A dynamic link between the basin-scale and zonal modes in the Tropical Indian Ocean

Transcription

1 Theor. Appl. Climatol. 78, (2004) DOI /s International Pacific Research Center, SOEST, University of Hawaii at Manoa, Honolulu, HI, USA A dynamic link between the basin-scale and zonal modes in the Tropical Indian Ocean Soon-Il An With 12 Figures Received January 30, 2003; revised September 19, 2003; accepted October 15, 2003 Published online June 3, 2004 # Springer-Verlag 2004 Summary The interannual variability of sea surface temperature (SST) anomalies in the tropical Indian Ocean is dominated mainly by a basin-scale mode (BM) and partly by an east west contrast mode (zonal mode, ZM). The BM reflects the basin-scale warming or cooling and is highly correlated with El Nino with 3- to 6-month lags, while the ZM is marginally correlated with El Nino with 9-month lags. During an El Nino, large-scale anomalous subsidence over the maritime continent occurs as a result of an eastward shift in the rising branch of the Walker circulation suppresses convection over the eastern Indian Ocean, allowing more solar radiation over the eastern Indian Ocean. At the same time, the anomalous southeasterly wind over the equatorial Indian Ocean forces the thermocline over the western Indian Ocean to deepen, especially in the southern part. As a result, SST over the whole basin increases. As El Nino decays, the subsidence over the maritime continent ceases and so does the anomalous southeasterly wind. However, the thermocline perturbation does not quickly shoal back to normal because of inertia and it disperses as Rossby waves. These Rossby waves are reflected back as an equatorial Kelvin wave, causing deepening of the thermocline in the eastern Indian Ocean, and preventing SSTs from cooling in that region. Moreover, the weaker wind speed of the monsoon circulation results in less latent heat loss, and thus warms the eastern Indian Ocean. These two processes therefore help to maintain warm SSTs over the eastern Indian Ocean until fall. During the fall, the warm SST over the eastern Indian Ocean and the cold SST over the western Indian Ocean are enhanced by air sea interaction and the ZM returns. The ZM dissipates through the seasonal reversal of the monsoon atmospheric circulation and the boundary-reflected Kelvin wave. In the same manner, a basin-scale cooling in the tropical Indian Ocean can induce the ZM warming in the west and cooling in the east. 1. Introduction The mechanism for changes in the sea surface temperature (SST) anomaly over the tropical Pacific, the El Nino=Southern Oscillation (ENSO), has been thoroughly investigated (Neelin et al., 1998; Wallace et al., 1998), but the mechanism in the Indian Ocean is less well understood, partly because fewer observations are available, and the interannual variation is relatively weak. Yet, Saji et al. (1999) and Webster et al. (1999) recently proposed a local Indian Ocean air sea interaction phenomenon, the Indian Ocean dipole Mode (a.k.a. Zonal Mode ; Annamalai et al., 2003) using observed data, and their analyses stimulated an effort to understand this mechanism related to interannual SST variation in the Indian Ocean. The atmospheric circulation over the Indian Ocean can be externally forced by El Nino by way of an atmospheric bridge (Chambers et al., 1999; Latif et al., 1994; Klein et al., 1999; Yu and Rienecker, 1999), but it can also be generated by the local air sea interaction, for example the zonal mode (Hereafter, ZM) (Saji et al.,

2 204 S.-I. An 1999; Webster et al., 1999). Because the ZM tends to be strongest during the fall, it is hard to evaluate the abnormal atmosphere ocean condition of the Indian Ocean, especially when the ZM occurs during the developing phase of ENSO (summer and fall followed, by ENSO winter), and even harder to determine whether it is locally excited or externally forced by ENSO. In fact, some strong ZM events are followed by the ENSO, which peaks frequently during the winter (Murtugudde et al., 2000). The basin-scale warming (cooling) (hereafter, BM as a counterpart of the zonal mode) of the Indian Ocean is recognized as a passive response to El Nino. The changes in radiative forcing and local wind stress can cause the BM (Kiladis and Diaz, 1989; Venzke et al., 2000). Nicholls (1989) showed that the time series of the principal component of the Indian Ocean SST, which resembles the BM, is strongly anti-correlated with the Southern Oscillation index at a lag of 3 months, and Venzke et al. (2000) also showed that the Nino-3 index leads the area averaged Indian ocean SST (20 N 20 S, E) by 4 months and suggested that this basin-scale warming is primarily due to the anomalous surface heat fluxes. During the decaying phase of ENSO (summer and fall, following ENSO mature phase), its direct impact is diminished but the thermal and dynamical inertia can remain for some time. Thus, it is natural to suppose that the thermal inertia reinitiates the local air sea interaction during summer and fall. In this paper, we investigate the mechanism of the establishment of basin-scale warming (or cooling) with relevance to the ENSO and the triggering of the zonal mode during the decaying phase of ENSO. First, the indices for the BM and ZM are identified (Sections 2 and 3), and the relationships between the El Nino, BM and ZM are demonstrated (Section 4). A possible mechanism is then hypothesized (Section 5), and to support the hypothesis a numerical model experiment is performed (Section 6). 2. Leading modes of the Indian Ocean SST anomaly Using the SODA data (Carton et al., 2000), which are ocean assimilation data from 1950 to 1998, the characteristics of the interannual variation of the Indian Ocean SST are investigated. Since 1970 the use of expendable bathythermograph (XBT) and conductivity-temperature-depth (CTD) sensors have become widespread worldwide, resulting in a great increase in the number of measurements below 200 m (Carton et al., 2000). Xie et al. (2002) showed that the SODA data have good agreement with XBT observations in their Indian Ocean analysis. Data are available at 1 1 resolution in the midlatitudes and longitude latitude resolution in the tropics, with 20 vertical levels of 15 m resolution near the sea surface. First, empirical orthogonal function (EOF) analysis is applied to the SST anomaly (SSTA). Figure 1a shows the first EOF mode represents the basin-scale warming=cooling pattern (BM), which is known as an ENSO forced mode (Klein et al., 1999). The following second EOF mode is always constrained by the orthogonality with the Fig. 1. First EOF mode of SST anomalies derived from SODA data for the period of : (a) original data, (b) filtered data. First EOF modes for original and filtered data explain 26.3% and 12.6% of total variance, respectively. The boxes in (a) and (b) indicate the region for the basin-scale mode index and for the zonal mode index, respectively

3 Link between the BM and ZM in the Tropical Indian Ocean 205 Fig. 2. Time series of (a) Nino-3 index, (b) basin-scale mode index, and (c) zonal mode index. 3-month running mean has been applied. Units of each time series are C first EOF mode so that the second EOF mode may or may not be the second leading mode in terms of the variability. To avoid the orthogonal constraint, the signal of the first EOF mode is removed from the original data by using the linear regression method (hereafter, the filtered SSTA), and then the EOF analysis is reapplied to the filtered SSTA. So, the first EOF mode obtained from the filtered data is not necessarily orthogonal with the first EOF mode of unfiltered data, and must be the second dominant mode of Indian Ocean SST. As shown in Fig. 1b, the first EOF mode of the filtered SSTA displays the east west contrast pattern and is similar to the Indian Ocean dipole mode (Saji et al., 1999) or zonal mode (Annamalai et al., 2003). Note that using Conditional Maximum Covariance Analysis, An (2003) also obtained the most dominant air sea coupled pattern in the tropical Indian Ocean when the ENSO signal was removed, in which the SSTA and surface wind stress patterns resemble the zonal mode. The EOF analysis revealed two leading modes of SSTA over the tropical Indian Ocean. Because the two leading modes have distinct spatial distribution we may identify a simple index time series to represent the leading modes. Saji et al. (1999) defined the dipole mode index (here, zonal mode index, ZMI), indicating the difference of SSTA between the tropical western Indian Ocean (50 70 E, 10 S 10 N) and the tropical southeast Indian Ocean ( E, 10 S EQ). In a similar manner, the basin-scale mode index can be identified as the domain average of SSTA over the tropical Indian Ocean ( E, 15 S 10 N). The basin-scale mode index (hereafter, BMI) is highly correlated with the time coefficients of the first EOF mode of the unfiltered data (the correlation coefficient is 0.97), and the Indian Ocean zonal mode index (hereafter, ZMI) is correlated with the first EOF Fig. 3. Root-mean-square of (a) Nino-3 index, (b) basinscale mode index, and (c) zonal mode index with respect to each calendar month. Units of each time series are C

4 206 S.-I. An mode of the filtered data (the correlation coefficient is 0.84). Thus, each index well represents each leading mode. Each index, including the Nino-3 index, is shown in Fig. 2. The fluctuation of the BMI is very similar to that of the Nino-3 index and its dominant frequency is 4 5 years, suggesting that the BM may indeed be directly forced by the ENSO. ZMI, however, shows a higher frequency signal (about months), indicating that the ZM may be an intrinsic mode of the Indian Ocean, rather than a direct response to external influence (e.g. An, 2003). This may be the case because the higher frequency signal of the ENSO is rather weak, and the narrow Indian Ocean basin barely allows a slow adjustment timescale like the ENSO timescale variation in the Pacific. Note that there have been three big ZM events since 1950, 1961, 1994, and The 1961 and 1994 events are not El Nino years, but 1997 is a strong El Nino year. The debate regarding the 1997 ZM event, as well as other ZM events, still continues as to whether this ZM is internally generated (Iizuka et al., 2000; Saji et al., 1999; Webster et al., 1999) or externally forced (Ueda and Matsumoto, 2000; Yu and Rienecker, 1999). 3. Seasonality of basin-scale and zonal modes The peaks of ZM events tend to appear most frequently around September and October (Saji et al., 1999), while those of ENSO appear between November and January (An and Wang, 2001). The seasonal phase-locking feature of these phenomena is clearly depicted in the rootmean-square plot of the indices with respect to Fig. 4. Horizontal distribution of the seasonal varying part of the surface wind stress vector for (a) Dec. Jan., (b) Feb. Mar., (c) Apr. May, (d) Jun. Jul., (e) Aug. Sep., and (f) Oct. Nov. The vector scale is displayed at the top right corner of (a)

5 Link between the BM and ZM in the Tropical Indian Ocean 207 calendar months (Fig. 3). The maximum variability of the Nino-3 index occurs around December (generally boreal winter), that of ZMI around October (generally boreal fall), and that of the BMI around February or June (generally boreal spring but the seasonality is slightly weaker). All three indices show seasonality but are locked into different seasons. The seasonal phase-locking characteristics of both the BM and ZM are obviously related to the seasonal change in the monsoon flow over the Indian Ocean (see Fig. 4). However, a somewhat different mechanism rules the seasonality of each mode. From a thermodynamic view point, the wind-evaporation feedback reflecting the positive=negative feedback process between the SST and the wind speed so as to change the latent heat flux, depends on the mean wind direction. When the southeasterly monsoon flow over the eastern Indian Ocean (between June and September, Fig. 4d and e) prevails, the wind-evaporation feedback tends to enhance the SSTA in that region, while the northwesterly monsoon flow (between December and March, Fig. 4a and b) plays the role of a damping mechanism on the SSTA. From an ocean dynamic aspect, the southeasterly flow intensifies the costal upwelling and offshore current near Java and Sumatra, while the upwelling is suppressed by the northwesterly flow. The strong upwelling brings the cold water up to the surface so that the change in the ocean subsurface effectively influences the surface temperature to some extent, which can be verified by the correlation map between the SST and 20 C isotherm anomalies (Fig. 5). Figure 5 shows the positive correlation over the eastern Indian Ocean is significant during May November when the upwelling is strong. On the other hand, the warm BM event during January March, concurrent with the equatorial easterly, corresponds to the deepening of the thermocline over the western Indian Ocean and the shoaling that occurs over the eastern Indian Ocean (see Fig. 8), resulting in a negative correlation over the eastern Indian Ocean during that season. A significant positive correlation in the central-to-western Indian Ocean is also noteworthy, and it shows the westward propagating feature as mentioned in Xie et al. (2002), who also showed that the changes in the thermocline depth (depth of Fig. 5. Time-longitude section of the local correlation between SST anomaly and 20 C isotherm depth anomaly along the equator 20 C isotherm) over the Southern Indian Ocean contribute strongly to the change in SST in that region. 4. Relationship between basin-scale mode, zonal mode, and ENSO To reveal the relationships between the ENSO, BM, and DM, the cross correlation between the indices the simplest possible approach is calculated. As shown in Fig. 6a, the Nino-3 index leads the BMI by 3 5 months with a significant correlation (>0.6). Even the simultaneous correlation between two indices is significant, whereas the maximum correlation between the Nino-3 index and ZMI at 9-month lags only exceeds the 90% significance level, so they are marginally correlated (Fig. 6b). It should also be noted that they are negatively correlated ( 0.3), which means the warm ENSO event is related to the cold ZM event (the negative SSTA over the western Indian Ocean and the positive SSTA over the eastern Indian Ocean), and vice versa. At 2- and 7-month lags, the BMI and ZMI are negatively correlated but not significantly (Fig. 6c). Yet, the lag correlation between the time coefficients associated with the first EOF mode of the original SST (shown in Fig. 1a) and those of the filtered SST (shown in Fig. 1b) is similar to that between the BMI and ZMI, but exceeds the 95% significance level (see the dash-dotted line in Fig. 6c).

6 208 S.-I. An Fig. 6. Cross correlation between (a) Nino-3 index and the basin-scale mode index, (b) Nino-3 index and the zonal mode index, and (c) basin-scale mode index and the zonal mode index. The long dashed and dotted lines in each panel indicate the 95% and 90% significance levels. The long dash-dotted line in (c) indicates the cross correlation between the EOF time coefficient of the original SST anomaly and that of the filtered SST anomaly In summary, El Nino (La Nina) leads the basin-scale warming (cooling) over the tropical Indian Ocean by 3 5 months, and the former obviously causes the latter. The basin-scale warming (cooling) of the tropical Indian Ocean is followed by the warming (cooling) over the eastern Indian Ocean and cooling (warming) over the western Indian Ocean about 7 months later. This lead=lag relationship is statistically significant but not as strong as the relationship between ENSO and the BM. The relationship between ENSO and the ZM event is also marginally correlated. Although the cross correlation between the indices showed a possible connection between two phenomena, an event-by-event checking will provide additional confirmation of the significance of the relationship. Because of the seasonality of each event, as shown in Fig. 3, one may focus on a particular season in which the peak of each event occurs most frequently. Here, the seasonal average has been taken out, which is the boreal winter average for the Nino-3 index, the spring average for the BMI, and the fall average for the ZMI, so that the time series of each index become annual data. Then, each annual time series was normalized, and if its normalized value was between 0.5 and 0.5, it was excluded from a count of how many events follow other events. Figure 7 shows counts of events following respective events, and the positive sign in parenthesis indicates the case when both indices are the same sign; otherwise it is negative. As shown in Fig. 7, nineteen BM events out of twenty-five cases follow ENSO events with a Fig. 7. Number of events that follow others. The bold arrow in the upper part indicates the time sequence. The light arrow and the number in the lower part indicate the case for counting and number of case, respectively. (þ) and ( ) indicates the correlation in the positive and negative sense, respectively. BM and ZM indicate the basin-scale and zonal modes, respectively

7 Link between the BM and ZM in the Tropical Indian Ocean 209 one season lag. This confirms that a majority of the BM events follow ENSO with a one season lag. Twelve ZM events out of twenty-five follow the BM events with a 2-season lag but they are negatively correlated to each other indicating that 44% of ZM events might be linked to BM events. Figures 6 and 7 suggest that ENSO primarily causes BM events, and that BM events frequently cause the ZM events. A dynamical interpretation of this sequence will be presented. Alternatively, there are fourteen ZM events that are followed by ENSO, and they are positively correlated. A good example of this case is the 1997=98 El Nino. During 1997 fall, the east west SST contrast over the tropical Indian Ocean was maximized and followed by the one of the strongest warm Pacific events of the century, the El Nino. It has been suggested that this particular ZM event was triggered by ENSO (Ueda and Matsumoto, 2000). However, the local air sea interaction over the Indian Ocean can also be as influential as the external forcing is, although which factor is the stronger is not yet fully resolved. 5. The establishment of basin-scale and zonal modes Based on the statistics shown in Fig. 7, the composite map for the nineteen winters (Dec. Feb.) when only ENSO is followed by the BM, is shown in Fig. 8. For this figure, the SODA data for the surface (wind stress, SST) and ocean subsurface (HC125 indicating the vertical averaged value of the sea water temperature from 125 m depth to the surface) variables compiled from 1950 to 1998 and the outgoing long-wave radiation (OLR, as a proxy for rainfall) from 1979 to 1998 have been utilized. In the Pacific, all variables show the general characteristics of ENSO such as; the warming over the central-to-eastern equatorial Pacific, the convergence of the surface wind into the warm center (Fig. 8a), the deepening of heat content over the eastern Pacific and shoaling of the heat content over the western Pacific (Fig. 8b), and the enhanced convection in the central eastern Pacific and the suppressed convection over the maritime content (Fig. 8c). In the Indian Ocean there is a warming over the Fig. 8. Composite map of (a) SST and surface wind stress anomalies, (b) HC 125, and (c) OLR based on the 19 cases, which the basin-scale mode events follow the ENSO. Units in (a) and (b) are C, and units in (c)wm 2. Positive values are shaded

8 210 S.-I. An whole tropical Indian Ocean basin concurrent with tropical eastern Pacific warming. The easterly flow over the southern Indian Ocean, and the southeasterly flow near Sumatra are enhanced at the same time. Associated with the surface wind, the heat content over the central to western Indian Ocean increases, and the heat content over the eastern Indian Ocean decreases (Fig. 8b). The increase of OLR over the eastern Indian Ocean, with its maximum over the maritime continent, is also prominent (Fig. 8c). This is mainly due to the suppressed convective motion in that region through an atmospheric bridge, the so-called Walker circulation, resulting in the increase in solar radiation (Klein et al., 1999). Therefore, the basin-scale warming associated with El Nino may be attributed to the increase in heat content over the western Indian Ocean and to the increase in solar radiation over the eastern Indian Ocean. The evolution that occurs after the BM during spring into the ZM during fall is shown in Fig. 9. For this figure, 12 ZM events that follow BM events were selected. Between March and May, the SSTA over the whole Indian Ocean becomes warmer and the surface wind patterns revert to northwesterly, indicating that the external forcing by ENSO is diminishing. The northwesterly wind cannot support the accumulated warm water as a form of the Rossby waves over the southwestern Indian Ocean, and thus the resulting relaxation of the warm water turns into a downwelling equatorial Kelvin wave through a boundary reflection process (upper panel of Fig. 9). Between June and August, the SST over the central-to-eastern Indian Ocean becomes much warmer and that over the far western Indian Ocean becomes cooler, and the anomalous equatorial wind becomes westerly. The heat content becomes larger than it is during the spring over the eastern Fig. 9. Composite map of (a) SST and surface wind stress anomalies, and (b) HC125 based on the 12 cases, which the zonal mode events follow the basin-scale mode events. Seasonal evolution from spring to fall is shown from the top to the bottom. Units are C, and the positive values are shaded

9 Link between the BM and ZM in the Tropical Indian Ocean 211 Indian Ocean (middle panel of Fig. 9b). Between September and November, the eastern Indian Ocean SST becomes warmer and the western Indian Ocean SST becomes cooler. Thus, the SST pattern is very similar to that for the ZM (actually negative ZM, warming in east and cooling in west). The corresponding surface winds point to the warm SST regions indicating the evidence of an air sea interaction. The strong east west contrast in the heat content higher in the east and lower in the west also corresponds well to the surface westerly wind (lower panel of Fig. 9). Figure 9 shows the warm (cold) water, which is accumulated by the easterly (westerly) wind anomaly during the El Nino (La Nina) winter, is reflected and propagates eastwards as a downwelling (upwelling) Kelvin wave during the spring and summer, in a manner similar to the equatorial waves in the Pacific (McPhaden and Yu, 1999). This Kelvin wave then turns into the coastal Kelvin wave that influences an alongshore thermocline (Sprintall et al., 2000). To confirm the role of these equatorial waves, the zonal evolution of the 10-day cycle TOPEX= POSEIDON (T=P) sea level height anomaly along the equator is shown in Fig. 10. T=P data are obtained from the Center for Space Research, University of Texas at Austin. As indicated by the solid-dashed line, for instance, the warm water accumulated during the spring of 1995 and 1998 propagates eastwards, and its speed is about 1.2 m s 1, which approximates the 2 nd baroclinic equatorial free Kelvin wave speed in the Indian Ocean (McCreary et al., 1993). Similar propagating features are also found during 1994, 1996, and Figures 9 and 10 show that the BM can initiate the ZM. The warm SSTA that lies over the entire Indian Ocean during winter begins to decrease from the late spring in time with the decay of ENSO. However, by supplying the warm water accumulated in the western off-equatorial Indian Ocean into the equatorial eastern Indian Ocean through the equatorial wave-guide, the SSTA over the eastern Indian Ocean can remain warm until the following fall. During the fall, the SSTA is reinforced by air sea interactions such as the decrease of the latent heat loss due to the decrease in wind speed and the deepening thermocline depth, due to the westerly wind anomaly Fig. 10. Time-longitude section of the anomalous sea level height along the equator obtained from the TOPEX= POSEIDON. Units are cm as discussed in Section 3. The same process, with an opposite anomaly, occurs in the case of cold BM events. 6. Numerical experiment As mentioned before, the accumulated water in the western Indian Ocean during the spring, as a form of the Rossby wave, can be reflected as the Kelvin wave after the equatorial wind weakens. This reflected Kelvin wave propagates to the equatorial eastern Indian Ocean along the equatorial wave-guide. Previously, the eastward propagation of the equatorial Kelvin wave was clearly demonstrated (Fig. 10), but the origin of this Kelvin wave is not yet confirmed. Since the

10 212 S.-I. An equatorial wave processes associated with the suggested hypothesis can be diagnosed using the model, a numerical experiment with an ocean dynamic model has been performed. A non-dimensionalized reduced gravity model to simulate an equatorial beta-plane shallow water system with a long-wave approximation is used. ð@ t þ "Þu yv x h ¼ F yu x h ¼ G ð1þ ð@ t þ "Þh þðu x þ v y Þ¼0 F and G represent the zonal and meridional wind forcing, respectively. The linear damping time scale is 30 months. The model domain spans E, with idealized straight north south meridional Ð boundaries, and as boundary conditions, udy ¼ 0 at the western boundary and u ¼ 0 at the eastern boundary are adopted. The model is forced for the period January 1973 to December 1997 with monthly wind stresses from Florida State University (FSU) pseudo wind stress data (Shriver and O Brien, 1995). Two experiments were performed. In the first experiment, no restriction was given, while in the second experiment western boundary reflection was not allowed so that the boundary-generated (reflected) Kelvin wave is excluded and only the directly wind-forced Kelvin wave is left. In each experiment, the model is run from 1973 to 1997, and the model output for only the period of 1993 through 1997 is discussed for an easy comparison with Fig. 10. The model is validated primarily with sea level data from T=P as shown in Fig. 11. Sea level is a proxy for heat content since both vary primarily as a result of changes in thermocline depth. High sea level is generally associated with a deep Fig. 11. Simulated Kelvin wave thermocline depth (a), wind-forced Kelvin wave thermocline depth (b), and western boundary-reflected Kelvin wave thermocline depth (c) along the equator. The contour interval is 5 m

11 Link between the BM and ZM in the Tropical Indian Ocean 213 thermocline and high heat content, and vice versa. Both the model (Fig. 11a) and T=P (Fig. 10) show a similar evolution feature. As shown in Fig. 11a (First experiment), the timing, formation, and propagation of the thermocline agree with the T=P data. Figure 11b represents the directly wind-forced Kelvin wave (hereafter forced Kelvin wave), which has been derived from the second experiment (i.e. no reflection at the western boundary), and Fig. 11c represents the boundary-generated Kelvin wave (hereafter reflected Kelvin wave), which is obtained by simply removing the directly wind-forced Kelvin wave signal (Fig. 11b) from the total Kelvin wave (Fig. 11a). During nearly every spring, the thermocline perturbation in the western Indian Ocean propagates all the way to the eastern Indian Ocean, and eventually the perturbations are intensified. For example, the downwelling reflected Kelvin waves during the spring of 1995 and 1996 propagate to the eastern Indian Ocean (Fig. 11c), and this thermocline becomes much deeper due to the direct wind forcing, after setting up air sea interaction during the late summer and fall (Fig. 11b). In the same manner, the upwelling-reflected Kelvin waves that appeared during 1994 winter and 1997 spring, subsequently contributed to the elevation in the thermocline depth in the eastern Indian Ocean. This indicates that the reflected Kelvin wave caused by the reflection of the Rossby wave seems to play a role of initiation for a strong variation of the eastern Indian Ocean SST. However, this point needs to be studied in more detail. The model also indicates that the reflected Kelvin waves erode the forced Kelvin waves. The thermocline depth of the reflected Kelvin waves clearly contributed to the drop in the thermocline depth (and elevation in thermocline depth) over the equatorial eastern Indian Ocean during 1993=94 winter, 1995 fall, 1995 fall, 1996 fall, and 1997 fall. It seems that about one-third of the forced Kelvin wave is dwarfed by the reflected Kelvin wave. Consequently, the reflected Kelvin waves play a role in both the triggering and dampening mechanisms for the zonal mode. 7. Summary and discussion Using ocean assimilation data (SODA), the TOPEX=POSEIDON sea level data, and OLR, the generation mechanism of the basin-scale and zonal modes of tropical Indian Ocean SST anomalies and their dynamic relationship have been investigated. Concurrent with the tropical Pacific warm (cold) event, convection over the tropical eastern Indian Ocean is suppressed and the equatorial westerly becomes weaker, which in turn allows more (less) solar radiation and less (more) latent heat loss over the eastern Indian Ocean and a deepening (shoaling) of the thermocline over the western Indian Ocean, especially the southern part. As a result, the SST over the whole tropical Indian Ocean increases (decreases). As El Nino decays, the warm SSTA over the Indian Ocean cannot be maintained. Nevertheless, the ocean inertia allows the accumulated heat content over the western Indian Ocean in the form of Rossby waves to turn into the equatorial Kelvin wave, resulting in a warming tendency in the eastern Indian Ocean. The monsoon circulation during the fall provides a favorable condition for the local air sea interaction, and thus the zonal mode can be developed. During the period selected for analysis ( ), about 44% of ZM events follow BM events. The composite feature belonging to this subgroup of 44% and results from a numerical experiment clearly verified that ZM events are physically linked to BM events. However, every BM event does not induce the ZM. In order to operate the triggering mechanism by the BM, it may require a pre-condition for the generation of ZM, such as the shoaling thermocline depth in the eastern Indian Ocean (e.g. Clarke and Lebedev, 1997). Investigation of this question will be a future research topic. A well-known climate regime shift during the late 1970s was accompanied by changes in the large-scale atmosphere ocean circulation and also changes in the characteristics of ENSO (An and Wang, 2000), including changes in the global impact of El Nino (e.g. Krishna Kumar et al., 1999). It is known that the connection between the Indian monsoon and El Nino became weaker during the most recent decade, while the correlation between the India monsoon rainfall index and the ZMI increased (Ashok et al., 2001). To see the interdecadal change in the relationship between the Indian Ocean and the El Nino, the cross correlation between the indices for two different decadal periods

12 214 S.-I. An and was calculated. Figure 12a shows that the relationship between the BMI and Nino-3 for two decadal periods does not change much, indicating that their relationship is robust. On the other hand, the lagged correlation coefficient between the ZMI and the other indices changes dramatically in two decades (Fig. 12b and c). During the most recent decade ( ), the ZMI significantly leads Nino-3 index by 3 months and the BMI by 9 months, which rarely happened during the earlier decades ( ). The temporal lead=lag relationship between the ZM and ENSO that appeared during the does not necessarily mean that the ZM induces ENSO. It may be caused by the seasonal phase-locking characteristics of each mode. Note that for both decadal periods, the ZMI follows both Nino-3 and BMI with the negative correlation. Thus, the mechanism that is postulated in this paper may not be influenced by the interdecadal change, however, the mechanism suggested for the interdecadal variation in the relationship between ENSO and the ZMI is worth further investigation. Acknowledgments The author has been supported by Frontier Research System for Global Change through its sponsorship of the International Pacific Research Center. The author thanks Diane Henderson and Gisela Speidel for their careful reading and editing of the manuscript. SOEST Contribution 6348 and IPRC Contribution IPRC-263. Fig. 12. As in Fig. 6 but with the different decades: indicated by the bold solid line and indicated by the bold dash-dotted line. The thin dotted and long-dashed lines indicate the 90% significance level for the and for the , respectively References An SI (2003) Conditional maximum covariance analysis and its application to the tropical Indian Ocean SST and surface wind stress anomalies. J Climate 16: An SI, Wang B (2000) Interdecadal change of the structure of the ENSO mode and its impact on ENSO frequency. J Climate 13: An SI, Wang B (2001) Mechanisms of locking of the El Nino and La Nina mature phases to boreal winter. J Climate 14: Annamalai H, Murtugudde R, Potemra J, Xie SP, Liu P, Wang B (2003) Coupled dynamics over the Indian Ocean: spring initiation of the Zonal mode. Deep-Sea Research II 50: Ashok K, Guan Z, Yamagata T (2001) Impact of the Indian Ocean Dipole on the relationship between the Indian Monsoon rainfall and ENSO. Geophys Res Lett 28: Carton JA, Chepurin G, Cao X, Giese B (2000) A simple ocean data assimilation analysis of the global upper ocean Part I: methodology. J Phys Oceanogr 30: Chambers DP, Tapley BD, Stewart RH (1999) Anomalous warming in the Indian Ocean coincident with El Nino. J Geophys Res 104: Clark AJ, Lebedev A (1997) Interannual and decadal changes in equatorial wind stress in the Atlantic, Indian, and Pacific Oceans and the eastern coastal response. J Climate 10: Iizuka S, Matsuura T, Yamagata T (2000) The Indian Ocean SST dipole simulated in a coupled general circulation model. Geophys Res Lett 27: Kiladis GN, Diaz HF (1989) Global climate anomalies associated with extremes in the Southern Oscillation. J Climate 2: Klein SA, Soden BJ, Lau NC (1999) Remote sea surface temperature variations during ENSO: Evidence for a tropical atmospheric bridge. J Climate 12:

13 Link between the BM and ZM in the Tropical Indian Ocean 215 Krishna Kumar K, Rajagopalan KB, Cane MA (1999) On the weakening relationship between the Indian monsoon and ENSO. Science 284: Latif M, Sterl A, Assenbaum M, Junge MM, Maier-Reimer E (1994) Climate variability in a coupled GCM. Part II: The Indian Ocean and Monsoon. J Climate 7: McCreary JP, Kundu PK, Molinari RL (1993) A numerical investigation of dynamics, thermodynamics and mixedlayer processes in the Indian Ocean. Prog Oceanog 31: McPhaden MJ, Yu X (1999) Equatorial waves and the El Nino. Geophys Res Lett 26: Murtugudde R, McCreary JP, Busalacchi AJ (2000) Oceanic processes associated with anomalous events in the Indian Ocean with relevance to J Geophys Res 105: Neelin JD, et al (1998) ENSO theory. J Geophys Res 103: Nicholls N (1989) Sea surface temperature and Australian winter rainfall. J Climate 2: Saji NH, Goswami BN, Vinayachandran PN, Yamagata T (1999) A dipole mode in the tropical Indian Ocean. Nature 401: Shriver JF, O Brien JJ (1995) Low-frequency variability of the equatorial Pacific Ocean using a new pseudostress dataset: J Climate 8: Sprintall J, Gordon A, Murtugudde R, Susanto D (2000) An Indian Ocean Kelvin wave observed in the Indonesian seas during May J Geophys Res 105: Ueda H, Matsumoto J (2000) A possible triggering process of East West asymmetric anomalies over the Indian Ocean in relation to 1997=98 El Nino. J Meteor Soc Japan 78: Venzke S, Latif M, Villwock A (2000) The coupled GCM ECHO-2. Part II: Indian Ocean response to ENSO. J Climate 13: Wallace JM, et al (1998) On the structure and evolution of ENSO-related climate variability in the tropical Pacific: Lessons from TOGA. J Geophys Res 103: Webster PJ, Moore AM, Loschnigg JP, Leben RR (1999) Coupled ocean atmosphere dynamics in the Indian Ocean during Nature 401: Xie SP, Annamalai H, Schott FA, McCreary JP (2002) Structure and mechanisms of South Indian Ocean climate variability. J Climate 15: Yu L, Rienecker MM (1999) Mechanisms for the Indian Ocean warming during the El Nino. Geophys Res Lett 26: Author s address: Soon-Il An ( sian@hawaii.edu), International Pacific Research Center, SOEST, University of Hawaii at Manoa, Honolulu, HI 96822, USA.

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