Influence of Indian Ocean Dipole on Poleward Propagation of Boreal Summer Intraseasonal Oscillations

Transcription

1 VOLUME 21 J O U R N A L O F C L I M A T E 1 NOVEMBER 2008 Influence of Indian Ocean Dipole on Poleward Propagation of Boreal Summer Intraseasonal Oscillations R. S. AJAYAMOHAN* AND SURYACHANDRA A. RAO FRCGC, Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan TOSHIO YAMAGATA FRCGC, Japan Agency for Marine-Earth Science and Technology, Yokohama, and Department of Earth and Planetary Science, The University of Tokyo, Tokyo, Japan (Manuscript received 7 November 2006, in final form 7 March 2008) ABSTRACT The influence of the Indian Ocean dipole (IOD) on the poleward propagation of boreal summer intraseasonal oscillations (BSISOs) is examined using observed datasets. This study finds that coherent (incoherent) poleward propagation of precipitation anomalies from 5 S to 25 N are observed during negative (positive) IOD years. Disorganized poleward propagation of BSISO in the south equatorial Indian Ocean is observed during positive IOD years. The rationale behind such an anomaly in the poleward propagation of BSISO in contrasting IOD years is identified based on the theory of northward-propagating BSISO, which suggests the influential role of air sea interaction on the genesis and propagation of BSISO. It is found that the mean structure of moisture convergence and meridional specific humidity distribution undergoes radical changes in contrasting IOD years, which in turn influences the meridional propagation of BSISO. This study assumes significance, considering the critical role of BSISO in modulating the seasonal mean summer monsoon rainfall. 1. Introduction The importance of the Indian Ocean dipole (IOD) and its impact on seasonal and interannual climate variations has been discussed widely in recent studies (Yamagata et al. 2004, and references therein). IOD is defined as the dipole mode in sea surface temperature (SST) anomalies in the tropical Indian Ocean coupled to zonal winds and convection (Saji et al. 1999). The coupled ocean atmosphere phenomenon, locked to the boreal summer and fall, is associated with cool (warm) SST anomalies in the southeastern equatorial Indian * Current affiliation: Canadian Centre for Climate Modelling and Analysis, Victoria, British Columbia, Canada. Current affiliation: Indian Institute of Tropical Meteorology, Pune, India. Corresponding author address: R. S. Ajayamohan, Canadian Centre for Climate Modelling and Analysis, University of Victoria, P.O. Box 1700, STN CSC, Victoria, BC V8W 2Y2, Canada. ajayanrs@gmail.com Ocean (SEIO) and warm (cool) SST anomalies in the western Indian Ocean. The unique teleconnection of IOD on various parts of the globe are being established. The studies that show the influences of IOD on the Southern Oscillation (Behera and Yamagata 2003), central Indian intense rains (Ajayamohan and Rao 2008), East African short rains (Black et al. 2003; Rao and Behera 2005), Sri Lankan Maha rainfall (Zubair et al. 2003), and southwestern parts of Australia (Saji and Yamagata 2003; Ashok et al. 2003) are noteworthy. The possible impact of IOD on Indian summer monsoon rainfall and its prediction is currently an active research area among climate scientists, because of the pronounced socioeconomic impacts of the monsoon. The positive IOD starts in May with anomalous cooling in the southeastern equatorial Indian Ocean (Saji et al. 1999; Guan et al. 2003); the cooling reaches maximum in boreal fall. Southeastern equatorial Indian Ocean SST anomalies in boreal summer [June September (JJAS)] in many positive (negative) IOD years are significantly below (above) normal in the peak monsoon time and have the potential to influence DOI: /2008JCLI American Meteorological Society 5437

2 5438 J O U R N A L O F C L I M A T E VOLUME 21 the monsoon. Ashok et al. (2001, 2004) found that IOD plays an important role as a modulator of Indian summer monsoon rainfall (ISMR) and influences the correlation between ISMR and El Niño Southern Oscillation (ENSO). They conclude that whenever the ENSO ISMR correlation is low (high), the IOD ISMR correlation is high (low). Gadgil et al. (2004) showed that large anomalies of ISMR are linked to the atmospheric component of the coupled Indian Ocean dipole. The role of winter intraseasonal oscillations on the termination of IOD has been studied in detail (Rao and Yamagata 2004; Rao et al. 2007; Han et al. 2006). It is found that atmospheric intraseasonal oscillations in winter excite westerlies, which in turn excite anomalous downwelling oceanic Kelvin waves, resulting in the termination of IOD events. Rao et al. (2007) also noticed a weakening of wintertime intraseasonal activity during positive IOD years. Shinoda and Han (2005), who studied the influence of IOD on atmospheric intraseasonal oscillations during boreal fall, found a high correlation between the interannual variation of 6 30-day surface zonal wind activity in the central and eastern equatorial Indian Ocean and the large-scale zonal SST gradient. They also observed a reduction in the intensity of submonthly variability and day wind activity during dipole events. The boreal summer intraseasonal oscillations (BSISOs) are associated with poleward propagation. It would be interesting to study the influence of IOD on poleward-propagating oscillations because these oscillations are known to influence the South Asian summer monsoon rainfall. This is the motivation of the present work. The summer monsoon rainfall over India is not uniform, and it is punctuated by active and break spells that are manifestations of the monsoon intraseasonal oscillations. It is also closely related to the annual evolution of the tropical convergence zone (Shukla 1987; Gadgil 2003). The most important character of BSISO is the marked meridional propagation of clouds and convection from about 5 S to 25 N over South Asian monsoon region (Yasunari 1979, 1980; Sikka and Gadgil 1980). The dominant monsoon ISO with a day period has a large spatial scale similar to that of seasonal mean and its interannual variability (Yamagata and Hayashi 1984; Sperber et al. 2000; Goswami and Ajayamohan 2001). This may result in a strengthening (weakening) of the seasonal mean in its active (break) phases. Hence, monsoon ISO has the potential to influence the seasonal mean and its predictability (Sperber et al. 2000; Goswami and Ajayamohan 2001). Modeling studies also confirm the critical role of BSISO in shaping the seasonal mean monsoon (Waliser et al. 2003; Ajayamohan and Goswami 2007; Ajayamohan 2007). Based on an index used to define ISO activity using outgoing longwave radiation (OLR) data, Lawrence and Webster (2001) have shown that the summertime ISO activity is relatively uncorrelated with ENSO, except for a weak positive correlation at the beginning of the South Asian monsoon season. They conclude that summertime ISO activity is uncorrelated with any other contemporaneous or leading SST variability. Krishnan et al. (2006) show that the extended monsoon break of July 2002 is associated with enhanced sea surface height anomalies in the eastern equatorial Indian Ocean. In this study, we focus on the question of whether the anomalous cooling/warming in the southeastern equatorial Indian Ocean influences the meridional propagation of the boreal summer ISOs. Are the propagation characteristics of BSISO disparate during contrasting IOD years? If so, what is the underlying mechanism and the dynamics responsible for the difference? The importance of these questions lies in the fact that BSISOs are the building blocks of the summer monsoon. To the best of our knowledge, this is the first study that addresses the impact of IOD on BSISO. Toward this goal, we have used a set of observational and reanalysis datasets. In the remaining part of the this article, a brief description of the used datasets and analysis methods are outlined in section 2, the identification of a relationship between IOD and the meridional propagation of BSISO is presented in section 3, the dynamical aspects of this relationship is addressed in section 4, and the work is concluded with a summary and discussion in section Data sources and methodology Pentad precipitation data from the Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP; Xie and Arkin 1997) for the period have been used. Daily averaged precipitation from the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40; Uppala et al. 2005) was also used for the verification of the CMAP results. For SST, weekly data from the Hadley Centre Global Sea Ice and Sea Surface Temperature (Had- ISST; Rayner et al. 2003) dataset during and weekly data from Reynolds SST (Reynolds and Smith 1994) during were used. Daily averaged ERA-40 data for zonal and meridional winds, vertical pressure velocity, and specific humidity were used for the period. The horizontal resolution for the above datasets is for ERA-40 and CMAP and for HadISST and Reynolds SST. The

3 1NOVEMBER 2008 A J A Y A M O H A N E T A L FIG. 1. JJAS seasonal mean SST anomalies averaged over 10 S 0, E, normalized by their own standard deviation. Lines indicate 0.5 standard deviation of SST anomalies. The SST dataset is a merged product of HadISST ( ) and Reynolds SST ( ). vertical resolution of the ERA-40 dataset is 13 pressure levels. Anomalies were calculated according to the data frequency (pentad/daily). The anomalies have been filtered using a Lanczos filter (Duchon 1979), by retaining periodicities between 20 and 100 days to obtain intraseasonal anomalies. These intraseasonal anomalies will be referred to as filtered anomalies hereafter. The SST data have been used for analysis after removing the obvious linear trends. 3. Relationship between IOD and meridional propagation of BSISO Positive (negative) IOD years are usually classified based on the concomitant cooling (warming) of SST in the southeastern equatorial Indian Ocean with the associated anomalous warming (cooling) of SST in the western Indian Ocean during boreal summer and fall (Saji et al. 1999; Rao et al. 2007). Two indices the east dipole mode index (east DMI; 10 S 0, E) and the west dipole mode index (west DMI; 10 S 10 N, E) are defined for this purpose. However, this study deals with the relationship between boreal summer (JJAS) ISOs and IOD, and hence slightly different criteria, as follows, are used to classify the negative and positive IOD years: 1) The years for which JJAS SST anomalies are averaged over a box in the southeast equatorial Indian Ocean (10 S 0, E) and are above or below 0.5 standard deviation (Fig. 1). 2) There must be simultaneous warming (cooling) in the western Indian Ocean during boreal summer, when there is cooling (warming) in the southeast equatorial Indian Ocean. Warming (cooling) in the western Indian Ocean is the consequence of the Rossby wave response emanating from the cool (warm) southeastern equatorial Indian Ocean (Rao et al. 2002). The cooling in the southeastern Indian Ocean usually begins in May, and it takes about 2 3 months to show strong warming in the west DMI box. Because we are interested in the boreal summer ISO signal, a stringent criterion for warming (cooling) is not imposed in the western Indian Ocean. A warming (cooling) signal in the western Indian Ocean with significant cooling (warming) in the southeastern equatorial Indian Ocean in the boreal summer is considered a positive (negative) IOD year (see Table 1). Observations indicate coherent poleward propagation of cloud bands from the south equatorial Indian Ocean ( 5 S) to about 25 N on intraseasonal time scales during the summer monsoon season (Yasunari 1979, 1980; Sikka and Gadgil 1980; Lawrence and Webster 2002; Jiang et al. 2004). Composite seasonal mean intraseasonal variance of filtered precipitation anomalies (see Fig. 2) identifies two zones of maximum pre- TABLE 1. Positive and negative IOD years considered for this study. Negative IOD years Positive IOD years * * * These years are excluded from the list of negative IOD years because they do not show significant cooling in the western Indian Ocean in boreal summer (JJAS). See text for details.

4 5440 J O U R N A L O F C L I M A T E VOLUME 21 FIG. 2. (a) JJAS composite mean variance of day filtered CMAP precipitation anomalies (mm 2 day 2 ) in contrasting IOD years (see Table 1 for the list of negative and positive IOD years). Contour levels are 3, 9, 15, 24, 36, 48, and 60. The box represents the base region (12 22 N, E) taken for the regression calculations. cipitation that coincide with the movement of the tropical convergence zone. While the primary zone of maximum precipitation is over the Indian subcontinent (12 22 N, E), the secondary zones of precipitation maximum stays over the warm waters of the Indian Ocean (10 S 0, E). The poleward propagation of cloud bands from the warm waters of the Indian Ocean to the subcontinent is responsible for the active and break cycles of monsoon precipitation over the Indian continent. Enhanced intraseasonal variance of precipitation anomalies is seen in most of the Indian monsoon domain in negative IOD years (Fig. 2a) compared to positive IOD years (Fig. 2b). This suggests enhanced intraseasonal activity in this region in negative IOD years compared to positive IOD years. Figure 3 shows time latitude plots of unfiltered (only annual cycle removed) precipitation anomalies averaged over the E longitude band during a typical negative and positive IOD year. A greater number of polewardpropagating precipitation anomalies (from 5 S to 25 N) is generally seen in negative IOD years in contrast to positive IOD years. The average number of polewardpropagating BSISO in negative IOD years is from three to four, compared to the one to two in positive IOD years. Although few poleward-propagating signals are active in June and July in some positive IOD years, in general there is a weakening of the intraseasonal signal in these years. To understand the propagation characteristics of BSISO in contrasting IOD years, filtered precipitation anomalies are regressed at different time lags with respect to a reference time series. The reference time series is computed based on the averaged filtered precipitation anomalies over a box in the monsoon trough region (12 22 N, E; see Fig. 2) during the boreal summer season from 1 June to 30 September. In general, these lag regression plots show the coherent poleward propagation of filtered precipitation anomalies in intraseasonal time scales from about 5 S (figure not shown; see Lawrence and Webster 2002; Ajayamohan and Goswami 2007). In negative IOD years, the poleward propagation of precipitation anomalies seems FIG. 3. Time latitude plot of unfiltered (only annual cycle removed) precipitation anomalies (mm day 1 ) averaged between 70 and 95 E during two typically contrasting IOD years. Slanted lines represent poleward-propagating anomalies that are well connected.

5 1NOVEMBER 2008 A J A Y A M O H A N E T A L FIG. 4. Regressed filtered anomalies of CMAP precipitation (mm day 1 ) during negative IOD years for different lags. Contours are labeled at an interval of 0.6. Only statistically significant (0.05 significance level using a t test) anomalies are plotted. to be more organized (Fig. 4). Precipitation anomalies over the equatorial Indian Ocean (Fig. 4a) move poleward in a coherent manner to form an active monsoon season with enhanced precipitation (Fig. 4c) over the continent. Meanwhile, reduced precipitation anomalies strengthen over the equatorial Indian Ocean (Fig. 4d), which moves poleward to form a break monsoon season (Fig. 4f) after 20 days. In contrast, a coherent poleward movement of precipitation anomalies is missing in positive IOD years (Fig. 5). The most striking difference is over the warm waters of the Indian Ocean, where a coherent band of precipitation anomalies is not seen during positive IOD years. In negative IOD years, BSISO anomalies in the equatorial region are close to the Sumatra coast, while for positive IOD years they are located in the central Indian Ocean. Because SSTs near Sumatra are below normal during positive IOD years, and these SSTs do not support strong convection in this region, significant anomalies are seen shifted to the central equatorial Indian Ocean. The characteristic large spatial scale of poleward-propagating BSISO is missing in positive IOD years, particularly in the regions south of the equator (Fig. 5). Poleward propagation characteristics of precipitation anomalies are summarized in Fig. 6, where the latitude time lag plot of regressed precipitation anomalies averaged over the E domain are plotted. Regression map of precipitation anomalies of all years of CMAP precipitation ( ) show coherent propagation from about 5 S to 25 N (Fig. 6a) illustrating the dominant character of monsoon ISOs. Organized coherent poleward propagation of BSISO with enhanced BSISO variance is observed in negative IOD years compared to normal years. On the other hand, during positive IOD years disorganized or incoherent propagation is observed (Fig. 6c). When compared with the poleward propagation of negative IOD years where there is coherent poleward propagation of precipitation anomalies from 5 S, positive IOD years show weak propagation, especially in the equatorial region. Thus, the fundamental characteristics of BSISO are different in contrasting IOD years with organized (disorganized) meridional propagation in warm (cool) phases of IOD. Figure 6 is based on CMAP precipitation data and is limited by the sample size of positive and negative IOD years. Similar regression maps calculated from the ERA-40 daily precipitation dataset for a longer period ( ) confirm the above results (figure not shown). The cloud bands form over the warm waters of the

6 5442 J O U R N A L O F C L I M A T E VOLUME 21 FIG. 5. Same as Fig. 4, but for positive IOD years. south equatorial Indian Ocean and propagate northward toward the monsoon trough region. The differences in spatial structure and strength of BSISO convection anomalies during contrasting IOD years are more prominent in the south equatorial Indian Ocean (Figs. 4 and 5). To find how the warm and cool anomalous SST phases associated with IOD affects the dominant periodicities in this region, we have used a wavelet spectrum analysis. The wavelet analysis decomposes a time series into time frequency space and thus provides detailed information on dominant modes in the data, showing how these modes vary in time (Torrence and Compo 1998). Figure 7 shows the composite wavelet spectrum of precipitation anomalies averaged over FIG. 6. (a) Regressed filtered anomalies of CMAP precipitation (mm day 1 ) averaged over E as a function of latitude and time lag during the period. As in (a), but for (b) negative and (c) positive IOD years. Contour interval is 0.6. Only statistically significant (0.1 significance level using a t test) anomalies are plotted.

7 1NOVEMBER 2008 A J A Y A M O H A N E T A L atmospheric parameters are examined to investigate whether there is a distinct difference in these profiles during contrasting IOD years, and, if so, how do changes in these fields influence the ISO characteristics. FIG. 7. Composite wavelet spectrum of precipitation anomalies averaged over 10 S 0, E for (a) negative and (b) positive IOD years. 10 S 0, E for negative and positive IOD years. While the 30-day period is dominant in June and July, a peak with a variance of around 50 days is also dominant during the entire monsoon season in negative IOD years (Fig. 7a). In positive IOD years, both the 30- and 50-day period variances become weak. The peak at the maximum variance at 60 days becomes more prominent in August and September during positive IOD years. There is enhanced BSISO activity in the south equatorial Indian Ocean during negative IOD years as compared to positive IOD years in the summer monsoon season (June September). 4. Mechanism In this section, we try to elucidate the dynamical reasons for the incoherent northward propagation of BSISO in positive IOD years. The vertical profiles of various a. Theory Several theories and hypotheses were proposed in the past to explain the poleward propagation of BSISO, which is the most striking character of summer monsoon intraseasonal oscillations (Goswami 2005; Wang 2005, and references therein). Based on many previous studies aimed at understand this intriguing problem, Jiang et al. (2004) propose an improved theory of the physical processes that are responsible for the poleward propagation using a simple zonally symmetric model to interpret the results from a GCM simulation and observations. According to Jiang et al. (2004), a combination of vertical wind shear and moisture convection feedback mechanisms is responsible for the meridional propagation of the convection band. They demonstrate that the easterly mean wind shear in the region gives rise to the generation of barotropic vorticity to the north of the convection center, which in turn generates barotropic divergence in the free atmosphere north of the convection center. This leads to boundary layer convergence north of the convection maximum. This mechanism is more effective away from the equator in the Northern Hemisphere. Near the equator, they suggest an alternate mechanism. This involves anomalous moisture convergence north of the heating by anomalous winds in the presence of a positive gradient of mean meridional specific humidity. The asymmetry in meridional specific humidity contributes to the northward shift of the convective heating. The summer mean flow and mean boundary layer humidity allow perturbation moisture convergence to be maximum north of the convection center. The presence of the basic mean flow is the key factor responsible for northward propagation (Wang and Xie 1997; Wu. et al. 2006). However, what specific features of summer mean circulation are responsible for northward propagation? As mentioned above, the effect of easterly vertical shear is an important factor. The advection of moisture by the mean flow and moisture advection resulting from mean meridional intraseasonal winds in the boundary layer are essential factors favoring northward propagation (Jiang et al. 2004). The third factor that may enhance northward propagation is the intraseasonal variation of SST, which is shown to guide convection in the northward-propagating BSISO (Sengupta et al. 2001; Fu et al. 2003).

8 5444 J O U R N A L O F C L I M A T E VOLUME 21 The above-mentioned theories demand the following on intraseasonal time scales: 1) The cyclonic vorticity at low levels and the associated boundary layer moisture convergence must be maximum north of maximum convection to initiate the poleward propagation of BSISO. This mechanism is more effective north of the equator. 2) Near the equator, the moisture convection feedback mechanism is responsible for poleward propagation of BSISO. 3) Intraseasonal SST variations. Warm (cool) SST ahead of nhanced (suppressed) convection. b. Changes in the mean state in contrasting IOD years A notable increase in summer mean vertically integrated specific humidity is observed over the southeastern Indian Ocean in negative IOD years compared to positive IOD years (Fig. 8a). Figure 8b shows mean integrated specific humidity averaged over E and plotted as a function of latitude for negative and positive IOD years. This plot highlights the differences in the mean meridional gradient of specific humidity during contrasting IOD years in the equatorial region. The mean meridional specific humidity profile of positive IOD years are distinctly different in the equatorial region compared to negative IOD years. In normal years, the specific humidity from south of equator to the equatorial region (12 5 S) increases sharply. This sharp positive meridional gradient is enhanced in negative IOD years (Fig. 8b). In positive IOD years, because of the decrease in specific humidity in southeastern Indian Ocean, the meridional gradient becomes weak. The presence of a positive mean meridional gradient of specific humidity is one of the essential features for coherent poleward propagation. Mean convergence of winds and mean moisture convergence is enhanced over the Indian Ocean region in negative IOD years compared to positive IOD years (Figs. 8c,e). The spatial maps of divergence and moisture divergence show similar patterns. This shows that changes in mean moisture convergence are mainly due to the convergence of winds. These seasonal mean changes in contrasting IOD years are also reflected in the equatorial Indian Ocean region (70 95 E), from where BSISO precipitation propagates poleward (Figs. 8d,f). The spatial pattern of SST and the divergent component of moisture transport anomalies during the contrasting IOD years are shown in Fig. 9, which illustrates the cool and warm phases of IOD. The cool (warm) SST anomalies in the southeast equatorial Indian Ocean suppress (enhances) anomalous convection over this region, which creates a divergence (convergence) zone of moisture anomalies in positive (negative) IOD years (Fig. 9). The resulting anomalous moisture transport also causes considerable changes in the atmosphere on intraseasonal time scales. The changes in the mean state specific humidity and surface convergence favor the northward propagation of BSISO (Jiang et al. 2004). The convergence at the surface level will induce upward motion in the atmospheric boundary layer, which will bring rich moisture to a certain level in the planetary boundary layer (PBL). The advection caused by summer mean meridional winds in the PBL may further shift specific humidity center to the north of convection. Another possible mechanism that leads to the northward shift of moisture is the advection of the mean state specific humidity by BSISO meridional winds in the boundary layer. c. Composite vertical structure of northwardpropagating ISO events Both the theoretical framework (Jiang et al. 2004) and the conceptual model (Goswami 2005) suggest that the northward shift of barotropic vorticity, barotropic divergence, specific humidity, and vertical velocity from the maximum convection center is responsible for the northward propagation of the BSISO signal. Composites are calculated using the methodology suggested by Jiang et al. (2004). First, a Hovmöller diagram of positive BSISO rainfall averaged over E is plotted as a function of latitude and time lag for positive and negative IOD years (figure not shown). From this plot, the reference latitude and the time of the extreme northward-propagating BSISO events (from 5 S to 20 N) are noted. Extreme northward-propagating BSISO is defined as the BSISO events that show coherent poleward propagation of precipitation anomalies from 5 S to 20 N. A phase composite structure based on these extreme northward-propagating BSISO is constructed at each reference latitude over which the maximum convection occurs. Because these phase structures bears great similarity, we further compose them with respect to the maximum convection center for different latitudes. In negative IOD years, we have nine extreme northward-propagating BSISO cases. Because a very few number of extreme northwardpropagating BSISO events are present in positive IOD years (two cases satisfying the criteria), composites for positive IOD years are calculated with respect to the maximum convection center only. The selected maximum convection centers (seven cases) do not show

9 1NOVEMBER 2008 A J A Y A M O H A N E T A L FIG. 8. (a) Composite of summer (JJAS) mean integrated specific humidity (kg hpa kg 1 ) for negative IOD years minus positive IOD years. (b) JJAS integrated specific humidity averaged over E plotted against latitude. Specific humidity integrated from surface to 300 hpa has been used. Red and blue lines represent negative and positive IOD years, respectively. Black line represents all years taken together. (c) Same as (a), but for low-level (1000 hpa) convergence of winds ( U; 10 6 s 1 ). (d) Same as (b), but for low-level convergence of winds. (e) Same as (a), but for low-level (1000 hpa) moisture convergence ( qu; 10 8 Kg kg 1 s 1 ). (f) Same as (b), but for low-level moisture convergence.

10 5446 J O U R N A L O F C L I M A T E VOLUME 21 FIG. 9. Composite seasonal mean anomalies (JJAS) of SST ( C) and divergent component of moisture transport anomalies (kg m 1 s 1 ) for contrasting IOD years. Vertically integrated moisture transport can be separated as rotational and irrotational components (see Behera et al. 1999). Moisture transport anomalies are integrated from 1000 to 300 hpa. marked northward propagation in positive IOD years. In other words, we have two categories of BSISO one with a strong northward-propagating character and other with a weak northward propagation. Figure 10 shows the meridional vertical structure of composite ISO in contrasting IOD years for specific humidity (Figs. 10a,b), vertical velocity (Figs. 10c,d), vorticity (Figs. 10e,f), and divergence (Figs. 10g,h). As noted earlier, meridional asymmetry in specific humidity is an important factor for northward propagation in the equatorial region. Such clear meridional asymmetry in the specific humidity profile is seen during negative IOD years (Fig. 10a). In contrast, the specific humidity profile of positive IOD years does not show marked meridional asymmetry (Fig. 10b). Similarly, the composite vertical velocity shows ascending (descending) motion north of the convection maximum for negative (positive) IOD years (Figs. 10c,d). The structure of vorticity associated with negative IOD years reveals a positive vorticity center north of the convection center and a negative vorticity center south of the convection maximum. This asymmetry is not seen during positive IOD years. The composite structure of divergence indicates strong low-level convergence north of the convection center associated with negative IOD years, whereas no such signal is seen during positive IOD years (Figs. 10g,h). These results suggest that the dynamical features essential for coherent poleward propagation of boreal summer ISO are not noticeable in positive IOD years. The fundamental character of BSISO is distinct in contrasting IOD years. The conjecture stated in the section 4a demands a peculiar feature for coherent northward propagation BSISO anomalies. The maximum positive vorticity anomalies shall be located north of the corresponding anomalous convection center. The positive vorticity, in turn, induces convergence in the boundary layer and triggers new convection north of anomalous convection, thus favoring northward propagation. Regressed filtered anomalies of relative vorticity and vertical velocity averaged over E, at different time lags for contrasting IOD years, are shown in Figs. 11 and 12. The positive vorticity contours are located north of the maximum convection center at different phase lags in negative IOD years, suggesting coherent poleward propagation of BSISO anomalies (Fig. 11). In contrast, this feature is not clearly visible during all BSISO phases in positive IOD years (Fig. 12). Moreover, the amplitude of relative vorticity and vertical velocity is weakened in positive IOD years compared to negative IOD years. It shall be noted that the relative vorticity anomalies have a dominant barotropic structure with a northward tilt. This asymmetric barotropic structure of relative vorticity is well maintained during different phases of an oscillation in negative IOD years. However, the asymmetric meridional structure of relative vorticity is not maintained during all phases in positive IOD years, resulting in incoherent poleward propagation of BSISO anomalies. Regression analysis of moisture divergence ( qu) at 1000 hpa shows coherent (incoherent) poleward propagation of moisture divergence anomalies in negative (positive) IOD years. On decomposition of mean and anomalous terms, it is found that the moisture convergence/divergence is caused by the anomalous convergence of winds in the presence of mean humidity (figure not shown).

11 1NOVEMBER 2008 A J A Y A M O H A N E T A L FIG. 10. Meridional vertical structure of northward-propagating ISO mode for (a), (b) specific humidity (10 4 kg kg 1 ); (c), (d) pressure vertical velocity (Pa s 1 ); (e), (f) vorticity (10 6 s 1 ); and (g), (h) divergence (10 7 s 1 ). Plots represent anomalies averaged over E as a function of latitude and pressure levels for contrasting IOD years. Horizontal axis is the meridional distance ( latitude) with respect to the convection center (0 ). The positive (negative) values represent to the north (south) of convection center.

12 5448 J O U R N A L O F C L I M A T E VOLUME 21 FIG. 11. Composite vertical structure of BSISO. (a) Regressed filtered anomalies of pressure vertical velocity (arrows; Pa s 1 ) and relative vorticity (contour, 10 6 s 1 ) with respect to a base time series averaged over E for negative IOD years at different time lags. The base time series is based on precipitation averaged in the domain 5 S 5 N, E. The data used here are derived from the National Centers for Environmental Prediction (NCEP)-II reanalysis (Kanamitsu et al. 2002). d. Air sea interactions Recent modeling and observation studies (e.g., Fu et al. 2003; Sengupta et al. 2001) emphasize the crucial role of air sea interactions in defining the observed phase structure of BSISO. Fu et al. (2003) suggests that warm (cold) SSTs lead the northward-propagating wet (dry) phase of convection in the north Indian Ocean by FIG. 12. Same as Fig. 11, but for positive IOD years. about 10 days. Sengupta et al. (2001) show that intraseasonal SST anomalies are in quadrature with intraseasonal net surface heat flux (Qnet) anomalies. Negative Qnet (ocean loses heat) corresponds to the wet phase while positive Qnet corresponds to the dry phase. The warm (cold) phase of the SST band follows the dry (wet) precipitation band with a time lag of 7 10 days. The positive SST anomaly in the wake of the dry phase of convection can account for enhanced moisture perturbation through enhanced evaporation (Shinoda et al. 1998) and result in moisture convergence north of the active phase of convection (Fu et al. 2006). The major contributors for intraseasonal Qnet fluc-

13 1NOVEMBER 2008 A J A Y A M O H A N E T A L FIG. 13. Regressed filtered anomalies of net surface heat flux (Qnet; W m 2 ) averaged over E as a function of latitude and time lag for (a) negative and (b) positive IOD years. Same as (a), but for SST anomalies ( C) during (c) negative and (d) positive IOD years. Qnet data are derived from NCEP-II reanalysis and SST data are derived from daily optimally interpolated (OI) SST (Reynolds et al. 2007). tuations are identified as solar radiation flux and latent heat flux (Fu et al. 2003). The above-mentioned studies also demonstrate that the phase relationship between northward-propagating BSISO and SST is very well maintained in a coupled ocean atmosphere system. The SEIO is a very important region in which BSISO amplification and reinitiation takes place (Fu and Wang 2004; Wang et al. 2006). Because SEIO anomalously cools in the positive IOD years because of the strong feedback from subsurface ocean through upwelling, the moisture north of the convection is not enhanced. These results, when juxtaposed together, lend credence to the inference that the coherent evolution of convection and SST may be rudiments of coherent propagation of BSISO anomalies in negative IOD years. To evaluate the evolution of SST and convection in contrasting IOD years, we have analyzed the propagation characteristics of Qnet and SST. Coherent (incoherent) propagation of BSISO anomalies even can be seen in Qnet and SST BSISO anomalies in negative (positive) IOD years (Fig. 13). This means that there is a coherent relationship between intraseasonal SST, Qnet anomalies, and intraseasonal precipitation in negative IOD years compared to positive IOD years. Because this figure (Fig. 13) is a composite latitude time plot regressed with a base time series, the phase relationship between SST and Qnet is not evident. The phase lag between SST and convection in contrasting IOD years is illustrated in Fig. 14, where we plot the composite lag correlation between these two fields in two different locations in the equatorial region. The equatorial region north of 5 S and south of 5 N, chosen as marked difference in poleward propagation characteristics between contrasting IOD years, is seen in this region (see Fig. 6). It is clear that SST systematically leads rainfall in intraseasonal time scales

14 5450 J O U R N A L O F C L I M A T E VOLUME 21 FIG. 14. The composite lag correlations of intraseasonal rainfall and SST over two locations in the equatorial Indian Ocean in contrasting IOD years. The solid (dashed) lines indicate negative (positive) IOD years. by about 5 6 days with a significant correlation of 0.5 in negative IOD years. For positive IOD years, the maximum correlation coefficients between intraseasonal rainfall and underlying SST are considerably reduced over both the locations. In addition, the time needed to attain maximum lead correlation between intraseasonal SST and rainfall in contrasting IOD years is dissimilar. To sum up, in the initiation phase, warm SST enhances moisture in the atmospheric boundary layer and results in moisture convergence north of the convection center. Even though this warm SST could be a response of the suppressed convection phase of BSISO as suggested by Sengupta et al. (2001), it helps in the propagation of the subsequent enhanced convection. In the reinitiation phase, the boundary layer convergence ahead of convection keep warming up the sea surface north of the convection through reducing the upward latent heat flux and increasing the downward solar radiation. This process ensures coherent northward propagation of convection in negative IOD years. In positive IOD years this does not happen because of strong subsurface upwelling at SEIO, which keeps cooling the SST even in the cloud-free conditions (Rao et al. 2007). Further, moisture divergence takes place in positive IOD years ahead of the strong convection in the Southern Hemisphere. This may be one of the reasons for incoherent propagation of BSISO in positive IOD years. The above analysis unveils the basis behind the incoherent (coherent) poleward propagation of BSISO during positive (negative) IOD years. The strong moisture convection feedback mechanism favors coherent poleward propagation of BSISO in negative IOD years compared to positive IOD years. The step-by-step summary of the above analysis is discussed as follows with the help of a schematic (Fig. 15). Two essential conditions are needed for the coherent poleward propagation of BSISO in the equatorial region, as follows: (a) anomalous moisture convergence north of the heating source by anomalous winds and (b) the presence of FIG. 15. Schematic showing the possible dynamical mechanisms responsible for the differences in poleward propagation of BSISO in contrasting IOD years.

15 1NOVEMBER 2008 A J A Y A M O H A N E T A L asymmetric distribution of meridional specific humidity. During a negative IOD event, warmer anomalous SST in the southeastern Indian Ocean enhances the convergence (Fig. 8c) and results in the enhancement of specific humidity (Fig. 8a) in this region. Both of the above changes enhance the moisture convergence (Fig. 8d) and positive meridional specific humidity gradient (Fig. 8b). This leads to meridional asymmetry in specific humidity in intraseasonal time scales (Fig. 10a). The BSISO convergence is located to the north of the convection maximum (Fig. 11). Therefore, the essential conditions for northward propagation are well established in a negative IOD year. However, in a positive IOD event, colder anomalous SST in the southeastern Indian Ocean suppresses the mean convergence (Fig. 8c) and results in the suppression of specific humidity (Fig. 8a), leading to a weak meridional specific humidity gradient (Fig. 8b). Here, the BSISO convergence is also not always toward the north of the convection maximum (Fig. 12) and there is no clear meridional asymmetry in BSISO specific humidity (Fig. 10b). Hence, the essential features required for the northward propagation of maximum convection center are not met with in the southern Indian Ocean. This leads to the incoherent poleward propagation of BSISO from 5 S. It shall be noted that we do not find marked differences in vertical easterly wind shear in contrasting IOD years. The coherent (incoherent) propagation of BSISO anomalies is also seen in the net surface heat flux anomalies and SST anomalies in negative (positive) IOD years (Fig. 13), illustrating the origin of these oscillations. It is further shown that the lead lag correlation between BSISO convection and SST are dissimilar in contrasting IOD years (Fig. 14). This suggests more coherent air sea interaction in negative IOD years compared to positive IOD years. 5. Summary and discussion The most significant character of monsoon ISOs is the northward propagation of precipitation anomalies from the south equatorial Indian Ocean to about 25 N. This study explores the influence of the dominant coupled ocean atmospheric phenomenon in the tropical Indian Ocean, namely, the Indian Ocean dipole on the poleward-propagating boreal summer ISO. We use a number of parameters from observations and reanalysis datasets for this purpose. We find that IOD modulates the basic character of BSISO. It is found that the coherent (incoherent) poleward propagation of BSISO from around 5 S to 25 N is observed during negative (positive) IOD years. The mechanisms for different propagation characteristics of BSISO during negative/ positive IOD years are identified within the context of the theory suggested by Jiang et al. (2004). It is found that while the negative IOD years are characterized by organized coherent poleward propagation of precipitation anomalies, positive IOD years are characterized by disorganized incoherent poleward propagation. The mean structure of specific humidity and moisture convergence in boreal summer undergo drastic changes in IOD years. Warmer (cooler)-thannormal SSTs in the southeastern Indian Ocean during a negative (positive) IOD year enhance (suppress) the mean convergence in the southeastern equatorial Indian Ocean and lead to strong (weak) asymmetry in meridional specific humidity distribution in this region. On intraseasonal time scales, the variance is enhanced (suppressed) in negative (positive) IOD years. In this scenario, if maximum convection is centered south of the equator, then changes in the mean and intraseasonal time scales favor (do not favor) its coherent northward propagation in negative (positive) IOD years. In negative IOD years, filtered moisture divergence anomalies propagate poleward and the barotropic convergence is seen north of the convection maximum. A stronger meridional gradient of specific humidity is also observed. All of the above-mentioned changes lead to coherent propagation of convection anomalies. However, such coherent propagation of barotropic divergence and precipitation anomalies is not observed during positive IOD years, especially in the equatorial region. The barotropic convergence/ divergence maximum is not always to the north of the convection maximum. The asymmetry in the specific humidity profile is weak in positive IOD years. The essential conditions required for the coherent propagation of convection anomalies are not met within positive IOD years. It is further shown that the lead lag correlation between BSISO convection and SST are distinctly different in contrasting IOD years. To summarize, positive (negative) phases of the Indian Ocean dipole mode generates significant distortion in the atmosphere to alter the behavior of monsoon ISOs. As with any interannual signal, there are event-toevent differences. This is true for the interannual modulation of northward-propagating BSISO and also for IOD evolution. In some positive IOD years, it is found that poleward-propagating BSISO signals are active in the month of June and/or early July. As the IOD signal gets strengthened in August and September, the distortion of the poleward-propagating signal becomes noticeable. Even though both monsoon and IOD are seasonally phase-locked phenomena, because we filter

16 5452 J O U R N A L O F C L I M A T E VOLUME 21 the data on intraseasonal time scales it is difficult to highlight the weakening of the poleward-propagating BSISO signals on a monthly basis within the intraseasonal band. Further, it may be noted that even a slight strengthening of BSISO variance in August/September in positive IOD years does not support coherent northward propagation because the mean conditions and the conditions necessary for poleward propagation are unfavorable. As annotated in the introduction, several observational and modeling studies confirm the important role of the poleward-propagating BSISO on the seasonal mean monsoon and its interannual variability. BSISOs contribute to about 40% of the seasonal mean monsoon rainfall (Ajayamohan and Goswami 2003; Goswami 2005). BSISOs also modulate synoptic activity over the monsoon trough and contribute to the rainfall over the Indian continent (Goswami et al. 2003). The present study establishes a relationship between the polewardpropagating monsoon ISOs and the Indian Ocean dipole. This leads to the conjecture that the IOD can influence the seasonal mean monsoon rainfall in an indirect manner. The coupled evolution of SST, circulation, and precipitation on intraseasonal time scales introduces constraints on the internal variability generated by intraseasonal oscillations. Our current understanding is that the statistics of BSISO over the Asian monsoon region are only weakly modulated by slowly varying SST, leading to poor predictability of monsoons (Goswami 2005). The present study, however, contrasts with this viewpoint, because we have shown that the slowly varying SST modulates BSISO characteristics in a noticeable manner. This leads to prospects of better predictability of monsoons. This study is limited by the sample size of reliable SST and precipitation data; we only have five negative IOD years and four positive IOD years after the satellite era (1980s). Some of the IOD years are El Niño La Niña years, and hence the Pacific influence in ISO characteristics may be expected. However, it is to be noted that the correlation between Niño-3 and east DMI is insignificant in boreal summer. Previously, Lawrence and Webster (2001) have shown that ENSO is uncorrelated with boreal summer ISO activity. To overcome the problem of the limited sample size of pure IOD years in observations, we have carried out a long integration of a coupled ocean atmospheric model (CGCM) that simulates the Indian Ocean climate realistically. The results from the CGCM study confirm the above findings, which will be reported elsewhere. The analysis using the ERA-40 datasets has a longer record and hence a sufficient number of contrasting IOD years. The summer monsoon is the lifeline of the agriculture-dependent South Asian communities, and hence affects the lives of one-sixth of the world s population. Several studies highlight the crucial role of BSISO in determining the seasonal mean monsoon and its interannual variability. The present study shows that the BSISO characteristics are influenced by the Indian Ocean dipole mode. In this context, the results presented here assume significance. However, how the IOD ISO relationship actually influences the seasonal mean monsoon rainfall in a quantitative manner is not known. This is probably one of the most intriguing questions to be answered in the light of results from this study. Acknowledgments. The authors thank anonymous reviewers for constructive suggestions and comments that led to great improvement of this manuscript. RSAM would like to acknowledge Prof B. N. Goswami, IITM, India, for several insightful discussions on boreal summer intraseasonal oscillations. REFERENCES Ajayamohan, R. S., 2007: Simulation of South-Asian summer monsoon in a GCM. Pure Appl. Geophys., 164, , and B. N. Goswami, 2003: Potential predictability of the Asian summer monsoon on monthly and seasonal time scales. Meteor. Atmos. Phys., 84, , and, 2007: Dependence of simulation of boreal summer tropical intraseasonal oscillations on the simulation of seasonal mean. J. Atmos. Sci., 64, , and S. A. Rao, 2008: Indian Ocean dipole modulates the number of extreme rainfall events over India in a warming environment. J. Meteor. Soc. Japan, 86, Ashok, K., Z. Guan, and T. Yamagata, 2001: Impact of the Indian Ocean dipole on the relationship between the Indian monsoon rainfall and ENSO. Geophys. Res. Lett., 28, ,, and, 2003: Influence of the Indian Ocean Dipole on the Australian winter rainfall. Geophys. Res. Lett., 30, 1821, doi: /2003gl ,, N. H. Saji, and T. Yamagata, 2004: Individual and combined influences of ENSO and Indian Ocean dipole on the Indian summer monsoon. J. Climate, 17, Behera, S. K., and T. Yamagata, 2003: Impact of the Indian Ocean dipole on the southern oscillation. J. Meteor. Soc. Japan, 81, , R. Krishnan, and T. Yamagata, 1999: Unusual oceanatmosphere conditions in the tropical Indian Ocean during Geophys. Res. Lett., 26, Black, E., J. Slingo, and K. R. Sperber, 2003: An observational study of the relationship between excessively strong short rains in coastal east Africa and Indian Ocean SST. Mon. Wea. Rev., 131, Duchon, C. E., 1979: Lanczos filtering on one and two dimensions. J. Appl. Meteor., 18,