JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 116, D01107, doi: /2010jd014522, 2011

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1 JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 116,, doi: /2010jd014522, 2011 The role of air sea interaction over the Indian Ocean in the in phase transition from the Indian summer monsoon to the Australian boreal winter monsoon Eun Chul Chang, 1 Sang Wook Yeh, 2 Song You Hong, 1 and Renguang Wu 3 Received 24 May 2010; revised 5 September 2010; accepted 6 October 2010; published 13 January [1] The Indian Ocean sea surface temperature (SST) can affect the regional climate in the surrounding regions, including the Indian summer monsoon (ISM) and the Australian summer monsoon (ASM) variability. Recently, it was demonstrated that the in phase ISM to ASM transition can be accomplished through monsoon Indian Ocean interaction solely. To investigate this issue, a long term simulation of a hybrid coupled model (HCM) is conducted, in which the atmospheric general circulation model is coupled with a slab ocean model in the Indian Ocean only. Air sea interactions are allowed only in the tropical Indian Ocean, and the climatological sea surface temperature is specified outside the tropical Indian Ocean. Results from the idealized simulation indicate that the Indian Ocean SST itself can induce in phase ISM to ASM transitions. A wet ISM is largely associated with cool SST in the tropical South Indian Ocean in summer. A wet ASM is also associated with cool SST anomalies in summer. These cool SST anomalies persist until fall and lead to anomalous downward flows over the center of the tropical South Indian Ocean. Consequently, anomalous low level convergences dominates over northern Australia until winter, which induces a wet ASM. Citation: Chang, E. C., S. W. Yeh, S. Y. Hong, and R. Wu (2011), The role of air sea interaction over the Indian Ocean in the in phase transition from the Indian summer monsoon to the Australian boreal winter monsoon, J. Geophys. Res., 116,, doi: /2010jd Introduction [2] The tropospheric biennial oscillation (TBO), which involves the Indian summer monsoon (ISM) and the Australian summer monsoon (ASM), is an important phenomenon associated with atmosphere ocean coupled processes in the Indian and the western tropical Pacific Ocean [Meehl, 1994, 1997]. In terms of ocean atmosphere land coupling, there are major features of the TBO including local or remote air sea interactions, and tropical extratropical teleconnection, which are closely associated with monsoon variability [Yang and Lau, 2006]. It is important to understand the impacts of atmosphere ocean variability on the weather and climate of the surrounding areas that are densely populated [Lau and Nath, 2004]. There are two key features of TBO in the Indian Australian monsoon system as discussed in the literature, the in phase ISM to ASM transition and the out of phase ASM to ISM transition [Wu, 2008] (hereafter W08). 1 Department of Atmospheric Sciences and Global Environment Laboratory, Yonsei University, Seoul, South Korea. 2 Department of Environmental Marine Science, Hanyang University, Ansan, South Korea. 3 Institute of Space and Earth Information Science, Chinese University of Hong Kong, Hong Kong. Copyright 2011 by the American Geophysical Union /11/2010JD [3] Previous studies have argued that both the tropical Pacific Ocean and the Indian Ocean sea surface temperature (SST) anomalies play an important role in the formation of in phase transition of ISM to ASM [Meehl and Arblaster, 2002; Yu et al., 2003; Hung et al., 2004;Wu and Kirtman, 2004, 2007a]. Among them, the Pacific Ocean SST anomalies could affect the ISM and ASM by shifting the east west Walker circulation [Hendon, 2003; Hung et al., 2004]. In particular, such a shift in the Walker circulation is considered as a role of El Niño ;Southern Oscillation (ENSO) in the in phase ISM to ASM transitions [Meehl, 1987; Meehl and Arblaster, 2002]. Since the El Niño events mature in boreal winter, they provide a similar impact on the ISM and ASM via a shift of the east west Walker circulation [Hung et al., 2004]. The peak of El Niño is preceded by a weak Indian summer monsoon and accompanied by a weak Australian monsoon. Thus, the existence of El Niño can reinforce the in phase relationship in this sense. [4] Without a role of ENSO, however, there exists the case of in phase ISM to ASM transition [Shukla, 1987; Allan, 1991; Webster et al., 1998], which suggests that the Indian Ocean SST anomalies also play a role in the in phase ISM to ASM transitions [Meehl and Arblaster, 2002; Yoo et al., 2006; Wu and Kirtman, 2007b]. Warm Indian Ocean SST anomalies could induce northwesterly flow onto Australia which led to increased moisture convergence and rainfall over the land [Joseph et al., 1991; Simmonds and Rocha, 1991; Frederiksen and Balgovind, 1994]. The Indian 1of9

2 Ocean SST could enhance the summer regional climate over India more strongly and affect the ASM variability [Streten, 1981; Hackert and Hastenrath, 1986; Nicholls, 1989; Drosdowsky, 1996; Frederiksen et al., 1999; Watterson, 2001; Hendon, 2003], which is indicated by the lag correlation between ASM and preceding Indian Ocean SST anomalies in observations [Joseph et al., 1991; Drosdowsky and Chambers, 2001; Yoo et al., 2006]. The influence of Indian Ocean SST anomalies on the ASM may be related to SST gradient induced changes in the east west circulations over the Indian Ocean through the Maritime Continent and northern Australia [Wu and Kirtman, 2007a]. Not surprisingly, it has been argued that the Indian Ocean plays a more critical role than the Pacific Ocean in the TBO transition from a strong ASM to weak ISM based on the simulation of coupled general circulation model [Yu et al., 2003]. Theses previous results call for more retrospective analysis to understand the role of the Indian Ocean and tropical Pacific SST anomalies on the in phase relationship of ISM to ASM. [5] Recently, W08 raised an important question how the Indian Ocean SST anomalies contribute to the in phase ISMto ASM transition. W08 explored the role of the Indian Ocean SST change in the in phase ISM to ASM transition in observations and provided the evidence that the in phase ISM to ASM transitions can occur without ENSO. W08 argued that the in phase ISM to ASM transitions in non ENSO years can be accomplished through monsoon Indian Ocean interactions. In details, an anomalous ISM leads to SST anomalies in the tropical Indian Ocean through windevaporation effects. The resultant Indian Ocean SST anomalies induce an anomalous ASM of the same sign as the ISM through an anomalous east west circulation over the Indian Ocean and the Maritime Continent northern Australia. To confirm this hypothesis, W08 conducted two types of numerical model experiments. In the first experiment, climatological SST is specified to force an atmospheric general circulation model (AGCM) and in the second experiment, a patch of warm or cold SST anomalies is added to the climatology SST in the tropical Indian Ocean. [6] While Wu08 emphasized the role of wind evaporation feedbacks, some other studies still indicated the role of ocean processes in the Indian Ocean SST anomalies [Chambers et al., 1999; Murtugudde and Bussalacchi, 1999; Murtugudde et al., 2000; Shinoda et al., 2004]. Thus, one may raise the question whether the Indian Ocean SST anomalies without the ocean dynamics can develop to favor the in phase ISMto ASM transition. To examine this we conduct an idealized numerical experiment in which an AGCM is coupled with a slab ocean model in the tropical Indian Ocean only. In contrast to the numerical experiments by Wu08, we may isolate air sea interactions over the Indian Ocean in the inphase ISM to ASM transitions. The question that this study addresses is how much local air sea interactions over the Indian Ocean contribute to the in phase ISM to ASM transition without the ocean dynamics which several studies have suggested as being important. Our result indicates that the Indian Ocean SST itself, which results from air sea interaction processes without the ocean dynamics, can lead to in phase ISM to ASM transition. [7] The organization of the text is as follows. The model description and the experimental design are described in section 2. Section 3 provides analyzed features of the in phase ISM to ASM transitions from a long term simulation of numerical model experiment. Section 4 describes interactions between the ISM and the Indian Ocean and the impact of the Indian Ocean on the ASM. Summary and discussions are given in section Model and Methodology [8] We use the Hybrid coupled model (HCM) which consists of the AGCM coupled into the slab oceanic model (SOM) in the tropical Indian Ocean only (25 N 30 S, 40 E 120 E). Outside the tropical Indian Ocean, the climatological SST is prescribed. The HCM is performed for the simulation period of 107 years and we analyzed the 100 years in this study. [9] 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 changed, including the land surface process, shallow convection, and Planet Boundary Layer (PBL) processes [Kim et al., 1998]. The slab ocean model allows for the simplest air sea interactions through thermodynamic processes. The prognostic equation for ocean mixed layer temperature, T 0,is 0 C 0 h ¼ F þ Q; where r 0 is the density of ocean water ( kg m 3 ), C 0 the heat capacity of ocean water ( Jkg 1 K 1 ), and Q the ocean mixed layer heat flux (W m 2 ) simulating deepwater heat exchanged and ocean transport. In addition, h 0 is the ocean mixed depth (m), which is set to a uniform depth of 60 m, and F is the net atmosphere into ocean heat flux (W m 2 ), which is defined in the absence of sea ice as F ¼ FS FL SH LH; where FS is solar flux absorbed by the ocean, FL the long wave cooling flux at the ocean surface, SH the sensible heat flux from the ocean to the atmosphere, and LH the latent heat flux from the ocean to the atmosphere. 3. Results 3.1. Features of In Phase ISM to ASM Transition [10] W08 defined the ISM as the time series of rainfall variability averaged over the region of 5 N 25 N, 60 E 100 E over the Indian monsoon region (IMR) during June September (JJAS) and the ASM is defined as the time series of rainfall variability averaged over the region 5 S 20 S, 100 E 150 E over the Australian monsoon region (AMR) during December February (DJF). To verify whether the above definition of ISM and ASM is useful in the HCM, the standard deviations of rainfall variability during JJAS and DJF for the entire analyzed period are shown in Figures 1a 2of9

3 Figure 1. Precipitation standard deviations for (a) June September (JJAS) and (b) December February (DJF) from 100 years model run. Standard deviations greater than 4 mm are shaded. IMR, Indian monsoon region; AMR, Australian monsoon region. and 1b, respectively. Note that box in Figures 1a and 1b indicates the IMR and the AMR to define the ISM and ASM, respectively. Large variability of rainfall is detected over the IMR during JJAS (Figure 1a) and over the AMR during DJF (Figure 1b). Therefore, it is useful to follow the definition of ISM and ASM in this study as in W08. [11] Figure 2 shows the normalized ISM and ASM from the simulation period of 100 years. The in phase ISM to Figure 2. Normalized ISM and ASM for the period of 100 years in the HCM. Positive and negative signs at the bottom of the plots are for wet to wet and dry to dry transitions, respectively. 3of9

4 Figure 3. Precipitation anomalies (millimeters) for wet to wet transition. Dark shaded areas indicate positive anomalies, and light shaded areas indicate negative anomalies. MAM, March May; JJA, June August; SON, September November; DJF, December February. IMR and AMR are indicated in each plot by boxes at upper left and lower right, respectively. ASM transitions are determined when a wet (dry) ISM is followed by a wet (dry) ASM as in the work by Wu [2008]. The wet and dry ISM is defined as when the ISM is above and below normal 0.4 standard deviations, respectively. Wet and dry ASM is also defined thus. Based on this definition, it is found that there are 34 in phase transitions for the 100 year period. Among them, there are 16 wet to wet transitions and 18 dry to dry transitions. Wu [2008] showed 10 in phase transitions for the period of (26 years) from observations, and among them there are 5 wet to wet and 5 dry to dry transitions. The frequency of the wet towet and the dry to dry ISM to ASM transition is 0.34/year in the HCM, which is slightly smaller than the observations (0.38/year). On the other hand, there are 17 simulated out of phase ISM to ASM transitions for 100 years model simulation. The observational analysis showed 2 out of phase transitions for 26 years [Wu,2008].It is clear that the in phase ISM to ASM transitions occur more frequently than the outof phase transitions in both the observations and the model simulation. However, the model simulated results present that the out of phase ISM to ASM transitions can occur without ENSO, whereas there are no out of phase transitions from observational results for non ENSO years. [12] Figures 3a 3d display the composite of rainfall anomalies for the 16 wet to wet transitions during the boreal spring (March May, MAM), summer (June August, JJA), fall (September November, SON) and winter (DJF), respectively. In spring (Figure 3a), there exists an asymmetric rainfall anomalies in the meridional direction over the northwestern Indian Ocean. Negative rainfall anomalies are observed in the IMR. In contrast, positive rainfall anomalies are located in the South Indian Ocean. In summer and fall, the signs of anomalies are reversed compared to those in spring (Figures 3b and 3c). It is clear that the ISM is strong in summer (Figure 3b) and an asymmetric rainfall pattern in the western and central Indian Ocean persists until fall (Figure 3c). In contrast, rainfall anomalies are small in the AMR until fall. During winter, however, a strong ASM is observed (Figure 3d), which is indicative of the in phase ISM to ASM transition. We also analyze composite of rainfall anomalies for the 18 dry to dry transitions from spring to winter (not shown). The overall features of rainfall anomalies over the Indian Ocean are similar except for a reversal of sign and some differences in the magnitude compared to the wet to wet transition. 4of9

5 Figure 4. (a) SST anomalies (kelvins), (b) 1000 hpa wind anomalies (m s 1, vector), and (c) latent heat flux anomalies (W m 2 ) in MAM for wet to wet transition. Dark and light shaded areas in Figure 4b indicate convergence and divergence, respectively. [13] Consequently, we can infer that the in phase ISM to ASM transitions can occur when the air sea interactions are allowed over the Indian Ocean without ENSO. This result indicates that changes in the Indian Ocean SST itself, which results from air sea interaction processes in the HCM, can trigger in phase ISM to ASM transition. When the same procedure is applied to the coupled general circulation model used by W08, it is found that general features of coupled general circulation model are similar to the HCM results; however, detailed structures are different from season to season (not shown). This indicates that ocean dynamics can modify the seasonal evolution of SST anomalies in the Indian Ocean in the in phase ISM to ASM transition. In section 3.2, we analyze detail processes of in phase ISM to ASM transition in the HCM to examine the role of air sea interactions over the Indian Ocean Roles of Indian Ocean SST [14] In this section, we analyze the wet to wet transitions simulated in the HCM, focusing on identifying the role of Indian Ocean SST. It is found that the processes for the dryto dry transition are similar to the wet to wet transition except for a reversal of sign in anomalies. [15] We first show the composite of SST and wind anomalies at 1000 hpa in spring prior to the strong Indian monsoon (Figures 4a and 4b). A triple like feature of SST anomalies is obvious in the meridional direction in the Indian Ocean, that is, anomalous cool SST is located in the south and northwestern Indian Ocean and anomalous warm SST exists in the central Indian Ocean. One may find that anomalous low level winds at 1000 hpa (Figure 4b) blow from cool SST to warm SST because cool (warm) SST acts to raise (lower) the surface pressure, resulting in a low level wind convergence in the equatorial Indian Ocean. This reflects the ocean forcing of the atmosphere in spring in the in phase ISM to ASM transitions. Both the low level wind convergence and anomalous warm SST is well matched with the region where positive rainfall anomalies are observed (Figure 3a). Anomalous warm SST may contribute to increase anomalous rainfall because it is favorable to evaporate more water vapor into the atmosphere in regions of warm SST. Overall, the SST pattern leads to anomalous rain and lowlevel winds over the Indian Ocean in spring. However, we cannot exclude the possibility that there is a contribution from the land temperature via producing a meridional temperature gradient in MAM. It is found that anomalous negative land temperature is dominant in the inland of India (Figure 5), which may contribute to the formation of a lowlevel wind convergence in the equatorial Indian Ocean together with anomalous cool SST in the northwestern Indian Ocean as shown in Figure 4a. [16] On the other hand, one may argue that such anomalous SST in the Indian Ocean in spring persists from the previous year of weak monsoons. Further analysis of the evolution of composite SST anomalies in the north (0 5 N, 65 E 90 E) and south (5 S 25 S, 60 E 100 E) Indian Ocean for wet to wet transition and dry to dry transition (not shown here) indicates that such possibility is dependent on the region. That is, the SST anomalies in the north Indian Ocean persists from the previous year of weak monsoons that would result from less latent heat flux and less heat removed from the surface and greater net solar radiation at the surface but, in contrast, the SST anomalies in the South Indian Ocean during strong monsoons are not from the previous year of weak monsoons. 5of9

6 Figure 5. Land surface temperature anomalies (kelvins) in MAM for wet to wet transition. [17] Because the climatological southerly winds at 1000 hpa are strong over the South Indian Ocean in spring (not shown here), anomalous southerly over the same region leads to an enhanced wind speed, which results in an increase of latent heat flux anomalies in spring (Figure 4c). Note that negative sign indicates anomalous latent heat flux into the atmosphere from the ocean. Such an increase of latent heat flux into the atmosphere induces a large cooling in the South Indian Ocean in summer (see Figure 6a). A center of anomalous latent heat flux in spring (Figure 4c) is coincident with that of anomalous cool SST in the South Indian Ocean in summer. In summer the SST anomalies show a pronounced cross equatorial gradient along with large negative anomalies in the South Indian Ocean and weak anomalies in the North Indian Ocean. Basically, this result indicates that surface heat flux (here, latent heat flux) in spring may significantly contribute to SST anomalies in the South Indian Ocean in summer. To examine this relationship, we calculate a lead lagged relationship between the net heat flux and SST anomalies averaged in the South Indian Ocean (5 S 25 S, 60 E 100 E). Note that we refer to the net atmosphere into ocean heat flux as the net heat flux; therefore, positive net heat flux indicates anomalous heat flux into the ocean. Figure 6b displays that the lagged correlations of SST anomalies in summer with the net heat flux in a lag and lead of 6 months. Positive lags indicate the net heat flux preceding the SST. The maximum correlation occurs at leads of approximately 1 2 months with positive correlation exceeding the 95% confidence level, indicating that the net heat flux induces the South Indian Ocean SST anomalies in summer. [18] Such an asymmetric feature of SST anomalies in summer induces an enhanced cross equatorial flow at low level from the tropical South to the North Indian Ocean as shown in Figure 6c. Anomalous cool SST in the tropical South Indian Ocean is favorable to induce anomalous high pressure. Therefore, it causes strong pressure gradient crossing the equator. The fact that anomalous cool SST is associated with anomalous high pressure, indicating that the ocean tends to force the atmosphere over the South Indian Ocean in summer. A southerly wind leads a strong convergence over the North Indian Ocean (Figure 6c). A strong wind convergence plays a role to induce large rainfall anomalies over the North Indian Ocean as shown in Figure 3b, that is, a wet ISM. These results indicate that the cooling in the South Indian Ocean in summer, which results from air sea interaction processes in previous spring, forms a meridional SST gradient that leads to a wet ISM via a low level wind convergence over the North Indian Ocean. In other words, the Indian Ocean SST plays a key role to induce a wet ISM. [19] In fall, negative SST anomalies are still strong in the South Indian Ocean (see Figure 7b) and positive net heat fluxes are observed over the same region (not shown here). Further analysis indicates that positive net heat fluxes are mainly due to an increase of net shortwave flux into the ocean. Cool SSTs in the South Indian Ocean in summer and fall induce a persistent anomalous high pressure over the same region, which play a role to enhance a net shortwave radiation. On the other hand, such a persistent anomalous high pressure in two successive seasons leads to anomalous downward motion over the center of tropical South Indian Ocean (Figure 7a). [20] To compensate anomalous downward flows over the tropical South Indian Ocean, both anomalous upward flow and low level convergence are dominated over the AMR (box in Figure 7a) in conjunction with warm SST anomalies in the west side of Australia (Figure 7b). The zonal distribution of SST anomalies averaged over 5 S 20 S, indicates an east west contrast of anomalous SST in the tropical South Indian Ocean and the AMR in fall, which is similar to a dipole like SST pattern as suggested in previous studies [Saji et al., 1999; Webster et al., 1999]. In fall, the climatological wind over the AMR is dominated by the southeasterlies (not shown). Therefore, low level convergence over the same region contributes to reduce the wind speed, which results in positive SST anomalies via a reduced latent heat flux into atmosphere. We argue that the atmospheric condition and anomalous SST over the AMR, which is favorable to induce a wet ASM, are largely attributed to persistent cool SST anomalies over the South Indian Ocean in summer and fall. 6of9

7 approximately 2 3 months, supporting the above argument. That is, anomalous warm SST in the AMR in fall is favorable to induce anomalous low level pressure, which is associated with a low level convergence as shown in Figure 7a. Figure 8b shows that upward flows in winter are enhanced compared to fall, which is concurrent with a large area of anomalous convergence at low level over the AMR, resulting in a wet ASM. Overall, these results indicate that the Indian Ocean SST plays a key role to the in phase ISM to ASM transition. 4. Summary and Discussion [22] In this study, we conducted an idealized coupled model experiment to further study the role of the Indian Ocean in the in phase ISM to ASM transitions. There are many previous studies which emphasize the role of the Indian Figure 6. (a) Composite of SST (contour) and net atmosphere to ocean heat flux (W m 2, shaded), (b) lagged correlations of SST in summer with net atmosphere to ocean heat flux over SIO, and (c) 1000 hpa wind anomalies (m s 1, vector). Dark and light shaded areas in Figure 6c indicate convergence and divergence, respectively, and IMR and AMR are indicated by boxes at upper left and lower right, respectively. [21] Figure 8a shows the lead lagged relationship between the precipitation anomalies and SST anomalies averaged in the AMR in fall. Results in Figure 8a clearly indicate that positive SST anomalies lead a wet ASM at leads of Figure 7. (a) Cross sectional streamlines (5 S 20 S) and horizontal convergence ( 10 6, shaded) and (b) SST anomalies in SON for wet to wet transition. Dark and light shaded areas in Figure 7a indicate horizontal convergence and divergence, respectively. Box over 100 E 150 E in Figure 7a indicates AMR. 7of9

8 Figure 8. (a) Lagged correlations of SST in fall with precipitation over AMR and (b) cross sectional streamlines (5 S 20 S) and horizontal convergence ( 10 6, shaded) in DJF for wet to wet transition. Dark and light shaded areas in Figure 8b indicate horizontal convergence and divergence, respectively. Box over 100 E 150 E in Figure 8b indicates AMR. Ocean on the Australian and the Asian monsoon system by using general circulation models. However, most of them focused on the interactions between the Indian Ocean SST and the ENSO, or examined the role of air sea interactions without ENSO by utilizing ocean models which include ocean dynamics such as advection. To confirm the hypothesis that local air sea interactions over the Indian Ocean contribute to the in phase ISM to ASM transitions without ENSO, we analyzed a long term simulation of a HCM in which an AGCM is coupled with a slab ocean model in the tropical Indian Ocean only and the climatological SST is specified outside the tropical Indian Ocean. Therefore, we could isolate the air sea interactions over the Indian Ocean and examine whether the Indian Ocean SST anomalies without the ocean dynamics can develop to favor the in phase ISM to ASM transition. [23] The HCM reasonably simulated both the ISM in summer and the ASM in winter along with in phase ISM to ASM transitions for the simulation period of 100 years. Thirty four in phase transitions are obtained for the period of 100 years occurred. Among them, there are 16 wet towet transitions and 18 dry to dry transitions, which is comparable with the observations in terms of their occurrence frequency. [24] A wet ISM is largely associated with cold SST anomalies in the tropical South Indian Ocean in summer. The SST anomalies showed a pronounced cross equatorial gradient, with large negative SST anomalies in the tropical South Indian Ocean and weak anomalies in the North Indian Ocean. Such an asymmetric feature of SST anomalies induces an enhanced cross equatorial flow at low levels from the tropical South to the North Indian Ocean. In other words, the cool SST anomalies in the South Indian Ocean SST anomalies induce the anomalous high pressure over the same region, which induces a meridional pressure gradient. This leads to a strong convergence over the tropical North Indian Ocean, which results in a wet ISM. Our analysis indicates that such cold SST anomalies in summer are mainly induced by air sea interactions in the previous spring. The lead lagged relationship between SST anomalies and the net heat flux indicated that a negative net heat flux in the previous spring acts to cool SST in the tropical South Indian Ocean. On the other hand, a wet ASM is also associated with cold SST anomalies in summer, which persist until fall. Negative SST anomalies in summer and fall induce anomalous high pressure over there, which leads to anomalous downward flows over the center of tropical South Indian Ocean. [25] In conjunction with downward flow, both anomalous upward flows and low level convergence dominate over the AMR in fall and winter, which induces a wet ASM. It is clear that the upward branch over AMR shifts the Walker circulation to the east in winter. Such a shift in the Walker circulation is considered as a traditional view of the role of ENSO in the in phase ISM to ASM transitions [Meehl and Arblaster, 2002]. However, our result indicated that a shift of Walker circulation embedded with in phase ISM to ASM transition can occur over the Indian Ocean without ENSO. That is, the Indian Ocean SST itself, which results from the air sea interaction processes, leads to a wet ASM. [26] Using the detrended data, we have examined the possibility whether a liner trend contributes to the in phase relationship. It is found that the results are not much changed whether we use the detrended time series, indicating that in phase relationship simulated in the HCM may not be due to the possible trend. [27] A central element of the TBO for strong monsoons is the concept that, as the precipitation maximum moves from India toward Australia, it moves over anomalously warm SSTs and is reinforced and strengthened by enhanced evaporation and greater moisture for more intense precipitation, thus producing a strong to strong monsoon sequence. Therefore, one may interpret a wet to wet transition simulated in the HCM in this way in that there are warm SSTs south of India and northwest of Australia in MAM (Figure 4a). This would set up conditions for strong monsoons. Figure 6 also indicates that the strong Indian monsoon has cooled SSTs across the western Indian Ocean and the precipitation maximum is moving across warmer SSTs in the eastern Indian Ocean during the SON season. Note that we could 8of9

9 find that the zonal distribution of rainfall anomalies averaged over 5 S 20 S is characterized by an east west contrast similar to the anomalous SST in fall (not shown). This suggests that such an SST pattern could be driven by local surface energy balance connected to the coupled nondynamic ocean. [28] In spite of the above argument, it is noteworthy that the wet to wet or dry to dry transients can occur even without air sea interaction as shown by Wu08. In the experiment by Wu08, climatological SST forcing is specified so that the interannual variability of the ISM and ASM is mainly due to atmospheric internal dynamics. Wu08 indicated that the number of in phase and out of phase ISM to ASM transitions is nearly the same but observed that feature (more frequent in phase transition) cannot be produced without air sea interactions. [29] Acknowledgments. S. W. Yeh was funded by the Korea Meteorological Administration Research and Development Program under grant CATER_2006_4202, and E. 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