On Impacts of ENSO and Indian Ocean Dipole events on the subregional Indian summer monsoon rainfall

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1 On Impacts of ENSO and Indian Ocean Dipole events on the subregional Indian summer monsoon rainfall Karumuri Ashok *,1 and N. H. Saji 2 1 Frontier Research Center for Global Change (FRCGC/JAMSTEC) , Showamachi, Kanazawa-Ku, Yokohama, Kanagawa, , Japan 2 International Pacific Research Center, University of Hawaii at Manoa, Honolulu, Hawaii, U.S.A. * address: ashok@jamstec.go.jp ABSTRACT The relative impacts of the ENSO and Indian Ocean Dipole (IOD) events on the Indian summer (June-September) monsoon rainfall at sub-regional scales have been examined in this study. GISST datasets from , along with Willmott and Matsuura gridded rainfall data, All India summer monsoon rainfall data, and homogeneous and sub regional Indian rainfall datasets were used in this study. The spatial distribution of partial correlations between the IOD and summer rainfall over India indicates a significant impact on the rainfall along the monsoon trough regions, parts of southwest coastal regions of India, and also over Pakistan, Afghanistan and Iran. ENSO events, have a wider impact, though opposite in nature over the monsoon trough region to that of IOD events. The ENSO (IOD) index is negatively (positively) correlated (significantly at 90% confidence level) with summer monsoon rainfall over 7(5) of the 8 homogeneous rainfall zones of India. During summer, ENSO events also cause drought over northern Sri Lanka, while the IOD events cause surplus rainfall to its south. On monthly scales, the ENSO and IOD events have significant impact on many parts of India. In general, the magnitude of ENSOrelated correlations is stronger than those related to the IOD. The monthly-stratified IOD variability during each of the months from July to September has a significant impact on the Indian summer monsoon rainfall variability over different parts of India, confirming that strong IOD events indeed influence the Indian summer monsoon. 1. Introduction The study of Indian summer monsoon variability is one of the important socially relevant scientific themes that receive lot of attention (see Webster et al., 1998) because of its complexity and 1

2 impact on general population. Its impact on the largely agriculture-based Indian economy can be gauged from the fact that rainfall over the Indian region during the summer monsoon season that lasts from June- September (JJAS) is 78% of the annual rainfall, based on the data from (Parthasarathy et al., 1994). El Niño has been known to be one of the most important forcings of the Indian summer monsoon variability (Sikka, 1980; Pant and Parthasarathy, 1981; Rasmusson and Carpenter, 1983; see Webster et al. 1998). However, the impact of ENSO on the Indian summer monsoon rainfall (ISMR) 1 has apparently weakened in the last two decades of the 20th century (Kumar et al., 1999). Along with Kumar et al. (1999), many other studies tried to understand the cause behind the weakening of the relationship (Kripalani and Kulkarni, 1999; Slingo and Annamalai, 2000; Ashok et al. 2001; Chang et al; 2001; Gershunov 2001). It is interesting to note that if the years 1983 and 1997 are excluded, the ENSO correlation with the Indian monsoon returns to a high (at 99% significance) level (Chang et al., 2001). However, it is now well known that 1997 witnessed a very strong positive Indian Ocean Dipole (IOD) event (Saji et al., 1999; Webster et al., 1999). Also during the summer of 1983, a strong positive IOD-like condition prevailed (see Fig.1; also Ashok et al., 2001; Saji et al., 2003a; Ashok et al., 2004), indicating the possible role of the IOD in the weakening of the ENSO-monsoon relationship; Guan et al. (2003) have, in fact, demonstrated by composite analysis of the observed boreal summer sea surface temperature anomalies (SSTA) during strong positive and negative IOD years that there exists a significant out of phase relationship between the SSTA in eastern and western tropical Indian Ocean during these years. Using observed dataset as well as an AGCM, Ashok et al. (2001) demonstrated that the weakening of the ENSO-ISMR relationship is apparently due to the frequent occurrence of strong positive IOD events that neutralized the ENSO impact; this is because the ISMR is positively correlated to the IOD mode index (IODMI) (Ashok et al., 2001; also see Table 1), whereas it is negatively correlated to NINO3 index that represents ENSO phenomenon. A recent paper by Sarkar et al. (2004) also supports this hypothesis. A brief description of the recently evolving research on IOD-Indian summer monsoon relationship follows. The impact of IOD type sea surface temperature anomalies (SSTA) on the Indian summer monsoon of 1961 was noted by Saji et al. (1999). Behera et al. (1999) showed that the strong anomalous surplus summer rainfall over India during 1994 was due to the cold SSTA off the coast of Indonesia that comprised the eastern pole of the intense positive IOD 1 Area-weighted average of the seasonal (June-September) total rainfall at 306 district stations spread over India (Mooley and Parthasarathy, 1984; see Fig. 6.1 of Pant and Kumar, 1997). 2

3 event of Ashok et al. (2001) and Guan et al. (2003), by use of an AGCM, demonstrated the role of this IOD-induced convergence/divergence pattern over the Bay of Bengal in influencing the ISMR. Saji and Yamagata (2003) confirmed that IOD influences the ISMR. Raju et al. (2002) stressed upon the role of Western Indian warming during the positive IOD event in the modulation of the ISMR. In another recent study, Ashok et al (2004a) showed, by use of observed datasets as well as an AGCM, the importance of the western pole of the IOD in modulating the rainfall over the northwest regions of India and neighboring Pakistan, and in reducing the ENSO impact on Indian summer rainfall. Other modeling studies that bring out the role of the IOD in modulating the ISMR are by Li et al. (2003) and Lau and Nath (2004). Bhaskar Rao et al. (2004) carried out a simulation case study of 1994 summer monsoon over the Indian region. Gadgil et al. (2003; 2004) indicate that an index derived from IOD s atmospheric variability, along with ENSO index, explains most of the interannual variability of the ISMR. Patra et al. (2005) found that the positive (negative) IOD-like conditions that persisted during July 2003 (2002) may have, along with changes in aerosol radioactive forcing, contributed to the surplus (deficit) rainfall during that particular month. IOD influence is also seen during boreal fall and winter seasons on the rainfall over India and Sri Lanka (Kripalani and Kumar, 2004; Zubair et al., 2003). This paper is an extension of our earlier research on relative impacts of the IOD and ENSO events on Indian summer rainfall, and the dynamical mechanisms that cause the impacts (Ashok et al., 2001; Guan et al., 2003; Ashok et al. 2004a). In this paper, using a partial correlation technique, we examine the impact of these largely independent coupled phenomena (Yamagata et al., 2003; 2004) on the ISMR from the subregional scale point of view. Apart from the seasonal relationships, we also try to understand these impacts on monthly scales. Finally, we show lead-partial correlations of the IODMI of the individual months from June to September with total seasonal rainfall to demonstrate that IOD events indeed influence the rainfall over India. 2. Data and Methodology In this study, the GISST 2.3b data set (Rayner et al., 1996) is used to compute the IODMI defined as the difference of the area-averaged SSTA between the regions 50 o E-70 o E, 10 o S-10 o N and 90 o E-110 o E, 10 o S-equator (Saji et al., 1999), and NINO3 index (area-averaged SSTA over 150 o W-90 o W, 5 o S-5 o N) that represents ENSO. We further use the land rainfall datasets from the gridded products (0.5 resolution) of Willmott and Matsuura (1995) to understand the spatial distribution of the impacts of the IOD, and ENSO events. These land rainfall data have been derived from the Global Historical Climatology Network 3

4 (GHCN version 2), and station records of monthly and annual total precipitation (Legates and Willmott, 1990). Extensive research has been carried out to identify homogeneous summer rainfall regions over India. A brief description of these homogeneous regions follows: based on the observations of the India Meteorological Department (IMD), Parthasarathy et al. (1993) have prepared the spatially coherent rainfall data series for largest possible homogeneous area by combining the rainfall of 14 subdivisions (Haryana, Punjab, west and east Rajasthan, west and east Madhya Pradesh, Gujarat, Saurastra, Konkan, Madhya Maharashtra, Vidarbha, Telangana, and north Karnataka, covering the northwestern, central and northern parts of peninsular India, covering about 55% of the whole country) having similar characteristics (see Fig. 6.5 of Pant and Kumar, 1997 for details). This region has been named as the homogeneous India. About 7 sub-divisions, namely east Rajasthan, west Madhya Pradesh, Gujarat, Konkan and Goa, Madhya Maharashtra, Marathwada, and Vidarbha, mostly from the previously mentioned largest homogenous rainfall region, comprise the core monsoon region. Subsequently, Parthasarathy et al. (1995, 1996) have redivided the whole country into 5 regions, based on their inter-correlation properties, and teleconnection patterns with global and regional circulation features. The regionalization reduces the noise-component that is present in the All India summer monsoon rainfall series, an index to represent the area-averaged rainfall over India (Pant and Kumar, 1997). In the next section, we study the relative influences of the IOD and the ENSO on the summer rainfall of these homogeneous regions mentioned before. To understand the impacts of the IOD/ENSO on Indian monsoon rainfall at regional scale, we used the available rainfall datasets of 29 subdivisions as well as several homogeneous Indian rainfall datasets (Parthasarathy, 1993) for the period These data have been derived (Parthasarathy et al., 1993) from the raw data collected by the IMD. To assess the contributions of individual ENSO, and IOD events as independent variables to the ISMR variability, we have used a partial correlation technique identical to that used by Guan et al. (2003), and Ashok et al. (2004b), with ISMR regressed on both the IODMI and the NINO3 SSTA. The rationale for using a multiple regression technique here follows. Let us suppose that we need to compute the regression co-efficient between the IODMI-ISMR. The IODMI is positively correlated to the ISMR because of their inherent relationship (Saji et al., 1999; Ashok et al., 2001). However, the dominant variability in the Indian Ocean is due to ENSO, which explains 37% of the variability of the Indian Ocean sea surface temperature anomalies, as compared to the 12% by the IOD (Saji et al., 1999). Apart from this, 4

5 during some years such as 1983, 1997, etc., IOD and ENSO events co-occur. The IODMI signal in such years is contaminated by the ENSO signal due to the Clarke-Meyers effect (Yamagata et al., 2003, Ashok et al., 2003a). Furthermore, the IOD and ENSO events are seasonally phase locked, and their influences on the ISMR are opposite to one another, as evident from the composite analyses of the pure 2 ENSO, and pure IOD events (Ashok et al., 2004a); this factor causes overestimation/underestimation of the individual influences. Therefore, a good way to understand the impact of IOD (or ENSO) on the Indian monsoon rainfall is the partial correlation technique. This technique has also been used to assess the influence of the Indian Ocean SST on Indian summer monsoon (Ashok et al., 2001; 2004a), Australian winter rainfall (Nicholls 1989, Ashok et al. 2003b) and other regions of the world (Saji and Yamagata 2003a). We adopted this procedure when assessing the respective impacts of the IOD and ENSO events on the rainfall over the homogeneous sub-regions of India. We also remove signals at frequencies slower than 7 years, as it is known that both IOD and ENSO exhibit significant decadal variability (Ashok et al., 2004b, Nitta and Yamada, 1989). Though the correlation between their decadal signals is weak, there are certain decades such as the 1990s when their phases are similar (See Fig.1, Ashok et al., 2004b; Tozuka et al., 2006). We used a Monte-Carlo technique (Ashok et al., 2001; Saji et al., 2003) to identify the significance levels of the partial correlations. 3. Results a. Interannual variability of seasonal summer monsoon rainfall over India To validate the gridded rainfall dataset (Willmott and Matsuura, 1995) over India, in Fig. 1, we present the JJAS mean rainfall over the Indian region. The distribution shows a band of accumulated rainfall maxima along the foothills of Himalayas, and another band to the west of the Western Ghats that extend from Kerala in south up to Maharashtra. Along the monsoon trough zone too, accumulated rainfall of not less than 80 cm can be found to the east of 75ºE. To the west of this line, we find regions of semiarid and arid regions. South of 18ºN, the regions east of Eastern Ghats and also receive relatively less rainfall. In general, the distribution of the rainfall is similar, both quantitatively as well as qualitatively, with the mean summer monsoon rainfall distribution (compare with Fig. 8.4 of Pant and Kumar, 1997) that has been directly based on India Meteorological Department (IMD) datasets. 2 When a coupled phenomenon in either of the Indian or Pacific Oceans is not associated with the coupled phenomenon of the same phase in the other Ocean (Rao et al, 2002; Ashok et al. 2003; 2004a) 5

6 The time series of the unfiltered JJAS ISMR, NINO3 SSTA and IODMI are presented in Fig. 2. Pure El Niño events consistently reduce the JJAS rainfall over India, except when accompanied by the positive IOD events such as during years such as 1983 and 1997 etc. The pure positive IOD events such as those during 1961, 1963, 1967, 1983, 1994 on the other hand, are associated with the anomalously surplus rainfall anomalies. The resultant impact, to a great extent, depends on the relative intensities (See Ashok et al., 2001; 2004a), as can be evidenced during years such as 1983, 1985, 1997 etc. when the IOD event during that summer apparently reduces the impact of the co-occurring ENSO event that has the same phase as that of the IOD. The regional extent of the impact of the negative IOD events on the magnitude of the modulated-ismr anomalies seem to be relatively weak as compared that affected by the positive IOD events; however, the distribution of the composite anomalies during the negative IOD events is nevertheless significant over parts of India (see Fig. 3c of Ashok et al., 2004a). The spatial distributions of the partial correlations between the anomalous summer monsoon rainfall with NINO3 index and with IODMI are presented in Figures 3a and 3b respectively. Over the Indian region, El Niño events apparently cause significantly (at 90% confidence level) deficit rainfall over most of the country. The IODMI, on the other hand, is positively correlated along the monsoon trough and the northwest region, as can be evidenced from the distribution of the significant correlations, in agreement with mechanisms hypothesized in the earlier studies (Ashok et al., 2001; Ashok et al., 2004a). Even along the western peninsula, we see a few locations where the positive IOD events cause surplus rainfall significantly (at 90% confidence level from a 2-tailed test). The influences of ENSO and IOD events on the Indian monsoon rainfall are, thus, opposite to one another; their impacts over Pakistan, Afghanistan and Iran are also, in general, opposite to one another. Over Sri Lanka, the location of significant correlations between the rainfall and NINO3 SSTA is to the north, while the IOD impact seems to be more prominent to the south. We present the anomaly partial correlation coefficients of the JJAS rainfall over different homogeneous regions with the IODMI and NINO3 SSTA for the period in Table 1. The partial correlations of the IODMI (NINO3 SSTA) with the ISMR indicate the degree of the impact of the IOD (ENSO) on the ISMR in the absence of ENSO (IOD). From this table, it can be seen that El Niño events impact strongly on summer rainfall over most of the homogeneous rainfall zones of India. Most of these zones are also significantly influenced by the IOD but the impacts are opposite.. The ENSO (IOD) index is negatively (positively) correlated (significantly at 90% confidence level) with summer monsoon rainfall 6

7 over 7(5) of the 8 homogeneous rainfall zones of India. It is seen that IODMI is positively correlated at 95% significant level with all India rainfall anomalies as well as those over the homogeneous zones of homogeneous India and West Central India, indicating that a vast region over India receives surplus rainfall during the positive IOD events. The partial correlations (not presented) of the available JJAS sub-divisional rainfall anomalies with the NINO3 index and the IODMI indicate that The ENSO (IOD) events have significant (at 90% confidence level) impacts on the rainfall variability of 19 (8) out of 29 subdivisions for which the rainfall data are available. The magnitudes of the partial correlations of ISMR with NINO3 index, in general, are higher than those between IODMI and ISMR. The impact of ENSO is distributed more widely. The IOD impact is mainly along the mean position of the monsoon trough over India that covers the subdivisions of Madhya Pradesh, Gujarat, Saurastra and Kutch, and also on the rainfall of the subdivisions of Orissa on the east coast, and coastal Karnataka on the southwest coast that receives high rainfall during JJAS (see Fig. 1). b. Interannual variability of monthly summer monsoon rainfall over India The monthly partial correlations of the Indian summer monsoon rainfall with the IODMI and NINO3 SSTA (after removing the influence of the other predictor) for June, July, August and September are presented in Fig. 4. Because of the monthly stratification, the rainfall and SST data contain signals with intraseasonal periodicities also. Hence the patterns are not entirely similar to those shown in Figures 2a and 2b. The IOD impact during June seems to be confined to central Indian plains and parts of southwestern India (Fig. 4a). By July, the impact is distinctly widespread, with significant positive correlations seen over the south India, Gujarat and regions to further west up to Afghanistan and Iran (Fig. 4b). The negative correlations to the northeast of the mean monsoon trough position over India and positive correlations to its south indicate an enhancement of the active monsoon-like conditions. The positive correlations can be seen along the monsoon trough during both August and September (Figures 4c and 4d). From Fig. 2b and Figures 4a-d, it appears that IOD enhances active monsoon conditions over central plains of India along the monsoon trough both on monthly i.e. sub-seasonal and seasonal timescales; interestingly, strong negative correlations are seen over south-central India during September (Fig. 4d). Thus, figures 4a-d show that the subseasonal characteristics of the IOD during the boreal summer (Saji and Yamagata, 2003b) have distinct signatures during different months, and thereby have a bearing for intraseasonal variability of Indian summer monsoon. During the month of June, the ENSO events impact significantly on the rainfall over the eastern part of north-india, and some regions of south India (Fig. 4d). During the next two months, the 7

8 impact shifts westward and southward (Figures 4e and 4f), and during September, the positive correlations are largely confined to the west central Indian region (Fig. 4h). To demonstrate further that IOD events do indeed influence the Indian monsoon, we present the lead correlations of the IODMI during different months from July-September with the mean seasonal JJAS rainfall anomalies (Figures 5a-c). Fig. 5 shows significant positive correlations with the anomalous JJAS rainfall distribution. IOD variability during each month of July to September apparently contributes more to JJAS rainfall as compared to that during June (Figure not shown). This is reasonable, partly because of the reason that IOD event is, during some years, still in pre-development phase till June and intensifies later (Saji et al., 2003b). This may also be due to the simple but important fact that about 60% of the monsoon rainfall - a very high proportion - comes from the rainfall during July-August (See discussion on Page 158, Pant and Kumar, 1997). However, if the IOD signal is strong by June, we see a wider impact of the IOD even during that month. Figures 5 and 4 demonstrate that the IOD variability during different months from June to September contributes to the aggregate summer monsoon rainfall variability as well as the high frequency intraseasonal variability. Similar lead correlations in the case of ENSO have also consistently significant negative correlations, spread over a wider area (Figures not shown). Interestingly, September variability of ENSO, just as that of the IOD variability, contributes relatively less as compared to that from the variabilities during July and August. This is despite the fact that the IOD and ENSO events, in general, are stronger during September, compared to earlier months; this may be probably because of the fact that monsoon starts to withdraw from the subcontinent during the month of September. This is a topic that needs further attention. 4. Concluding Remarks In this study, we examine the impact of the IOD and ENSO on the Indian summer monsoon variability from the vantages of homogeneous and subdivisional rainfall distribution. We also examine the relationships on a monthly scale basis. The data used are the Willmott and Matsuura rainfall datasets, Indian rain-gauge datasets, and GISST datasets. The period of analysis is from The ENSO events have a very significant negative correlation with rainfall over most of India, while the IOD events have an opposite impact. IOD events have maximum impact around the monsoon trough areas, and a few parts of the west coast. Positive IOD events, in particular, seem to accentuate the 8

9 active monsoon-like conditions. It is seen that IOD and ENSO impacts are also opposite over Pakistan, Afghanistan and Iran. The monthly-stratified partial correlation analysis indicates distinct impacts of the ENSO and IOD events during every month of the JJAS season on the summer monsoon rainfall over different parts of India. The maximum impacts are seen during July and August. The ENSO (IOD) events are negatively (positively) correlated (significant at 90% confidence level) with summer rainfall over 7 (5) out of 8 homogeneous rainfall regions in India. Positive IOD events, in particular, seem to accentuate the active monsoon-like conditions. The partial correlations of ENSO indices with ISMR are, in general stronger than those with IODMI; despite that, strong positive IOD events such as during 1997 reduce the droughts due to co-occurring El Niño events, and thus play an important role in ISMR variability. From this point, potential use of IODMI in reducing the errors in monsoon prediction for years such as 1997 should be considered. We also computed the partial correlations of the JJAS rainfall with the monthly indices of IOD and ENSO from June till September. From this analysis, it is confirmed that IOD and ENSO variabilities during these months indeed influence the monsoon variability. The significant lead correlations that the IODMI has with the rainfall anomalies over the Indian region have implications for prospective changes in planning of the economy and agriculture of the region even after the onset of the monsoon. The IOD events are in the development stage during JJAS, and evolve gradually during these months (Saji and Yamagata, 2003b). This statement holds true for ENSO as well. Also, modulations of monsoon, IOD, and ENSO events by some external factors have a bearing on the IOD/ENSO-monsoon relationship. The impact of these phenomena on the Indian summer monsoon ultimately depends on their individual as well as relative intensities during the particular season (Ashok et al., 2001, Gadgil et al., 2004). Acknowledgements The subdivisional and homogeneous Indian rainfall sets have been downloaded from the IITM website. The figures in this paper have been made using COLA/GrADS software. The authors acknowledge Prof. T. Yamagata, Drs. S. K. Behera and Anguluri S. Rao for discussions. Figure Captions Fig.1 JJAS rainfall climatology ( ) in cm. 9

10 Fig.2 Normalized anomalies of ISMR (Bar), NINO3 SSTA (red line) and IODMI (green line). Fig. 3 JJAS partial correlations between anomalies of rainfall and (a) NINO3 SSTA (b) IODMI. Fig. 4 Partial correlations between anomalies of rainfall and IODMI for the month of (a) June (b) July (c) August (d) September (g-h) same as Figures 4.a-d but for those between the rainfall anomalies andnino3 SSTA. Fig. 5 Partial correlations between JJAS rainfall anomalies, and monthly anomaly of IODMI for the month of (a) July (b) August (c) September. Table 1: The partial correlation coefficients of the homogeneous-regional rainfall with the Indian Ocean Dipole Mode Index (IODMI), and NINO3 SSTA, an appropriate ENSO index. These are computed for the period Values more than 0.3 exceed the 90% level of confidence for partial correlation, and are shown in bold letters. Values at 95% and 99% significance levels are 0.34 and 0.44 respectively. Homogeneous region Partial Correlations with the Partial Correlations with the IODMI NINO3 SSTA All India Homogeneous India Core Monsoon North West India West Central India Central North East India North East India Peninsular India

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15 Fig.2 Normalized anomalies of ISMR (Bar), NINO3 SSTA (red line) and IODMI (green line). Fig.1 JJAS rainfall climatology ( ) in cm. Fig.3 JJAS partial correlations between anomalies of rainfall and (a) NINO3 SSTA (b) IODMI. 15

16 Fig. 4 Partial correlations between anomalies of rainfall and IODMI for the month of (a) June (b) July (c) August (d) September (g-h) same as Figures 4.a-d but for those between the rainfall anomalies andnino3 SSTA. 16

17 Fig. 5 Partial correlations between JJAS rainfall anomalies, and monthly anomaly of IODMI for the month of (a) July (b) August (c) September. 17