Wet and dry years of Indian summer monsoon and its relation with Indo-Pacific sea surface temperatures


H. Varikoden, Indian Institute of Tropical Meteorology, Dr. Homi Bhabha Road, Pashan, Pune-08, India. E-mail: hamza@tropmet.res.in


Regional characteristics of Indian Summer Monsoon Rainfall (ISMR) during dry and wet monsoon years associated with El Niño Southern Oscillation (ENSO) and non-ENSO (normal) periods are investigated. Four cases such as wet monsoon years associated with ENSO (EW) and non-ENSO (NW) events and dry monsoon years associated with ENSO (ED) and non-ENSO (ND) events have been discussed. The dry and wet years associated with ENSO and non-ENSO periods show significant differences in the spatial pattern of rainfall. During wet years (EW and NW), the rainfall is abnormally high in the west coastal stations. However, during EW years, anomalous heavy rainfall is observed in the northern part of the west coast while during the NW years, the rainfall is high in southwest coastal regions. In central India, the rainfall is abnormally high only during the wet years associated with non-ENSO events (NW). The west coastal areas receive below normal rainfall during ED, ND years, however, the negative anomalies are large during ED years compared to ND years. The low level (1000 hPa) moisture transport is coherent with the changes in the spatial patter of ISMR. The Indo-Pacific sea surface temperatures (SSTs), low level circulation (at 850 hPa) features and general circulation associated with the four cases of ISMR have also been studied in detail. During wet phases of ISMR, the SST over the pacific ocean is abnormally cool and that during dry phases of ISMR is abnormally warm. However, the areas of cool and warm are different during ENSO and normal years. Copyright © 2012 Royal Meteorological Society

1. Introduction

Indian summer monsoon rainfall (ISMR) contributes about 70–90% of annual rainfall over India. The interannual variation of ISMR significantly affects the Indian agriculture and thus the Indian economy (Webster et al., 1998). The frequent droughts and floods are the manifestation of year to year variability of ISMR (Krishnamurthy and Shukla, 2000). The magnitudes of these variabilities are different at different locations over the subcontinent. Understanding of these variabilities especially the extreme droughts and floods are of great importance for the Indian economy because the country's economy has bounded with the availability of monsoon rainfall, for example, a 19% deficient of ISMR in 2002 resulted in significant economic hardship in India. It has been established that the year to year variability of ISMR has a good teleconnective relation with El Niño Southern Oscillation (ENSO) (Webster and Yang, 1992; Ju and Slingo, 1995), Indian Ocean Dipole (IOD) (Saji et al., 1999; Webster et al., 1999), North Atlantic Oscillation (Gupta et al., 2003), etc. Apart from these teleconnective relations, the ISMR shows floods and droughts in the absence of IOD/ENSO and this interannual variability may be due to some internal feedbacks (Goswami, 1998).

Characterizing the interannual variability of ISMR remains a vexing problem to the monsoon scientist because the Asian summer monsoon encompasses complex, multi-scale variability in temporal and spatial domains. The most dominant mode of variability of the Asian summer monsoon is that arising as a response to the basin-scale SST anomalies during ENSO (Lau and Wu, 2001). Evidently ENSO has an impact on the Indian summer monsoon variability and which influences the dry and wet conditions over the Indian Peninsula (Meehl, 1987; Bhatt, 1989; Yasunari, 1990; Webster and Yang, 1992). Gu et al. (2010) studied the relation between ISMR and ENSO and found that the ISMR weakens during the El Niño early onset years because the warm eastern Pacific SSTA induces cyclonic wind shear that suppresses convection over India. During the El Niño decaying summer, a strong ISMR results from the El Niño to La Niña transition or a basin wide Indian Ocean warming. Numerous studies have shown that the warm phase of ENSO (El Niño) is associated with a weakening of the Indian monsoon with an overall reduction in rainfall. Conversely, the cold phase of ENSO (La Niña) is associated with the strengthening of the Indian monsoon and enhancement of rainfall (e.g. Sikka, 1980; Rasmussen and Carpenter, 1983). The exact reason for the occurrence of the Indian summer monsoon drought and wet conditions are still unknown. There are cases of strong ENSO, having little or no impact on the Indian monsoon, and there are years in which monsoon droughts and floods occur without strong ENSO. Studies have shown that the influence of ENSO on ISMR during the last two decades has been weakening considerably while compared to previous decades (Krishnakumar et al., 1999; Kinter et al., 2002).

Other important oceanic feature that directly influences the ISMR is the air sea interaction process in the Indian Ocean named as IOD mode. This dipole mode can be represented by an index called Dipole Mode Index (Saji et al., 1999). Behera et al. (1999) inferred that the IOD phenomenon modulates the meridional circulation by inducing anomalous convergence (divergence) patterns over the Bay of Bengal during positive (negative) IOD events, leading to excessive (deficit) monsoon rainfall over the monsoon domains. In a study by Ashok et al. (2001) reported that there are apparent complementary interdecadal changes between the ENSO-ISMR and IOD-ISMR relationships. Ashok et al. (2004) examined the ISMR anomalies during pure IOD, pure ENSO events and found that while they are co-occurring, positive IOD significantly reduces the effect of El Niño on ISMR. Yang et al. (2007) reported that the evolving and decaying stages of ENSO influences the Indian Ocean basin mode warming and hence the summer monsoon rainfall pattern over the Indian subcontinent. Li et al. (2001) established that the Indian Ocean SST has positive correlation with ISMR due to high surface evaporation. These studies does not give a clear idea about the influence of Indian Ocean on ISMR in the absence of ENSO. The occurrence of droughts and floods in the absence of ENSO indicates that Indian Ocean plays an important role in modulating ISMR. This study is therefore focused on identifying the role of sea surface temperature (SST) in the Indo-Pacific region on the distribution of ISMR. The spatial structure of rainfall anomaly during different phases (wet and dry years) of ISMR associated with ENSO and non-ENSO conditions have been studied in detail using long period data sets. Further, the features of Walker and Hadley circulation during these different phases of Indian summer monsoon have been explored in this study.

2. Data and methods

2.1. Data

This study utilizes daily gridded rainfall data set with 1° latitude × 1° longitude grid resolution available from India Meteorological Department (IMD) for a period from 1901 to 2004. The geographical area, 6.5°N to 37.5°N; 66.5°E to 101.5°E was considered for interpolating (Shepard, 1968) the station rainfall data to gridded rainfall data. The interpolation method is based on the weights calculated from the distance between the station and the grid point and also the directional effects. Standard quality controls were performed before carrying out the interpolation analysis (Rajeevan et al., 2006; Rajeevan and Bhate, 2009). For this work, we considered the daily data for Indian summer monsoon season (June to September) and it have been converted to monthly values.

In addition to the IMD gridded data, IITM monthly rainfall data set available in the www.tropemt.res.in website was also used for this study. IITM All India rainfall data set was constructed from 306 stations almost uniformly distributed all over India, except Himalayan mountain regions, for which rainfall data are available from 1871. All India average rainfall for the southwest monsoon period was selected from both the IMD gridded and IITM station rainfall data sets for the comparison. Figure 1 shows the scatter plot of IITM station rainfall versus IMD gridded rainfall. The rainfall anomalies are normalized with its own standard deviation. The plot shows good agreement between the two rainfall data sets. The linear correlation coefficient value is 0.88 with a root mean square error (RMSE) of 0.05. The correlation value is significant at 0.01% significant level. The high value of correlation coefficient and very low value of RMSE indicates that there is no significant changes between the two rainfall data sets. Therefore in further studies we used IMD gridded spatial rainfall data to study the characteristics during the southwest monsoon period.

Figure 1.

Regression analysis of rainfall from 306 stations prepared by IITM and gridded rainfall data prepared by IMD. The linear correlation coefficient is 0.88 between the two rainfall data set

We also used the extended reconstruction sea surface temperature (ERSST) for the last century. This monthly data set have 2° latitude × 2° longitude grid spatial resolution. The analysis of ERSST is based on the International Comprehensive Ocean-Atmosphere Data Set release 2.4. ERSST.v3 (Smith and Reynolds, 2003, 2004; Smith et al., 2008). ERSST.v3b is generated using in situ SST data and improved statistical methods that allow stable reconstruction using sparse data. The monthly analysis extends from January 1854 to the present, but because of sparse data in the early years, the analyzed signal is damped before 1880. After 1880, the strength of the signal is more consistent over time. The v3 improvements are justified by testing with simulated data. ERSST v3 has improved low-frequency tuning that reduces the SST anomaly damping before 1930 using the optimized parameters. Both in situ and satellite (AVHRR) SST data were used as inputs in ERSST.v3 (Smith et al., 2008). However, the addition of satellite SSTs introduced a small residual cold bias (in the order of 0.01 °C). AVHRR is an infrared (IR)-based instrument. IR measurements can only be obtained in clear-sky conditions, and cloud contaminated data are often difficult to identify. This small difference did not strongly impact the long term trend, it was sufficient to change rankings of warmest month in the time series, etc.

Zonal, meridional wind at 850 hPa and 1000 hPa and specific humidity at 1000 hPa are used form 20th centuary reanalysis products. These data set have a spatial resolution of 2° × 2° latitude longitude spatial grid and is available from 1871 to present. This data set is a valuable resource to the climate research community for both model validations and diagnostic studies (Compo et al., 2011).

2.2. Methods

An ENSO index is created using the ERSST data set as SST anomaly (SSTA) over NINO3.4 region (area averaged SSTA over 170°W to 120°W, 5°S to 5°N). We considered an year as El Niño year when 5 month running mean of the nino 3.4 index exceed 0.4 °C for six consecutive months and a La Niña year is when the index is less than − 0.4 °C for six consecutive months. Further a positive ENSO year refers El Niño and negative ENSO year refers La Niña. To understand the effect of ENSO on SSTs, we regressed the ERSST during summer monsoon season (June to September) with the ENSO Index (Nino 3.4 SST anomaly) of the season to remove the effect of ENSO from the SST anomaly at all the grid points.

The 104 years (from 1901 to 2004) have been classified as wet and dry years of ISMR based on the summer monsoon rainfall anomaly normalized with its own standard deviation. We call an year as wet (dry) when the normalized rainfall anomaly is greater than 1 (less than − 1). The years are further classified based on occurrence and nonoccurrence of ENSO as ENSO-Wet (EW), ENSO-Dry (ED), Normal Wet (NW) and Normal Dry (ND). When a wet year is associated with ENSO event then we called the year as EW year and a wet year without ENSO is denoted by NW. Similarly the dry years are classified as ED (with ENSO) and ND (without ENSO). All the wet and dry years of ISMR associated with ENSO and non-ENSO events (EW, ED, NW and ND years) occurred during the study period, 1901–2004, is tabulated in Table 1. The significance of the difference in spatial pattern of rainfall between wet and dry years of ISMR with and without ENSO have been tested using student's t-test. Composite analysis of moisture convergence at 1000 hPa, SST and 850 hPa circulation have been carried out to understand the possible gate way through which Indo-Pacific SSTs influence the ISMR.

Table 1. Wet and dry years of ISMR from 1901 to 2003 during EW, ED, NW and ND years
1994 19711986

3. Results and discussion

3.1. ISMR climatology

In this study, we made an attempt to characterize the drought and wet years during the ENSO and non-ENSO years. For this, we utilized monthly data of ERSST and IMD gridded rainfall for more than a century (1901–2004). Figure 2 presents the Nino 3.4 SST anomaly during Indian summer monsoon season for the entire period of study derived from the ERSST data set along with the standardized anomaly of ISMR. It is clear that the flood and drought years are occurring without El Niño or La Niña. In view of this we tried to study the features of dry and wet years of ISMR separately with the ENSO index. Climatology of ISMR for the period from 1901 to 2003 is presented in Figure 3. The figure depicts that the rainfall is high over the Konkan coast and the northeastern regions of the country, where the monsoon rainfall exceeds 20 mm d−1. Over the central India, the rainfall is about 10 mm d−1 and it is less than 5 mm d−1 over southeast India and northwest Indian regions. This spatial distribution of rainfall varies considerably during wet and dry years of monsoon, moreover the occurrence and nonoccurrence of ENSO can again alter the spatial pattern of rainfall over the country. Hence a detailed understanding of the rainfall climatology is made by considering the wet and dry years of monsoon associated with ENSO and non-ENSO conditions.

Figure 2.

Nino 3.4 SST anomaly (ENSO index) during the summer monsoon period along with anomaly of ISMR standardized with its own standard deviation, The horizontal lines are ± 0.4 which corresponds to El Niño/La Niña

Figure 3.

ISMR (mm d−1) climatology for the period 1901–2004.

3.2. Rainfall characteristics

ISMR anomalies for EW, ED, NW, and ND years and the difference between ENSO and non-ENSO periods are presented in Figure 4. Figure 4(a) and (d) represents wet and dry monsoon rainfall anomalies associated with ENSO (El Niño and La Niña) events, while Figure 4(b) and (e) represents rainfall anomaly during wet and dry years of ISMR, which are not associated with ENSO events. Rainfall anomalies are computed based on the 104 years (1901–2004) of ISMR climatology. The figure shows that the spatial structure of rainfall pattern during ENSO and non-ENSO years are different. During wet years associated with ENSO, the rainfall maximum is found along the west coastal region from 15°N to 20°N, where the rainfall anomaly exceeds 80 mm month−1. Whereas in central and northern India, the rainfall is above normal with more than 25 mm month−1. Pockets of less rainfall were also noticed over southeast and northeastern parts of the country (Figure 4(a)). However, wet years not associated with ENSO is more interesting because the above normal rainfall activity is not uniformly distributed over the country (Figure 4(b)). The rainfall is abnormally high in the west coastal areas especially towards south coast, where the high values of positive rainfall anomaly above 80 mm month−1 is noticed. Over central India also the rainfall anomaly is high, more than 80 mm month−1. It is interested to note that the rainfall anomaly is below normal in southeast and northeastern regions of Indian Peninsula. During the ENSO years, the values of negative anomaly is low compared to NW years and the positive anomaly is abnormally high especially over the Maharashtra coasts. In the case of NW years, certain regions experience heavy rainfall activity while over certain other regions the rainfall activity is very low compared to EW years. Hence during NW years the country witnesses highest maximum and lowest minimum amount of rainfall compared to EW years. This is very peculiar character of the ISMR during wet seasons in the absence of ENSO. This can be clearly seen from Figure 4(c), which presents the difference in rainfall during wet years from ENSO to non-ENSO periods. The contours in the graph is the 0.01% significant level based on the two tailed student's t-test. Significant differences are observed in the spatial distribution of rainfall over the western coast, central Indian regions and over the northeastern regions. In the southwest coastal areas (south of 15°N) rainfall is lower during the wet years associated with ENSO. Rainfall is registered more to the north of 15°N up to about Gujarath coast especially in the coastal belt. These differences are confident at 99.99% level. In the central India, during EW years, rainfall is significantly low. However, during the NW years, rainfall in some parts of central India is about 75 mm month−1 more than that of the EW years.

Figure 4.

Rainfall anomaly (mm month−1) for (a) EW, (b) NW, (c) difference between (a) and (b), (d) ED, (e) ND and (f) difference between (d) and (e).

During dry monsoon years, the spatial distribution of ISMR is opposite from that of the wet years. During ED years, the rainfall anomaly is too low except in the areas of south eastern and north eastern regions of the country. The maximum deficiency of rainfall is observed over areas of western coasts. In the case of ND years, even though the west coastal regions experienced low rainfall activity, the negative anomalies are less compared to ED years. The summer monsoon rainfall is above normal to the south of 12°N, while the entire India experiences drought. In the years of ED, the below normal rainfall is observed in the areas of west coastal and northern India, however, during ND years, the dryness has mostly experienced in the central parts of India. Even though there is large spatial variability in the rainfall distribution, the analysis show that large scale floods and droughts occur with (Figure 4(a) and (d)) and without ENSO events (Figure 4(b) and (e)). The difference between the ED and ND rainfall and its significance at 0.01% level is given Figure 4(f). In the entire western regions, the difference is significant at 0.01% level. During ED years, the entire western coastal belt receives lesser rainfall than during the dry years associated with non-ENSO periods except Goa region and there the rainfall is more during ED years. We also observed significant reduction in rainfall over the northern and West Bengal regions during ED years. However, more rainfall is received over the central parts of India during ED events than during the ND years.

During ENSO events, the anomalous warming or cooling in the eastern Pacific plays an important role in altering the large scale circulation and thereby affects the rainfall distribution over India (Chakraborty and Krishnamurti, 2003). However, occurrence of wet and dry monsoon associated with non-ENSO situation lead us to understand the role of Indian Ocean in modulating ISMR. Li and Zhang (2002) argued that positive SST anomaly in the Indian Ocean increases moisture transport due to enhanced surface evaporation. The accumulation of moisture leads to a strong monsoon through anomalous moisture advection due to summer monsoon mean flows. Hence, before understanding the influence of Indian Ocean in modulating the ISMR, the role of moisture transport from Indian Ocean in determining the rainfall characteristics over Indian region have been studied.

3.3. Low level moisture transport

The association between ISMR and moisture transport have been examined by computing moisture convergence at 1000 hPa during wet and dry years of monsoon with ENSO and non-ENSO periods. The difference in moisture divergence during wet and dry years is given in Figure 5. In wet years, the moisture convergence is seen in the west coastal regions especially in the northern parts of Konkan coast eastern regions of Indian subcontinent (Figure 5(a)). The divergence patterns (positive values of the contour) are noticed in the central parts of India. These spatial pattern of the low level moisture divergence is almost in consistence with the rainfall distribution of difference in wet years between ENSO and normal years. Similarly, the difference in moisture divergence between ENSO and normal periods during dry summer monsoon years is given in Figure 5(b). During the dry years, rainfall deficiency is noticed in the northern and southern regions of western coast (Figure 4(f)). The moisture divergence also shows the same result. In the northern and southern regions of west coastal areas positive divergence of moisture is observed indicating the low level divergence of moisture during normal dry years. This divergence pattern prohibits the organization of clouds during the normal dry years over the western coastal belts. The negative values of divergence pattern indicating the low level convergence is observed in the central parts of Indian region. This convergence zone of moisture enables convection over the region and hence above normal rainfall activity.

Figure 5.

Moisture divergence (m s−1 g kg−1) at 1000 hPa during summer monsoon season (a) difference between ENSO wet and normal wet years of ISMR and (b) difference between ENSO dry and normal dry years of ISMR.

3.4. Low level circulation

The wind structure during ENSO years are distinctly different from normal years (Rasmusson and Wallace, 1983; Ju and Slingo, 1995). Moreover, differences are observed during wet and dry years irrespective of the ENSO and normal years. Wind anomaly structure at 850 hPa during summer monsoon period of EW, ED, NW, and ND years is given Figure 6. The figure clearly shows considerable differences in circulation during all the four cases of monsoon. Figure 6(a) shows the 850 hPa circulation anomaly during ENSO wet years. During ENSO wet years, the low level jetstream (LLJ) is weak over the Indian peninsula; however, it makes a convergence zone over the west coastal areas leading to convective rain bands over the west coastal belts. During the ENSO dry periods, the wind pattern is completely different from the wet cases. In the dry years, El Niño is prominent in the Pacific ocean and the circulation induced by the El Niño makes a divergence over the Indian subcontinent (Figure 6(b)). In addition to that, Indian Ocean SST also do a prominent modulation to drive the low level circulation. In this case SST is warm over the Arabian Sea, which makes the LLJ weak. Warm SSTs over the Pacific Ocean (due to El Niño conditions) induces a westerly wind anomaly over Bay of Bengal region. These two flows, make a divergent circulation over Indian region and leads to dry conditions over the subcontinent during southwest monsoon period. This divergent flow pulls the dry air from the continental higher latitude region and further increases the dryness over the country.

Figure 6.

Wind anomaly (m s−1; magnitudes in shading) at 850 hPa during summer monsoon period for (a) EW, (b) ED, (c) NW and (d) ND years.

Summer monsoon rainfall is abnormally high during some normal monsoon years, i.e without La Niña. During such periods, the monsoon LLJ is abnormally strong over the Indian peninsula (Figure 6(c)). This strong LLJ enhances the moisture supply to the peninsula and leads to organized convective cloud bands. The easterlies in the equatorial Indian ocean is also strengthened due to the high SST over the west equatorial Indian Ocean, leading to strong cross equatorial flow of the LLJ and therefore moisture supply over the subcontinent. During the wet phase of monsoon, warm SSTs over west Equatorial Indian Ocean resembles a positive IOD structure and this is concurrent with the findings of Behera et al. (1999) and Ashok et al. (2001). In the case of normal dry years, the LLJ is too weak during the summer monsoon period. It is also clear from Figure 6(d) that the dry air draws from the continental areas and this leads to dryness of the atmosphere and subsequently leads to below normal rainfall or dry condition during the summer monsoon period.

3.5. Influence of Indo-Pacific SSTs on ISMR

The role of Indo-Pacific SST during wet and dry periods associated with ENSO and non-ENSO have been studied. Figure 7(a)–(d) represents the spatial structure of SST anomaly during EW, NW, ED, and ND events of ISMR. The SST anomaly pattern shows that during EW years, negative anomaly in the western side (a basin wide cooling) and a positive SST anomaly in the eastern side of the Indian Ocean near Indonesia. This spatial structure is very similar to the negative IOD mode (even though the magnitude of the SST anomaly is small). Negative ENSO years (La Niña: a cool phase in the eastern Pacific) are characterized by abnormal cooling of the waters over eastern Pacific Ocean and suppressed convection in the atmosphere above it. Studies by Sikka (1980), Pant and Parthasarathy (1981) and Rasmusson and Carpenter (1981) have shown that there is an increased number of droughts during El Niño and an excess rainfall during La Niña over the Indian subcontinent.

Figure 7.

SST anomaly for the summer monsoon period in the Indo-pacific region during (a) EW, (b) NW, (c) ED and (d) ND years

During wet years not associated with ENSO (Figure 7(b)), ISMR is above normal and the SST pattern is different from the ENSO years in the Indo-Pacific region. During these years ISMR is above normal in most parts of the Indian Peninsula as observed in Figure 4(b). The SST pattern shows a positive IOD structure: positive SST anomaly over the west equatorial Indian Ocean and negative anomaly over Indonesian regions (Saji et al., 1999). In addition to the positive IOD structure, we have observed a negative SST pattern over a wide area in the central Pacific (180°E to 110°W and 10°S to 10°N). This negative SST anomaly plays a vital role in controlling the general circulation in the atmosphere to regulate Indian summer monsoon.

During dry years of Indian summer monsoon, SST anomaly pattern is almost opposite from ENSO years to normal years. There is no unique methodology to predict frequency of droughts, its occurrence and its cessation (Dugam, 2010). The present scientific knowledge is insufficient for accurate and early prediction of droughts, so it is essential to understand the causes of drought and associated oceanic conditions and thus it may lead to a better prediction of droughts. As we discussed in section '3.2. Rainfall characteristics', the negative anomaly of rainfall is high over west coast and northern regions during positive ENSO (El Niño) years, however, during non-ENSO years, negative rainfall anomaly is higher in the central and northern regions of Indian Peninsula (Figure 4(e)). The related oceanic features are different for ENSO and non-ENSO years. Figure 7(c) shows the SST anomaly pattern during ENSO with a drought in ISMR. This depicts that most of the Indian Ocean is warm (more warmer towards the south), however, the ENSO induced circulation causes subsidence over the Indian subcontinent (Ju and Slingo, 1995) and thus the rainfall is below normal.

During non-ENSO years, the SST anomaly is negative in the entire Arabian Sea and Bay of Bengal (Figure 7(d)). The below normal temperature in the Arabian Sea and Bay of Bengal leads for subsidence and thus it prevents the organized convection. Moreover, the Pacific SST influence also show a strong influence on the Indian summer monsoon by regulating the general circulation of the atmosphere. The SST over the Pacific Ocean is positive extending from 160°E to 125°W from the either side of the equatorial belt. This positive anomaly in the Pacific Ocean behaves very similar to the La Niña events in the western Pacific.

3.6. Walker and Hadley circulations during non-ENSO years

Walker and Hadley circulations refer as large scale atmospheric circulations in the zonal and meridional directions respectively in the height plane. These circulations changes with the SST pattern changes over the Equatorial pacific and Indian Ocean regions. The Walker and Hadley circulations associated with the El Niño and La Niña are well defined in literatures. The teleconnections of El Niño and La Niña with ISMR is also well known by the scientific community through the Walker and Hadley circulations.

However, the teleconnection or influence of the Pacific Ocean with the wet and dry years of ISMR associated with non-ENSO periods is not well studied so far. In this view, we tried to explain the teleconnections of the Pacific through the zonal and meridional circulations with the ISMR. Figure 8 shows that Walker and Hadley circulation during wet years of ISMR associated with non-ENSO events. The composite structure of Walker circulation averaged over the equatorial region (5°S to 5°N) and the Hadley circulations average over the Indian regions (72.5°E to 85°E) shows clear teleconnections between the negative SST anomaly over the central equatorial Pacific and ISMR. From the Walker circulation, it is clear that the circulation shows a descending limb over the negative SST anomaly region in the Pacific. This downward circulation move towards west through the surface as easterlies and ascend over the maritime continent due to the continental effect. This ascending component move towards west in the upper atmosphere and descend again over the equatorial Indian Ocean. Moreover, the positive anomaly of SST over the western Indian Ocean (Figure 7(b)) helps to rise the Walker circulation over it and this leads to intensify the descending component at the central equatorial Indian Ocean. The Hadley circulation shows the descending limb at the central equatorial Indian Ocean and this down ward limb moves towards north through the surface and rises at the peninsular region. This rise of the atmospheric circulation leads to organized convection and ultimately surplus amount of rainfall over the Indian region during summer monsoon season.

Figure 8.

Walker (upper) and Hadley (lower) circulations during wet years of ISMR not associated with ENSO

The Walker and Hadley circulations during dry summer monsoon years not associated with ENSO are clearly different from the circulations during ENSO events. The SST anomaly shows negative in the central Pacific Ocean almost similar to the La Niña events, except the area of negative SST anomaly and the magnitude of SST. The atmospheric circulation associated with this event is also similar to the circulations that associated with La Niña. During ND years, Walker circulation (Figure 9) shows descending component at the maritime continent and ascending components over the central equatorial Indian ocean and Pacific oceans. The ascending component at the equatorial Indian Ocean shows a descending circulation of the Indian peninsular regions. This descending component of Hadley circulation in the meridional direction leads to inhibit the formation of organized convention over the Indian sub continents and thus ISMR show a below normal rainfall.

Figure 9.

Walker (upper) and Hadley (lower) circulations during dry years of ISMR not associated with ENSO

4. Summary

In this study, we made an attempt to unravel the different regional characteristics of rainfall during wet and dry years of Indian summer monsoon associated with ENSO (El Niño and La Niña) and non-ENSO events. The features of rainfall, low level moisture transport and circulation, SST and general (Walker and Hadley) circulation features were analyzed by compositing the different cases, EW and NW, ED and ND. From the composite analysis of rainfall anomaly, we found that there is significant difference in the spatial characteristics of ISMR between wet years associated with and without ENSO. The regional distribution of rainfall during dry years also differs significantly between ENSO and non-ENSO years. During EW years, the rainfall anomaly is higher in the northern part of the west coastal stations. However, for NW years, the rainfall anomaly is more in the southern part of the Konkan coast. In the EW years, the rainfall anomaly is almost uniformly distributed all over central India but in the case of NW years central and northern India receives higher rainfall amount. During ED years, Indian Peninsula experiences below normal rainfall and minimal rainfall were noticed in the west coastal stations and northern India. However, during ND years the west coastal belt is not experiencing severe drought similar to that during ED years. Below normal rainfall is occurring mostly in the central India during the ND years. The low level moisture transport is almost concurrent with the changes in rainfall pattern between ENSO and non-ENSO years. Moisture convergence is associated with abnormally high rainfall and divergence zones consistent with below normal rainfall.

During ENSO wet years, a negative SST anomaly over the southern Indian Ocean and a negative mode of IOD like structure are observed. The Pacific influence during La Niña period is well explained by the zonal and meridional circulations. However, the wet years in the absence of ENSO, SST in the Indian Ocean shows a positive IOD structure. The Pacific regions also shows a negative anomaly in the central equatorial regions and this leads to modification of Walker circulation and the zonal direction favouring the sinking motion over the central equatorial Indian Ocean. Hadley circulation over the Indian region favours uplift over the peninsular regions of Indian subcontinent leading to wet ISMR.

During dry years, the occurrence of ENSO produces positive SST anomaly over the Indian Ocean and positive anomaly in the east Pacific. The ENSO influences the ISMR and leads to the drought condition. During non-ENSO years, the below normal SST over Indian Ocean and above normal SST in the equatorial central Pacific leads to drought.

The low level circulation is mainly governed by the SST over the region. During ENSO years, circulation induced by ENSO and SST over the Indian Ocean regulate the low level circulation for the wet/dry of ISMR. During the wet phases associated with non-ENSO years, the LLJ is strong over the Indian subcontinent and it may be due to the positive IOD structure in the Indian Ocean. In the case of dry years of ISMR associated with non-ENSO, the LLJ is weak over the subcontinent and it may be due to the basin wide cooling of the Indian Ocean.


We thank Prof. B.N. Goswami, Director, IITM for all the support and necessary infrastructural facilities. The data sets used in this study are properly acknowledged. Comments from reviewers helped a lot to improve the manuscript.