Chinese Academy of Sciences CASKey Lab of Ocean

Circulation and Waves, 7 Nanhai Road, Qingdao 266071, China.

E-mail: [email protected]

1 MAY 2013 YUAN ET AL . 2845

DOI: 10.1175/JCLI-D-12-00117.1

� 2013 American Meteorological Society

Walker circulation (easterly anomalies over the equa-

torial 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 sug-

gested 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 IndianOcean

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 bathy-

thermograph (XBT) measurements along a line between

western Australia and the Java island (the so-called IX1

line) range from below 0 (from the IndianOcean toward

the Pacific) to over 20 Sv (1 Sv [ 106 m3 s21) 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

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.

2846 JOURNAL OF CL IMATE VOLUME 26

(1 PW 5 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 sug-

gest 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 ob-

served to decreases during El Nino and increases during

La Nina (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 sug-

gesting 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). Re-

peated 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.

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.

1 MAY 2013 YUAN ET AL . 2847

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 18 latitude 3 18 longitude grid for the

period of 1990–2009 based on in situ and satellite ob-

servations. 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 28 latitude 3 58longitude 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 sub-

surface temperature of the equatorial Pacific Ocean by

the Tropical Atmosphere Ocean array are available.

The XBT data along the IX1 section have been com-

bined 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 358 to58S, from 1008 to 1178E, and from 1987 to 2008. In-

terannual monthly anomalies of the geostrophic trans-

port 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 Nino and the 1994 IOD are excluded in the

calculation of the climatology. The South Java Current

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.


refers to the transport through the IX1 section north

of 108S.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 At-

mospheric Research (NCEP–NCAR) reanalysis data

(Kalnay et al. 1996) for the period of 1990–2009 on

a 2.58 3 2.58 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 dif-

ference of SSTA between the western (108S–108N,

508–708E) and eastern (108S–08, 908–1108E) equatorialIndian Ocean defined by Saji et al. (1999). The Nino-

3.4 index is calculated as the average SSTA in the area

of (58S–58N, 1708–1208W). The warm pool SSTA is

averaged over (1308E–1208W and (SST above 28.58C).The surface zonal wind anomalies (SZWA) over the

western Pacific are averaged in the area of (58S–58N,

1308–1508E). 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 De-

cember to the next February. Boreal seasons are used

throughout the text of this paper.

The interannual anomalies of the subsurface tem-

perature are calculated with their seasonal cycle of

1990–2003 removed. The SSTA and SZWA are in-

terannual 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 corre-

lations 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 in-

terannual 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

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.

1 MAY 2013 YUAN ET AL . 2849

a regression against the Nino-3.4 index. A Gaussian fil-

ter with a cutoff period of 13 months was used to smooth

the monthly transport anomalies of ITF, South Java

Current, the surface Ekman flow, theDMI, andNino-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 (08–108S, 908–1108E)and the SSTA over the Indo-Pacific basin in the fol-

lowing 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 tele-

connection above the 95% significance level is in-

dicated 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.

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 subtrop-

ical southern Pacific Ocean, which diminishes quickly

within the next month or so. The weak correlation in

spring suggests that the atmospheric bridge process be-

tween 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

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.


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 south-

eastern tropical Indian Ocean in fall and those in the

Indo-Pacific basin beyond the time lag of 1 yr are gen-

erally 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 re-

gression of the anomaly time series on the Nino-3.4

index. The lag correlation with the ENSO signal re-

moved is a rigorous test of the teleconnection mecha-

nism, because IOD and ENSO are highly correlated so

that the removal of the ENSO signal has inevitably re-

moved some of the IOD signal. Yet, the teleconnection

is still significant in the non-ENSO SSTA fields.

The lag correlation between the warm pool (1308E–1208W; SST above 28.58C) 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 re-

sults suggest that the Walker circulation over the trop-

ical Pacific Ocean is probably of short memory.

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

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.


tropical Indian Ocean in fall and non-ENSO temperature anom-

alies 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.

1 MAY 2013 YUAN ET AL . 2851

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 rem-

iniscent of the SSTA correlations (Fig. 4). The lag cor-

relation with the Indo-Pacific SSHA in the immediate

following winter shows the typical ENSO–IOD tele-

connection 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 andLiu 2009). The significant lag correlation

in the narrow equatorial zone in the western equatorial

Pacific Ocean and the Indonesian Seas suggests the in-

fluence 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 Pa-

cific 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 con-

necting the IOD forcing with the Pacific ENSO events.

It is worth mentioning that the delayed oscillator

theory of ENSO dynamics suggests that western bound-

ary 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 upwell-

ing Rossby wave anomalies dominated the western Pa-

cific Ocean in the summer through winter seasons of

1997 andwere reflected into the equatorial Kelvin waves

to terminate the 1997/98 El Nino in the coming spring.

However, these Rossby waves are generally not linked

to the oceanic anomalies in the eastern equatorial In-

dian Ocean, because the waveguide from the western


tropical Indian Ocean in fall and temperature anomalies in the

vertical section along 68N 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.


tropical Indian Ocean in fall and non-ENSO temperature anom-

alies in the vertical section along 68N 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.


Pacific Ocean to the eastern IndianOcean is through the

Indonesian Seas and along the western coasts of New

Guinea andAustralia (Clarke and Liu 1994;Wijffels and

Meyers 2004; McClean et al. 2005) and the ocean

thermocline anomalies are generally not in strong cou-

pling with the atmosphere over the western Pacific

Ocean (Lukas and Lindstrom 1991;Wang andMcPhaden

2001; Yuan 2009). Therefore, the SSHA in the south-

eastern 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 south-

eastern 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

Nino-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 in-

dependent of ENSO.

c. Subsurface correlation

The forcing of IOD on ENSO through the ITF vari-

ability is also suggested by the subsurface temperature

FIG. 10. ITF transport anomalies and the IOD, Nino-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 In-

dian Ocean. The Ekman transport anomalies (grey, dotted) are

drawn for comparison. The cutoff period of the filter is 13 months.

(bottom) The Nino-3.4 SSTA (black, dashed) and DMI (black,

solid) indices are shown.

FIG. 11. Lag correlations between ITF transport anomalies at the IX1 section and the Nino-3.4

index over the period of 1990–2008. Positive months indicate that ITF lags the Nino-3.4 index.

Solid and dashed horizontal lines stand for the 95% and 99% significance levels, respectively.

1 MAY 2013 YUAN ET AL . 2853

anomalies. The correlation between the SSTA in the

southeastern tropical Indian Ocean in fall and the sub-

surface 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% signifi-

cant 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 in-

dicates eastward propagation into the central and east-

ern 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 corre-

lation 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 consis-

tent 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.

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 IndianOcean 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 68N 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 Nino-3.4 SST index are removed (Fig. 9). The situ-

ation is about the same along 68S (not shown). The lack

of significant propagating signals in the off-equatorial

Pacific Ocean associated with IOD suggests that the

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 hori-

zontal lines stand for the 95% and 99% significance levels, respectively.


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 east-

ern equatorial Indian and Pacific Oceans at the 1-yr time

lag is most likely induced by the ITF variability.

d. Variability of the ITF

The interannual anomalies of the ITF volume trans-

port is calculated from the geostrophic currents in ref-

erence to the 700-m level of nomotion in the IX1 section

based on the XBT data (Fig. 10). In addition, the

transport anomalies of the South Java Current flow-

ing along the Sumatra–Java coast north of 108S 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 Nino-3.4 index are

shown in the bottom panel for reference.

The ITF transport anomalies show major signals as-

sociated 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 is20.35, above the 99% significance level. The

correlation between the filtered Nino-3.4 SST index

and the filtered monthly transport anomalies of the

ITF is, however, only 20.05, below the 95% signifi-

cance level.

Further calculations indicate that the correlations

between ITF and Nino-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 be-

tween the filtered Nino-3.4 SST index and ITF anoma-

lies 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 equa-

torial Pacific Ocean. The correlations between ITF and

Nino-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

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.

1 MAY 2013 YUAN ET AL . 2855

Ocean peak in the coming winter through spring

seasons.

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 21 through 25 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 equa-

torial 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.

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 108S through the IX1 section (Fig. 10). The surface

Ekman transport of the winds has a smaller amplitude

on average, suggesting the dominance of oceanic ther-

mocline processes in the ITF transport variations. The

correlation between the DMI and the transport anom-

alies of the South Java Current is20.19, above the 98%

significant level. In comparison, the correlation between

the Nino-3.4 SST index and the transport anomalies of

the South Java Current is 0.02, way below the 95% sig-

nificance level.

e. Effects of the atmospheric bridge

The atmospheric bridge process of IOD forcing on

ENSO suggests that variations of theWalker circulation

over the equatorial Pacific Ocean are forced by the In-

dian Ocean SSTA through the western Pacific wind

anomalies, which in turn drive the ENSO variability in

the equatorial Pacific Ocean (Izumo et al. 2010). How-

ever, the SZWA in the western equatorial Pacific (58S–58N, 1308–1508E) in fall are in poor correlation with the

FIG. 14. Lag correlations between western Pacific SZWA in fall and Indo-Pacific SSTA in




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 SZWAover 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.

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 sum-

mer through fall seasons are all weak and insignificant

(Fig. 14), suggesting that the strong teleconnection be-

tween the eastern equatorial Indian and Pacific Oceans

at the 1-yr time lag seen in the SSTA and SSHA corre-

lations is unlikely induced by the atmospheric bridge

process. The lack of persistent atmospheric bridge con-

nection 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 sub-

surface temperature anomalies in the cold tongue ther-

mocline at the 1-yr time lag (Fig. 15).

The longitude–time plot of the SZWA averaged be-

tween 58S and 58Nover 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 signifi-

cant correlation with the SZWA in the eastern equato-

rial Pacific 1 yr later, except during the 1997/98 El Nino.

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.

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

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 cor-

relations above the 95% significance level.

1 MAY 2013 YUAN ET AL . 2857

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 per-

sistence of the ENSO events in the ocean’s main ther-

mocline, 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 domi-

nating effects of the oceanic channel dynamics over the

atmospheric bridge process in forcing the cold tongue

anomalies at the 1-yr lag.

One may argue that the signal of the far western Pa-

cific SZWA in fall could be first buried into the western

Pacific thermocline, which then drives the ensuing cou-

pled evolution of the tropical Pacific Ocean and atmo-

sphere 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

FIG. 16. Hovmoller plot of monthly SZWA of NCEP–NCAR

reanalysis data averaged between 58S and 58N. The contour in-

terval is 1 m s21, with the zero-value contour omitted. Shading

indicates positive anomalies greater than 1 m s21. The domain of

1308–1508E is marked with two vertical lines.

FIG. 17. Lag correlations between western Pacific SZWA in fall

and temperature anomalies in the Pacific equatorial vertical sec-

tion 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.


correlation between the SSHA in the southeastern trop-

ical Indian Ocean in fall and those in the cold tongue at

the 1-yr time lag is unlikely induced by the atmospheric

bridge process.

4. Discussion and summary

Stimulated by the dynamics study ofYuan 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 Indo-

nesian Seas in the main ocean thermocline (Figs. 4–7),

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).

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 con-

trast to the significant teleconnection in the ocean be-

tween the eastern Indian and Pacific Ocean. The results

FIG. 18. Lag correlations between western Pacific SZWA in fall and Indo-Pacific SSHA in



1 MAY 2013 YUAN ET AL . 2859

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 in-

traseasonal 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 in-

terannual 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 at-

mospheric bridge process are based primarily on cor-

relations 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 chan-

nel dynamics have been incorporated into the correla-

tion 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 cli-

mate 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 sub-

surface 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 bar-

rier. The disclosed dynamics and structure of the corre-

lations 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 shar-

ing 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 ofLTO (1101), are gratefully acknowledged.

H. Zhou was supported by a Fund of GCMAC, SOA

(1102).

REFERENCES

Alexander,M.A., I. Blade,M. Newman, J. R. Lanzante, N.-C. Lau,

and J. D. Scot, 2002: The atmospheric bridge: The influence of

ENSO teleconnections on air–sea interaction over the global

oceans. J. Climate, 15, 2205–2231.

Annamalai, H., S. P. Xie, J. P. McCreary, and R. Murtugudde,

2005: Impact of Indian Ocean sea surface temperature on

developing El Nino. J. Climate, 18, 302–319.

Battisti, D., 1988: Dynamics and thermodynamics of a warming

event in a coupled tropical atmosphere-ocean model. J. At-

mos. Sci., 45, 2889–2919.Behera, S. K., and T. Yamagata, 2003: Influence of the Indian

Ocean dipole on the Southern Oscillation. J. Meteor. Soc.

Japan, 81, 169–177.——, J. J. Luo, S.Masson, S. A. Rao, H. Sakuma, and T. Yamagata,

2006: A CGCM study on the interaction between IOD and

ENSO. J. Climate, 19, 1688–1705.

Clarke, A. J., and X. Liu, 1994: Interannual sea level in the

northern and eastern Indian Ocean. J. Phys. Oceanogr., 24,

1224–1235.

——, and S. VanGorder, 2003: Improving El Nino prediction using

a space-time integration of Indo-Pacific winds and equatorial

Pacific upper ocean heat content.Geophys. Res. Lett., 30, 1399,

doi:10.1029/2002GL016673.

Cresswell, G. R., and J. R. Luick, 2001: Current measurements in

the Maluku Sea. J. Geophys. Res., 106, 13 953–13 958.

Delcroix, T., B. Dewitte, Y. duPenhoat, F. Masia, and J. Picaut,

2000: Equatorial waves and warm pool displacements during

the 1992–1998 El Nino South Oscillation events: Observation

and modeling. J. Geophys. Res., 105, 26 045–26 062.

Drushka, K., J. Sprintall, S. T. Gille, and I. Brodjonegoro, 2010:

Vertical structure of Kelvin waves in the Indonesian Through-

flow exit passages. J. Phys. Oceanogr., 40, 1965–1987.Fieux, M., R. Molcard, and A. G. Ilahude, 1996: Geostrophic

transport of the Pacific-Indian oceans throughflow. J. Geo-

phys. Res., 101, 12 421–12 432.

Ganachaud, A., and C. Wunsch, 2000: Improved estimates of

global ocean circulation, heat transport and mixing from hy-

drographic data. Nature, 408, 453–456.

Gordon, A. L., R. D. Susanto, and A. Ffield, 1999: Throughflow

within Makassar Strait. Geophys. Res. Lett., 26, 3325–3328.

——, ——, ——, B. A. Huber, W. Pranowo, and S. Wirasantosa,

2008: Makassar Strait throughflow, 2004 to 2006. Geophys.

Res. Lett., 35, L24605, doi:10.1029/2008GL036372.

FIG. 19. Schematic pathway of the Indo-Pacific sea level anomaly

propagation.


Izumo, T., and Coauthors, 2010: Influence of the state of the Indian

Ocean dipole on the following year’s El Nino. Nat. Geosci., 3,

168–172.

Jury, M. R., and B. Huang, 2004: The Rossby wave as a key

mechanism of IndianOcean climate variability.Deep-Sea Res.

I, 41, 2123–2136.

Kalnay, E., and Coauthors, 1996: The NCEP/NCAR 40-Year Re-

analysis Project. Bull. Amer. Meteor. Soc., 77, 437–471.Kandaga, P., A. L. Gordon, J. Sprintall, and R. D. Susanto, 2009:

Intraseasonal variability in the Makassar Strait thermocline.

J. Mar. Res., 67, 757–777.

Kug, J.-S., T. Li, S.-I. An, I.-S. Kang, J.-J. Luo, S. Masson, and

T. Yamagata, 2006: Role of the ENSO–IndianOcean coupling

on ENSO variability in a coupled GCM. Geophys. Res. Lett.,

33, L09710, doi:10.1029/2005GL024916.

Lau, N. C., andM. J. Nath, 2000: Impact of ENSOon the variability

of the Asian–Australian monsoons as simulated in GCM ex-

periments. J. Climate, 13, 4287–4309.

——, and ——, 2003: Atmosphere–ocean variations in the Indo-

Pacific sector during ENSO episodes. J. Climate, 16, 3–20.

——, A. Leetmaa, M. J. Nath, and H. L. Wang, 2005: Influence of

ENSO-induced Indo-western Pacific SST anomalies on ex-

tratropical atmospheric variability during the boreal summer.

J. Climate, 18, 2922–2942.

Luick, J. L., and G. R. Cresswell, 2001: Current measurements in

the Maluku Sea. J. Geophys. Res., 106, 13 953–13 958.

Lukas, R., and E. Lindstrom, 1991: The mixed layer of the western

equatorial PacificOcean. J.Geophys. Res., 96 (Suppl.), 3343–3357.

Luo, J.-J., R. Zhang, S. K. Behera, Y. Masumoto, F.-F. Jin,

R. Lukas, and T. Yamagata, 2010: Interaction between El Nino

and extreme Indian Ocean dipole. J. Climate, 23, 726–742.

MacDonald,A.M., 1998:The global ocean circulation:Ahydrographic

estimate and regional analysis. Prog. Oceanogr., 41, 281–382.

Masumoto, Y., and G. Meyers, 1998: Forced Rossby waves in the

southern tropical IndianOcean. J. Geophys. Res., 103, 27 589–

27 602.

McClean, J. L., D. P. Ivanova, and J. Sprintall, 2005: Remote ori-

gins of interannual variability in the Indonesian Throughflow

region from data and a global Parallel Ocean Program simu-

lation. J. Geophys. Res., 110,C10013, doi:10.1029/2004JC002477.

McPhaden, M. J., and X. Yu, 1999: Equatorial waves and the 1997–

1998 El Nino. Geophys. Res. Lett., 26, 2961–2964.

Meyers, G., 1996: Variation of Indonesian Throughflow and the El

Nino-Southern Oscillation. J. Geophys. Res., 101, 12 255–12 263.

Molcard, R., A. G. Ilahude, M. Fieux, J. C. Swallow, and

J. Banjarnahor, 1994: Low frequency variability of the

currents in Indonesian channels (Savu-Roti M1 and Roti-

Ashmore Reef M2). Deep-Sea Res., 41, 1643–1662.

——, M. Fieux, and A. G. Ilahude, 1996: The Indo-Pacific through-

flow in the Timor Passage. J. Geophys. Res., 101, 12 411–12 420.

——,——, and F. Syamsudin, 2001: The throughflowwithinOmbai

Strait. Deep-Sea Res., 48, 1237–1253.Murtugudde, R., A. J. Busalacchi, and J. Beauchamp, 1998: Seasonal-

to-interannual effects of the Indonesian Throughflow on the

tropical Indo-Pacific basin. J. Geophys. Res., 103, 21 425–21 441.

Nof, D., 1996: What controls the origin of the Indonesian

throughflow? J. Geophys. Res., 101, 12 301–12 314.

Picaut, J., F. Masia, and Y. du Penhoat, 1997: An advective-

reflective conceptual model for the oscillatory nature of the

ENSO. Science, 277, 663–666.Qiu, B., M. Mao, and Y. Kashino, 1999: Intraseasonal variability in

the Indo-Pacific Throughflow and the regions surrounding the

Indonesian seas. J. Phys. Oceanogr., 29, 1599–1618.

Rayner, N. A., D. E. Parker, E. B. Horton, C. K. Folland, L. V.

Alexander, D. P. Rowell, E. C. Kent, and A. Kaplan, 2003:

Global analyses of sea surface temperature, sea ice, and night

marine air temperature since the late nineteenth century.

J. Geophys. Res., 108, 4407, doi:10.1029/2002JD002670.

Saji, N.H., B.N.Goswami, P.N.Vinayachandran, andT.Yamagata,

1999: A dipole mode in the tropical Indian Ocean.Nature, 401,

360–363.

Schopf, P. S., and M. J. Suarez, 1988: Vacillations in a coupled

ocean-atmosphere model. J. Atmos. Sci., 45, 549–566.

Sprintall, J., A. L. Gordon, R. Murtugudde, and R. D. Susanto,

2000: A semiannual IndianOcean forcedKelvinwave observed

in the Indonesian seas in May 1997. J. Geophys. Res., 105,

17 217–17 230.

Uppala, S. M., and Coauthors, 2005: The ERA-40 Re-Analysis.

Quart. J. Roy. Meteor. Soc., 131, 2961–3012.

Vranes, K., A. L. Gordon, and A. Field, 2002: The heat transport

of the Indonesian Throughflow and implications for Indian

Ocean heat budget. Deep-Sea Res. II, 49, 1391–1410.Wang, W., and M. J. McPhaden, 2001: Surface layer temperature

balance in the equatorial Pacific during the 1997–98 El Nino

and 1998–99 La Nina. J. Climate, 14, 3393–3407.

Webster, P. J., V. O.Magana, T. N. Palmer, J. Shukla, R.A. Tomas,

M. Yanai, and T. Yasunari, 1998: Monsoons: Processes, pre-

dictability, and the prospects for prediction. J. Geophys. Res.,

103 (C7), 14 451–14 510.

——,A.Moore, J. Loschnigg, andM. Leban, 1999: Coupled ocean–

atmosphere dynamics in the Indian Ocean during 1997–98.

Nature, 401, 356–360.

White, W. B., 1995: Design of a global observing system for gyre-

scale upper ocean temperature variability. Prog. Oceanogr.,

36, 169–217.

Wijffels, S. E., and G. Meyers, 2004: An intersection of oceanic

waveguides: Variability in the Indonesian Throughflow re-

gion. J. Phys. Oceanogr., 34, 1232–1253.

——, ——, and J. S. Godfrey, 2008: A 20-yr average of the Indo-

nesian Throughflow: Regional currents and the interbasin

exchange. J. Phys. Oceanogr., 38, 1965–1978.

Wu, G., and W. Meng, 1998: Gearing between the Indo-monsoon

circulation and the Pacific-Walker circulation and the ENSO,

Part 1. Data analyses (in Chinese). Chin. J. Atmos. Sci., 22, 470.Wu, R., and B. Kirtman, 2004: Understanding the impacts of

the Indian Ocean on ENSO variability in a coupled GCM.

J. Climate, 17, 3037–3054.

Wyrtki, K., 1987: Indonesian Throughflow and the associated

pressure gradient. J. Geophys. Res., 92, 12 941–12 946.

Yamagata, T., and Y.Masumoto, 1989: A simple ocean-atmosphere

coupled model for the origin of a warm El Nino Southern Os-

cillation event. Philos. Trans. Roy. Soc. London, 329A, 225–236.Yuan, D., 2009: Interannual horizontal heat advection in the sur-

face mixed layer over the equatorial Pacific Ocean: Assimi-

lation versus TAO analyses. Theor. Appl. Climatol., 97, 3–15,doi:10.1007/s00704-008-0068-7.

——, and H. Liu, 2009: Long-wave dynamics of sea level variations

during Indian Ocean dipole events. J. Phys. Oceanogr., 39,

1115–1132.

——, M. M. Rienecker, and P. S. Schopf, 2004: Long wave dy-

namics of the interannual variability in a numerical hindcast

of the equatorial Pacific Ocean circulation during the 1990s.

J. Geophys. Res., 109, C05019, doi:10.1029/2003JC001936.——, and Coauthors, 2011: Forcing of the Indian Ocean dipole on

the interannual variations of the tropical Pacific Ocean: Roles

of the Indonesian Throughflow. J. Climate, 15, 3597–3608.

1 MAY 2013 YUAN ET AL . 2861

Date post:	09-Dec-2016
Category:	Documents
Upload:	xia
View:	212 times
Download:	0 times

Interannual Climate Variability over the Tropical Pacific Ocean Induced by the Indian Ocean Dipole...

Documents