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Indo-Pacific Climate Interactions in the Absence of an Indonesian
Throughflow
JULES B. KAJTAR,AGUS SANTOSO,AND MATTHEW H. ENGLAND
ARC Centre of Excellence for Climate System Science, and Climate Change Research Centre, University of
New South Wales, Sydney, New South Wales, Australia
WENJU CAI
CSIRO Marine and Atmospheric Research, Aspendale, Victoria, Australia
(Manuscript received 5 February 2014, in final form 9 September 2014)
ABSTRACT
The Pacific and Indian Oceans are connected by an oceanic passage called the Indonesian Throughflow
(ITF). In this setting, modes of climate variability over the two oceanic basins interact. El Niño–Southern
Oscillation (ENSO) events generate sea surface temperature anomalies (SSTAs) over the Indian Ocean that,
in turn, influence ENSO evolution. This raises the question as to whether Indo-Pacific feedback interactions
would still occur in a climate system without an Indonesian Throughflow. This issue is investigated here for the
first time using a coupled climate model with a blocked Indonesian gateway and a series of partially decoupled
experiments in which air–sea interactions over each ocean basin are in turn suppressed. Closing the Indo-
nesian Throughflow significantly alters the mean climate state over the Pacific and Indian Oceans. The Pacific
Ocean retains an ENSO-like variability, but it is shifted eastward. In contrast, the Indian Ocean dipole and the
Indian Ocean basinwide mode both collapse into a single dominant and drastically transformed mode. While
the relationship between ENSO and the altered Indian Ocean mode is weaker than that when the ITF is open,
the decoupled experiments reveal a damping effect exerted between the two modes. Despite the weaker
Indian Ocean SSTAs and the increased distance between these and the core of ENSO SSTAs, the interbasin
interactions remain. This suggests that the atmospheric bridge is a robust element of the Indo-Pacific climate
system, linking the Indian and Pacific Oceans even in the absence of an Indonesian Throughflow.
1. Introduction
The El Niño–Southern Oscillation (ENSO) is the dom-
inant global climate mode on interannual time scales,
exerting profound impacts upon the environment and
economies worldwide. Originating in the equatorial Pa-
cific Ocean, ENSO impacts air–sea processes over re-
mote oceans (Klein et al. 1999;Lau and Nath 2003;Liu
and Alexander 2007;Du et al. 2009), generating anoma-
lous sea surface temperature anomalies (SSTAs). These
remote SSTAs can, in turn, feed back onto ENSO vari-
ability in the Pacific. A particularly strong feedback is
exerted by the Indian Ocean [see Santoso et al. (2012)
and references therein], to which the Pacific Ocean is
directly connected via the Indonesian Throughflow.
During their growth phase, El Niño and La Niña
events occasionally induce positive and negative Indian
Ocean dipole (IOD) events, respectively, which peak in
boreal autumn (Saji et al. 1999). The IOD is a coupled
mode of variability, involving a seesaw pattern in SSTAs
in the western and eastern tropical Indian Ocean. A
positive phase of the IOD corresponds with anoma-
lously cool SST in its eastern pole and anomalously
warm SST in the western pole (vice versa for the nega-
tive phase). As El Niño and La Niña events peak in
boreal winter, they tend to induce basinwide warming
and cooling, respectively, over the tropical Indian Ocean,
which is commonly referred to as the Indian Ocean
basinwide mode (IOBM; Klein et al. 1999;Lau and Nath
2003;Du et al. 2009). These Indian Ocean modes of
variability in turn feed back onto ENSO processes in the
Pacific, influencing its period, amplitude, and thus its
Corresponding author address: Jules B. Kajtar, ARC Centre of
Excellence for Climate System Science and Climate Change Re-
search Centre, University of New South Wales, Sydney, New South
Wales 2052, Australia.
E-mail: j.kajtar@unsw.edu.au
VOLUME 28 JOURNAL OF CLIMATE 1JULY 2015
DOI: 10.1175/JCLI-D-14-00114.1
Ó2015 American Meteorological Society 5017
predictability (e.g., Kug and Kang 2006;Luo et al. 2010;
Izumo et al. 2010;Santoso et al. 2012).
It has long been believed that the primary way in
which Indian Ocean climate modes feed back onto
ENSO is via their influence on the atmospheric Walker
circulation (Lau and Nath 2000,2003;Alexander et al.
2002;Wu and Kirtman 2004;Annamalai et al. 2005;
Behera et al. 2006;Potemra and Schneider 2007a). This
can be inferred, for instance, by suppressing air–sea in-
teractions over the Indian Ocean in climate models (e.g.,
Wu and Kirtman 2004;Behera et al. 2006;Dommenget
et al. 2006;Santoso et al. 2012). Warm IOBM has been
found to drive easterly wind anomalies over the western
Pacific that act to dampen El Niño events (Santoso et al.
2012) and accelerate the transition to a La Niña event
(Kug and Kang 2006;Kug et al. 2006). These easterly
wind anomalies induce eastward propagating upwelling
Kelvin waves along the equatorial Pacific that eventu-
ally terminate the El Niño event (Wang et al. 1999).
The IOD has also been thought to influence ENSO dy-
namical processes in the Pacific through the atmospheric
bridge, via an alteration of the Walker circulation
(Izumo et al. 2010,2014). During a negative IOD for
instance, which tends to co-occur with a developing La
Niña, the anomalously warm eastern Indian Ocean in-
duces easterly wind anomalies that deepen the thermo-
cline over the western Pacific warm pool. The eastward
propagating upwelling Kelvin waves reinforce the shal-
lowing thermocline in the eastern Pacific, thus enhancing
development of the ensuing La Niña. The abrupt termi-
nation of these wind anomalies at the end of the negative
IOD event in November allows the anomalous warming
to spread toward the eastern Pacific, preconditioning for
an El Niño in the following year (Izumo et al. 2010).
The atmospheric bridge is thus an important element
of the Indo-Pacific climate system. However, its role in
coupling the two basins is likely to be complicated by the
presence of the Indonesian Throughflow (ITF). Yuan
et al. (2011,2013) argued that the ocean channel mech-
anism is more important than the atmospheric bridge in
the coupling between ENSO and the IOD at longer time
lags. With both models and observations, they showed
that IOD events can generate upwelling anomalies in
the eastern tropical Indian Ocean, inducing Kelvin waves
that propagate through the Indonesian seas to the equa-
torial Pacific Ocean.
The idea of a dominant ocean channel mechanism
seems reasonable given that the ITF transports a signif-
icant volume of water and heat from the Pacific to Indian
Ocean. On average, the flow rate is 10–15 Sv (1 Sv [
10
6
m
3
s
21
;Potemra 1999;Gordon 2005;Wijffels et al.
2008), and the heat transport is on the order of 0.5–1.0
PW (Vranes et al. 2002;England and Huang 2005). In
addition, the ITF exhibits significant interannual vari-
ability (e.g., Meyers 1996;England and Huang 2005;van
Sebille et al. 2014) controlled by pressure differences
between the western Pacific and eastern Indian Ocean,
which are in turn linked to ENSO and Indian Ocean
variability. The ITF can also be directly influenced by
changes in oceanic circulation induced by both ENSO
and the IOD (e.g., England and Huang 2005;Potemra
and Schneider 2007b;Sprintall et al. 2009;Sprintall and
Révelard 2014).
Blocking the Indonesian Throughflow in coupled cli-
mate models significantly alters the global climate and
ENSO (Schneider 1998;Wajsowicz and Schneider 2001;
Song et al. 2007;Santoso et al. 2011). For instance,
blocking the ITF results in weaker trade winds, flatter
equatorial thermocline, and weaker upwelling across the
Pacific Ocean, leading to an alteration of ENSO char-
acteristics. This demonstrates that the ITF is an impor-
tant component of the Indo-Pacific climate system.
Thus, it is illuminating to assess the significance of the
atmospheric bridge mechanism in light of the prominent
presence of the ITF. An understanding of the role of the
two available pathways may help to improve ENSO,
IOD, and IOBM forecasting, and it can shed light on
Indo-Pacific feedback interactions throughout Earth’s
history, over which the ITF has varied substantially (e.g.,
Cane and Molnar 2001;Kuhnt et al. 2004).
One way to evaluate the role of the atmospheric
bridge is to utilize a climate model with a closed ITF and
conduct partially decoupled experiments in which the
air–sea interactions are suppressed over each oceanic
basin independently. Each of the two experimental
elements—that is, a closed ITF and suppressed air–sea
interactions—has been analyzed separately in previous
studies (Santoso et al. 2011,2012). Here we combine
these elements for the first time. This experimental de-
sign enables us to assess the extent to which Indo-Pacific
coupled interactions occur when the ITF is blocked. In
this case, the interbasin feedback interactions, if any,
will necessarily occur through the atmospheric bridge.
We proceed by outlining the numerical model and
experimental design in section 2.Insection 3, the change
to the mean climate with ITF closed is presented, along
with the transformations of the dominant Pacific and
Indian Ocean climate modes. In section 4, the impor-
tance of the atmospheric bridge is assessed with the aid
of partially decoupled experiments. A discussion and
summary follows in section 5.
2. The climate simulations
The simulations were conducted using version 1.2 of
the Commonwealth Scientific and Industrial Research
5018 JOURNAL OF CLIMATE VOLUME 28
Organization (CSIRO) Mark 3L (Mk3L) climate system
model. CSIRO Mk3L is a coupled general circulation
model (GCM) designed for running millennial-scale
simulations (Phipps 2010;Phipps et al. 2013). The at-
mospheric GCM (AGCM) has a resolution of ;5.68
longitude 3;3.28latitude, with 18 levels in the hybrid
vertical coordinate. The oceanic GCM (OGCM) has
double the horizontal resolution (i.e., ;2.88longitude 3
;1.68latitude) and 21 levels in the vertical zcoordi-
nate. The AGCM and OGCM are spun up indepen-
dently, after which the two are coupled with constant
flux adjustments to minimize drift and maintain a re-
alistic seasonal climatology. The version of the model
used here includes the updated configuration of the
Indonesian Archipelago, as employed by Santoso et al.
(2012);Santoso et al. (2011) used an earlier version of
the CSIRO Mk3L.
The properties of ENSO, the IOD, and the IOBM are
all reasonably well simulated by the model; neverthe-
less, some biases exist, as already noted by Santoso et al.
(2012). Here we briefly outline the principal biases. As
in many Intergovernmental Panel on Climate Change
(IPCC)-class climate models (e.g., Guilyardi et al. 2009),
the ENSO displays a ‘‘cold tongue bias.’’ Additionally,
SST variability peaks 2–3 months earlier with weaker
magnitude and a longer period than observed. The sim-
ulated IOD is stronger than in observations, consistent
with the climatological biases in SST, trade winds, and
rainfall over the eastern Indian Ocean. This bias renders
the warm phase of the IOBM to exhibit slight cooling in
the southeastern Indian Ocean (and the opposite for the
cool phase), which is not apparent in observations. Fur-
thermore, the mean ITF rate in the model is approxi-
mately 21 Sv, which is larger than the observed estimate of
about 15 Sv. This is associated with the coarse model
resolution partly through the joint effect of baroclinicity
and relief (JEBAR; England et al. 1992;Santoso et al.
2011). Despite these shortcomings, the overall model
performance is reasonable considering its resolution,
which makes it ideal for centennial- to millennial-scale
climate simulations.
After the initial spinup of the AGCM and OCGM, the
coupled model is integrated for 400 yr, with CO
2
con-
centration held fixed at the preindustrial value of
280 ppm. At this point, two experiments are branched off
and integrated a further 1100yr out to year 1500. In the
first, the Indonesian passage is blocked by a land bridge
[as done by Santoso et al. (2011)] and, in the second, the
ITF remains open. The last 200 yr of each run is used as
the control experiments that we henceforth denote as
CTRL
clsd
and CTRL
open
, respectively. The CTRL
open
experiment was run primarily to compare the changes to
the mean climate and modes over the Indian Ocean,
which has not been previously published. Epochs from
within the CTRL
clsd
experiment are then used to initial-
ize an ensemble of three 100-yr partially ‘‘decoupled’’
experiments, wherein air–sea interaction over the Indian
and Pacific Oceans are separately suppressed. These ex-
periments are referred to as DCPLIO
clsd for suppressed air–
sea interaction over the Indian Ocean, and DCPLPO
clsd for
suppressed air–sea interaction over the Pacific Ocean.
The decoupling is achieved by fixing SST over the re-
spective oceans using the climatological seasonal mean
field, as done by previous studies (e.g., Baquero-Bernal
et al. 2002;Behera et al. 2005;Dommenget et al. 2006;
Santoso et al. 2012). The decoupled regions were boun-
ded by 308Sand308N, by the coast to the east and west,
and by the Indonesian archipelago. As such, the western
side of the Maritime Continent warm pool is considered
as part of the Indian Ocean, and the eastern side (the
western Pacific warm pool) is considered as part of the
Pacific Ocean. Despite this setting, the warm pool sea-
sonal variation is retained, since the SST is fixed to the
seasonally varying climatology. However, it should be
noted that, in this way, our study does not explicitly
consider the potential influence that variability over the
Indonesia seas has on ENSO (Annamalai et al. 2010)as
well as on Indian Ocean variability.
Each 100-yr partially decoupled run was initialized
from the matching CTRL
clsd
experiment at the corre-
sponding epoch over the 200-yr period, taken at in-
tervals of 50 yr (i.e., initialized at year 1 for set one, year
51 for set two, and year 101 for set three). An ensemble
set of three partially decoupled experiments for each
case allows for the inference of statistical significance.
The purpose of using 100-yr-long experiments was to
ensure sufficient sampling of the low-frequency ENSO
variability in each scenario, but they were limited to that
length so that the decoupling did not introduce sub-
stantial model drift resulting from any potential error in
air–sea heat fluxes (Fischer et al. 2005). The mean climate
drift in our 100-yr-long partially decoupled experiments is
small. For DCPLIO
clsd, the difference in mean SST across
the equatorial Pacific Ocean over 100 yr compared to
CTRL
clsd
is less than 0.05 K. For DCPLPO
clsd, the difference
across the equatorial Indian Ocean is less than 0.01 K.
3. Mean climate and modes in the closed ITF
control experiment
Blocking the ITF results in significant changes to the
mean climate. Figure 1 shows the annual mean climate
in CTRL
open
and CTRL
clsd
and the differences between
the two experiments, featuring SST, surface wind stress,
ocean surface currents, rainfall, and sea level pressure.
Changes to the Pacific Ocean shown by Santoso et al.
1JULY 2015 K A J T A R E T A L . 5019
(2011) with the earlier version of the CSIRO Mk3L
model are reproduced here in Fig. 1 and briefly de-
scribed for completeness. Closing the ITF dramatically
changes the ocean circulation, with notable strengthen-
ing of the East Australian Current and weakening of the
Agulhas Current. The equatorial Pacific thermocline
slope declines, resulting in a warmer eastern Pacific that
leads to higher and lower sea level pressure (SLP) in the
western and eastern Pacific, respectively. This drives
westerly wind anomalies and weakens the westward
equatorial surface currents and upwelling. As a result,
rainfall increases in the eastern Pacific and decreases in
the western Pacific.
The changes to the mean climate over the Indian
Ocean in the model, not discussed by Santoso et al.
(2011), include cooling of the eastern Indian Ocean
waters resulting from the absence of heat transported
from the western Pacific via the ITF. The concurrent
increase in SLP drives stronger southeasterly winds that
promote equatorial upwelling and lifts the thermocline
depth, thereby further cooling the eastern Indian Ocean.
The most significant change to precipitation is a large
decrease over the southeastern Indian Ocean, which is
consistent with the cooler SST and higher SLP in that
region.
The intense cooling in the eastern Indian Ocean and
lesser warming in the eastern Pacific render a slowdown
of the Walker circulation across the two basins. Zonal
atmospheric wind speeds over the Pacific Ocean are
typically reduced by half. These changes to the mean
climate and Walker circulation are illustrated as a sche-
matic in Fig. 2 and are qualitatively consistent with
previous studies that used coupled climate models to
study the issue (Wajsowicz and Schneider 2001;Song
et al. 2007). The other elements of Fig. 2 are discussed in
section 4.
Closing the ITF significantly alters the modes of cli-
mate variability as a result of changes to the mean cli-
mate upon which they evolve (Song et al. 2007;Santoso
et al. 2011). Figures 3 and 4show the spatial patterns of
FIG. 1. Annual mean climate in CTRL
open
and CTRL
clsd
, along with the differences. Mean SST field is shown in color and surface wind
stress with arrows for (a) CTRL
open
, (b) CTRL
clsd
, and (c) the differences. Mean vertical ocean velocity at 50m is shown in color and
horizontal depth-integrated ocean currents over the top 50 m are shown with arrows for (d) CTRL
open
, (e) CTRL
clsd
, and (f) the dif-
ferences. Mean precipitation is shown in color and SLP (hPa) with contours for (g) CTRL
open
, (h) CTRL
clsd
, and (i) the differences.
5020 JOURNAL OF CLIMATE VOLUME 28
the dominant empirical orthogonal function (EOF)
modes for SST. The EOF analyses were performed on
the full 200-yr CTRL
open
and CTRL
clsd
sets for each
ocean separately and bounded by 208S–208N. In the In-
dian Ocean, the IOBM (Fig. 3a) and the IOD (Fig. 3b)
are the leading modes of climate variability, explaining
22% and 19% of the total variance, respectively. How-
ever, when the ITF is closed, the Indian Ocean essen-
tially exhibits only a single mode, with the first EOF
mode (EOF-1) explaining 31% of the total variance,
(EOF-2 and EOF-3 correspond to only 9% and 8%, re-
spectively). The spatial pattern exhibits a broad warming
(or cooling) signature that extends westward from the
eastern Indian Ocean. The pattern and temporal char-
acteristics, as shown in section 4, are unlike that of either
the IOD or the IOBM in CTRL
open
.Themode,whose
SSTA pattern is of uniform polarity, bears closer re-
semblance to an El Niño signature in the Indian Ocean
given the equatorial region in CTRL
clsd
is marked by
strong upwelling, with trade wind and oceanic current
patterns similar to those in the Pacific Ocean. For sim-
plicity, we will refer to this mode in the CTRL
clsd
ex-
periment as the Indian Ocean mode and abbreviate it to
IOM
clsd
to emphasize its occurrence is unique to the
closed ITF experiments.
Consistent with observations, ENSO in CTRL
open
(Fig. 4a) is the leading mode, explaining 41% of the total
variability. ENSO-like variability persists in CTRL
clsd
(Fig. 4b) despite having its characteristics altered, in
agreement with that reported by Santoso et al. (2011) in
the earlier version of the model. The core of the ENSO
SSTAs is confined farther to the east in CTRL
clsd
. The
overall variability is reduced, primarily through the
collapse of the decadal component, as the magnitude of
the interannual component is largely retained (see
Fig. 6f of Santoso et al. 2011), without involving any
apparent change in seasonality. These alterations to
ENSO are a result of the changes to the Walker circula-
tion, which drives weaker easterly wind stresses over the
equatorial Pacific (see schematic in Fig. 2).
4. Effect of suppressed air–sea interactions
To examine the response of the modes in CTRL
clsd
to
suppressed air–sea interactions, we constructed repre-
sentative SST indices for each oceanic basin that best
capture the modes of variability. An Indian Ocean cen-
tral index (IOCI) was constructed by averaging SST over
the region of strongest variability (58S–58N, 508–1008E)
for the Indian Ocean mode (IOM
clsd
), as indicated in
Fig. 3c. Note that the overall results do not change when
the averaging box is shifted slightly to the south. To ac-
count for the eastward shift in the core region of the
ENSO SSTAs in CTRL
clsd
, the Niño-3 index (58S–58N,
1508–908W) was adopted for this analysis, as indicated
in Fig. 4b, which is captured better by the Niño-3.4 index
in CTRL
open
. The monthly standard deviation, power
spectral densities, and autocorrelations of these two
FIG. 2. Schematic of changes to the Walker circulation and surface wind stress upon closure
of the ITF. In the background image, the color shading shows the mean SST field for CTRL
open
(as in Fig. 1a), and the color contours represent the difference in the mean SST field between
CTRL
clsd
and CTRL
open
(as in Fig. 1c). The black loops illustrate the typical Walker circulation
in CTRL
open
. When the ITF is closed, the Walker circulation weakens, as shown by the dashed
red loops, designating the change. The interactions between oceanic modes in CTRL
clsd
are
discussed in section 4, and they are illustrated by the following: The large gray arrows along the
equator denote the mean surface wind stress anomalies during the growth phase of a warm
event (July–November for El Niño in the Pacific and March–June for the warm Indian Ocean
phase) for CTRL
clsd
. The dark green and dark purple arrows denote the same wind stress
anomalies but for DCPLIO
clsd and DCPLPO
clsd, respectively. The brighter green and purple arrows
denote the effective influence of the opposite ocean basin. The arrows for the wind stress
anomalies are reversed for La Niña and cool Indian Ocean events.
1J
ULY 2015 K A J T A R E T A L . 5021
indices are shown in Fig. 5, for CTRL
clsd
, DCPLPO
clsd, and
DCPLIO
clsd. A notable feature of IOM
clsd
is that the vari-
ability peaks during May–July (Fig. 5a), in contrast to the
IOD (which peaks during August–November) and the
IOBM (peaking during January–May). This seasonal
phase locking in CTRL
clsd
is consistent with the peak of
the southeasterly winds and equatorial upwelling in
austral winter (not shown).
Suppressing air–sea interactions in either oceanic
basin results in amplification of the overall variability of
both Niño-3 and the IOCI. This occurs without any
change to the seasonality (Figs. 5a,b). The modes oper-
ate on notably different time scales: interdecadal for
IOM
clsd
(Fig. 5c) and interannual for ENSO (Fig. 5d).
The tendency for an increase in the decorrelation time
scale (Figs. 5e,f), more prominently for the IOCI, cor-
roborates a shift in the modes toward longer periodicity.
The partially decoupled experiments show that the
removal of the SST mode from one basin strengthens the
other, relative to the CTRL
clsd
simulations. Therefore,
we conclude that damping occurs between the Pacific
and Indian Ocean SST modes. Such interactions neces-
sarily occur through the atmospheric bridge, since the
ITF is blocked. Unlike the situation in CTRL
open
,in
which IOD and IOBM are strongly correlated with
ENSO (Santoso et al. 2012), IOM
clsd
tends to occur
more independently from ENSO, as evidenced by
a weak positive correlation between Niño-3 and the
IOCI, with a maximum correlation coefficient of ap-
proximately 0.2, occurring at zero lag (not shown). Al-
though it is statistically significant at the 95% confidence
level, the weak correlation also implies that the cool
phase of IOM
clsd
can co-occur with an El Niño and the
warm phase with a La Niña. The tendency for slightly
more frequent occurrences of paired warm IOM
clsd
phase with El Niño and cool IOM
clsd
phase with La Niña
allows the damping to occur as explained below.
The atmospheric bridge underpins the coupling be-
tween the Indian and Pacific Oceans in CTRL
clsd
.
FIG. 3. The dominant EOF modes for Indian Ocean SST pre-
sented as regression maps. The two dominant modes in CTRL
open
are (a) the IOBM and (b) the IOD. In CTRL
clsd
, the variance is
dominated by a single (c) Indian Ocean mode (IOM
clsd
). The
percentage of the variance explained by each mode is shown above
each panel. Additionally, the variance explained by EOF-2 in
CTRL
clsd
is given in parentheses. The overlaid box in (c) denotes
the region chosen for the Indian Ocean central index (IOCI; 58S–
58N, 508–1008E). The gray shading indicates the land cells in the
model.
FIG. 4. The dominant EOF modes for Pacific Ocean SST pre-
sented as regression maps for (a) CTRL
open
and (b) CTRL
clsd
.
The percentage of the variance explained by each mode is shown
above each panel, with the variance explained by EOF-2 in pa-
rentheses. The overlaid box in (b ) denotes the Niño-3 region (5 8S–
58N, 1508–908W), which encapsulates the core of the ENSO
SSTAs in CTRL
clsd
.
5022 JOURNAL OF CLIMATE VOLUME 28
Figure 6a shows that suppressing air–sea coupling over
the Indian Ocean results in strengthened equatorial
zonal wind stress (t
x
) variability over the eastern Pacific.
This is the signature of the enhanced Niño-3 variability
seen in Fig. 5b. The significant weakening of t
x
vari-
ability over the Indian Ocean (between 508and 1008E)
during March–June is due to the absence of IOM
clsd
in
DCPLIO
clsd (Fig. 6a). The weakened variability extends
across to the western Pacific (1508E–1608W) over the
latter half of the year. Thus we infer, and reinforce later,
that the weakened t
x
variability over the western Pacific
represents weaker t
x
anomalies in that region, which
leads to enhanced t
x
anomalies over the eastern Pacific
and thus permits stronger ENSO events in the absence
of IOM
clsd
. The damping effect of ENSO on IOM
clsd
is
apparent by the strengthening of t
x
variability over the
Indian Ocean in DCPLPO
clsd (Fig. 6b). The enhanced t
x
variability manifests over the western Pacific (between
1008and 1508E) during July–October because of the
removal of ENSO, and extends over the Indian Ocean
(508–1008E) during November–March.
The composite SSTA evolution of the warm and cool
phases of IOM
clsd
, shown in Figs. 7a and 7b, respectively,
illustrates the weak correlation between the modes. In
Fig. 7 and later figures, Jul(0) corresponds to July (cal-
endar months abbreviated) in the year of the warm or
cool event, and 21 or 1 in parentheses denotes the year
before or after the event. In addition to the weak cor-
relation between warm or cool events in the Indian and
Pacific Oceans, there appears to be a degree of non-
linearity. Specifically, the composites show that while
the warm IOM
clsd
phase coincides with some anomalous
warming in the Pacific [Fig. 7a; east of 1608W during
Jul(21)–Jul(0)], the cool phase does not as frequently
FIG. 5. Comparison of CTRL
clsd
with DCPLPO
clsd and DCPLIO
clsd for the SST index corresponding to each ocean basin.
The monthly standard deviations are shown for (a) the IOCI and (b) the Niño-3 index. Note that for both cases
variability is enhanced when air–sea interaction in the opposite ocean is suppressed, but the seasonality is unchanged.
(c),(d) The power spectral densities for the respective indices and (e),(f) the autocorrelations. For each plot, the thick
curves indicate the ensemble means. The color-shaded areas indicate the 95% confidence intervals, which were
computed by dividing each 100-yr series into three 90-yr series shifted by 5 years. From the resulting nine 90-yr
samples, the confidence interval was estimated based on 1000 bootstrapped means.
1J
ULY 2015 K A J T A R E T A L . 5023
co-occur with a La Niña (hence the weaker cool SST
signature in the eastern PacificinFig. 7b).
The influence of IOM
clsd
becomes apparent when the
Pacific Ocean is decoupled. Figure 7c shows easterly t
x
anomalies over the western Pacific between Jul(0) and
Oct(0) following the warm IOM
clsd
phase and, con-
versely, westerly t
x
anomalies following the cool phase
(Fig. 7d). Since this is in CTRL
clsd
, it is difficult to infer
the origin of these anomalies, and they may, in fact, be
induced by either or both the IOM
clsd
and ENSO modes.
By decoupling the Pacific Ocean, Figs. 7e,f show that the
origin of the t
x
anomalies over the western Pacific is due
in large part to the Indian Ocean SSTAs, since ENSO is
absent. However, the westward shift of the anomalies
in DCPLIO
clsd indicates a degree of coupling to ENSO in
CTRL
clsd
.Thet
x
anomalies correspond with a re-
sponse in SLP that is anomalously low over the Indian
Ocean and anomalously high over the Pacific for the
warm IOM
clsd
phase, with opposite anomalies for the
cool phase. This influence of IOM
clsd
, which is stron-
gest during the latter half of the year (around the
mature phase of ENSO), is consistent with a Kelvin
wave response to zonally uniform diabatic heating
over the Indian Ocean (Annamalai et al. 2005). It
is further evidenced by spatially uniform rainfall
changes, which can be seen by comparing Figs. 8a,
bwith Figs. 8c,d.
Composites of SSTAs associated with El Niño and La
Niña events in Figs. 9a,b reaffirm the weak and asym-
metric pairing with the warm and cool phases of IOM
clsd
respectively. A warming signature can be seen over the
Indian Ocean during Jan(1)–Apr(1) in Fig. 9a, but
a corresponding cooling signature is absent in Fig. 9b.
The Indian Ocean induced easterly wind anomalies over
the western Pacific enhance the wind components that
are directly related to El Niño evolution (and the op-
posite for La Niña), which in turn exert a damping effect
on ENSO variability (Santoso et al. 2012). Figures 9g,h
show the differences in magnitudes of t
x
anomalies be-
tween DCPLIO
clsd (Figs. 9e,f) and CTRL
clsd
(Figs. 9c,d) for
El Niño and La Niña events. For El Niño (Fig. 9g), the
weakened t
x
anomalies in the western Pacific [between
1508E and 1608W during Jul(0)–Jan(1)] due to the re-
moval of IOM
clsd
are apparent, but for La Niña (Fig. 9h)
it is less so. Conversely, the enhancement of the t
x
anomalies in the eastern Pacific (between 1608and
1108W) are more pronounced for La Niña. The weak-
ening of the t
x
anomalies in the western Pacific is ex-
pected to be masked to some extent by the concurrent
enhancement of the t
x
anomalies to the east, since the
two are linked via ENSO amplitude. Nevertheless, the
ENSO magnitude for both phases is consistently en-
hanced because of the removal of IOM
clsd
induced wind
stress anomalies in the western Pacific.
The damping effect of ENSO on IOM
clsd
is apparent
by the strengthening of the t
x
anomalies over the Indian
Ocean sector in DCPLPO
clsd (Figs. 7e,f) relative to that in
CTRL
clsd
(Figs. 7c,d). The strengthening occurs over the
entire 24-month span that is shown, further illustrating
the shift in IOM
clsd
to longer periods in DCPLPO
clsd.El
Niño events induce easterly t
x
anomalies in CTRL
clsd
over the western Pacific (Fig. 9c). In the absence of
IOM
clsd
, the easterly t
x
anomalies are stronger across
the Indian Ocean basin during Jan(1)–Jul(1) (Fig. 9g).
These induced t
x
anomalies over the Indian Ocean
are in response to the high SLP anomalies associated
with anomalous cooling seen in Fig. 9a between 1508E
and 1408W commencing in Jan (1), which appears to
propagate eastward resulting from the more dominant
ENSO thermocline feedback in CTRL
clsd
than in
CTRL
open
(Santoso et al. 2011). The easterly t
x
anom-
alies induced by El Niño are favorable for upwelling and
latent-heat-driven cooling in the Indian Ocean, and
hence they exert a damping effect on the co-occurring
warm phase of IOM
clsd
.LaNiña and cool IOM
clsd
events
interact similarly but with t
x
, SST, and SLP anomalies of
the opposite signs to the scenario described for El Niño
and warm IOM
clsd
events. Thus, when the Pacific Ocean
is decoupled, the wind stress variability associated with
IOM
clsd
is enhanced, including that over the western
Pacific (Fig. 5b). This allows IOM
clsd
to grow stronger
and persist longer.
The interactive feedback between the warm IOM
clsd
phase and El Niño in CTRL
clsd
is summarized in the
FIG. 6. Differences in monthly standard deviation of the equa-
torial t
x
averaged over 58S–58N for (a) DCPLIO
clsd minus CTRL
clsd
and (b) DCPLPO
clsd minus CTRL
clsd
. The regions with different
variance at the 90% confidence level under an Ftest are marked
with solid lines.
5024 JOURNAL OF CLIMATE VOLUME 28
schematic of Fig. 2. In CTRL
clsd
, divergent t
x
anomalies
manifest over the Pacific Ocean during the growth phase
of an El Niño (large gray arrows). When the Indian
Ocean is decoupled, the easterly component (western
Pacific) is weakened, while the westerly component
(eastern Pacific) is enhanced (dark green arrows) in as-
sociation with the stronger El Niño. Thus the effective
influence of the warm phase of IOM
clsd
on El Niño in
CTRL
clsd
is through the strengthened easterly t
x
anomalies over the western Pacific (depicted by the
difference between the t
x
anomaly in CTRL
clsd
and
DCPLIO
clsd; bright green arrow). Over the Indian Ocean,
a westerly t
x
anomaly emerges during the warm IOM
clsd
phase (large gray arrow). When the Pacific Ocean is
decoupled, the t
x
anomaly is enhanced (dark purple
arrow) in association with the stronger IOM
clsd
. In this
case, the effective influence of the developing El Niño
on the warm IOM
clsd
phase is marked by the weakened
westerly t
x
anomaly over the Indian Ocean (bright
purple arrow denoting the difference between the t
x
anomaly in CTRL
clsd
and DCPLPO
clsd).
5. Discussion and conclusions
Using a suite of coupled and partially decoupled cli-
mate model experiments, this study examined the
importance of the atmospheric bridge on feedback in-
teractions between the Pacific and Indian Oceans. To
isolate the atmospheric bridge, any possible influence of
the Indonesian Throughflow (ITF) was negated by in-
troducing a land bridge across the Maritime Continent.
First, it was shown that closing the ITF resulted in sig-
nificant changes to modes of variability linked to changes
in the mean climate. Over the Pacific, the core of the
ENSO SSTAs shift eastward into the Niño-3 region, and
the IOBM and IOD collapse into a single dominant In-
dian Ocean mode (IOM
clsd
).
With the ITF closed, further experiments were con-
ducted with air–sea interactions suppressed, firstly over
the Indian Ocean, and then separately over the Pacific.
Decoupling in this way eliminates any possible influence
of modes of variability in that ocean basin on the other,
since SSTAs in that basin are prohibited from perturbing
FIG. 7. Composites over 24 months of (a),(c),(e) warm and (b),(d),(f) cool Indian Ocean events for (a),(b) SST
anomalies in CTRL
clsd
and for t
x
and SLP anomalies in (c),(d) CTRL
clsd
and (e),(f) DCPLPO
clsd. The monthly quan-
tities are averaged over the equatorial zone (58S–58N). Warm and cool events are selected when the IOCI averaged
over May–July is above and below one standard deviation, respectively. Only regions that are significantly different
from zero at the 90% confidence level under a ttest are plotted. The SLP contours (hPa) are black for positive
anomalies and gray for negative anomalies. These panels span from Jul(21) to Jun(1), so that the peak of the IOM
clsd
events are centered near Jul (0).
1J
ULY 2015 K A J T A R E T A L . 5025
the atmosphere. It was shown that when one ocean is
decoupled, SST variability over the other ocean basin is
enhanced. Thus, it was inferred that the modes of vari-
ability in opposite basins act to dampen one another.
This negative feedback occurs despite the fact that the
occurrences of Indian Ocean SSTAs appear to be in-
dependent of the ENSO mode, and vice versa, unlike in
the case when the ITF is open (Santoso et al. 2012).
Nevertheless, the simulations produce a slightly stronger
tendency for a warm Indian Ocean to co-occur with an El
Niño. The damping effect is shown to occur through this
combination. Specifically, the warm Indian Ocean SSTAs
induce easterly winds over the western Pacific that exert
a damping effect on the ensuing El Niño. Removing
IOM
clsd
weakens these wind anomalies, and thus
strengthens the ENSO mode. El Niño is also shown to
induce easterly wind anomalies over the Indian Ocean
that has a cooling effect through upwelling and evapo-
ration. Such conditions are not favorable for the gener-
ation of the warm IOM
clsd
phase. This mechanism is
similar but with anomalies of the opposite sign, for La
Niña and the cool IOM
clsd
phase. The removal of the
mode in each basin through the partial decoupling
technique thus strengthens the mode in the other basin.
With the ITF blocked, Indo-Pacific interactions can only
occur via the atmospheric bridge.
The damping influence of the Indian Ocean on ENSO
with the ITF closed is analogous to the results with ITF
open. Santoso et al. (2012) found that variability of
ENSO is enhanced by a similar magnitude when the
Indian Ocean is decoupled. With the ITF open, they
revealed that the IOBM influences equatorial zonal
wind stress t
x
over the Pacific, which acts to dampen
ENSO. Specifically, the warm phase of the IOBM, which
generally follows the peak of an El Niño, induces east-
erly t
x
anomalies over the western Pacific Ocean. This
weakens the westerly t
x
anomalies that are conducive
for the Bjerknes coupled air–sea feedback, so this con-
sequently results in a weaker El Niño phase. The con-
verse applies for the cool IOBM phase and La Niña.
Thus, suppressing air–sea interactions in the Indian
Ocean with the ITF open weakens t
x
variability over the
western Pacific during January–April because of the
removal of the IOBM, which generally coincides with
the decay phase of ENSO events. We have found that
a similar mechanism exists when the ITF is closed, but
with an altered seasonality. The variability of the dom-
inant climate mode in the Indian Ocean peaks during
May–July with a much longer persistence, so it tends to
influence ENSO during the latter half of the year, cor-
responding to its growth phase.
The results of Santoso et al. (2012) were consistent
with Dommenget et al. (2006), who also found that In-
dian Ocean variability acts to dampen ENSO. Both sets
of authors also agreed that the ENSO period becomes
longer with the Indian Ocean decoupled. Some earlier
studies had concluded that coupling ocean modes tends
to increase the variability of ENSO (e.g., Barsugli and
Battisti 1998;Yu et al. 2002;Wu and Kirtman 2004), but
many of these were based on a single experiment with
a shorter run time (on the order of 50 yr) or used an
overly simplified GCM.
FIG.8.AsinFigs. 7c–f, but for composites of rainfall for (a),(c) warm and (b),(d) cool Indian Ocean events in (a),(b)
CTRL
clsd
and (c),(d) DCPLPO
clsd.
5026 JOURNAL OF CLIMATE VOLUME 28
In the present study, it is somewhat surprising that the
atmospheric bridge mechanism remains strong despite
the fact that, when the ITF is closed, the core region of the
ENSO SSTAs is shifted eastward, thereby increasing the
spatial separation from the Indian Ocean. The mainte-
nance of the interbasin interactions is also reflected in the
influence of the Indian Ocean mode, which is significant
in CTRL
clsd
. This is consistent with the uniform polarity
pattern of the tropical SSTA of IOM
clsd
. Such structure is
more conducive for a stronger Kelvin wave response than
if it were of a dipole pattern, which would otherwise
generate an interference of Kelvin waves of opposite
signs (Annamalai et al. 2010). We also note that the po-
tential influence of SSTAs over the Indonesian seas on
the coupling between the two basins (Annamalai et al.
2010) has not been explicitly assessed here. The present
study demonstrates nonetheless that the atmospheric
bridge is a robust element of the Indo-Pacific climate that
would allow complex climate feedback interactions to
occur even in the absence of the oceanic channel.
While the ITF has never been completely blocked in
the real system [see Santoso et al. (2011) and references
therein] and the behavior of the atmospheric bridge
may be a function of changes in the mean climate, our
results point to the possibility that modes of variability
in the Indian and Pacific Oceans have been in constant
interaction throughout Earth’s history. This could have
important implications for our understanding of Indo-
Pacific climate variability in the context of past and
future climates.
Acknowledgments. This study was supported by the
Australian Research Council (ARC) Centre of Ex-
cellence for Climate System Science. The model sim-
ulations were conducted on the NCI National Facility
in Canberra, which is supported by the Australian
Commonwealth government. W. Cai is supported by
the Australian Climate Change Science Programme.
We thank the three anonymous reviewers for their
FIG. 9. Composites over 24 months of (a),(c),(e) El Niño and (b),(d),(f) La Niña events for (a),(b) SST anomalies in
CTRL
clsd
and for t
x
and SLP anomalies in (c),(d) CTRL
clsd
, and (e),(f) DCPLIO
clsd. (g),(h) The corresponding dif-
ferences of the wind stress anomaly magnitudes jDCPLIO
clsdj2jCTRLclsd j. The monthly quantities are averaged over
the equatorial zone (58S–58N). El Niño and La Niña events are selected when the Niño-3 index averaged over
September–December is above and below one standard deviation, respectively. Only regions that are significantly
different from zero at the 90% confidence level under a ttest are plotted. The SLP contours (hPa) are black for
positive anomalies and gray for negative anomalies. These panels span from Jan(0) to Dec(1), so that the peak of the
ENSO events are centered near Dec(0).
1J
ULY 2015 K A J T A R E T A L . 5027
comments and suggestions, which helped to greatly
improve the manuscript.
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