Abstract

An interactive feedback between ENSO and the Indian Ocean is investigated using a Center for Ocean–Land–Atmosphere Studies (COLA) interactive ensemble coupled model. From a long-term simulation of the coupled GCM, it is shown that El Niño events terminate relatively rapidly when the Indian Ocean SST is anomalously warm. The anomalous Indian Ocean warming induces the anomalous easterlies over the western Pacific by modulating the Walker circulation. In turn, the anomalous easterlies generate oceanic-upwelling Kelvin waves over the western Pacific, which propagate eastward and accelerate the decay of the warm SST in the eastern Pacific. As a result, El Niño terminates relatively quickly, and the phase transition from El Niño to La Niña progresses rapidly. These interactive processes are consistent with those derived from the previous observational analyses.

1. Introduction

Recently, a number of studies have documented Indian Ocean variability. In particular, some of these studies have investigated covariability between ENSO and Indian Ocean (IO) sea surface temperature (SST) (Klein et al. 1999; Venzke et al. 2000; Baquero-Bernal et al. 2002; Huang and Kinter 2002; Xie et al. 2002; Lau and Nath 2000, 2003, 2004; Krishnamurty and Kirtman 2003; Wu and Kirtman 2004a; Kug et al. 2005, 2006). To a large extent, they all agreed that the modulation of the Walker circulation provides a mechanism linking ENSO and IO variability.

Anomalous Walker circulation associated with ENSO can affect IO variability by two different processes; the first is associated with atmospheric processes (Klein et al. 1999; Lau and Nath 2003), and the second is due to oceanic processes (Xie et al. 2002; Li et al. 2002). Regarding the first process, Klein et al. (1999) suggested an “atmospheric bridge” mechanism linking Pacific and Indian Ocean variability. The proposed mechanism works as follows: over the eastern Indian Ocean, enhanced subsidence during El Niño reduces cloud cover and increases the solar radiation absorbed by the ocean, thereby enhancing SST warming. Therefore, the eastern Indian Ocean SST lags the ENSO SST forcing because the ocean has a large heat capacity, although atmospheric teleconnections are associated with a simultaneous response. On the other hand, Xie et al. (2002) showed that the anomalous equatorial wind associated with ENSO in the equatorial Indian Ocean can explain the warming of the western Indian Ocean. The equatorial easterly wind produces an anticyclonic wind stress curl and forces downwelling oceanic Rossby waves off of the equator. The downwelling Rossby waves, which result in SST warming, propagate slowly westward. Because of the propagation time of the Rossby waves, there is a time lag between the Indian Ocean warming the eastern Pacific warming associated with ENSO.

As well as the ENSO affect on IO variability, there is evidence that IO variability can modulate the tropical Pacific variability in observations (Yu et al. 2002; Saji and Yamagata 2003; Kug et al. 2005; Kug and Kang 2006, hereafter KK06), simple atmospheric models (Watanabe and Jin 2002, 2003; Wu and Kirtman 2004a), and atmospheric (Annamalai et al. 2005; KK06) and coupled (Yu et al. 2002; Wu and Kirtman 2004a) GCMs. In particular, KK06 emphasized an interactive feedback between ENSO and the Indian Ocean. They pointed out that IO warming tends to be accompanied by El Niño during the developing phase in the historical record, and their coherence leads to a rapid decay of the El Niño event and a relatively fast transition from El Niño to La Niña.

The dynamics of interaction between El Niño and the Indian Ocean work as follows. During the boreal summer El Niño developing phase, the El Niño–induced anomalous Walker circulation leads to anomalous SST warming over the western Indian Ocean. The anomalous warming enhances anomalous easterlies along the equatorial IO, which can generate additional warming in the western IO through a positive air–sea feedback process, such as Bjerknes feedback (Webster et al. 1999; Saji et al. 1999; Li et al. 2002). The IO SST continues to warm during the boreal autumn. Concurrently, however, the anomalous easterlies associated with the warming are confined to the IO having little impact on the tropical Pacific. During the boreal winter, however, when El Niño is in its mature phase, the anomalous warming extends eastward to cover the entire tropical IO. Associated with these SST changes, the anomalous easterlies extend into the western Pacific (WP). The anomalous easterlies in the WP generate oceanic-upwelling Kelvin waves, which propagate eastward and accelerate the decay of the warm SST in the eastern Pacific. As a result, the El Niño is quickly terminated, and the phase transition from El Niño to La Niña progresses relatively rapidly.

Resulting from limitation in the historical record, numerical models are useful tools in understanding the natural phenomena and for testing hypotheses. In particular, several studies have used state-of-the-art coupled GCMs to understand ENSO dynamics (Yu and Mechoso 2001; Guilyardi et al. 2003, 2004) and the relationship between ENSO and other climate phenomena (Yu et al. 2002; Gualdi et al. 2003; Wu and Kirtman 2004a, b; Lau and Nath 2004), In this study, we analyze results of a Center for Ocean–Land–Atmosphere Studies (COLA) interactive coupled model (Kirtman and Shukla 2002), and show that the model results support KK06’s hypothesis regarding the interactive feedback between ENSO and the Indian Ocean.

Section 2 gives a brief description of the COLA interactive coupled GCM utilized in this study. In section 3, we demonstrate the interactive feedbacks between ENSO and the Indian Ocean in the interactive ensemble model simulation. The summary and discussion are given in section 4.

2. COLA interactive ensemble coupled model

The interactive ensemble strategy uses multiple realizations of the atmospheric model coupled to a single realization of the ocean model. The purpose of the interactive ensemble strategy is to significantly reduce the stochastic forcing of the ocean resulting from internal atmospheric dynamics. In some sense, the interactive ensemble is a sophisticated coupled GCM (CGCM) in which numerical experiments can be implemented that had previously only been possible in simple theoretically motivated models. The interactive ensemble strategy works as follows. The atmospheric GCM (AGCM) is identical for each ensemble member, and the AGCM realizations only differ in their initial conditions. Because the atmosphere is sensitively dependent on initial conditions, the AGCM realizations evolve differently. As the interactive ensemble evolves, each AGCM realization experiences the same SST predicted by the oceanic GCM (OGCM). The OGCM, on the other hand, experiences surface fluxes of heat, momentum, and freshwater that are the ensemble average of the AGCM realizations. The AGCM realizations are noise independent (i.e., the noise among the ensemble members is uncorrelated), but because they are all coupled to the same SST, they have the same signal. The models are fully interactive in that the component models exchange fluxes and SST once a day.

The interactive ensemble produces reasonably realistic ENSO events. The interactive ensemble ENSO variance is about 25% lower than the standard coupled model. The interactive ensemble ENSO is dominated by a biennial period, whereas the standard coupled model has a broader spectral peak between 2 and 4 yr. The ENSO events are primarily a standing oscillation in the tropical Pacific SST anomaly (SSTA), but are too narrowly confined to the equator and extend too far to the west. These shortcomings are typical of current coupled GCMs. The simulation on the Indian Ocean variability was described in Wu and Kirtman (2004a). The model produces both the basinwide Indian Ocean mode and the so-called zonal mode, and both of these modes are too strongly correlated with ENSO compared to the observations. The amplitude and life cycle agrees with observations, but the frequency is too high as with the ENSO model. See Kirtman and Shukla (2002) and Kirtman et al. (2005) for a more complete description and a comparison to the standard coupled model.

3. Role of the Indian Ocean SST

We defined El Niño and La Niña events from a 900-yr model simulation. To define El Niño and La Niña events we introduce a somewhat new index, Niño-3.3 SST, which is an averaged SSTA over 5°S–5°N and 180°–120°W. Because the SST pattern of the model associated with El Niño is slightly shifted to the central Pacific compared to that of observation, we used the SST index slightly shifted to the central Pacific from typical Niño-3.4 SST. The El Niño (La Niña) events are defined when the Niño-3.3 SST exceeds its standard deviation during January–March (JFM), when ENSO is mostly in its mature phase. The standard deviation of Niño-3.3 SST during JFM is 0.64 K. Based on this definition we found a total of 164 El Niño events and 158 La Niña events. In addition, we defined a western Indian Ocean SST (WISST) index by averaging the SSTA over 10°S–10°N and 40°–65°E and during November through December of previous season of the ENSO mature phase (JFM). This western Indian Ocean SST indicates the state of the Indian Ocean one season in advance of the maturing ENSO. This definition is similar to that of KK06. To avoid confusion related to different time averaging for these two indices, the years for the ENSO-developing and -decaying phases are denoted “(0)” and “(1),” respectively. The ENSO-developing (decaying) phase is defined as a prior (posterior) period of the ENSO mature phase (JFM).

First, to simply check the relation between Indian Ocean SST and ENSO transitions as KK06 suggested, we plot scatter diagrams of Niño-3.3 SST and WISST (Fig. 1). Red (blue) circle indicates El Niño (La Niña) events. To a large extent, El Niño (La Niña) events tend to be accompanied by Indian Ocean warming (cooling) as shown in Fig. 1a. The correlation coefficient between two indices is 0.59. This correlation coefficient is similar to that of the observational value of 0.62 from KK06. Subsequent to the El Niño mature phase, the Niño-3.3 SST starts to decay, and most El Niño events transition into a cold event during July–September [JAS(1)] as shown in Fig. 1b. However, in some cases the SST anomaly remains near normal or even weak warm during JAS(1), indicating a relatively slow transition. Note that these slow transition cases tend to occur when the WISST represents normal conditions.

Fig. 1.

Scatter diagram between WISST and (a) Niño-3.3 SST during JFM(1), (b) Niño-3.3 SST during JAS(1), and (c) the tendency of Niño-3.3 SST. The tendency is calculated from the JAS(1) Niño-3.3 SST by subtracting the JFM(1) Niño-3.3 SST. Red, black, blue circles indicate El Niño, normal, and La Niña state during JFM(1), respectively.

Fig. 1.

Scatter diagram between WISST and (a) Niño-3.3 SST during JFM(1), (b) Niño-3.3 SST during JAS(1), and (c) the tendency of Niño-3.3 SST. The tendency is calculated from the JAS(1) Niño-3.3 SST by subtracting the JFM(1) Niño-3.3 SST. Red, black, blue circles indicate El Niño, normal, and La Niña state during JFM(1), respectively.

To show clearly the relation between the ENSO transition and WISST, we calculated the SST tendency by subtracting the JFM(1) Niño-3.3 SST from the JAS(1) Niño-3.3 SST. The SST tendency basically depends on the ENSO phase during JFM. Because the JFM(1) season is the mature phase of ENSO, El Niño (La Niña) is linked to negative (positive) SST tendency. However, we found a strong relationship between the SST tendency and WISST, as shown in Fig. 1c. Here, warm (cold) WISST is correlated with a negative (positive) SST tendency. The correlation coefficient is −0.64. In addition, when the ENSO state is near normal during JFM, the tendency remains correlated to the WISST. The correlation coefficient is 0.48, which is significant at the 99.9% confidence level. This indicates that the SST tendency is determined by not only ENSO phase but also the WISST phase. In other words, negative (positive) SST tendency is larger when El Niño (La Niña) and warm (cold) WISST occur in phase during the ENSO cycle.

To examine the relationship between WISST and the ENSO transition, we divided 164 El Niño events into five groups based on the amplitude of WISST. The criteria and number of events for each case are listed in Table 1. The first group corresponds to very strong Indian Ocean SST (VSIO), which we interpret as having strong feedbacks between the Indian and the Pacific Oceans. The last group corresponds to a no–Indian Ocean SST (NIO) signal, indicating little or no interaction between the Indian and Pacific Oceans. The other groups are intermediate cases between VSIO and NIO. It should be noted that there is no case for which the WISST is less than its −0.5 standard deviation among the 164 El Niño events.

Table 1.

Group lists for classified El Niño events, their criteria, and number of El Niño cases. The standard deviation (SD) of WISST is 0.15 K.

Group lists for classified El Niño events, their criteria, and number of El Niño cases. The standard deviation (SD) of WISST is 0.15 K.
Group lists for classified El Niño events, their criteria, and number of El Niño cases. The standard deviation (SD) of WISST is 0.15 K.

Figure 2 shows a composite evolution of the Niño-3.3 SST for each category listed in Table 1. Consistently, the modeled El Niño has its onset during the boreal summer, while the observed onset is most prominent during boreal spring (e.g., Kug et al. 2005). This may be related to the relatively short ENSO period in the model simulation. During the boreal summer at year (0), the Niño-3.3 SST monotonically increases with increasing WISST (during the following November–December). This means that the summer Niño-3.3 SST leads the following winter WISST. In other words, a large El Niño during the boreal summer is correlated with large WISST during the following boreal winter.

Fig. 2.

Niño-3.3 SST evolution for five El Niño composites: VSIO (red line), SIO (orange line), MIO (yellow line), WIO (green line), and NIO (blue line) composites.

Fig. 2.

Niño-3.3 SST evolution for five El Niño composites: VSIO (red line), SIO (orange line), MIO (yellow line), WIO (green line), and NIO (blue line) composites.

Although the Niño-3.3 SST evolution is different for each classification during the developing boreal summer, the Niño-3.3 SST state is nearly the same during November of the developing year. Following November, the Niño-3.3 SST develops rapidly reaching its peak phase during JFM(1). The VSIO category has the largest warm anomalies during the peak phase. The demise of the warm event can also be stratified by the state of the Indian Ocean SST. For example, stronger coupling with the Indian Ocean (i.e., large VSIO) leads to a faster transition from El Niño to La Niña. This difference in the transition to the cold phase is quite systematic in terms of the magnitude of the WISST index. For the VSIO composite, the Niño-3.3 SST decays rapidly so that La Niña is fully developed by the next summer. On the other hand, for the NIO composite the Niño-3.3 SST decays slowly so that a near-normal condition prevails during the next summer.

The differences between the five classifications appear not only in the Niño-3.3 SST but also throughout the equatorial SST. Figure 3 shows the equatorial SST anomalies for each classification. For every case, positive SST anomalies are apparent over the central and eastern Pacific starting in boreal summer. At the same time, cold SST anomalies develop over the equatorial IO. The cold SST anomalies tend to be stronger in the decoupled case (i.e., NIO). As El Niño develops, the cold SST anomalies decay and warm SST anomalies begin to develop over the IO. A significant difference between the five categories is the timing of the transition from cold to warm SSTs over the IO. As expected, the stronger the WISST index, the earlier the transition. The warm IO appears during October(0) in the VSIO composite, but does not appear until January(1) in the NIO composite. The warm IO SST persists through the following summer in every case. Another significant difference is found in the central and eastern Pacific during the boreal summer of the decaying phase. Here the SST anomalies gradually decrease from the VSIO case to the NIO case over the tropical Pacific, as seen in Fig. 3. These results support KK06’s arguments that a warm IO SST leads to a fast transition from El Niño to La Niña.

Fig. 3.

SST anomalies along the equator for five cases.

Fig. 3.

SST anomalies along the equator for five cases.

To further examine the feedback processes between the Indian and Pacific Oceans, SST patterns and their related wind anomalies are shown in Figs. 4 and 5. For simplicity, we just display the VSIO and NIO cases. During JAS(0) with the VISO composite, warm SST anomalies and the related westerly anomalies develop over the tropical Pacific. In addition, cyclonic wind anomalies develop over the western North Pacific. The equatorial westerlies are part of the cyclonic flow centered in the western North Pacific. Over the far western IO, weak, warm costal SST anomalies are apparent. These initial warm SST anomalies are related to the anomalous easterlies over the IO. During October–December [OND(0)], the warm SST and westerly anomalies continue to develop via local air–sea feedbacks. Over the Indian Ocean, the warm SST and easterly anomalies continue to strengthen and start to extend throughout the Indian Ocean by external ENSO forcing (Lau and Nath 2004) and local air–sea coupled feedbacks (Webster et al. 1999; Saji et al. 1999; Li et al. 2002). However, the anomalous easterlies are confined to the Indian Ocean basin, so that they have little direct influence on the tropical Pacific at that time. We also note that anticyclonic flow develops over eastern India and the Bay of Bengal region, similar to the observational findings of Wang and Zhang (2002).

Fig. 4.

Composites of SST anomalies: (left) VSIO case and (right) NIO case. Shading indicates standardized SST anomaly.

Fig. 4.

Composites of SST anomalies: (left) VSIO case and (right) NIO case. Shading indicates standardized SST anomaly.

Fig. 5.

The same as Fig. 4, except for 850-hPa winds.

Fig. 5.

The same as Fig. 4, except for 850-hPa winds.

During JFM(1), the SST anomalies reach their peak phase over the tropical Pacific and the IO SST has evolved into basinwide warming. However, the wind anomalies over the Indian Ocean have weakened. It is interesting that the anticyclonic flow over Bay of Bengal region has migrated to the east and has intensified. As a result, over the western North Pacific, the wind has changed from cyclonic to anticyclonic during the El Niño developing phase. Recently, Watanabe and Jin (2002) and Annamalai et al. (2005) showed, using their different models, that Indian Ocean warming is critical for developing the western North Pacific anticyclone as well as the local SST forcing. The equatorial western Pacific wind changes from westerly to easterly in association with the SST changes. This wind change is critical for ENSO variability because the eastern Pacific SST is closely related to the equatorial western Pacific wind via equatorial Sverdrup balance (e.g., Weisberg and Wang 1997a, b; Wang et al. 1999; Wang et al. 2001). In other words, the anomalous easterlies generate oceanic-upwelling Kelvin waves, which propagate eastward into the eastern Pacific. The Kelvin waves hasten the decay of the eastern Pacific SST anomalies, and lead to a relatively fast transition from El Niño to La Niña. Figures 4c–e clearly show the role that the western Pacific wind anomalies have in hastening the transition. The strong, warm SST anomalies decrease rapidly (Figs. 4c–d) and cold SST anomalies rapidly develop (Figs. 4d–e). During JAS of the decaying year, the cold SST and easterly anomalies are observed over much the tropical Pacific, indicating that the La Niña state is already well established.

Conversely, in the case of the NIO composite, the warm SST anomaly in the Pacific is weak compared to that of the VSIO composite during JAS(0) of the developing year. In addition, the Indian Ocean warming is not found, and anomalous westerlies are apparent unlike in the VSIO composite. During OND(1), there is no significant equatorial SST and thus the anomalous easterlies are weak over the Indian Ocean, indicating that local air–sea coupled processes are relatively weak in this case. During the El Niño peak phase, the IO SST warming is relatively weak. A significant difference from the VSIO composite is also apparent in the western North Pacific. Though there is still anticyclonic flow in this region, the magnitude is weak and the location is shifted to the west compared to the VSIO composite. The equatorial western Pacific easterlies associated with the anticyclone flow are much weaker than those of the VSIO composite, and they have little impact on eastern Pacific SST. As a result, El Niño slowly decays and development of La Niña is delayed relative to the VSIO composite.

As mentioned earlier, the western Pacific wind anomaly is a key variable in the connection between ENSO and the Indian Ocean. To examine the role of the IO SST on the western Pacific, we show the evolution of western Pacific wind anomalies for five separate WISST categories (Fig. 6). For simplicity, a western Pacific zonal wind index is defined as the averaged zonal wind anomaly over 5°S–5°N and 120°–160°E. For every case, the western Pacific zonal wind index shows a common feature, namely, that the anomalous westerly wind has changed to near normal or easterly during November through the following January. The anomalous westerly during November is strongest for the VSIO composite and weakest for the strong Indian Ocean (SIO; not “NIO”) composite. This indicates that the magnitude of wind anomaly may not be strongly dependent on the Indian Ocean SST (i.e., WISST) at that time. However, a sudden change of the western Pacific wind anomaly seems to be related to the Indian Ocean SST at a later time. During the El Niño mature phase JFM(1), strong western Pacific easterlies are detected in the VSIO composite and relatively weak easterlies in the western Pacific are noted for the NIO composite. In addition, the magnitude of the easterly anomaly varies monotonically with the WISST index. Warmer WISST yields stronger western Pacific easterly anomalies.

Fig. 6.

The same as Fig. 2, except for WP zonal wind index (5°S–5°N, 120°–160°E).

Fig. 6.

The same as Fig. 2, except for WP zonal wind index (5°S–5°N, 120°–160°E).

Figure 7 shows the heat content composites for the VSIO and NIO cases. During the El Niño mature phase, the two composites have similar patterns and amplitudes. Consistent with the wind anomalies, there is a strong zonal contrast in the heat content over the tropical Pacific basin. The negative heat content in the western Pacific is a precursor of La Niña development (Wyrtki 1975, 1985; Jin 1997a, b). With the development of the western Pacific easterly anomalies for the VSIO composite, the western Pacific negative heat content anomalies migrate to the east. During April–June [AMJ(1)] the east Pacific positive heat content anomaly is abruptly collapsed. Strong negative heat content anomalies become well established in the eastern Pacific during following summer, consistent with the rapid development of La Niña. On the other hand, when ENSO is “not coupled” to the Indian Ocean (i.e., the NIO case), the western Pacific heat content anomaly propagates to the eastern Pacific relatively slowly, so that the negative heat content in the eastern Pacific develops slowly.

Fig. 7.

The same as Fig. 4, except for heat content anomalies.

Fig. 7.

The same as Fig. 4, except for heat content anomalies.

To diagnose the relationship between the western Pacific easterly wind and heat content changes in more detail, we have separately examined the evolution of the eastern Pacific heat content for the five categories of WISST. We have already shown (Fig. 6) that the strong Indian Ocean SST is linked to strong western Pacific easterly wind anomaly. During the El Niño mature phase the eastern Pacific heat content is strongly positive. We found that this is also consistent with the evolution of the east Pacific heat content (Fig. 8). It is clear that the decay of the east Pacific heat content is easily stratified based on Indian Ocean SST. Stronger Indian Ocean SST (stronger western Pacific easterly anomaly) is linked to a more rapid change of eastern Pacific heat content, while the weaker Indian Ocean SST (weaker western Pacific easterly anomaly) is linked to a slower transition.

Fig. 8.

The same as Fig. 2, except for the eastern Pacific heat content anomaly (3°S–3°N, 180°–270°E).

Fig. 8.

The same as Fig. 2, except for the eastern Pacific heat content anomaly (3°S–3°N, 180°–270°E).

4. Summary and discussion

In this study, the interactive feedback between ENSO and the Indian Ocean is investigated by analyzing a 900-yr simulation of the COLA interactive coupled GCM. The model results show that the transition from El Niño to La Niña occurs more rapidly when El Niño is accompanied by Indian Ocean warming. The model results also indicate that warm Indian Ocean SST is consistent with western Pacific–enhanced easterly anomalies, which ultimately lead to the rapid demise of eastern Pacific warm heat content anomalies. Consequently, Indian Ocean SST leads to a relatively fast transition from the warm phase to the cold phase of ENSO. These interactive processes are consistent with those derived from the previous observational analyses.

Given the rather long model simulation (i.e., 900 yr), it was possible to subdivide the El Niño events into five classes or categories based on the Indian Ocean SST. The composite analyses for the five categories show that the role of the IO SST on ENSO transition is working not only in extreme cases but also in intermediate cases. These results are consistent with those derived from the observational analyses of KK06, suggesting that the interactive feedback between ENSO and the Indian Ocean is quite plausible.

Recently, Wu and Kirtman (2004a) carried out a “decoupled” experiment in which the atmosphere and ocean GCMs are decoupled over the Indian Ocean sector using the present interactive coupled GCM. In this experiment, climatological Indian Ocean SST is used to force the atmosphere in the Indian Ocean sector. Although their focus is somewhat different from ours, their results give a good opportunity to test our hypothesis on the interactive feedback. They showed that the dominant period of simulated ENSO changes from about 2.3 yr in the control (i.e., coupled run) to about 2.8 yr in the decoupled run. This result supports our argument that the coupling with the Indian Ocean is connected to the fast transition of ENSO.

In addition, Yu et al. (2002) carried out similar experiments using the University of California, Los Angeles (UCLA) coupled GCM. They performed a “Pacific run” and an “Indo-Pacific run.” For the Pacific run case, the Indian Ocean SST is prescribed climatological SST, similar to the “decoupled” experiment of Wu and Kirtman (2004a). They showed that the dominant period of the simulated ENSO cycle increases from about 4 yr in the Pacific run to about 4.4 yr in the Indo-Pacific run. Though the 4-yr ENSO period is slightly increased, the power of the biennial component has significantly increased in the Indo-Pacific run [see Fig. 4 from Yu (2005)]. This indicates that the model has two dominant ENSO modes of the biennial ENSO and low-frequency ENSO. Among them, the biennial ENSO must be linked to the Indian Ocean variability. We can interpret that the biennial mode of ENSO may be contributed to by a relatively fast transition of ENSO. Therefore, two modeling results from different coupled GCMs support the position that our arguments are robust and not model-dependent results.

It is interesting that the interaction is asymmetric relative to the phase of ENSO. We find that the association between the Indian Ocean SST and La Niña evolution is weak compared to that associated with El Nino (see Figs. 1b and 1c). This asymmetric feature is also apparent in observations (KK06) and other coupled GCMs (Kug et al. 2006). This asymmetry has important implications for ENSO prediction. For example, weaker interactions with the Indian Ocean indicate that the probability of a strong, fast transition is relatively low. Similarly, the duration of La Niña is expected to be longer than that of El Niño. This is in fact the case in the observations and in the coupled simulation examined here. Therefore, the asymmetry of the present interactive feedback may induce the asymmetric ENSO characteristics without oceanic (An and Jin 2004) and atmospheric (Kang and Kug 2002) nonlinearity. These possible implications need to be further examined in future studies.

In addition, the present interactive feedback between ENSO and the Indian Ocean has implications for how ENSO predictions are made. Currently, there are two classes of dynamical El Niño prediction models: one is an intermediate coupled model, which typically does not include Indian Ocean processes, and the other is a full coupled GCM. The intermediate coupled models, which do not include the Indian Ocean, cannot, by design, include the western Pacific wind variability associated with Indian Ocean SST. This implies that they are unlikely to predict the fast phase transition of ENSO.

On the other hand, most coupled GCMs include both the tropical Pacific and Indian Oceans. The coupled GCM, therefore, has the potential to predict the rapid phase transition of ENSO. In this case, however, it is required that the coupled GCM accurately predict Indian Ocean SST. Unfortunately, from our preliminary results for various tier-one seasonal predictions made with the state-of-art coupled GCMs, the forecast skill for the Indian Ocean SST is relatively low. This may result from an oceanic initialization problem over Indian Ocean and a poor simulation of air–sea coupled process over the Indian Ocean. These deficiencies have to be overcome, and further research is required.

Acknowledgments

This research was partly supported by the Korea Meteorological Administration Research and Development Program under Grant CATER_2006-4206. J.-S. Kug was supported by the SRC program of the Korean Science and Engineering Foundation. This work was also partially supported by grants from the National Science Foundation (ATM-9814295 and ATM-0122859), the National Oceanic and Atmospheric Administration (NA16-GP2248), and National Aeronautics and Space Administration (NAG5-11656).

REFERENCES

REFERENCES
An
,
S-I.
, and
F-F.
Jin
,
2004
:
Nonlinearity and asymmetry of ENSO.
J. Climate
,
17
,
2399
2412
.
Annamalai
,
H.
,
S-P.
Xie
,
J. P.
McCreary
, and
R.
Murtugudde
,
2005
:
Impact of Indian Ocean sea surface temperature on developing El Niño.
J. Climate
,
18
,
302
319
.
Baquero-Bernal
,
A.
,
M.
Latif
, and
L.
Stephanie
,
2002
:
On dipolelike variability of sea surface temperature in the tropical Indian Ocean.
J. Climate
,
15
,
1358
1368
.
Gualdi
,
S.
,
A.
Navarra
,
E.
Guilyardi
, and
P.
Delecluse
,
2003
:
Assessment of the tropical Indo-Pacific climate in the SINTEX CGCM.
Ann. Geophys.
,
46
,
1
26
.
Guilyardi
,
E.
,
P.
Delecluse
,
S.
Gualdi
, and
A.
Navarra
,
2003
:
Mechanisms for ENSO phase change in a coupled GCM.
J. Climate
,
16
,
1141
1158
.
Gualdi
,
S.
, and
Coauthors
,
2004
:
Representing El Niño in coupled ocean–atmosphere GCMs: The dominant role of the atmospheric component.
J. Climate
,
17
,
4623
4629
.
Huang
,
B.
, and
J. L.
Kinter
III
,
2002
:
Interannual variability in the tropical Indian Ocean.
J. Geophys. Res.
,
107
.
3199, doi:10.1029/2001JC001278
.
Jin
,
F-F.
,
1997a
:
An equatorial ocean recharge paradigm for ENSO. Part I: Conceptual model.
J. Atmos. Sci.
,
54
,
811
829
.
Jin
,
F-F.
,
1997b
:
An equatorial ocean recharge paradigm for ENSO. Part II: A stripped-down coupled model.
J. Atmos. Sci.
,
54
,
830
847
.
Kang
,
I-S.
, and
J-S.
Kug
,
2002
:
El Niño and La Niña sea surface temperature anomalies: Asymmetry characteristics associated with their wind stress anomalies.
J. Geophys. Res.
,
107
.
4372, doi:10.1029/2001JD000393
.
Kirtman
,
B. P.
, and
J.
Shukla
,
2002
:
Interactive coupled ensemble: A new coupling strategy for CGCMs.
Geophys. Res. Lett.
,
29
.
1367, doi:10.1029/2002GL014834
.
Kirtman
,
B. P.
,
K.
Pegion
, and
S. M.
Kinter
,
2005
:
Internal atmospheric dynamics and tropical Indo-Pacific climate variability.
J. Atmos. Sci.
,
62
,
2220
2233
.
Klein
,
S. A.
,
B. J.
Soden
, and
N. G.
Lau
,
1999
:
Remote sea surface temperature variation during ENSO: Evidence for a tropical atmosphere bridge.
J. Climate
,
12
,
917
932
.
Krishnamurthy
,
V.
, and
B. P.
Kirtman
,
2003
:
Variability of the Indian Ocean: Relation to monsoon and ENSO.
Quart. J. Roy. Meteor. Soc.
,
129
,
1623
1646
.
Kug
,
J-S.
, and
I-S.
Kang
,
2006
:
Interactive feedback between the Indian Ocean and ENSO.
J. Climate
,
19
,
1784
1801
.
Kug
,
J-S.
,
S-I.
An
,
F-F.
Jin
, and
I-S.
Kang
,
2005
:
Preconditions for El Niño and La Niña onsets and their relation to the Indian Ocean.
Geophys. Res. Lett.
,
32
.
L05706, doi:10.1029/2004GL021674
.
Kug
,
J-S.
,
T.
Li
,
S-I.
An
,
I-S.
Kang
,
J-J.
Luo
,
S.
Masson
, and
T.
Yamagata
,
2006
:
Role of the ENSO-Indian Ocean Coupling on ENSO variability in a coupled GCM.
Geophys. Res. Lett.
,
33
.
L09710, doi:10.1029/2005GL024916
.
Lau
,
N-C.
, and
M. J.
Nath
,
2000
:
Impact of ENSO on the variability of the Asian–Australian monsoons as simulated in GCM experiments.
J. Climate
,
13
,
4287
4309
.
Lau
,
N-C.
, and
M. J.
Nath
,
2003
:
Atmosphere–ocean variations in the Indo–Pacific sector during ENSO episodes.
J. Climate
,
16
,
3
20
.
Lau
,
N-C.
, and
M. J.
Nath
,
2004
:
Simulation of tropical Indian Ocean SST variability with east–west asymmetry using a coupled atmosphere–ocean GCM.
J. Climate
,
17
,
245
265
.
Li
,
T.
,
Y.
Zhang
,
E.
Lu
, and
D.
Wang
,
2002
:
Relative role of dynamic and thermodynamic processes in the development of the Indian Ocean dipole: An OGCM diagnosis.
Geophys. Res. Lett.
,
29
.
2110, doi:10.1029/2002GL015789
.
Saji
,
N. H.
, and
T.
Yamagata
,
2003
:
Structure of SST and surface wind variability during Indian Ocean dipole mode events: COADS observation.
J. Climate
,
16
,
2735
2751
.
Saji
,
N. H.
,
B. N.
Goswami
,
P. N.
Vinayachandran
, and
T.
Yamagata
,
1999
:
A dipole mode in the tropical Indian Ocean.
Nature
,
401
,
360
363
.
Venzke
,
S.
,
M.
Latif
, and
A.
Villwock
,
2000
:
The coupled GCM ECHO-2. Part II: Indian Ocean response to ENSO.
J. Climate
,
13
,
1371
1383
.
Wang
,
B.
, and
Q.
Zhang
,
2002
:
Pacific–East Asian teleconnection. Part II: How the Philippine Sea anomalous anticyclone is established during El Niño development.
J. Climate,
,
15
,
3252
3265
.
Wang
,
B.
,
R.
Wu
,
R.
Lukas
, and
S. I.
An
,
2001
:
A possible mechanism for ENSO turnabout.
Dynamics of Atmospheric General Circulation and Climate, IAP/Academia Sinica, Ed., China Meteorology Press, 552–578
.
Wang
,
C.
,
R. H.
Weisberg
, and
J. I.
Virmani
,
1999
:
Western Pacific interannual variability associated with the El Niño-Southern Oscillation.
J. Geophys. Res.
,
104
,
5131
5149
.
Watanabe
,
M.
, and
F-F.
Jin
,
2002
:
Role of Indian Ocean warming in the development of Philippine Sea anticyclone during ENSO.
Geophys. Res. Lett.
,
29
.
1478, doi:10.1029/2001GL014318
.
Watanabe
,
M.
, and
F-F.
Jin
,
2003
:
A moist linear baroclinic model: Coupled dynamical–convective response to El Niño.
J. Climate
,
16
,
1121
1140
.
Webster
,
P. J.
,
A. M.
Moore
,
J. P.
Loschnigg
, and
R. R.
Leben
,
1999
:
Coupled ocean-atmosphere dynamics in the Indian Ocean during 1997-98.
Nature
,
401
,
356
360
.
Weisberg
,
R. H.
, and
C.
Wang
,
1997a
:
Slow variability in the equatorial west-central Pacific in relation to ENSO.
J. Climate
,
10
,
1998
2017
.
Weisberg
,
R. H.
, and
C.
Wang
,
1997b
:
A western Pacific oscillator paradigm for the El Niño-Southern Oscillation.
Geophys. Res. Lett.
,
24
,
779
782
.
Wu
,
R.
, and
B.
Kirtman
,
2004a
:
Understanding the impacts of the Indian Ocean on ENSO variability in a coupled GCM.
J. Climate
,
17
,
4019
4031
.
Wu
,
R.
, and
B. P.
Kirtman
,
2004b
:
Biennial oscillation of the monsoon–ENSO system in an interactive ensemble coupled GCM.
J. Climate
,
17
,
1623
1640
.
Wyrtki
,
K.
,
1975
:
El Niño—The dynamic response of equatorial Pacific Ocean to atmospheric forcing.
J. Phys. Oceanogr.
,
5
,
572
584
.
Wyrtki
,
K.
,
1985
:
Water displacements in the Pacific and genesis of El-Niño cycles.
J. Geophys. Res.
,
90
,
7129
7132
.
Xie
,
S-P.
,
H.
Annamalai
,
F. A.
Schott
, and
J. P.
McCreary
Jr.
,
2002
:
Structure and mechanisms of South Indian Ocean climate variability.
J. Climate
,
15
,
864
878
.
Yu
,
J-Y.
,
2005
:
Enhancement of ENSO’s persistence barrier by biennial variability in a coupled atmosphere-ocean general circulation model.
Geophys. Res. Lett.
,
32
.
L13707, doi:10.1029/2005GL023406
.
Yu
,
J-Y.
, and
C. R.
Mechoso
,
2001
:
Coupled atmosphere–ocean GCM study of the ENSO cycle.
J. Climate
,
14
,
2329
2350
.
Yu
,
J-Y.
,
C. R.
Mechoso
,
J. C.
McWilliams
, and
A.
Arakawa
,
2002
:
Impacts of the Indian Ocean on the ENSO cycle.
Geophys. Res. Lett.
,
29
.
1204, doi:10.1029/2001GL014098
.

Footnotes

Corresponding author address: Prof. In-Sik Kang, School of Earth and Environmental Sciences, Seoul National University, Seoul 151-742, South Korea. Email: kang@climate.snu.ac.kr