Impact of the Indian Ocean on ENSO variability in a hybrid coupled model

QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY Q. J. R. Meteorol. Soc. 133: 445 457 (27) Published online in Wiley InterScience (www.interscience.wiley.com).18 Impact of the Indian Ocean on ENSO variability in a hybrid coupled model Sang-Wook Yeh, a * Renguang Wu b and Ben P. Kirtman b,c a Korea Ocean Research and Development Institute, Ansan, Korea b Center for Ocean Land Atmosphere Studies, 441 Powder Mill Rd, Suite 32, Calverton, MD 275, USA c George Mason University, Climate Dynamics Program, Fairfax, VA 223, USA ABSTRACT: This study examines the impact of the Indian Ocean on El Niño and the Southern Oscillation (ENSO) variability through a series of numerical experiments with a hybrid coupled model. In the control run, an atmospheric general circulation model (AGCM) is coupled to the Zebiak Cane simple ocean model in the tropical Pacific. Outside the tropical Pacific climatological sea surface temperatures are prescribed in the control simulation. In the first experiment, sea surface temperature anomalies (SSTAs) in the Indian Ocean are statistically predicted based on the state of the Pacific, and used to force the atmosphere. In the second experiment, a slab thermodynamic mixed layer model is coupled to the AGCM in the Indian Ocean. The Indian Ocean modifies the ENSO frequency via interactions with the Indian monsoon, but only when air sea interactions in the Indian Ocean are included in the experimental design (i.e. the second experiment). The inclusion of the Indian Ocean, however, has little impact on the ENSO amplitude, which is at variance with other coupled simulations, suggesting that some missing dynamics or physics (i.e. Indian Ocean dynamics, Indonesian Throughflow, etc.) may play an important role. The Indian summer monsoon is more tightly coupled to ENSO in the second experiment than in the control run and the first experiment. The power spectrum of the Indian monsoon rainfall has a significant biennial timescale of around 2 3 months in the second experiment, which may enhance the biennial time-scale of ENSO variability through a shift of the horizontal structure of zonal wind stress variability in the central equatorial Pacific. Copyright 27 Royal Meteorological Society KEY WORDS Indian Ocean; ENSO variability; Indian monsoon rainfall; zonal wind stress Received 31 October 25; Revised 12 July 26; Accepted 14 July 26 1. Introduction Recent studies have brought renewed interest in Indian Ocean variability and its relationship with El Niño and the Southern Oscillation (ENSO). Most of the previous studies focus primarily on the influence of the ENSO variability on the Indian Ocean (Klein et al., 1999). However, Indian Ocean variability can affect ENSO variability either through atmospheric wind and pressure changes (Yasunari 1985, 1987; Chung and Nigam, 1999; Kirtman and Shukla, 2; Kim and Lau, 21; Yu et al., 22; Behera and Yamagata, 23; Wu and Kirtman, 23; Yu et al., 23; Wu and Kirtman, 24a,b; Annamalai et al., 25; Kug and Kang, 25; Terray and Dominiak, 25; Wu and Kirtman, 25; Yu, 25) or through modulation of the Indonesian Throughflow (Wyrtki, 1987; Wajsowicz and Schneider, 21; Matthew and Huang, 25). Recently, Yu et al. (22) showed that a coupled atmosphere-ocean general circulation model (CGCM) simulation of ENSO, including both the tropical Indian and Pacific oceans, produces ENSO events with larger * Correspondence to: Sang-Wook Yeh, Korea Ocean Research and Development Institute, Ansan, Korea. E-mail: swyeh@kordi.re.kr amplitude than when the coupling is restricted to the tropical Pacific Ocean only. The dominant period of the simulated ENSO cycle increased from about 4 years in the Pacific run to about 4.4 years in the Indo-Pacific run. Changes in the simulated ENSO period in Yu et al. (22) are somewhat controversial because the quasi-biennial tendency in ENSO is strengthened by the ENSO monsoon interaction when the effects of the Indian Ocean are included (Yasunari and Seki, 1992; Shen and Lau, 1995; Kim and Lau, 21; Lau and Wu, 21), indicating the need for further analysis of the ENSO Indian Ocean relationship. (A recent paper by Yu (25), however, showed that the biennial variability is increased in the Indo-Pacific run based on the same CGCM used in Yu et al. (22).) Using similar CGCM experiments as in Yu et al. (22), Wu and Kirtman (24b, hereafter, WK4) examined the impacts of the Indian Ocean on ENSO variability by performing experiments with a CGCM. Their results showed that the ENSO variability in the coupled model is significantly reduced when the Indian Ocean is decoupled from the atmosphere (i.e. the climatological Indian Ocean sea surface temperature (SST) is used to force the atmosphere). WK4 argued that the Indian Ocean affects ENSO variability by modulating convective heating over the Indian Ocean SST Copyright 27 Royal Meteorological Society

446 S.-W. YEH ET AL. and the Walker circulation over the tropical Indian and Pacific Oceans. Previous studies have suggested that there is an essential need to understand the role of air sea coupling on climate variability in the tropical Pacific Indian Ocean (Kumar and Hoerling, 1998; Kitoh and Arakawa, 1999; Clark et al., 2; Krishnamurthy and Kirtman, 23; Wang et al., 24). Our study aims to identify how Indian Ocean variability affects ENSO variability in the presence of local thermodynamic coupling only. This is examined mainly through numerical experiments with a hybrid coupled model (HCM). We adopt two different strategies for simulating the sea surface temperature anomalies (SSTAs) of the Indian Ocean in the HCM. In the first strategy, the SSTA in the Indian Ocean is statistically predicted based on the state of the tropical Pacific (i.e. no local thermodynamic air sea coupling). In the second strategy, the atmospheric general circulation model (AGCM) is coupled to a thermodynamic slab mixed layer model in the Indian Ocean (i.e. active thermodynamic air sea coupling). The design of the experiments is similar to the CGCM experiments mentioned above. However, the HCM used in this study has a different model hierarchy and coupling strategy in order to focus on the role of thermodynamic air sea coupling versus the atmosphere forced by the SST in the Indian Ocean sector. Outside the tropical Pacific Indian Ocean domain, climatological SSTs are prescribed, thereby excluding the impact of midlatitude ocean variability. Moreover, since the HCM employs simple ocean models coupled to the tropical Pacific and the Indian Ocean, respectively, the impact of the Indonesian Throughflow and Indian Ocean dynamics is neglected. The results presented here indicate that the biennial time-scale of ENSO frequency is enhanced when air sea interaction in the Indian Ocean is included. The inclusion of Indian Ocean variability in the experiments presented here has little impact on the amplitude of ENSO, which contrasts with both Yu et al. (22) and WK4, suggesting the potential importance of Indonesian Throughflow, Indian Ocean dynamics or extratropical Pacific processes. Section 2 describes the model experiments. In section 3, we describe the influence of the Indian Ocean on the ENSO variability and ENSO Indian monsoon relationship. We examine how the ENSO frequency is modified based on composite analysis in section 4. Our summary isgiveninsection5. 2. Model and ethodology 2.1. Model component The HCM presented here was developed by Yeh (21) and applies a coupling strategy that is similar to that originally developed by Kirtman and Zebiak (1997). The HCM has a complex AGCM, which is coupled to an intermediate-level anomaly ocean model in the tropical Pacific region (13 E 27 W, 19 N 19 S). Outside the oceanic model domain, SSTs for the AGCM are prescribed based on the observed annually varying climatology. The atmospheric component is the Seoul National University AGCM (SNUAGCM, Kim et al., 1998), which is a global spectral model with T31 resolution (approximately 3.5 longitude 2.5 latitude). There are 17 unevenly spaced sigma-coordinate vertical levels in the model. The SNUAGCM is based on the Center for Climate System Research/National Institute of Environmental Studies AGCM of Tokyo University (Numaguti et al., 1995), but has several major changes, including the land surface process, shallow convection, and Planet Boundary Layer (PBL) processes (Kim et al., 1998). The ocean component is the Zebiak and Cane (ZC) ocean model (Zebiak and Cane, 1987) described by linear shallow-water equations, which produce thermocline depth anomalies and depth-averaged baroclinic currents. The annual cycle is included in the model by the prescribed mean currents, temperature, and thermocline depth. The ZC ocean model used in this study has a new parameterization for the temperature of subsurface water entrained into the ocean mixed layer (Yeh, 21). The heat and momentum of the component models are anomaly coupled. The coupling frequency of the model is once every 1 days with mean values being exchanged between the ocean and atmosphere. The privilege of the anomaly coupling strategy is to prevent the rapid climate drift seen in the coupled models. 2.2. Model performance The performance of the SNUAGCM has been rigorously tested by Kang et al. (22) for the climatological variations of summer monsoon rainfall in the CLIVAR/Monsoon Intercomparison Project. Yeh (21) tested the performance of the HCM for simulated precipitation and the 5 hp geopotential height for the period of 1 years. Yeh (21) showed that the precipitation simulated in the HCM, which corresponds to El Niño and La Niña, is similar to the observation along the equator in the tropical Pacific. Yeh (21) also showed that the dominant mode of 5 geopotential height variability simulated in the HCM exhibits a notable spatial correspondence with the Pacific/North American pattern, indicating that the tropics midlatitude teleconnection is effectively simulated in the HCM. In addition, Yeh et al. (24) intensively analysed the characteristics of ENSO variability simulated in the HCM on interannual and decadal time-scales. They showed that the spatial structure of the interannual ENSO variability in the HCM is similar to the observations. The spectral density of ENSO simulated in the HCM is similar to the observation with a peak around 4 months. The El Niño simulated in the HCM has a tendency to be locked to the end of calender year. Moreover, fluctuations in the ENSO period and amplitude are significant on decadal timescales, which is evident in the observations (Wang and Wang, 1996).

ENSO VARIABILITY IN A HYBRID COUPLED MODEL 447 2.3. Methodology We integrated the HCM for another 1 years for this study. Hereafter, we refer to this second 1 year simulation as the control run. In addition, two experiments are performed in this study, which will be referred to as Exp1 and Exp2. In the tropical Pacific Ocean domain, Exp1 and Exp2 are the same as the control run. The difference is in the Indian Ocean domain. In Exp1, SSTAs in the Indian Ocean (4 E 12 E, 3 N 3 S) are statistically predicted based on the simulated NINO3 SST index. The statistical relationship between the Indian Ocean SSTA and the NINO3 index is based on simple linear regression using observed data from 195 to 2. These SSTAs are superposed on the annually varying climatology in the Indian Ocean. For both the regression and the climatology, monthly observed SSTAs analysed by the National Centers for Environmental Prediction (NCEP; Reynolds and Smith, 1994) were used. In Exp1 there is no air sea interaction over the Indian Ocean, and the SSTA is merely predicted based on the current state of the Pacific Ocean in the coupled simulation. No lag-lead relationships are explicitly included in the statistical prediction. In Exp2, the atmosphere in the Indian Ocean is coupled to a slab ocean model (SOM, Hansen et al., 1983), which allows for simple air sea interactions. Exp1 and Exp2 are integrated for 55 years and 52 years, respectively. Only the last 5 years of Exp1 and Exp2 are analysed in this study. A more complete description of the SOM is presented in the Appendix. 3. Changes due to the Indian Ocean influence In this section, we document changes in ENSO amplitude and frequency, relative frequency for occurrence of warm and cold events, and the Indian monsoon ENSO relationship. 3.1. ENSO amplitude and frequency To show the influence of the Indian Ocean on the ENSO amplitude, we display in Figure 1(a) (c) the standard deviation of total SSTA simulated in the control run, Exp1, and Exp2, respectively. The anomaly is defined as the deviation from the mean annual cycle calculated over the entire record for each experiment. The standard deviation of the SSTA variability averaged over the NINO3 region (5 N 5 S, 21 E 27 E) is.86 C (control run),.77 C (Exp1), and.78 C (Exp2). Note that the NINO3 SST index has a standard deviation of.89 C in observations and that both experiments have a slightly smaller NINO3 amplitude than the control run. This result disagrees with the previous results (Yu et al., 23; WK4), in which the ENSO amplitude is larger when coupled feedbacks in the Indian Ocean are (a) Control run 1S 2S 3S 3E 6E 9E 12E 15E 18 15W 12W 9W (b) Exp 1 1S 2S 3S 3E 6E 9E 12E 15E 18 15W 12W 9W (c) Exp 2 1S 2S 3S 3E 6E 9E 12E 15E 18 15W 12W 9W Figure 1. Maps of the standard deviation of SSTA variability simulated in the control run (a), Exp1 (b), and Exp2 (c) for the analysed period of each run. The contour interval is.1 C. This figure is available in colour online at www.interscience.wiley.com/qj

448 S.-W. YEH ET AL. 3 (a) Obs. 3 (c) Exp 1 Spectral Density 2 1 2 1 48 24 period (month) 48 24 3 (b) Control run 3 (d) Exp 2 Spectral Density 2 1 2 1 48 24 period (month) 48 24 Figure 2. Power spectra of the observed NINO3 SST index (a) and simulated NINO3 SST index in the control run (b), Exp1 (c), and Exp2 (d). The solid curve shows the power spectra and the dashed curve shows the power spectra for red noise process at 95% significance level. included in the coupled model experimental design. This may be due to shortcomings and deficiencies of the HCM (i.e. no Indonesian Throughflow, Indian Ocean dynamics, extratropical processes). In other words, this lack of sensitivity suggests the potential importance of Indonesian Throughflow, Indian Ocean dynamics or extratropical Pacific processes on changes in the ENSO amplitude. In contrast to ENSO amplitude, changes in ENSO frequency are significant, in particular for Exp2. We first show the spectral density of the observed NINO3 SST index for the period 195 2 (Figure 2(a)). The spectral density calculation is based on the Fourier method. For additional details, the reader is referred to Jenkins and Watts (1968). The dominant observed period for the NINO3 SST index is about 48 months, with a broad spectrum between 2 and 6 months. Note that the dashed curve shows the power spectra for red noise at a 95% significance level. The simulated spectral density in the control run has a peak around 37 months (Figure 2(b)), showing less power both at lower frequencies and at higher frequencies than the observations. The control run fails to reproduce the quasibiennial tendency of the observed ENSO variability. Figures 2(c) and (d) for Exp1 and Exp2 are the same as in Figure 2(b). The power spectrum simulated in Exp1 (Figure 2(c)) does not differ much from the control run (Figure 2(b)). However, the power on quasi-biennial time-scales (i.e. around 24 months) is enhanced in Exp 2 (Figure 2(d)). (We tested the spectrum of the NINO3 SST index in Exp2 against the 95% significance plot of the control run. We multiplied the spectrum for the control run by r/br, where r is the degree of freedom and br is the χ 2 value corresponding to the degree of freedom r (von Storch and Zwiers, 1999). It was found that the spectrum of the NINO3 SST index in Exp2 was significantly different from that in the control run.) Apparently, maintaining the coupled feedbacks in the Indian Ocean enhances the biennial tendency of ENSO. We also examined the change in the propagation of tropical Pacific SSTAs in Exp2 versus the control run (not shown). While the Exp2 SSTA is primarily a standing mode, the eastward migration of the SSTA is slightly apparent in the control run. There is a suggestion of change in propagation of tropical Pacific SSTAs associated with ENSO owing to the coupled feedbacks in the Indian Ocean. 3.2. Indian monsoon ENSO relationship In observations, the Indian summer monsoon rainfall is negatively correlated with the central-eastern equatorial

ENSO VARIABILITY IN A HYBRID COUPLED MODEL 449 Pacific SST in the post-summer monsoon season (e.g. Lau and Yang, 1996). Here, we examine this relationship in the model. To represent the variability of the Indian summer (June July August September, hereafter, JJAS) monsoon, a rainfall index is defined. The Indian summer monsoon rainfall index (ISMRI) is defined as the mean rainfall averaged over the India (1 N 3 N, 65 E 1 E) during JJAS. Note that the above area corresponds to the largest rainfall variability over the Indian summer monsoon region in the three runs (not shown). The interannual precipitation anomalies are constructed by removing the long-term mean in each run. The correlation coefficient between ISMRI and the NINO3 SST index during December January February is.17 in the control run,.4 in Exp1, and.4 in Exp2. Note that the 9% significant confidence levels are ±.37. It is noteworthy that Exp1 fails to reproduce the relationship between ENSO and Indian monsoon despite the fact that Indian Ocean SSTAs are statistically predicted. The difference in Exp1 may be due to the experimental design, in which the atmospheric response is the forced response to the prescribed SSTs over the Indian Ocean sector there are no coupled feedbacks. Inclusion of the Indian Ocean air sea interaction (Exp2) increases the Indian monsoon ENSO correlation. This result is consistent with Wu and Kirtman (24a) who showed that it is necessary to consider the atmosphere ocean coupling in the Indian Ocean in order to capture the observed Indian monsoonenso relationship in the coupled model. All three simulations presented here have monsoon variability and the potential for the monsoon to impact ENSO (i.e. Kirtman and Shukla, 2; Wu and Kirtman, 23) so, why is coupling in the Indian Ocean needed in order to capture the ENSO monsoon correlation? Wu and Kirtman (24a) argued that the ENSO monsoon relationship depends, in part, on a competition between two processes: (1) remote teleconnections from the tropical Pacific impacting the monsoon and (2) local internal atmospheric dynamics associated with monsoon variability. When the local internal atmospheric dynamics dominates, the remote teleconnections are overwhelmed by the noisiness and the ENSO monsoon relationship is undetected. On the other hand, when the noise due to internal dynamics is relatively weak, the remote teleconnection dominates, which, in turn allows for the monsoon to impact the evolving ENSO, and the negative correlation emerges. Furthermore, Wu and Kirtman (25) argued that retaining coupled feedbacks in the Indian Ocean has the effect of reducing the variability due to local internal dynamics. The reduction in variability is easily detected in precipitation and is due to negative feedbacks between latent heat flux and SSTs. As a diagnostic of this negative feedback we show here the ratio (control run/exp2) of the standard deviation of rainfall variability during JJAS in the monsoon region (Figure 3). The rainfall variability over the India (1 N 3 N, 65 E 1 E) during JJAS is much reduced when the model retains coupled feedbacks in the Indian Ocean, i.e. Exp2. This reduction in rainfall variability simulated in Exp2 is associated with the existence of coupled feedbacks in the Indian Ocean, which has the effect of reducing the rainfall variability due to local internal dynamics compared to the control run. To further demonstrate the importance of the local negative feedbacks we examine the temporal relationship 4N (S.D.[Control] / S.D. [Exo2]) (JJAS Rainfall) 6E 7E 8E 9E 1E 11E Figure 3. Ratio of standard deviations (control/exp2) for rainfall variability during JJAS. Shading shows values above 1.. This figure is available in colour online at www.interscience.wiley.com/qj

45 S.-W. YEH ET AL. (a) (b) Figure 4. The lag-lead correlation of SSTAs (thick) and evaporation (thin) with respect to the rainfall anomalies averaged over the region 1 N 3 N: 65 E 1 E calculated based on monthly means for the entire analysed period in Exp2, and that of SSTAs (dotted) with respect to the rainfall anomalies averaged over the same region for the period 1979 23 in the observations (a). (b) As for (a) except that the correlations are calculated with respect to JJAS rainfall anomalies in the observation (thin) and Exp2 (thick). Negative lags indicate that SSTAs and Evaporation leads the rainfall anomalies. The zero lag in (b) denotes July. between SSTAs and atmospheric anomalies, i.e. rainfall and evaporation simulated in Exp2. Figure 4(a) shows the lag-lead correlation of SSTAs (thick) and evaporation anomalies (thin) with respect to the rainfall anomalies averaged over the area 1 N 3 N, 65 E 1 E calculated based on monthly means for the entire analysed period in Exp2, and that of SSTAs (dotted) with respect to the rainfall anomalies averaged over the same region for the period 1979 23 in the observations. For observations, we use the Climate Prediction Center Merged Analysis of Precipitation (CMAP; Xie and Arkin, 1997) data for precipitation and the SST from NCEP. It is apparent that the SSTAs change from positive to negative around zero-lag when considerable evaporation occurs. This suggests that the surface evaporation contributes to a change in the sign of the SSTAs, i.e. the atmosphere provides a negative feedback to the SST. In turn, the rainfall anomalies are reduced in the following months. As in Exp2, the north Indian Ocean SSTAs from observations also change from positive to negative around zero-lag. This supports the model results. We performed a similar correlation analysis with respect to the Indian summer (JJAS) rainfall in the observations (thin line in Figure 4(b)) and Exp2 (thick line in Figure 4(b)). As for Figure 4(a), a considerable amount of the Indian summer monsoon rainfall anomalies leads to the reversal of the north Indian Ocean SSTAs after summer in both the observations and Exp2. This feature resembles that seen in a coupled GCM (Figure 5a of Wu and Kirtman, 25). In the forced simulation, however, the negative feedback is suppressed because SST cannot respond to changes in atmospheric anomalies. This explains why the atmospheric variability is reduced when air sea coupling is included. The standard deviation of the rainfall simulated in the Exp2 throughout the Indian Ocean region during JJAS is substantially smaller than that of the control run as shown in Figure 3. Despite the large reduction of the rainfall variability in this large region during JJAS, the reduction of the total Indian monsoon rainfall variability (just in the vicinity of the Indian Peninsula) between the control run and Exp2 is not significant. The standard deviation of the total Indian monsoon rainfall index is.79 mm/day (control run) and.71 mm/day (Exp2). Although Exp2 captures the Indian monsoon ENSO relationship, the simultaneous correlation coefficient between ISMRI and ENSO in Exp2 is not as high as in the observation (<.6; Kirtman and Shukla, 2). This may be owing to deficiencies in simulation of the Indian Ocean SSTA. To examine the Indian Ocean SSTAs associated with ENSO we display the simultaneous regression map between the Indian Ocean SSTAs and NINO3 SST index in Figure 5 for observations (a), Exp1 (b) and Exp2 (c). Because the Indian Ocean SSTAs in Exp1 are statistically determined by the observed relationship, the regression pattern for the observation and Exp1 is quite similar. The Indian Ocean SSTAs associated with ENSO variability show a basin wide warming, with the exception of cooling in the southeastern part in both observations and Exp1. Despite the well simulated ENSO Indian Ocean correlation in Exp1, the model fails to capture the ENSO monsoon relationship. Again, this result suggests the importance of local air sea feedbacks in the Indian Ocean.

ENSO VARIABILITY IN A HYBRID COUPLED MODEL 451 (a) Obs..1.18.12.14 1S 2S.16.2 3S.14 3E 6E 9E 12E (b) Exp 1 air sea interface is more important than accurately simulating the structure of the Indian Ocean variability. On the other hand, the poor response is probably due to the absence of Indian Ocean dynamics in Exp2. In fact, Figure 5(a) (c) shows that in the north Indian Ocean and southeastern tropical Indian Ocean heat flux feedbacks are sufficient to simulate the basic variability. However, throughout the rest of the basin, Indian Ocean dynamics are critical as can be seen from the poor agreement with observations (Figure 5(a) and (c)). This is to be expected since several studies have shown that Indian Ocean dynamics are important in the southern Indian Ocean (e.g. Murtugudde and Busalacchi, 1999; Huang and Kinter, 22). Furthermore, we cannot eliminate the potential contribution due to AGCM errors, particularly their ability to simulate remote teleconnection. 1S 2S 3S.1.1.8 3E 6E 9E 12E (c) Exp 2 1S 2S.12.2.4.6.4.2 3S 3E 6E 9E 12E Figure 5. The regression coefficients between the Indian Ocean SSTAs and the NINO3 SST index for observations during periods of 195 2 (a); and for Exp1 (b) and Exp2 (c) for the entire analysed period. The NINO3 SST index is normalized by its standard deviation. The contour interval is.2 and shading indicates positive values. This figure is available in colour online at www.interscience.wiley.com/qj The regression pattern in the Indian Ocean in Exp2 has some differences compared with that of the observations. Considerable cooling in the South Indian Ocean is shown in Exp2, which is markedly different from observations. This inconsistency in the southern Indian Ocean might help to explain the underestimation of the Indian monsoon ENSO relationship in Exp2 compared with the observations. For capture of the ENSO monsoon relationship, it appears that the energetic consistency at the 4. Understanding the change in ENSO frequency In this section, we discuss how the Indian Ocean impacts on the ENSO frequency by comparing the control run with Exp2. The Indian Ocean modifies the ENSO frequency via interactions with the Indian monsoon, but only when air sea interactions in the Indian Ocean are included (i.e. Exp2). Previous studies have suggested that Indian summer monsoon anomalies can trigger ENSO events (Barnett, 1991; Yasunari and Seki, 1992; Nigam, 1994; Wainer and Webster, 1996; Kirtman and Shukla, 2). Wainer and Webster (1996), for example, prescribed idealized monsoon forcing in a simple coupled model and argued that a variable monsoon might explain the irregularity of ENSO. On the other hand, biennial variability is one of the dominant modes of interannual variability in the tropical Pacific and Indian regions (Kawamura, 1988; Rasmusson et al., 199; Clarke and Shu, 2). It is also well known that the biennial tendency in ENSO variability is likely to be a fundamental timescale involved in monsoon ENSO interaction (Yasunari and Seki, 1992; Meehl, 1993; Shen and Lau, 1995; Kim and Lau, 21; Lau and Wu, 21; Meehl and Arblaster, 22a,b). Kim and Lau (21) argued that the frequency of ENSO is more towards a biennial frequency when there is a strong coupling between ENSO and monsoon wind forcing. Here, we show that the Indian monsoon variability has more power at biennial frequency when the Indian Ocean coupling is included. This is seen in Figure 6(a) and (b), which displays the spectral analysis of the monthly Indian monsoon rainfall index simulated in the control run and Exp2, respectively. The monthly rainfall anomaly is defined as the deviation from the mean annual cycle calculated over the entire record in each run. The dominant period of the Indian monsoon rainfall shows an obvious shift from around 36 48 months (Figure 6(a)) in the control run to around 2 3 months in Exp2 (Figure 6(b)). Note that the dominant period of the Indian

452 S.-W. YEH ET AL. 5 (a) Control Run 5 Obs. 4 4 Spectral Density 3 2 Spectral Density 3 2 1 1 48 24 period (month) 48 24 period (month) 5 (b) Exp 2 Figure 7. As in Figure 6 except that observations are for the period 1979 23 based on the CMAP data. Spectral Density 4 3 2 1 48 24 period (month) Figure 6. Power spectra of the simulated monthly Indian monsoon rainfall anomaly in the control run (a) and Exp2 (b). The solid curve shows the power spectra and the dashed line shows the power spectra for red noise. monsoon rainfall simulated in the Exp1 is similar to that of the control run (not shown). Without air sea thermodynamic coupling in the Indian Ocean the Indian monsoon and ENSO variability is mainly at low frequency in the control run and Exp1. In this case the Indian monsoon is not predisposed to biennial periods and the ENSO biennial variability is weak. It is only in the Indian Ocean air sea coupled run (i.e. Exp2) that the biennial peak of the Indian monsoon emerges and the ENSO variability shifts towards the biennial frequency. This result indicates that the biennial time-scale of the Indian monsoon and ENSO variability is more pronounced when the Indian monsoon and ENSO are tightly coupled, which is consistent with Wu and Kirtman (24c). To support our argument we show the spectral density of Indian monsoon rainfall index in the observations. Figure 7 is the same as in Figure 6(a) and (b) except tha the observations are during the period 1979 23. The dominant period of observed Indian monsoon rainfall is around 2 3 months, which is quite similar to that of the Exp2. (We performed the spectral analysis of JJAS rainfall (i.e. ISMRI) for observations in the period 1979 23 and for the control and Exp2 model simulations. However, the JJAS rainfall time series cannot resolve the 2-year period because of the temporal resolution. This is the reason why we used the monthly mean time series instead of JJAS time series.) This result is consistent with the hypothesis that the inclusion of India Ocean air sea coupling leads to a shift in the Indian monsoon rainfall frequency towards the biennial timescale. The shift of the Indian summer monsoon to the biennial frequency might be related to the local air sea interaction mechanism suggested by Meehl (1997) (see his Figure 1(a)). A wet monsoon induces strong winds, high surface evaporation, and enhanced oceanic mixing. This leads to the reversal of the SSTAs at the end of the monsoon season. The feedback of the Indian summer monsoon on the North Indian SST is supported by Figure 4. If the reversed SSTAs persist for 1-year via oceanic memory, they can lead to a dry monsoon in the following summer. This local air sea interaction mechanism favours the biennial period of the Indian summer monsoon. To demonstrate the monsoon SST relationship, we provide the lead and lag correlations between the ISMRI and the Indian Ocean SSTs during the previous spring (March April-May), summer (June July August), and the following autumn (September October November) in Exp2 (Figure 8(a) (c)) and the observations (Figure 8(d) (f)), respectively. The most striking feature is that the correlation changes its sign from positive to negative in the north Indian Ocean from the previous spring and summer to the following autumn in both Exp2 and the observations. During the previous spring and the subsequent summer warm north Indian Ocean SSTs provide a large amount of surface evaporation, resulting in large rainfall over the India. However, the large Indian summer monsoon rainfall contributes to a change in the sign of the SSTAs in the autumn after the summer monsoon, i.e. the atmosphere provides a negative

ENSO VARIABILITY IN A HYBRID COUPLED MODEL 453 (a) MAM : HCM (D) MAM : Obs. 25N 25N 15N 5N.1 5E 6E 7E 8E 9E 1E 15N 5N.35.3.15.2.25.2 5E 6E 7E 8E 9E 1E (b) JJA (e) JJA 25N 25N 15N 15N 5N.5.1.5 5E 6E 7E 8E 9E 1E 5N.35 6E.25.25 9E (C) SON (f) SON 25N 25N 15N.1 15N.25 5N 5E 6E 7E 8E 9E 1E 5N 6E.3 9E Figure 8. Lead and lag correlations between the ISMRI and the Indian Ocean SSTs during the previous spring (a), summer (b), and the following autumn (c) in Exp2. (d) (f) As for (a) (c) except that the observation period is 1979 23. The contour interval is.5 and shading indicates positive values. This figure is available in colour online at www.interscience.wiley.com/qj feedback to the SST, which is consistent with Figure 4 and the mechanism proposed by Meehl (1997). How does the Indian monsoon induce changes in the ENSO frequency when the Indian Ocean coupling is induced? Previous studies indicate that the Indian monsoon can induce surface wind stress anomalies in the equatorial Pacific (e.g. Wainer and Webster, 1996; Wu and Kirtman, 23; Wu and Kirtman, 25). On the other hand, the ENSO frequency is related to the zonal structure of wind stress anomalies along the equatorial Pacific (An and Wang, 2; Wang and An, 21a,b). Using the ZC coupled model, An and Wang (2) showed that the equatorial zonal wind stress for the high frequency ENSO periods tends to shift westwards compared with that of low frequency ENSO periods. Here, we hypothesis that the change in the ENSO period towards an enhanced biennial time-scale is related to the shift of equatorial Pacific zonal wind stress anomalies owing to the impact of the Indian monsoon. To support this, we examine zonal wind stress anomalies associated with the Indian monsoon variability during summer. Figure 9(a) and (b) shows the linear regression coefficients between the ISMRI and JJAS zonal wind stress anomalies simulated in the control and Exp2. The spatial structure of tropical Pacific zonal wind stress is similar in the control run and Exp2. The horizontal structure of zonal wind stress anomalies is localized in the central equatorial Pacific in both the control run and Exp2. However, the maximum centre of regressed zonal wind stress variability in Exp2 is shifted to the west compared with that of the control run. Figure 9(c) shows the zonal structure of the regressed zonal wind stress variability against the ISMRI along the equator. The maximum centre of the regressed zonal wind stress variability in Exp2 is located about 15 longitude westward compared with that of the control run. We performed numerical experiments using a simple atmosphere model with specified SST forcing to demonstrate the influence of anomalous heating in the Indian monsoon region on surface winds over the Pacific Ocean. The atmosphere model is a 2 1/2-layer primitive equation model on the equatorial beta plane originally developed by Wang and Li (1993) and improved

454 S.-W. YEH ET AL. (a) Control 1S 14E 16E 18 16E 14W 12W 1W (b) Exp 2 1S.5.5.1.15.2.25.3.35 14E 16E 18 16W 14W 12W 1W (c) Control (thin), Exp2 (thick) along the equator 14E 16E 18 16W 14W 12W 1W Figure 9. Linear regression coefficients between the Indian summer (June September) monsoon rainfall index and JJAS zonal wind stress anomalies simulated in the control run (a) and Exp2 (b). The ISMRI is normalized by its standard deviation in the control run and Exp2. The contour interval is.2. Dashed lines indicate negative values. (c) As for (a) and (b) except along the equator. The thin line corresponds to the control run and the thick line to (Exp2). This figure is available in colour online at www.interscience.wiley.com/qj Precipitation & Lower Troposhere Wind Response 2m/s 1S 2S 3S 3E 6E 9E 12E 15E 18 15W 12W 9W 6W Imposed SST Anomalies 6E.4 9E 2 1.6 1.2.8.4 1 2 3 5 7 Figure 1. Precipitation (shading) and lower tropospheric wind (vector) response to the imposed north Indian Ocean SST anomalies (inserted plot) derived from the simple atmosphere model under JJAS climatology. The contour interval is.2 C for SST. The wind scale is shown at the top right. This figure is available in colour online at www.interscience.wiley.com/qj by Fu and Wang (1999). The JJAS mean SST used to force the simple atmosphere model is derived from the Shea Trenberth Reynolds SST climatology (Shea et al., 1992). We performed two simulations. In the control simulation, only climatology SST is imposed. In the experiment, an idealized SST patch in the north Indian Ocean with maximum SSTAs of 1 C is added to the SST climatology. We imposed an idealized SST in the north Indian Ocean to generate the local atmospheric heating. We then examine how this local heating leads to a remote Pacific wind response. This does not preclude the role of remote forcing (e.g. ENSO) in generating monsoon heating and the feedback of the monsoon on local SST, and it is intended as a diagnostic to show how the monsoon can impact Pacific winds, which can then impact ENSO. The difference as derived from the

ENSO VARIABILITY IN A HYBRID COUPLED MODEL 455 experiment minus the control simulation is considered as the response to the imposed SSTAs. Figure 1 shows the results obtained. The wind difference features a Matsuno Gill-type response with a Kelvin wave response to the east and a Rossby wave response to the west. In particular, the induced anomalous easterly winds extend from the Bay of Bengal to the western tropical Pacific. The wind anomalies over the eastern tropical Pacific are weak. This longitudinal zonal wind distribution can shift westwards the ENSO-related easterly wind anomalies in the equatorial Pacific. Based on the delayed oscillator theory (Suarez and Schopf, 1988) the westward (eastward) shifts of the anomalous zonal wind stress fetch will lead to a shorter (longer) basinwide thermocline adjustment time, because the time for Rossby waves to reach the western boundary is considerably reduced. This may explain the enhancement of the high frequency ENSO period (i.e. quasibiennial) in Exp2. 5. Concluding remarks Using a HCM we investigated how the Indian Ocean SST affects ENSO variability. The control run employs an AGCM coupled to an ocean model in the tropical Pacific only. Two additional numerical experiments were conducted to investigate the impact of the Indian Ocean SST. In the first experiment (Exp1), the Indian Ocean SSTAs are statistically predicted and then these SSTAs are superposed on an annually varying SST climatology. In the second experiment (Exp2), the AGCM is coupled to a SOM in the Indian Ocean. Both Exp1 and Exp2 are the same as the control run except for Indian Ocean SST processes. Neither experiment shows large changes in ENSO amplitude, which is in contrast to several previous studies (i.e. Yu et al., 22; WK4) and may suggest the importance of Indonesian Throughflow, Indian Ocean dynamics or extratropical processes. Changes in ENSO frequency are significant in Exp2, but not in Exp1. The ENSO simulated in Exp2 has an enhanced biennial time-scale. The Indian monsoon ENSO correlation is higher in Exp2 than in the control run, whereas Exp1 cannot simulate the Indian monsoon ENSO relationship. Our result indicates that air sea coupling in the Indian Ocean is necessary for simulating the Indian monsoon ENSO relationship and for studying the influence of the Indian Ocean on the ENSO variability. On the other hand, the rainfall variability over India during JJAS is reduced when the model retains coupled feedbacks in the Indian Ocean, i.e. Exp2. This reduction in rainfall variability simulated in Exp2 is associated with the existence of thermodynamic coupled feedbacks in the Indian Ocean, which has the effect of reducing the rainfall variability. In essence the local thermodynamic coupling has a damping effect on the local rainfall variability. If we view the interannual variability of Indian Monsoon rainfall as due to both remote forcing (e.g. ENSO) and local processes (i.e. internal dynamics), then if the variability due to local processes is diminished, we expect the remote connections to be easier to detect (i.e. a stronger ENSO ISMRI relationship). Since we are concerned with the interaction of the Indian monsoon and ENSO (via correlation analysis), it is possible for the amplitude of the monsoon and ENSO to go down. This has no reflection on the amplitude of the interaction (i.e. correlation) between the monsoon and ENSO. In observations, the strength of the Indian summer monsoon ENSO correlation is not necessarily related to their amplitudes (Slingo and Annamalai, 2). We discussed how the change in ENSO frequency is induced by comparing the control run with Exp2. The Indian Ocean modifies the ENSO variability via interactions with the Indian monsoon, but only when air sea interactions in the Indian Ocean are included. The Indian summer monsoon is more tightly coupled to ENSO in Exp2 than in the control run and Exp1. Moreover, the spectrum of the Indian monsoon rainfall index simulated in Exp2 has a significant biennial timescale of around 2 3 months, which may help to enhance the biennial time-scale of the NINO3 SST index. The spectrum of the observed Indian monsoon rainfall variability is quite similar to that of Exp2, indicating that the biennial time-scale of the Indian monsoon and ENSO variability is more pronounced when the Indian monsoon and ocean are coupled in the Indian Ocean. The Indian monsoon variability in Exp2 affects the horizontal structure of zonal wind stress variability in the central equatorial Pacific. The centre of zonal wind stress associated with the Indian monsoon is shifted westward in Exp2 compared with that of the control run, which enhances the high frequency ENSO period (i.e. quasibiennial). A number of recent studies have confirmed the importance of air sea interactions in the Indian Ocean for the proper simulation of the whole monsoon ENSO system (Kumar et al., 25; Wang et al., 25; Wu and Kirtman, 25). One of the most challenging questions which remains to be solved about the effect of Indian Ocean SSTs in the Indian summer monsoon ENSO coupled system on interannual timescales is how the various positive feedbacks in the Indian Ocean (e.g. the wind-evaporation-sst, coastal upwelling and equatorial wind-thermocline-sst feedbacks) may constructively or destructively interfere in the framework of the annual cycle and have an impact on both the Indian summer monsoon and the ENSO. There is no doubt that such problems must be investigated with fully coupled models which include complex air sea coupled feedbacks. The results presented in this study are based on a simple HCM, and thus have limitations for model hierarchy, coupling strategy, and model biases. We consider that similar experiments with a fully coupled model would be helpful for validation of the results presented here.

456 S.-W. YEH ET AL. Acknowledgements We thank Richard Seager and two anonymous reviewers, whose comments provided many suggestions that have improved this manuscript. S.-W. Yeh is supported by the Korea Ocean Research & Development Institute (PP741, PE9764). Renguang Wu and Ben P. Kirtman are supported by grants from the National Science Foundation ATM-33291, the National Ocean and Atmospheric Administration NA4OAR43134 and NA5OAR4311135, and the National Aeronautics and Space Administration NNG4GG46G. Appendix The SOM provides a simple ocean model for coupling with the SNUAGCM over the Indian Ocean sector. When the SST and net surface heat flux is used, the ocean heat flux can be defined and the SNUAGCM coupled run with SOM, allowing for simple ocean/atmosphere interaction. The open ocean slab model is taken from Hansen et al. (1983). 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