Clim Dyn DOI 10.1007/s00382-016-3451-6

Potential modulations of pre‑monsoon aerosols during El Niño: impact on Indian summer monsoon S. Fadnavis1 · Chaitri Roy1 · T. P. Sabin1 · D. C. Ayantika1 · K. Ashok2 

Received: 5 May 2016 / Accepted: 9 November 2016 © Springer-Verlag Berlin Heidelberg 2016

Abstract The potential role of aerosol loading on the Indian summer monsoon rainfall during the El Niño years are examined using satellite-derived observations and a state of the art fully interactive aerosol-chemistry-climate model. The Aerosol Index (AI) from TOMS (1978–2005) and Aerosol Optical Depth (AOD) from MISR spectroradiometer (2000–2010) indicate a higher-than-normal aerosol loading over the Indo-Gangetic plain (IGP) during the pre-monsoon season with a concurrent El Niño. Sensitivity experiments using ECHAM5-HAMMOZ climate model suggests that this enhanced loading of pre-monsoon absorbing aerosols over the Indo-Gangetic plain can reduce the drought during El Niño years by invoking the ‘Elevated-Heat-Pump’ mechanism through an anomalous aerosol-induced warm core in the atmospheric column. This anomalous heating upshot the relative strengthening of the cross-equatorial moisture inflow associated with the monsoon and eventually reduces the severity of drought during El Niño years. The findings are subject to the usual limitations such as the uncertainties in observations, and limited number of El Niño years (during the study period). Keywords  El Niño · ECHAM5-HAMMOZ · MISR AOD · TOMS AI

Electronic supplementary material  The online version of this article (doi:10.1007/s00382-016-3451-6) contains supplementary material, which is available to authorized users. * S. Fadnavis [email protected] 1

Indian Institute of Tropical Meteorology, Pune, India

2

University of Hyderabad, Hyderabad, India



1 Introduction The persistent weakening trend of Indian summer monsoon (ISM) precipitation during recent decades (Guhatakurtha and Rajeevan 2006; Rajeevan et al. 2010; Bollasina et al. 2011; Kumar et al. 2013) has catastrophic effects on agriculture, water resources, food security, economy and social life in India. Several recent studies (Ramanathan et al. 2005; Meehl et al. 2008) suggest that anthropogenic aerosols potentially act as a key element responsible for the observed decrease in the ISM precipitation. Further, enhanced aerosols, in general, induce energy imbalance between hemispheres, which may slow down the monsoon through weakening the meridional circulation (Forster et al. 2007; Hansen et al. 2011; Bollasina et al. 2011; Ganguly et al. 2012a; Sajani et al. 2012). It has been noted that in the recent years aerosol optical depth (AOD) over the Indian region is increasing at an alarming rate (>40% during 2000–2009: Ramachandran et al. 2012) which may eventually increase the frequency of drought in coming decades (Ramanathan et al. 2005). The potential influence of absorbing aerosols, especially Black carbon (BC) and dust, on Indian summer monsoon precipitation has become evident in recent years (Meehl et al. 2008; Lau et al. 2008; Wang et al. 2009; Vinoj et al. 2014; Ganguly et al. 2012b). The elevated levels of these aerosols reduce the precipitation through a direct effect (Ramanathan et al. 2005; Meehl et al. 2008). As per Ramanathan et al. (2005) and Meehl et al. (2008) any increase in the BC loading during the summer monsoon is likely to reduce the monsoon precipitation over India. In contrast, Vinoj et al. (2014) suggest that there is an anomalous intensification of the intensity of the Indian monsoon rainfall (on a time scale of weeks) due to the dust transported from West Asia and the Arabian Peninsula. Further, Wang et al.

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(2009), states that the response to anthropogenic aerosols, on the ISM rainfall shows a clear north–south contrast. Specifically, these aerosols cause a reduction (~40%) in ISM rainfall to the south of 20°N and an increase to the north of this zone. The Aerosol Robotic Network (AERONET) observations reveal that during pre-monsoon season (March–May) absorbing aerosols (BC and dust) are abundant in the IGP region (Kedia et al. 2014). IGP region extends between ~23°N and 33°N and 70°E–90°E. The AOD gradually increases in this region throughout the pre-monsoon season (Kedia et al. 2014; Singh et al. 2004). MODIS observations show that there is a two-fold increase in aerosols over the IGP during pre-monsoon season (April through June) of 2009 (Gautam et al. 2011). Lau and Kim (2006) suggest that an enhancement of absorbing aerosols during pre-monsoon season (mostly during April and May) over IGP and northern and southern slopes of Tibetan Plateau (TP) facilitates an early onset and intensification of ISM during June and July through an ‘Elevated Heat Pump (EPH)’ effect. As per EPH hypothesis, during the April–May months, dust aerosols are transported from Afghanistan, the Middle East, and the Taklimakan desert can accumulate over southern and northern slopes of the TP (Lau and Kim 2006, Gautam et al. 2009). The heavy loading of dust, along with locally emitted black carbon (BC) over the IGP, may result in an anomalous warm core in the upper troposphere over the TP. This warming, in turn, draws warm and moist air from the Indian Ocean and amplifies the meridional overturning, which strengthens the South Asian summer monsoon. On a slightly different note, El Niño is a vital factor responsible for anomalous suppression of the Indian summer monsoon (Sikka 1980; Keshavamurthy 1982; Ropelewski and Halpert 1987; Ummenhofer et al. 2013). The historical rainfall records confirm that severe droughts in India generally associated with the co-occurring strong El Niño events (Webster et al. 1998; KrishnaKumar et al. 2006). However, not every El Niño significantly reduces the summer monsoon rainfall (e.g. KrishnaKumar et al. 1999). There could be several other factors influencing the extent and intensity of drought during the co-occuring El Niño years which can alter the strength of ISM. For example, the critical role that aerosol can interplay during the co-occuring El Niño year is not yet understood clearly. From this perspective, we raised an unexplored question: how do the pre-monsoon aerosol loading, modulate the rainfall over Indian region in El Niño years. In this study, we presented a detailed analysis of available observation, reanalysis and results from model sensitivity experiments to understand the potential modulation of the aerosol loading during pre-monsoon season with a co-occurring El Niño. For this, we carry out several sensitivity experiments as shown in the next section using the ECHAM5-HAMMOZ,

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a state of the art fully interactive aerosol-chemistry-climate model. The paper organized as follows. A model description, our experimental setup and some details of observed datasets used in this work briefly presented in Sect. 2. The impact of pre-monsoon aerosol loading on India summer monsoon in the background of a co-occurring El Niño explored in Sect. 3. Section 4 provided a detailed discussion and followed by the conclusions in Sect. 5.

2 Model simulations and data used 2.1 Model description and experimental setup The ECHAM5-HAMMOZ aerosol-chemistry-climate model comprises of an atmospheric general circulation model, ECHAM5 (Roeckner et al. 2003), a tropospheric chemistry module MOZ (Horowitz et al., 2003) and an aerosol module Hamburg Aerosol Model (HAM) (Stier et al. 2005). The HAM module takes into account the primary aerosol compounds namely sulfate (SU), Black Carbon (BC), Organic Carbon (OC), sea salt (SS) and mineral dust (DU). It represents aerosols as internal and external mixtures with four soluble and three insoluble modes (Vignati et al. 2004). The aerosols interact with ‘meteorology’ through internal mixing and by altering cloud microphysics (i.e. through indirect effects). Details of the aerosol categorization and their parameterization schemes are documented in Stier et al. (2005). Further particulars of the model parameterizations, emissions, etc., are discussed in Pozzoli et al. (2011) and Fadnavis et al. (2013, 2014, 2015). The model run at a spectral resolution of T42 corresponding to about 2.8 × 2.8 degrees in the horizontal dimension and 31 vertical hybrid (σ–p) levels from the surface to 10 hPa. Note that the aerosol and trace gas emission for our run is for the year 2000. The anthropogenic and fire emissions of SU, BC and OC, are based on the AEROCOM emission inventory (Dentener et al. 2006), and representative of the year 2000. The anthropogenic and natural emissions are also described in detail by Pozzoli et al. (2008a, b). We conducted four sensitivity experiments listed in Table  1, each experiment consists of 10 ensemble members with a perturbed initial condition from March (1 to 10) 2003. The year 2003 was a neutral year (no IOD, ENSO etc.). The simple ensemble mean approach is adopted to represent the experiment. Emissions are the same in each simulation. To understand the effects of aerosols, we analyze the difference between the simulations with fully interactive aerosols (ON) and only passively transported aerosols (OFF). The impact of pre-monsoon aerosols during monsoon season is deciphered from simulations with

Potential modulations of pre-monsoon aerosols during El Niño: impact on Indian summer monsoon Table 1  Details of experiments conducted in the present study Sr. No Experiment description 1.

2.

3.

4

Name of experiment

Interactive aerosols (AERO-ON) with Climatological CTR_aero_pre-mon SST and effects of aerosols during pre-monsoon and monsoon season CTR_aeroOFF_pre-mon Passively transported aerosol (NO-AERO) with Climatological SST and effects of aerosols during pre-monsoon and monsoon season ElNiño_aero_pre-mon Interactive aerosols (AERO-ON) with El Niño SST and effects of aerosols during pre-monsoon and monsoon season Passively transported aerosol (NO-AERO) with El Niño SST and effects of aerosols during pre-monsoon and monsoon season

Prescribed SSTs

Time period of the simulation

Climatological SST March(1–10)–September

Climatological SST March(1–10)–September

El Niño SST

March(1–10)–September

ElNiño_aeroOFF_pre-mon El Niño SST

March(1–10)–September

initial conditions starting from 1 to 10 March 2003. All the simulations conducted until the end of monsoon season, 30 September 2003. In two sets of experiments, namely, CTR_aero_premon, CTR_aeroOFF_pre-mon, the model is forced with a monthly varying climatological SST derived from HadISST SST for the period 1979–2010 as lower boundary condition can consider as the ‘control’ experiment. In another two set of experiments termed as the ‘El Niño’ experiment (ElNiño_aero_pre-mon, ElNiño_aeroOFF_pre-mon), the model is forced with El Niño SST. These canonical SSTs are obtained following earlier studies such as Ashok et al. (2001, 2004), Guan et al. (2003), etc. by imposing monthly SST anomalies of 1997, a strong El Niño year, on the climatological SSTs only in the tropical Pacific region (110°E–90°W, 20°S–20°N) from March to September, as shown in Supplementary Figure S1. The simulations with the climatological SST are referred as control simulations while simulations forced with the El Niño type of SST referred as El Niño simulations. The detailed list of various experiments and their nomenclatures are provided in Table 1. 2.2 Observed and reanalyzed data sets used We have used the distribution of daily aerosol index (AI) from Total Ozone Mapping Spectrometer (TOMS) for the pre-monsoon (April–May) and monsoon seasons (June– September) for the period 1979–2005. TOMS detects aerosols over bright targets such as deserts, but the detection technique is insensitive to aerosols layers at low altitudes. (Zhang and Christopher 2003; Wonsick et al. 2014). The TOMS aerosol detection method utilizes the spectral contrast of two ultraviolet channels, (A and B) (Herman et al. 1997) in which the central wavelengths of the channels used for determination of the aerosol index. The aerosol index measured by TOMS instrument aboard two different

satellites Nimbus-7 (Nov. 1978–May 1993) and Earth Probe (July 1996—current) are used in this study. The aerosol retrievals are also available from other satellites e.g. Moderate Resolution Imaging Spectroradiometer (MODIS) and Multi-Angle Imaging Spectroradiometer (MISR) since 2000. However, the MODIS measurements give aerosol properties over the global oceans and land with low surface reflectance (Remer et al. 2002; Chu et al. 2002) and MISR over the global ocean and land with bright targets such as deserts (Kahn et al. 2001) for the period 2000– 2013. The spatial distribution of MISR and TOMS measurements are consistent over most of the regions (Zhang and Christopher 2003). The current study utilized the longest data set of TOMS AI as well as MISR AOD. TOMS AI detects mid-upper tropospheric absorbing aerosols since the distribution of mid-upper troposphere absorbing aerosols over elevated TP plays a fundamental role in modulation of the Indian monsoon. AERONET retrieved AOD at Kanpur (a station in IGP) reveals that more than 50% of AOD contributed from aerosols above 4 km (Dumka et al. 2014). We used the gridded daily measurements of rainfall (1°  × 1°) from India Meteorological Department (IMD) for the period 1979–2005 (Rajeevan et al. 2006) averaged over North India (70–90°E, 20–30°N) to represent the summer monsoon rainfall. The Hadley Centre Sea Ice and Sea Surface Temperature (HadISST) data set (Rayner et al. 2003) from the 1979–2005 used to identify the El Niño years. Note that, for simplicity in carrying out the model experiments; we do not distinguish between the canonical and Modoki ENSOs (Ashok et al. 2007). This is acceptable from the point that the impact of the Modoki and canonical ENSOs on the Indian summer monsoon rainfall is qualitatively similar (e.g. Keshavamurthy 1982; Navarra et al. 1999; KrishnaKumar et al. 2006; Ashok et al. 2007, 2009), though the magnitude of the anomalies differs. Also, we used the wind fields from National Center

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S. Fadnavis et al. Fig. 1  Distribution of seasonal mean (June–September) precipitation (mm/day) as obtained from experiments a CTR_aeroOff_pre-mon b ElNiño_aeroOff_pre-mon. The black arrows indicate wind vectors at 850 hPa

for Environmental Prediction (NCEP) reanalysis, available for the period 1948–2009 (Kistler et al. 2001). Following Ashok et al. (2001) and (2004), we consider all the ‘strong’ El Niño years as 1957, 1965, 1967, 1972, 1977, 1982, 1991, 1994, 1997, 2002, and 2004, 2009 considering the amplitude of the SST anomalies in either eastern or central tropical Pacific exceed one standard deviation. The year has been treated as an El Niño year if Nino3 index is more than the threshold value (>0.5) from June to September. Supplementary Figure S2 shows lead–lag correlations between all Indian summer monsoon years derived from the IMD data (June–September) and the monthly Niño-3 index from the HadISST (for the period 1951-2009). It exhibits maximum negative correlation for zero lag year. Therefore pre-monsoon/monsoon season of the same year, when El Nino develops is termed as concurrent year. Previous study (Swapna et al. 2015; Shukla and Paolina 1983) has also shown that lead–lag correlations between all Indian summer monsoon years derived from the IMD rainfall data sets (June–September) and the monthly Niño-3.4 index from the HadISST (for the 1935–2010 period) is maximum (negative correlation) during concurrent year (Figure 8 there in). Therefore we consider pre-monsoon season of the concurrent El-Nino year.

3 Results and discussions 3.1 El Niño effect on ISM precipitation Figure  1a, b shows the simulated seasonal (June–September) mean precipitation and circulation at 850 hPa obtained from the CTR_aeroOFF_pre-mon and ElNiño_ aeroOFF_pre-mon experiments. Though the magnitude of simulated mean rainfall underestimated in the model, the general spatial pattern of precipitation and low-level circulation are reasonably well simulated. Importantly, comparing these two simulations one can clearly infer that the ElNiño_aeroOFF_pre-mon simulation produces relatively

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less precipitation over the monsoon region. This result is in line with the many earlier studies (e.g. Keshavamurthy 1982; Webster et al. 1998; Navarra et al. 1999; Lau and Nath 2000; Ashok et al. 2001, 2004), which confirm the observational findings that most of the El Niños result in anomalous deficit in the ISM rainfall. This further confirms that model is able to capture the ENSO-related variability reasonably well. 3.2 The distribution of aerosols over India during pre‑monsoon season The aerosol influence monsoon on the various time scales, starting from weekly to seasonal through heating the atmospheric column, changing the cloud microphysics and circulation. Vinoj et al. (2014) observe that, over the time scale of a week, the rainfall over central India is positively correlated with the concentration of natural aerosols such as desert dust and sea salt over the Arabian Sea. Further, Wang et al. (2009) show that absorbing aerosols during the summer monsoon season affect the moist static energy, eventually leading to a significant reduction in precipitation south of 20°N and a substantial enhancement at the north of this zone. Several studies (e.g. Ramanathan et al. 2005; Bollasina et al. 2011; Hansen et al. 2011; Ganguly et al. 2012a; Sajani et al. 2012) reported a reduction in Indian monsoon precipitation due to enhanced aerosol loading through decreasing the net surface solar radiation, thus cooling the surface. This increases the atmospheric stability, leading to suppression of convection, and a reduction in precipitation. In addition to the concurrent effect, aerosol provides the buildup cell before the onset of the monsoon, especially over the IGP. Most of accumulation happens during pre-monsoon season (April and May) which we are focusing mainly in this study. To understand the role of pre-monsoon aerosol loading over the Indian monsoon, we analyze the TOMS AI, and MISR AOD averaged for the April and May months. Figure  2a–d shows the distribution of climatological mean

Potential modulations of pre-monsoon aerosols during El Niño: impact on Indian summer monsoon

Fig. 2  Longitude-latitude distribution of April–May average a TOMS aerosols index (AI) climatology of period 1978–2005, b TOMS aerosols index during El Niño years (1982, 1991, 1997, 2002, 2004), c climatology (2000–2010) of MISR aerosols optical depth (AOD), d MISR aerosols optical depth (AOD) during El Niño years (2002, 2004, 2009), ECHAM5-HAMMOZ simulated AOD

obtained from e CTR_aero_pre-mon f ElNiño_aero_pre-mon experiments. Anomalies (mean of El Niño years—Climatology) (indicated as shaded contours) of g TOMS aerosols index h MISR AOD i ECHAM5-HAMMOZ simulated AOD. Black line contours in figure g–i indicates 90% Student’s t test significance level

(April and May) from TOMS AI and MISR AOD and during strong El Niño years. The composite of El Nino is obtained from available numbers of sample year; TOMS AI 5 years (1982, 1991, 1997, 2002, 2004), and MISR 3 years (2002, 2004, 2009). TOMS Earth Probe AI show consistently higher amounts during 2002–2006 therefore we exclude this data and exhibit climatology of TOMS AI for the period 1979–2001 and El Niño years in supplementary figure S3 (a–c). There are only three El Niño years (1982, 1991 and 1997) in this time span. The El Niño in year 1997 is accompanied by the Indian Ocean Dipole (IOD) event and ISMR was normal in this year (Ashok et al. 2001, 2003, 2007; Ashok and Saji 2007; Pokhrel et al. 2012; Cherchi and Navarra 2013). The supplementary figure S3 (b–c) shows distribution of TOMS AI excluding year 1997 (Figure S3 (b)) and including year 1997(Figure S3 (c)). Aerosol loading over the IGP is higher than normal if we exclude the year 1997 (supplementary figure S3 (b))

and otherwise, if we consider the year 1997. It indicates that year 1997 influences the distribution of Aerosols, as there are only 3 El Niño years during 1979–2001. However influence of the year 1997 year is averaged out in the composite of all 5 El Niño years (Fig. 2b). Figure 2g–h shows difference between El Niño composite and climatology for TOMS AI and MISR AOD. Relatively high AOD loading (significant at 90% level) is clearly visible over the IGP during the El Niño years in these data. Some in situ observations have recorded heavy aerosol loading over the IGP during pre-monsoon season (Soni et al. 2010; Bonasoni et al. 2010; Dumka et al. 2010; Giles et al. 2011). From Fig. 2a–d it is clear that during El-Niño years, the aerosol loading is higher than normal, especially over the IGP. The simulated pre-monsoon (April–May) AOD, obtained from climatological (CTR_aero_pre-mon) and Niño (ElNiño_aero_pre-mon) simulations is shown in Fig.  2e, f. Figure 2e shows that model’s climatology is

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consistent with observations. In the El Niño simulation, the AOD is higher than that from the control simulation especial over the IGP (also Fig. 2i), which is consistent with MISR and TOMS observations. The more aerosols loading over the North Bay of Bengal, western side of Myanmar and southern peninsula India is mainly due to the contribution of sea salt aerosols. It is particularly notable that TOMS AI is sensitive only to absorbing types of aerosols and shows a large difference between MISR and model simulated AOD. It is well known that TOMS detect aerosols over bright targets such as deserts and is insensitive to scattering aerosols (Hu et al. 2007). That’s why, TOMS AI providing relatively less magnitude over the Arabian Sea. The simulated AOD values (Fig. 2e, f) are, in general, comparable with MISR observations (Fig. 2c, d) although there are differences in spatial distribution. One can clearly notice an underestimation in simulated AOD values with respect to MISR or TOMS observations. For example, AOD over Thar Desert, Iran, Turkestan and Kirgizstan is underestimated in the model while overestimates over Arabian Sea. This may be related to the complex parameterization of dust aerosols. Recently the “Aerosol Comparison between Observations and Models” (AeroCom) phase I experiments showed significant discrepancies in dust emission, distribution and transported particle size between models and observations (Huneeus et al. 2011; Koffi et al. 2012; Kim et al. 2014). This underestimation is a fundamental problem with most of the aerosol-chemistry-climate models. Many studies reported this underestimation by comparing the AOD values from the AERONET observations and sun photometer retrievals at different stations over the IGP (Dumka et al. 2014; Gautam et al. 2011). BC emission inventories are underestimated in most of the model and there are few measurements over the IGP (Soneja et al. 2016). There are uncertainties in model estimates of sea salt emission and parameterization too (Spada et al. 2013). We analyze distribution of BC, OC, Dust and sulfate aerosols as obtained from CTR_aero_pre-mon and ElNiño_aero_pre-mon simulations. Distribution of the OC and sulfate aerosols is similar to BC aerosols (Figures not shown). Hence, here we only present the distribution of BC and dust aerosols in supplementary Figure S4. In El Niño simulation (figure S4 (b)) the amount of dust over the IGP and TP is higher than climatology (figure S4(a)) Difference of El Niño and climatology simulations (figure S4(c)) show that higher dust loading over the TP during El Niño. The distribution of the BC aerosols apparently looks similar in both, CTR_aero_pre-mon and ElNiño_aero_pre-mon simulations (figure S4 (d and e) however the difference between these two simulations (figure S4 (f)) shows that BC aerosols are higher over the IGP in ElNiño_aero_premon than CTR_aero_pre-mon simulation. During the premonsoon season, distribution of BC aerosols does not show

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a significant difference in CTR_aero_pre-mon and ElNiño_ aero_pre-mon simulations, since BC is emitted locally and there is less wet scavenging during pre-monsoon. 3.3 The pre‑monsoon aerosols and El Niño monsoon precipitation relation During the pre-monsoon season, dust from the southwest Asian arid regions and deserts of western China is transported to IGP and TP (Wonsick et al. 2014; Lau and Kim 2006). The transported dust and locally emitted BC progressively build up over the IGP and TP from April through June. Lau and Kim (2006) suggest through their EPH hypothesis that these absorbing aerosols create a warm core in the upper troposphere and intensify Asian monsoon. In this section, we evaluate the role of pre-monsoon aerosols on monsoon precipitation during co-occurring El Niños. The simulated precipitation from the control (CTR_aero_ pre-mon) and El Niño (ElNiño_aero_pre-mon) experiments are shown in Fig. 3a, b). Comparing the two simulations it is clear that during the El Niño year precipitation drastically reduced over the monsoon region (~0.9–30 mm/day in climatology but 0.9–10 mm/day during El Niño years). The difference in precipitation from CTR_aero_pre-mon and CTR_aeroOFF_pre-mon clearly show the effect of aerosols during pre-monsoon season (Fig. 3c). The anomalous positive rainfall depicted over North India and subdued precipitation over peninsular India. Further, the monthly distribution of aerosol-induced change in ISM precipitation shows more impact of aerosol over the Northern India during June to August (supplementary figure S5 (a–c)). Thus, it can be noted that the pre-monsoon aerosols play a significant role in the enhancement of precipitation over North India through enhanced warming over TP (Lau and Kim 2006; Kim et al. 2015). Figure 3d shows aerosol-induced changes in Indian summer monsoon precipitation from El Niño (ElNiño_aero_pre-mon–ElNiño_aeroOFF_pre-mon) simulation. It shows positive precipitation anomalies over North India. The simulated precipitation pattern is similar to the difference in control simulation (Fig. 3c), but the magnitude of anomalies in the El Niño simulation is much less than the previous. The time series of daily TOMS AI and rainfall averaged over the IGP, (70–90°E, 20–30°N) during the period 01-011979 to 31-12-2005 shown in Fig. 4a–c. These plots show that every year there is a progressive increase in AI from April through June. It should be noted that mean aerosol amount varies due to change of satellites Nimbus-7 (1979– 1993) and Earth Probe (1996–2005). However higher aerosol amount during strong El Niños 1991, 2002 and 2004 is evident (except 1982). The higher aerosols during the pre-monsoon season of El Niño years followed by relatively high rainfall (~15–20 mm) over the North India. The

Potential modulations of pre-monsoon aerosols during El Niño: impact on Indian summer monsoon Fig. 3  Distribution of seasonal mean (June–September) precipitation (mm/day) as obtained from experiments a CTR_aero_ pre-mon b ElNiño_aero_premon. The black arrows indicate wind vectors at 850 hPa. Aerosol-induced changes in seasonal mean precipitation as obtained from c difference between CTR_aero_pre-mon- and CTR_ aeroOFF_pre-mon d difference between ElNiño _aero_pre-mon and ElNiño _aeroOFF_pre-mon experiments. Solid black line indicates the 95% Student’s t test confidence interval

TOMS Earth Probe AI is correlated (r = 0.54, p = 0.0001) with rainfall with a lag of 3 months, indicating an important role of pre-monsoon aerosols in increasing precipitation over IGP. Figure 4d shows time series of MISR AOD and rainfall averaged over the IGP, (70–90°E, 20–30°N) for the period 1 March 2003 to 31 December 2010. MISR AOD also built up of AOD starting from March through July/August every year. MISR is sensitive oceanic aerosols hence high amount AOD persists till July/August. Similar to TOMS AI, MISR AOD also shows higher than normal amount during El Niño years (2002 and 2004). The MISR AOD shows two months lag correlation (correlation coefficient ~0.42) with rainfall over the IGP. These results are in agreement with model simulations. It should be noted that relationship between aerosol loading with monsoon precipitation does not hold one to one relation since there are multiple factors (e.g. IOD, extratropical intrusion, etc.) affecting the aerosol transport and monsoon. The observational studies noted that the pre-monsoon aerosol loading with co-occurring El Niño in general increases the severity of monsoon drought (Kim et al. 2015). The pre-monsoon season of the El Niño years considered in this study is different. However present study considered the concurrent pre-monsoon season based in correlation between Indian summer monsoon rainfall and NINO-3 index. Swapna et al. (2015) has also shown negative correlation relation between ISMR and NINO3.4 index is highest during June–September of the concurrent year. Interestingly, the Indian summer monsoon during the 1997 was near neutral. But the El Niño impact that year was apparently reduced substantially due to anomalous convection associated with a co-occurring very strong positive IOD

event and the consequent modulation of local meridional circulation (e.g. Slingo and Annamalai 2000; Ashok et al. 2001; Guan et al. 2003; Pokhrel et al. 2012). The influence of the positive IOD events on the Indian monsoon rainfall has apparently increased relative to that of the ENSO in the recent decades owing to non-stationary processes (Krishnaswamy et al. 2015). This may be the reason that TOMS AI show lower values in 1997. As Ashok and Saji (2007) discuss, there are several other factors that reduce the impacts of a co-occurring El Niño. Having said this, at least from a linear perspective, it is apt to state that a strong canonical El Niño with the amplitude seen during the years such as 1982, 1997, etc., without any co-occurring monsoon-favorable driver, can affect the co-occurring ISMR substantially (e.g. Wang et al. 2009).

4 Discussion The latitude-altitude cross-section (averaged over 70–100°E) of aerosols-induced change in temperature during the ISM season obtained from control and El Niño simulations are plotted in Fig. 5a, b respectively. From these figures it is evident the elevated levels of aerosols during the pre-monsoon season with subsequent El Niño enhance the upper tropospheric warming. The Fig. 6a, b exhibit seasonal mean zonal circulation averaged over 15–30°N for CTR_aeroOFF_pre-mon and ElNiño_aeroOFF_premon simulations. The pre-monsoon aerosols-induced changes in zonal circulation shows that aerosol-induces vertical motion is stronger at foothills of the Himalayas and TP (Fig. 6c). In El Niño simulation (Fig. 6d) there is a

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S. Fadnavis et al. Fig. 4  Time series plot of daily TOMS aerosols index (red color) and precipitation (mm/ day) (blue color) (averaged over north India, lon:70–90°E, lat:20–30°N) for the period a 1979–1988, b 1989–1997, c 1998–2005, MISR AOD (red color) and precipitation (mm/ day) (blue color) (averaged over north India, lon:70–90°E, lat:20–30°N) for the period d 2000–2004 e 2005–2010

further increase in mid-upper tropospheric upward motion leading to enhanced convection. The anomalous monsoon Hadley circulation (Fig. 7a, b) also shows a stronger upward motion over northern India in El Niño simulation. Thus mid-tropospheric warming and increased instability induced by pre-monsoon aerosols result in enhancing the precipitation anomalies over North India as per EHP hypothesis.

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5 Conclusions We demonstrated the impacts of the pre-monsoon aerosols in the presence of a co-occurring El Niño on the Indian summer monsoon using observation and state of the art aerosol-chemistry-climate models. Importantly, our analysis of the pre-monsoon distributions of TOMS AI indicates that during an El Niño year the aerosol amount over the

Potential modulations of pre-monsoon aerosols during El Niño: impact on Indian summer monsoon Fig. 5  Latitude-pressure section of seasonal mean temperature anomalies (K) averaged for 70–100°E obtained from difference between a CTR_aero_premon and CTR_aeroOFF_premon b ElNiño_aero_pre-mon and ElNiño_aeroOFF_pre-mon. Black arrows indicate wind vectors. The vertical velocity field has been scaled by 300 and the units are ms−1

Fig. 6  Seasonal mean zonal circulation (averaged over 15–30°N) obtained from a CTR_aeroOFF_pre-mon b difference between ElNiño_ aeroOFF_pre-mon and CTR_ aeroOFF_pre-mon c aerosol induced changes zonal circulation (averaged over 15–30°N) obtained from difference between CTR_aero_Pre-mon and CTR_aeroOFF_Pre-mon d same as c but for difference between ElNiño aero_pre-mon and CTR_aeroOFF-pre-mon. In a–d background contours indicate vertical velocity omega. The vertical velocity field has been scaled by 100 units are ms−1

Indo-Gangetic plains (IGP) is anomalously high. Model simulations reveal that these aerosols are mostly dust and BC (absorbing aerosols) since large amount of dust transported from West Asia and locally emitted BC piles up over the IGP. During the pre-monsoon season of subsequent El Niño, the TOMS AI and model AOD show high aerosol loading over the IGP. The higher amount of AOD in El Niño year may be due to the stronger (than climatology) northerly winds in, the lower troposphere and subtropical westerly winds in the upper troposphere, transport more dust aerosols from west/northwest arid regions to the IGP and TP.

The aerosol induced increase in precipitation in the control simulation is ~0.5–4 mm/day and that in El Niño is ~0.5–1.5 mm/day over North India. The simulations with the pre-monsoon aerosols show an early onset and intensification of summer monsoon during June–July, in conformation with the EHP mechanism through an anomalous aerosol-induced warm core in the atmospheric column as proposed by (Lau and Kim 2006). This anomalous heating upshot the relative strengthening of the cross-equatorial moisture flow associated with the cross equatorial monsoon circulation and is responsible to a reduction in severity of drought in El Niño years.

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S. Fadnavis et al. Fig. 7  Seasonal mean change in the meridional circulation due aerosols. The fields have been averaged from 70°E to 110°E a obtained from difference CTR_aero_pre-mon and CTR_aeroOFF_pre-mon b obtained from difference ElNiño aero_pre-mon and ElNiño aeroOFF_pre-mon. The background contours indicate vertical velocity omega. The vertical velocity field has been scaled by 300 units are ms−1

We must mention that our experiments are necessarily canonical in design, and do not include many of the observed complexities such as the atmosphere–ocean coupling, ENSO flavor distinction etc. Notwithstanding this, the work provides valuable insight into the relevance of pre-monsoon aerosols on monsoon in the presence of an El Niño, the most famous natural driver affecting the Indian summer monsoon. Acknowledgements Authors acknowledge their gratitude towards Dr. R. Krishnan, the Executive Director, Centre for Climate Change Research, IITM for his encouragement during this study. All the simulations are carried out in HPC facility of IITM. IITM is a fully funded institute of Ministry of Earth Science, Government of India. We thank the two anonymous reviewers for their valuable suggestions.

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