Impact of ENSO and the Indian Ocean Dipole on the north-east monsoon rainfall of Tamil Nadu State in India

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1 HYDROLOGICAL PROCESSES Hydrol. Process. 23, (2009) Published online 9 January 2009 in Wiley InterScience ( Impact of ENSO and the Indian Ocean Dipole on the north-east monsoon rainfall of Tamil Nadu State in India V. Geethalakshmi, 1 * Akiyo Yatagai, 2 K. Palanisamy 1 and Chieko Umetsu 2 1 Tamil Nadu Agricultural University, Coimbatore , Tamil Nadu, India 2 Research Institute for Humanity and Nature, Kyoto, Japan Abstract: Tamil Nadu State in south-eastern India receives most of its rainfall from October through December, a phenomenon known as north-east monsoon rainfall (NEMR). Tamil Nadu s south-west monsoon rainfall (SWMR), received between June and September, correlates positively with the Southern Oscillation Index (SOI), whereas NEMR correlates negatively. We undertook a study to investigate the relationship between global teleconnection indicators, namely the El Niño-Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD), and precipitation over Tamil Nadu during NEMR. The results showed that NEMR had significant positive correlation with Niño-3 sea-surface temperatures (SST) in July. The statistical relationships between the IOD and NEMR were much weaker than those between ENSO and NEMR. To understand the relationship and/or local dynamic structure, composites of the circulation field for the extreme El Niño/La Niña years were compared with the mean state for July, September and November. Composite circulation analysis clearly showed that in extreme El Niño years, the Bay of Bengal exhibiteda positivesea-level pressure (SLP) anomaly, and the Arabian Sea exhibited a negative SLP anomaly, which resulted in strong north-easterly winds, bringing moisture and precipitation to the southern part of India in November. The reverse was also true: A strong negative anomaly was observed in the Bay of Bengal during La Niña years, which resulted in a weak NE monsoon. However, local circulation anomalies (Bay of Bengal through Arabian Sea) did not continue from July to November. Copyright 2009 John Wiley & Sons, Ltd. KEY WORDS north-east monsoon rainfall of India; El Niño Southern Oscillation; Indian Ocean Dipole; water vapour Received 5 August 2006; Accepted 28 February 2008 INTRODUCTION Rainfall during the 4-month period of June through September is termed the Indian summer monsoon season in a large-scale sense; however, the actual rainy period differs widely over different parts of the Indian subcontinent. Over Tamil Nadu, in south-eastern peninsular India (Figure 1), the most important rainfall season is autumn and winter. Rainfall during October through December over India is referred to as north-east monsoon rainfall (NEMR) when the south-westerlies of the summer monsoon give way to north-easterlies. The increase in rainfall activity over coastal areas of Tamil Nadu, which occurs in the middle of October, is generally considered as the setting in of the north-east monsoon. The normal date of the onset is around 20th October, with a variation of about a week on either side. It plays a crucial role in both the agriculture and the economy of Tamil Nadu (Srinivasan and Ramamurthy, 1973). As shown in Figure 2, major rains are received from September to December, hence, it is important to understand NEMR activity and to investigate the relationship between global teleconnection indicators and * Correspondence to: V. Geethalakshmi, Tamil Nadu Agricultural University, Coimbatore , Tamil Nadu, India. geetha@tnau.ac.in the precipitation over Tamil Nadu during the north-east monsoon season. NEMR exhibits large variations from place to place and year to year. According to Bhatnagar (2003), the reason for the difference in the distribution and the amount of NEMR is due to the influence of various parameters, including the El Niño Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD). Several studies have examined the correlation between the Southern Oscillation Index (SOI) and the Indian summer monsoon rains that occur between June and September (Sikka, 1980; Rasmusson and Carpenter, 1983; Shukla and Paolino, 1983; Parthasarathy and Pant, 1985; Ropelewski and Halpert, 1989; Mooley, 1997; and Krishna Kumar et al., 1999). Studies on the influence of ENSO and IOD on NEMR are, however, limited (Pant and Rupa Kumar, 1997; Kripalani and Kumar, 2004; Kumar et al., 2007). According to previous studies that investigated the relationship between ENSO and global precipitation signals (Ropelewski and Halpert, 1987, 1989; Curtis et al., 2001), most of India has less (more) rainfall during El Niño (La Niña) years. In contrast, the southernmost part of India shows an opposite trend: The region tends to have wet anomalies during El Niño (SOI negative) years (Singh and Chattopadhyay, 1998; Jayanthi and Govindachari, 1999; Khole and De, 2003). Copyright 2009 John Wiley & Sons, Ltd.

2 634 V. GEETHALAKSHMI ET AL. N 78 E 80 E Andra Pradesh Thirur Chennai Vellore Kanchipuram Krishnagiri Thiruvannamalai Karnataka Dharmapuri Villupuram Salem Nilgiris Erode Cuddalore Namakkal Perambalur Coimbatore Karur Trichirapalli Thanjavur Thiruvarur Bay of Bangal Dindigal Pudukkotai Kerala Theni Madurai Sivagangai Virudhunagar Ramnad Tuticorin Tirunelveli Kanyakumari Scale: 1:2,700,000 1cm = 27 kms International Polyconic Projection Indian Ocean 78 E 80 E Figure 1. Location of Tamil Nadu State, India Recent investigations regarding the relationships of the SOI and Niño-3 sea-surface temperatures (SST of 150 W 90 W, 5 N 5 S) with NEMR in Tamil Nadu have concluded that the SOI is negatively correlated with NEMR in Tamil Nadu (Geethalakshmi et al., 2002, 2005) and that Niño-3 SST is positively correlated (Geethalakshmi et al., 2003), which implies that autumn winter precipitation over Tamil Nadu is influenced by global climatological signals. Zubair and Ropelewski (2006) and Kumar et al. (2007) have recently pointed out that the relationship between ENSO and NEMR is strengthening. As the length of the growing period and the distribution of rainfall during the northeast monsoon season is important for agricultural production in Tamil Nadu (Geethalakshmi et al., 2003), a detailed analysis and investigation of global signals and their precursors as they influence local precipitation is necessary for crop planning. In the last decade of the twentieth century, Indian summer monsoon rainfall was normal or above normal during

3 ENSO AND THE IOD ON THE NORTH-EAST MONSOON RAINFALL IN TAMIL NADU mm/month Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Month TamilNadu All India Figure 2. Forty-year ( ) seasonal precipitation climatology values averaged for Tamil Nadu State and the Indian subcontinent. Data sets from the Indian Institute for Tropical Meteorology, the two major ENSO events witnessed (Krishna Kumar et al., 1999), although it was widely recognized that less summer monsoon rainfall tended to be observed during the ENSO years. Therefore, the long-term relationship between the ENSO and precipitation over Tamil Nadu must also be investigated. Recently, the IOD was catalogued as one of the major ocean-atmosphere-coupled phenomena in the tropical Indo-Pacific (Saji et al., 1999; Webster et al., 1999). Kripalani and Kumar (2004) reported the large-scale impact of the IOD on the rainfall variability of south India. In this paper, we attempt to evaluate the influence of the ENSO and the IOD on NEMR over Tamil Nadu. In most cases, local meteorologists and/or hydrologists have discussed only statistical relationships such as correlation coefficients between global indices (ENSO and IOD) and local precipitation. However, in the near future it will be possible and also necessary to use atmospheric hydrometeorological information to understand the climatological impact on local water resources. Therefore, we investigated the linkages between the statistical trends and the synoptic hydrological patterns that are related to significant climate anomalies. Some recent studies (Kripalani and Kumar, 2004; Zubair and Ropelewski, 2006) have shown circulation patterns that may relate to the global signals and NEMR over south India. However, localized or synoptic patterns more directly related to the precipitation of Tamil Nadu must be studied for their climatic implications. We provide some composite charts of precipitation and water vapour flux that were recently compiled following reanalysis of meteorological data sets to understand the physical linkage between global signals and local precipitation anomalies. DATA DESCRIPTION The sources of precipitation data sets for India and Tamil Nadu used in this study are the Regional Meteorological Centre (RMC), Chennai, for NEMR over Tamil Nadu; and the Indian Institute of Tropical Meteorology, which created the regional averaged monthly precipitation data set ( The Southern Oscillation and IOD indices were compiled by the Bureau of Meteorology Research Centre (BMRC), Australia. The data sets for rainfall, IOD, and SOI used for this study span 100 years ( ). We also used SST data for five different regions obtained from BMRC, Australia, in this study: (1) El Niño-3 ( W, 5 N 5 S); (2) the eastern box of Saji s dipole (50 70 E, 10 N 10 S); (3) the western box of Saji s dipole ( E, 10 S Eq); (4) the eastern box of Webster s dipole (45 55 E, 5 N 5 S); and (5) the western box of Webster s dipole ( E, 10 S Eq). We used the same areas that are defined in the papers Saji et al. (1999) and Webster et al. (1999). For composite analysis of precipitation anomalies, we used daily gridded precipitation data sets from the India Meteorological Department (IMD) (Rajeevan et al., 2005). For this study, we also used two meteorological reanalysis data sets compiled by the European Centre for Medium-Range Weather Forecasts (ECMWF), namely, the so-called ERA15 (ECMWF 15-Year Re-analysis; Gibson et al., 1997) and ERA40 (ECMWF 40-Year Reanalysis; Uppala et al., 2005) data sets. By using these reanalysis data sets, we can understand the inter-annual variation in atmospheric water balance, and quantitative study relies on data availability. The computational procedure combining vertically integrated precipitable water, moisture flux, and its divergence from ERA15 (Figures 4 and 5) is the same as that used in Yatagai (2003). Since it is known that ERA15 performs better than ERA40 over the tropics for climatology, we adopted climatological mean values from ERA15 (Yatagai, 2003) for Figures 4 and 5. We adopted well-known and common definitions of the El Niño and La Niña years based on the following websites: jh.html

4 636 V. GEETHALAKSHMI ET AL elnino1.htm. As a result, we used the following years to make general composite charts. El Niño: 1965, 1969, 1972, 1977, 1982, 1987, 1991, 1992, 1993, 1994 and 1997; La Niña: 1964, 1970, 1973, 1974, 1975, 1988 and (e.g El Niño means 1994/1995 winter). In addition, we used SOI values for some composite charts. Details are described below. RESULTS AND DISCUSSION General features Figure 2 shows the seasonal change of monthly precipitation averaged for the years Different from most of India, southern peninsular India gets its maximum rainfall during the north-east monsoon season. Conventionally, October to December is treated as the portion of the north-east monsoon season that mainly affects the state of Tamil Nadu. The rainfall over Tamil Nadu during October to December accounts for about 48% of the annual rainfall in the region. The probabilities of monthly rainfall for Tamil Nadu are presented in Figure 3. The variability of rainfall is also higher during the NEMR period (Kripalani and Kumar, 2004), which leads to difficulty in agricultural decision making. Therefore, it is important to know the precursor signals that relate significantly to the NEMR of Tamil Nadu. Large-scale hydrological conditions over India are shown in Figure 4 (for July) and in Figure 5 (for November) to reveal both SW and NE monsoon characteristics. In July, cross-equatorial moisture flow over the Arabian Sea brings moisture to the Indian subcontinent. Moisture evaporated over the Arabian Sea brings moisture to the west coast of India, owing to the westerly (or south-westerly) flow in the lower troposphere. The evapotranspiration from the surface can be roughly estimated by summing up divergence (Figure 4c) and precipitation (Figure 4d). Two centres of moisture flux convergence (negative divergence in Figure 4c) are observed, and these match the maxima of precipitation (Figure 4d). Tamil Nadu is located leeward of the Western Ghats and hence SW monsoon precipitation is not large. Moisture from the Bay of Bengal normally brings precipitation to the north-eastern part of India and Bangladesh during the SW monsoon season. Conversely, the dominant lower wind is north-easterly (or easterly) over the Indian subcontinent in November (Figure 5), and total moisture over India and the Bay of Bengal is much less than in July. During that season, the surface pressure over the ocean is lower than it is over the land. The convection centre locates not over land, but around the maritime continent. Owing to the low pressure over the south of India (Figure 14, top centre), an onshore north-easterly flow from the Bay of Bengal brings moisture and precipitation to Tamil Nadu. Relationship between NEMR, El Niño Southern Oscillation (ENSO), and IOD As the heaviest rainfall for Tamil Nadu is received in the NEMR season, the same period was chosen for analysis. The correlation coefficients between the climate indices (SOI and SST at different regions) of different months (January September) and NEMR are given in Table I. The NEMR totals for Tamil Nadu had the highest positive correlation with El Niño-3 SST for the month of July, indicating that rising SST in the Niño-3 region led to more NEMR, and vice versa. There was also a significant negative correlation between the SOI of the summer monsoon season (June September) and NEMR, Figure 3. Probabilities of monthly rainfall for Tamil Nadu, India. Each bar indicates the fluctuations of rainfall amounts in mm per month and the probability of occurrence of rainfall in that month from 0 to 100%. There is a 0% chance that monthly rainfall will exceed the highest value indicated by the bar and a 100% chance that it will exceed the lowest value. With respect to the box on the bar, there is a 20% probability that the monthly rainfall will be within the range indicated by the upper portion of the bar, and an 80% chance that it will exceed the range indicated by the lower portion. The horizontal line within the box on the bar indicates the median rainfall for the month, that is, 50% of the time the amount of rain will be below this value and 50% of the time above

5 ENSO AND THE IOD ON THE NORTH-EAST MONSOON RAINFALL IN TAMIL NADU 637 Figure 4. July climatological mean (a) precipitable water (mm; regions exceeding 30 mm month 1 are shaded), (b) vertically integrated moisture flux (mm ms 1 ) and (c) divergence (mm month 1 regions of negative divergence are shaded); estimated by ERA15. Climatological precipitation (mm month 1, regions exceeding 100 mm month 1 are shaded) as provided by Climate Prediction Center Merged Analysis of Precipitation (CMAP) is shown in (d) Figure 5. Same as Figure 4, but for November

6 638 V. GEETHALAKSHMI ET AL. Table I. Correlations between NEMR over Tamil Nadu and the indices SOI and SST for different months Particulars Region (SST) Jan Feb Mar Apr May Jun Jul Aug Sep SOI 0Ð064 0Ð092 0Ð111 0Ð195 Ł 0Ð210 Ł 0Ð314 ŁŁ 0Ð384 ŁŁ 0Ð318 ŁŁ 0Ð375 ŁŁ Niño W, 5 N 5 S 0Ð209 Ł 0Ð191 0Ð301 ŁŁ 0Ð326 ŁŁ 0Ð314 ŁŁ 0Ð321 ŁŁ 0Ð388 ŁŁ 0Ð334 ŁŁ 0Ð344 ŁŁ Saji-E E, 10 S 10 N 0Ð124 0Ð033 0Ð023 0Ð017 0Ð067 0Ð203 Ł 0Ð111 0Ð139 0Ð115 Saji-W E, 10 S Eq 0Ð080 0Ð151 0Ð113 0Ð198 Ł 0Ð164 0Ð011 0Ð126 0Ð100 0Ð112 Webster-E 4 55 E, 5 N 5 S 0Ð152 0Ð105 0Ð058 0Ð008 0Ð014 0Ð075 0Ð029 0Ð098 0Ð034 Webster-W E, 10 S Eq 0Ð105 0Ð142 0Ð123 0Ð193 0Ð151 0Ð015 0Ð117 0Ð079 0Ð104 A 100-year ( ) data set was used to compute the correlation coefficients. ŁŁ And Ł show correlations above the 1 and 5% significance levels, respectively. Figure 6. Simultaneous correlation between monthly rainfall of Tamil Nadu and monthly SOI ( ). The 95 and 99% confidence levels of the correlation coefficient are š0ð250 and š0ð325, respectively in the sense that NEMR was higher when SOI was negative. These results show that an El Niño (La Niña) condition during the boreal summer season was related to a wet (dry) NEMR condition over Tamil Nadu. A study conducted by Jayanthi and Govindachari (1999) revealed that the SST anomaly over the Niño-3/Niño-4 regions and NEMR were always positively correlated. It also showed that the 10-year running correlation coefficient ranged from 0Ð3 to 0Ð8, indicating that El Niño exerted a positive influence on NEMR over Tamil Nadu. The SST associated with the IOD signals (Saji-E, Saji-W, Webster-E and Webster-W boxes) did not show strong correlations, except for Saji-E (June) and Saji-W (April). According to Kripalani and Kumar (2004), during the past 40 years, the correlation between their NEMR and IOD totals (the differences between Saji-E and Saji-W) during the summer (June August) and autumn (September November) was not strong but showed a positive trend. Our results using a 100-year data set (Table I) are consistent with those of Kripalani and Kumar (2004). Saji-E data exhibit a positive correlation with our NEMR results for the period June to September, and Saji-W data exhibit a negative correlation during July to September. Conversely, the SST averaged by Saji et al. (1999) and Webster et al. (1999) showed a very weak relationship with NEMR activity for Tamil Nadu. Figure 6 reveals a simultaneous correlation between the monthly rainfall of Tamil Nadu and the monthly SOI ( ). A significant positive correlation was found for July September. To the contrary, a negative correlation coefficient was observed for October November. Historical analyses have shown clear evidence of an association between weak south-west monsoon rainfall (SWMR) and large negative SOI/El Niño events. These same analyses also have revealed an association between a strong SWMR and large positive SOI/absence of El Niño events (Sikka, 1980; Pant and Parthasarathy, 1981; Parthasarathy and Sontakke, 1988). The positive correlation during the south-west monsoon season (i.e. less SWMR for the El Niño years) for Tamil Nadu is consistent with results for most of India. Figure 7 shows the precipitation anomalies over Tamil Nadu during the SWMR for El Niño and La Niña events based on station data. For this composite, 11 El Niño years and 7 La Niña years were chosen as described in the previous section. In most of Tamil Nadu, less (more) precipitation was observed during the SWMR for El Niño (La Niña) years. Conversely, during the NEMR (Figure 8), more precipitation than average was observed for El Niño years. For La Niña years, the tendency was towards less precipitation, but this trend was not as clear as for El Niño years. Below we describe the linkage between hydrometeorogical dominant factors, i.e. moisture flow anomalies that bring precipitation anomalies to the region. The seasonal transitions of such anomalies from SWMR to NEMR have not been fully investigated, although it is interesting to note that a signal opposite to ENSO appeared between SWMR and NEMR.

7 ENSO AND THE IOD ON THE NORTH-EAST MONSOON RAINFALL IN TAMIL NADU 639 El Nino N 78 E 80 E Andra Pradesh Thirur Vellore Kanchipuram Chennai Krishnagiri Thiruvannamalai Karnataka Dharmapuri Villupuram Salem Nilgiris Erode Cuddalore Coimbatore Namakkal Karur Perambalur Trichirapalli Thanjavur Thiruvarur Bay of Bangal Dindigal Pudukkotai Kerala Theni Madurai Sivagangai Virudhunagar Ramnad Tuticorin Tirunelveli Kanyakumari Scale: 1:2,700,000 1cm = 27 kms International Polyconic Projection Indian Ocean 78 E 80 E Rainfall deviation negative side from the normal Rainfall deviation positive side from the normal Figure 7. Precipitation anomalies during the SWMR (June September) for El Niño (left) and La Niña (right) events Long-term changes between north-east monsoon rainfall and El Niño Southern Oscillation The relationship between NEMR and the SOI is susceptible to decadal changes. Krishna Kumar et al. (1999) reported that the impact of ENSO on SWMR is now weakening. The correlation between July Niño- 3 region SST values and NEMR totals over a period of 100 years from 1901 to 2000 is only 0Ð338. To check the consistency of these relationships, we obtained decadal correlations between climate indices (SOI and SST of Niño-3 region) for July and NEMR totals for Tamil Nadu (Figure 9). The results clearly indicate a consistent negative correlation between the SOI of different months of the SWMR and NEMR values for Tamil Nadu, except in the decades and Since the SOI is a good predictor, as shown in Table I as well as by Yang et al. (2007), we used mainly the SOI in the following analysis and discussion. Twenty-year sliding correlations between NEMR over Tamil Nadu

8 640 V. GEETHALAKSHMI ET AL. La Nina N 78 E 80 E Andra Pradesh Thirur Vellore Kanchipuram Chennai Krishnagiri Thiruvannamalai Karnataka Dharmapuri Villupuram Salem Nilgiris Erode Cuddalore Coimbatore Namakkal Karur Perambalur Trichirapalli Thanjavur Thiruvarur Bay of Bangal Dindigal Pudukkotai Kerala Theni Madurai Sivagangai Virudhunagar Ramnad Tuticorin Tirunelveli Kanyakumari Scale: 1:2,700,000 1cm = 27 kms International Polyconic Projection Indian Ocean 78 E Rainfall deviation negative side from the normal Rainfall deviation positive side from the normal Figure 7. (Continued) 80 E and the SOI in different months were also obtained (Figure 10). Among the different months, the strongest correlation between NEMR and SOI could be seen in July. It is interesting to note that in the last decade ( ), the correlation between July SOI and NEMR totals is not as high as it had been in the earlier decades, indicating a weakening of their relationship. However, the relationship between SST of the Niño-3 region and NEMR is also not as strong as that of the SOI with NEMR for the different decades studied (Figure 9). Hence, further in-depth analysis was carried out only to understand the influence of the SOI on NEMR. Effect of extreme Southern Oscillation Index conditions on north-east monsoon rainfall totals To see the relationship between the July SOI and NEMR more clearly, SOI values above or below 1 standard deviation were assumed to represent extreme SOI conditions, and we studied their impact on NEMR values of the corresponding year (Figure 11). During the 100-year study period, there were 17 extremely negative and 14 extremely positive SOI conditions for July. The

9 ENSO AND THE IOD ON THE NORTH-EAST MONSOON RAINFALL IN TAMIL NADU 641 El Nino N 78 E 80 E Andra Pradesh Thirur Chennai Vellore Kanchipuram Krishnagiri Thiruvannamalai Karnataka Dharmapuri Villupuram Salem Nilgiris Erode Cuddalore Namakkal Perambalur Coimbatore Karur Trichirapalli Thanjavur Thiruvarur Bay of Bangal Dindigal Pudukkotai Kerala Theni Madurai Sivagangai Virudhunagar Ramnad Tuticorin Tirunelveli Kanyakumari Scale: 1:2,700,000 1cm = 27 kms International Polyconic Projection Indian Ocean 78 E 80 E Rainfall deviation negative side from the normal Rainfall deviation negative side from the normal Figure 8. The same as Figure 7, but for NEMR (October December) normal NEMR total averaged over the study period was 481 mm. The average rainfall received during the extremely negative July SOI conditions was 566 mm, which is 17Ð7% more than the normal NEMR; that of the extremely positive conditions was 395 mm, which is 17Ð9% less than the normal NEMR. Under extremely negative conditions, only 2 out 17 NEMR values were below average. This indicates a shift towards wetter (drier) conditions in relation to the extreme negative (positive) SOI conditions in July across Tamil Nadu. Circulation/precipitation patterns As Tamil Nadu s SWMR totals are positively correlated with the SOI and its NEMR totals are negatively correlated, we compared composites of the circulation field for the extreme El Niño/La Niña years with the

10 642 V. GEETHALAKSHMI ET AL. La Nina N 78 E 80 E Andra Pradesh Thirur Vellore Kanchipuram Chennai Krishnagiri Thiruvannamalai Karnataka Dharmapuri Villupuram Salem Nilgiris Erode Cuddalore Coimbatore Namakkal Karur Perambalur Trichirapalli Thanjavur Thiruvarur Bay of Bangal Dindigal Pudukkotai Kerala Theni Madurai Sivagangai Virudhunagar Ramnad Tuticorin Tirunelveli Kanyakumari Scale: 1:2,700,000 1cm = 27 kms International Polyconic Projection Indian Ocean 78 E Rainfall deviation negative side from the normal Rainfall deviation negative side from the normal Figure 8. (Continued) 80 E mean state to understand their relationship and/or local dynamic structure. As ERA40 data are available for after 1957, we chose the July SOI values for the eight El Niño years and four La Niña years, as described in the previous subsection and shown in Figure 12. Some of the years we selected for this definition are different than those we selected for making Figures 7 and 8. Previously, our selection followed the widely used ENSO-year definition to show general anomaly patterns. However, to investigate the relevant moisture patterns, here we selected the years according to their July SOI extreme values. Figure 12 shows the mean July circulation fields and precipitation, El Niño condition anomalies (based on the difference between 8-year and climatological averages), and La Niña condition anomalies (based on the difference between 4-year and climatological averages). The climatological means (top diagrams) were averaged over

11 ENSO AND THE IOD ON THE NORTH-EAST MONSOON RAINFALL IN TAMIL NADU 643 Correlation coefficient SOI Nino 3 SST Figure 9. Decadal correlations between NEMR of Tamil Nadu and two climate indices [SOI and SST of the El Niño-3 region (150 W 90 W, 5 N 5 S)] for July Correlation coefficient Jan Feb Mar Apr May Jun Jul Aug Sep Figure 10. Twenty-year sliding correlations between NEMR values over Tamil Nadu and the SOI in different months for January to September. The horizontal axis (year) indicates the first year of the 20-year period selected for determining the correlation 40 years ( ). During El Niño (La Niña) events, there was less (more) precipitable water over the Arabian Sea and northern part of India. The wind field during an El Niño condition showed a more divergent structure over Tamil Nadu as well as over the whole of India, resulting in less precipitation over Tamil Nadu. Precipitation anomalies of the El Niño and La Niña conditions over Tamil Nadu are consistent with those shown in Figure 7. To understand the anomaly change, we checked the circulation field for September before moving on to the November condition. Figure 13 reveals a clearer difference in the circulation field than that appears in Figure 12. In September, less (more) precipitable water, less (more) precipitation, and higher (lower) sea-level pressure (SLP) are observed over India during El Niño (La Niña) events. In November (Figure 14), the pressure-pattern anomaly was not similar to those in Figures 12 and 13. In El Niño years, a positive SLP anomaly was observed in the Bay of Bengal and a negative SLP anomaly was observed in the Arabian Sea, resulting in a strong east west pressure difference over south India. This pressure difference caused strong NEMR in Tamil Nadu as well as over all of south India. During La Niña phases, the opposite situation was observed. Ropelewski and Halpert (1989) reported that during La Niña years, surface wind anomalies in

12 644 V. GEETHALAKSHMI ET AL. SOI % Deviation of Rainfall July - SOI % deviation of NEMR Figure 11. Comparison between July SOI and NEMR for Tamil Nadu. Only the years with strong SOI (>1 standard deviation) or weak SOI (< 1 standard deviation) are shown Figure 12. Top left: climatological mean precipitable water (mm) and surface wind (m s 1 ) for July as provided by ERA40. Regions exceeding 30 mm month 1 (50 mm month 1 ) are lightly (darkly) shaded. Contour spacing is 5 mm. Top centre: climatological mean SLP. Regions exceeding 1002 hpa (1010 hpa) are lightly (darkly) shaded. Contour spacing is 2 hpa. Top right: climatological mean precipitation (mm month 1 ) as provided by the IMD. Contour spacing is 50 mm. Middle row: anomaly composite for the year of extreme El Niño (SOI < 1 standard deviation). Strongly positive (negative) anomalies are darkly (lightly) shaded, as shown in the scales in the left and centre plots. Positive precipitation anomaly areas are shaded, whereas negative precipitation anomaly areas are not. Bottom row: anomaly composite for the years of extreme La Niña events (SOI >1 standard deviation)

13 ENSO AND THE IOD ON THE NORTH-EAST MONSOON RAINFALL IN TAMIL NADU 645 Figure 13. Same as Figure 11, but for September the Indian Ocean were westerly, indicating weaker than normal NEMR circulation. The September pattern (Figure 13) resembles the July pattern more than it does November s. We speculate that moisture flux anomalies accompanying an extreme ENSO event (indicated here by the SOI signals in July) are reflected by subcontinental scale (Arabian Sea India Bay of Bengal) circulation anomalies separately in the SWMR and NEMR seasons. It is also clear that the circulation anomaly, which is based on SOI signals in July over India, the Bay of Bengal, and the Arabian Sea, lessens during September to November. These observations imply that the global-scale physical mechanisms related to the positive correlation with SOI on SWMR and the negative correlation with SOI on NEMR are not the same. The pressure gradient between the Bay of Bengal and the Arabian Sea affects NEMR precipitation over Tamil Nadu more directly than the SOI signals. The November patterns of SLP and wind revealed in the El Niño composite (Figure 14, middle row) are reminiscent of the composite SST patterns for the autumnal positive IOD of Kripalani and Kumar (2004), as well as those of El Niño (Zubair and Ropelewski, 2006). That is, the pressure difference between the Bay of Bengal and the Arabian Sea as shown in this paper is the essential factor that directly affects the NEMR. Hence, we have shown that the NEMR subcontinental circulation anomaly is different between the SWMR and NEMR seasons. This means that SWMR and NEMR affect the global SOI signal differently, which may be consistent with Yang et al. (2007), who show that the Indian Ocean anomaly is not just a passive response to ENSO. Our analysis of the relationship between the NEMR and IOD modes was limited to an investigation using regional SST boxes those are used for defining the mode. Detailed circulation analyses are necessary in the future. SUMMARY AND CONCLUSION From our study, we concluded that the SOI and the El Niño-3 SST exert a significant influence on NEMR. Of the different months we studied, July s SOI and Niño-3 SST show significant correlations with NEMR.

14 646 V. GEETHALAKSHMI ET AL. Figure 14. Same as Figure 11, but for November The July SOI correlates negatively with NEMR, and an extremely negative SOI (El Niño years) results in wetter-than-average conditions during NEMR. SWMR over Tamil Nadu has significant positive correlation with a simultaneously strong SOI. That is, drier conditions occur in SWMR during El Niño years. No significant correlations were observed between NEMR values and the previous season s (spring through autumn) precursor SST, averaged over the regions that are used to define the IOD. To investigate a physical linkage between the July SOI, SWMR and NEMR, composite circulation analyses were examined for July through November. The composite circulation analysis for November clearly showed that in extreme El Niño years defined by the July SOI, the Bay of Bengal displayed a positive SLP anomaly, and the Arabian Sea displayed a negative SLP anomaly, which resulted in strong north-easterly winds across southern India. A strengthened NE monsoon brings more moisture and precipitation than average to the Tamil Nadu region during El Niño years. The reverse is also true. A strongly negative anomaly was observed in the Bay of Bengal during La Niña years as defined by the SOI, which resulted in a weak NEMR. However, no continuous anomalies subject to the global SOI signal from July to November were found around the Bay of Bengal and the Arabian Sea. ACKNOWLEDGEMENTS This work was supported by the Vulnerability and Resilience of Social-Ecological Systems project at the Research Institute for Humanity and Nature, India, and by the Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation (APHRODITE) water resources project funded by the Japan Ministry of Environment. We appreciate Dr Shinjiro Kanae of the University of Tokyo for kindly providing constructive comments. REFERENCES Bhatnagar AK Chennaionline: Another rainless year, chennaionline.com/cityfeature/chennai/2004/11meterology.asp [March 2007].

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