Science of the Total Environment 490 (2014) 570–578

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Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv

Influence of monsoons on atmospheric CO2 spatial variability and ground-based monitoring over India Yogesh K. Tiwari a,⁎, Ramesh K. Vellore a, K. Ravi Kumar a, Marcel van der Schoot b, Chun-Ho Cho c a b c

Centre for Climate Change Research, Indian Institute of Tropical Meteorology, Pune, India Centre for Australian Weather and Climate Research, CSIRO Marine and Atmospheric Research, Aspendale, Victoria, Australia National Institute of Meteorological Research, Seoul, Korea

H I G H L I G H T S • • • •

Impact of the Indian monsoon circulations on surface CO2 observations over India Continental and marine tracer transport mechanism influence the Indian subcontinent The lower CO2 events along the west coast of India are due to marine transport CO2 variability is 8–10 ppm during summer monsoon and N 15 ppm for rest of the year

a r t i c l e

i n f o

Article history: Received 13 January 2014 Received in revised form 9 May 2014 Accepted 13 May 2014 Available online xxxx Editor: Xuexi Tie Keywords: FLEXPART Sinhagad Cape Rama Carbon Tracker

a b s t r a c t This study examines the role of Asian monsoons on transport and spatial variability of atmospheric CO2 over the Indian subcontinent, using transport modeling tools and available surface observations from two atmospheric CO2 monitoring sites Sinhagad (SNG) and Cape Rama (CRI) in the western part of peninsular India. The regional source contributions to these sites arise from the horizontal flow in conduits within the planetary boundary layer. Greater CO2 variability, greater than 15 ppm, is observed during winter, while it is reduced nearly by half during summer. The SNG air sampling site is more susceptible to narrow regional terrestrial fluxes transported from the Indo-Gangetic Plains in January, and to wider upwind marine source regions from the Arabian Sea in July. The Western Ghats mountains appear to play a role in the seasonal variability at SNG by trapping polluted air masses associated with weak monsoonal winds. A Lagrangian back-trajectory analysis further suggests that the horizontal extent of regional sensitivity increases from north to south over the Indian subcontinent in January (Boreal winter). © 2014 Elsevier B.V. All rights reserved.

1. Introduction The total greenhouse gas (GHG) emission in India is estimated to have increased at a growth rate of 3.3% per year during the period 1994–2007 (http://moef.nic.in/downloads/public-information/Report_ INCCA.pdf). Estimates of total fossil-fuel CO2 emissions from the Indian subcontinent indicate an alarming annual rate of increase of 7% in recent decades [source: Carbon Dioxide Information Analysis Center (CDIAC), USA; Boden et al., 2009]. It is generally observed that some sectors such as the cement production, electricity generation, and transport have provided greater contribution to this growth. The CO2 sources and sink mechanisms over the Indian subcontinent are of ongoing interest for atmospheric carbon cycle research (Valsala et al., 2013; Rayner et al., 2008; Canadell et al., 2007; Sarma et al., 2003). Lal and Singh

⁎ Corresponding author. E-mail address: [email protected] (Y.K. Tiwari).

http://dx.doi.org/10.1016/j.scitotenv.2014.05.045 0048-9697/© 2014 Elsevier B.V. All rights reserved.

(2000) indicated that the GHG emissions over the Indian subcontinent are likely compensated by the local biospheric sink. Most of the atmospheric CO2 investigations conducted over the Indian subcontinent are based on aircraft measurements or from the model simulations (Patra et al., 2011, 2013; Baker et al., 2011, 2012; Schuck et al., 2010, 2012; Sawa et al., 2012), and not based on groundbased atmospheric observations. Despite the paucity of surface observations in this region, several recent studies have revealed the general connections between GHGs and the Indian summer monsoon (ISM) (Rayner et al., 2008; Schuck et al., 2010; Cherchi et al., 2010; Patra et al., 2011; Polson et al., 2013; Chadwick et al., 2014). In response to larger scale land-sea thermal contrast, the largest volume of precipitation over the Indian subcontinent is observed during the summer and winter monsoon periods [June to September (JJAS) in summer and December to February (DJF) in winter] (Asnani, 1993; Das, 2002; Wang, 2006; Goswami, 2012). Monsoon core regions in the summertime are defined over the Indo-Gangetic Plains (IGP) region, central part of the subcontinent and the mountainous west coast of India, as well as over

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the southern part of peninsular India during wintertime (Das, 2002) (refer Fig. 1). From a meteorological perspective, the maritime transport mechanisms are essential for source region CO2 estimates as the southwesterly summer monsoon from the Arabian Sea initially reaches the coastal regions in the western part of India during the ISM (Fig. 1). The influences of the continental source regions during the ISM cannot be disregarded due to the winter monsoon wind reversal regime. Therefore, quantification of the GHG source/sink regions and the atmospheric residence times over the oceanic and continental regions is necessary in the context of understanding the atmospheric carbon cycle in this region. Only a very few studies have addressed the CO2 monitoring from the perspective of different ecosystems over the Indian landmass, which pointed out that the CO2 seasonality at the coastal environment is influenced by monsoon meteorology and terrestrial ecosystem variability (Bhattacharya et al., 2009; Tiwari et al., 2011). An interesting component of this is the regional and large-scale transport responses to wind reversals associated with Indian monsoons over mountainous and coastal ecosystems. The objective of the present study is to investigate the impact of the Indian summer and winter monsoon circulations on atmospheric CO2 observations at two ground-based air sampling sites located in western India (refer Fig. 2). This is supported by atmospheric transport modeling results to provide some insights into CO2

Fig. 2. Locations of the sites Sinhagad (SNG), Cape Rama (CRI), Nagpur (NGP), Pondicherry (PON), Agra (AGR), Varanasi (VNS), and Hanle (HLE) over the Indian subcontinent referenced in this study. AGR and VNS are located over the Indo-Gangetic Plains (IGP) and the Western Ghats mountain region is indicated in the figure (source: https://maps. google.co.in).

variability over the subcontinent. The details of the two air sampling sites, observational techniques and data analysis methods are described in the next section. 2. Data and methodology 2.1. Study sites and CO2 monitoring

Fig. 1. NCEP-diagnosed 850 hPa mean winds (vectors; maximum vector size = 15 m s−1) during the months of (a) December–January–February 2010 and (b) June–July–August 2010. The approximate position of the monsoon trough is indicated by a solid black line in (b). High and low pressure regions are marked by H and L.

Currently, there are two GHG air sampling (receptor) sites in western India: Sinhagad (SNG), 200 km east of the Arabian Sea (73.75° E, 18.35° N, elevation = 1600 m asl) located over the Western Ghats mountains; and a coastal site at Cape Rama (CRI), Goa (15.08° N, 73.83° E, elevation = 50 m asl) (refer Fig. 2). Routine air sampling at SNG, collected from a 10 m meteorological tower at weekly intervals, has been operational since November 2009. This site is free from major vegetation where the inlet of sampling tube is installed. SNG generally has prevailing light winds from the south and south-east during afternoon hours and an ambient temperature range from 25 to 30 °C. The mean wind speed at the time of sampling is typically about 0.5 to 1 m s−1 so that the samples are free from the effects of vegetation and local influences. The air sampling methodology at SNG is described in detail by Tiwari and Ravi Kumar (2012), and Ravi Kumar (2014a, 2014b). The CRI site is located closer to the shoreline, is also free of any major vegetation, and is isolated from local anthropogenic influences in contrast to SNG (Bhattacharya et al., 2009). The mean wind speed at the time of sampling varies from about 10 to 12 m s−1 in the summer monsoon and 4 to 6 m s− 1 during the winter monsoon. Air samples were collected in two separate 0.5 l glass flasks from 6 m above ground. The filled glass flasks were then analyzed at the Commonwealth Scientific and Industrial Research Organization (CSIRO) Atmospheric Research GASLAB (Global Atmospheric Sampling LABoratory), Australia, for CO2 and other related trace gases (Francey et al., 2003). Routine

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air sampling at CRI commenced in February 1993 and continued until October 2002, when the program was suspended until resuming again in July 2009. 2.2. Model simulations Two models are used in this study to analyze the spatial distribution of CO2 and the identification of source regions. The models are (i) Lagrangian particle dispersion model FLEXPART (Stohl et al., 2005, 2009) and (ii) Carbon Tracker (Peters et al., 2005, 2007). FLEXPART is a comprehensive atmospheric transport modelling tool originally designed for air quality studies to investigate the long-range and mesoscale dispersion of pollutants from point sources. FLEXPART computations provide three-dimensional positions of the inert particles instantaneously released from the receptor locations and their paths are traced either backward or forward in time. A back-trajectory analysis from the FLEXPART model provides a good basis for the assessment of source–receptor relationships (Seibert and Frank, 2004). Three hourly meteorological fields from the National Center for Environmental Prediction Global Forecasting System (NCEP GFS) data products (http://www. nco.ncep.noaa.gov/pmb/products/gfs/) archived at 0.5° × 0.5° grid resolution serve as inputs to FLEXPART simulations. Twenty thousand particles were released 10 m from the respective receptor site elevations, and their trajectories were traced 15 days backward in time. The starting time of the back-trajectory analysis was set on the 25th day of the month, i.e., flow during the days 10–25 of the month is a reasonably good assumption to characterize the transport evolution for the respective month. The FLEXPART simulated signatures of stochastic particle dispersion and emission sensitivity are used to demonstrate the particle residence times over different regions, identification of significant upwind source regions and their sensitivity to the receptor locations. The Carbon Tracker (CT) is a global reanalyzed product used to synthesize the spatio-temporal variability of CO2 (Huijnen et al., 2010; Hungershoefe et al., 2010; Jacobson et al., 2007; Krol et al., 2005; Keeling and Whorf, 2007). The atmospheric transport is simulated using the global two-way nested Transport Model version 5 (TM5) which is forced by the time-varying meteorology from the European Center for Medium Range Weather Forecast (ECMWF; http://www. ecmwf.int/) reanalysis products. The model configuration has a regional domain in the horizontal centered over Asia at 1° × 1° resolution nested into the global domain 3° × 2° resolution, and 34 levels in the vertical hybrid–sigma coordinate. Fluxes are provided based on fossil fuel use, wildfire, vegetation and the oceans. The CT provides the surface fluxes of CO2, estimated from a large set of atmospheric CO2 mole fractions via a data assimilation system developed at the National Oceanic Atmospheric Administration Earth Systems Research Laboratory (NOAA/ ESRL) in cooperation with many partners, e.g., Carbon Tracker Asia (CT-Asia) by the National Institute of Meteorological Research (NIMR), Korea. We used CT-Asia CO2 concentration products for the period 2000–2010 in this study. The results from the observations and the aforementioned modeling tools are discussed in the next section. 3. Results and discussions 3.1. CO2 transport and emission sensitivity Figure 3a–d shows the FLEXPART simulated particle backtrajectories reaching the surface level at SNG during the months of January and July 2010. The particles predominantly showed an eastward mass transport over larger distances of continental length scales as well as gently descending from the upper to lower troposphere. Particles are transported over warmer land regions of northern hemisphere in January (Fig. 3a and b), i.e., from western part of Asia and northern Africa, and from the oceanic regions in the southern hemisphere in July, i.e., over the cooler waters of Atlantic Ocean and southern Indian

Ocean. The IGP region is one of the notable source regions for carbonaceous emissions due to biomass burning, coal combustion, and traffic exhaust (Sastangi et al., 2010). Upon the particle descent and entry into the Indian monsoon domain, the regional source contributions to the receptor site primarily arise from the horizontal flow in conduits within the planetary boundary layer (PBL). The summertime conduit in July is apparently linked to the low-level cross-equatorial and southwesterly monsoonal flows (shown in blue color; Fig. 3c). The winter time conduit in January (Fig. 3a) is primarily due to the prevalence of subsidence over the north Indian landmass, and cold wave conditions associated with passage of extra-tropical western disturbances (Rao and Srinivasan, 1969; Puranik and Karekar, 2009) originating from the Mediterranean juxtaposed with the northeasterly monsoon winds from the Tibetan Plateau (refer Fig. 2). The particle residence times appear to respond to the effects of large-scale monsoon circulation over a larger region of the marine atmosphere in July and to the effects of the strong continental atmospheric stability over a smaller region in January. This can be seen in the surface sensitivity maps shown in Fig. 3b and d. These figures show the footprints of emission sensitivity during January and July. Longer residence times of the particles due to upwind sources indicate stronger emission sensitivity or surface sensitivity in a given grid cell, i.e., these footprints represent the sensitivity of mole fraction at the receptor site to upwind surface sources/sinks. The surface sensitivity magnitudes are rather significant over a larger area in the central Arabian Sea and southern Indian Ocean in July, and are associated with the continental sources in the IGP region and central-eastern parts of India in January. This clearly suggests that the CO2 atmospheric mole fractions (concentrations) at SNG are more sensitive to regional terrestrial fluxes in January, and marine/oceanic fluxes in July. Further, SNG is apparently influenced by the long-range transport of CO2 in July with a wider marine domain of source regions, approximately covering a large distance of about 104 km, while it is only sensitive to short-range (to about 1500 km) transport, e.g., CO2 fluxes primarily from the sources in the background of strong atmospheric stability due to subsiding air and air stagnation within the subcontinent. During the seasonal transition months April and October, the particles primarily remained over the Indian subcontinent and over the northern Arabian Sea (not shown). Although CRI is located along the coastline, the transport mechanisms are generally similar to that of SNG in response to Indian monsoons, except that the surface sensitivity is much stronger in northern side of the Arabian Sea during summer, and also sensitive to marine influence from the Bay of Bengal induced by the northeasterly monsoon winds during winter. A detailed discussion of the transport and sensitivity features at CRI is provided by Tiwari et al. (2013a, 2013b). To further understand the influence of monsoonal circulations over the Indian subcontinent on the upwind source regional sensitivities, the back-trajectory analysis is also conducted for a few other locations Agra (AGR) and Varanasi (VNS) from central India, and Pondicherry (PON) from the southern side. The surface sensitivities at these locations displayed similar features as seen at SNG, and the particles resided within the PBL for a period of 5–7 days. The particle descent into the PBL is evident due to strong subsidence (see the anti-cyclonic wind streams in Fig. 1) centered over the subcontinent in January. The sensitivity signatures at AGR and VNS are different in July due to the changing wind direction with respect to the positioning of the northward propagation of the monsoon trough (indicated in Fig. 1) as VNS (AGR) is located north (south) of the monsoon trough. Notice that the dispersion in the back-trajectories at VNS and AGR is rather significant in the Arabian Sea, while it is confined in the Bay of Bengal. The continental signatures are more pronounced at AGR than at VNS. The dry continental air streams from the Tibetan Plateau (see Fig. 2 for the location) juxtaposed with the easterly air streams from far-eastern Asia appear to be instrumental for the particle trajectories reaching PON in January, which brings a mixed type of continental and marine tracers, i.e., the

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Fig. 3. FLEXPART simulated air parcel back-trajectories and normalized surface emission sensitivity reaching the receptor sites (a and c) Sinhagad (SNG), (e and g) Agra (AGR), (i and k) Varanasi (VNS), and (m and o) Pondicherry (PON) in January (left two panels) and July (right two panels). The emission sensitivity maps represent particle residence times (larger for darker red) displayed for SNG (b and d), AGR (f and h), VNS (j and l), and PON (n and p). The trajectories from SNG are derived backward in time for 15 days and others are for 7 days for the year 2010.

continental tracers from the IGP region advected through the marine environment. Unlike at SNG, the other aforementioned receptor sites showed narrow region of upwind regional sensitivity during July, while the extent of regional sensitivity increases from northern to southern sectors of the Indian subcontinent. 3.2. CO2 variability Concerning the accuracy of CT-Asia, the NIMR Korea has compared the model results against 48 various surface sites all over the globe. It is found that at the tropical latitudes (0–30 N; the region of interest used in this study) the model results were in exact agreement with

the measurements. It is further seen that there was a significant uncertainty reduction (by about 5%) in the model-estimated flux through assimilation of surface observations over the Asian region —the spatial–temporal distribution of these flux estimates is a priori in the CT-Asia simulation (personal communications with Dr. Andy Jacobson, NOAA, Boulder, Colorado, USA; see also http://www. nimr.go.kr/2/carbontracker/concentration.html). Fig. 4 shows the mean CO2 concentration for different seasons from the CT-Asia dataset. In response to stronger monsoonal flow north of the monsoon trough (ref. Fig. 1), one can see greater CO2 concentrations (≥384 ppm) widespread in the IGP region, one of the source regions for carbonaceous emissions (Sastangi et al., 2010). These elevated CO2

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Fig. 4. Mean atmospheric CO2 mole fractions/concentrations (ppm) during 2000–2009 valid for the (a) winter, (b) pre-monsoon, (c) monsoon, and (d) post-monsoon seasons over the Indian subcontinent from CT-Asia dataset.

events indicate a contribution of both continental and marine source regions with greater continental influences. The lower CO2 concentration events along the west coast of India are primarily driven by marine transport mechanisms with emission source regions closer to the coastline. Upon summer monsoon withdrawal in September, there is a decrease in the mean CO2 concentration values by about 6 to 8 ppm in the IGP region, and a very notable drop of about 10 ppm in the peninsular Indian region. In particular, there is a pronounced decrease (b372 ppm) evident in the vicinity of the SNG and CRI sites across the Western Ghats region in October. Prevalent dry conditions associated with higher surface pressures over the central and northern parts of India which tend to draw the extra-tropical cold and dry air streams into the IGP region, which is juxtaposed with the advent of northeasterly winter monsoons, suggest that the continental source regions significantly impact the SNG site with sources from the IGP region. Standard deviations in CO2 concentrations at SNG during July [October] is less [more] than 5 ppm. The difference in the mean CO2 between postsummer monsoon months September to November (SON) and summer monsoon months (June to August) is about 6 ppm. A subjective analysis is further carried out to assess the spatial variability of CO2 through the use of vegetation/land cover changes. Fig. 6d shows the monthly climatological mean of Normalized Difference Vegetation Index (NDVI) during the period 2000–2010 at SNG, CRI as well as at Nagpur (NGP; located 800 km east from the Arabian Sea; marked by NGP in Fig. 1). Irrespective of coastal and inland site locations, NDVI values during the summer monsoon (JJAS) months are about the same and the corresponding NDVI values closely represent a dense green area. However, there are notable differences behind and ahead of the summer monsoon season (JJAS months) at the SNG and CRI sites, in part due to little or no precipitation or vegetation losses during this time. NDVI shows a minimum value at SNG to represent bare soil during April to May, which contrasts the maximum in October to

represent the loss of vegetation canopy. Although the NDVI magnitude at CRI is larger, the month-to-month variability in the vegetation cover is found to be weaker at CRI as compared to SNG. This suggests that crop harvesting in the vicinity of SNG appears to play a role ahead of the summer monsoon season. Although the NDVI at NGP indirectly suggests similar vegetation characteristics to the responses associated with the Indian monsoons, the CO2 variability at NGP is quite contrary to the scenario seen at SNG, i.e., the variability is significant during the summer monsoon months and more pronounced during the monsoon withdrawal month in September. Fig. 6a shows the de-trended atmospheric CO2 mole fractions (concentrations) observed at SNG and CRI that are derived for the corresponding dates of observations at these sites. Fig. 6b and c shows the monthly standard deviation of these observations. One can see that there is a smaller CO2 variability (8–10 ppm) during summer monsoon months (JJAS) compared to values greater than 15 ppm for the remainder of the year. This is in part due to higher vegetation cover in these months due to intermittent precipitation spells. The observational record also indicated larger variances seen at SNG during post-monsoon months (later than September) than seen at CRI (Fig. 3). These fluctuations are likely due to the effects of crop harvesting and associated extensive biomass burning in the vicinity of the receptor sites which needs further examination with more data support. For the seasonal variability at the SNG and CRI sites, the transport mechanism by the easterly winds going over the Western Ghats mountain barrier to reach CRI is not likely strong during wintertime. Smaller variances at CRI suggest that continental trapping of pollutants due to much stronger atmospheric stability in the vicinity of SNG plays an important role as the coastal site CRI which is located on the leeside of the north–south oriented Western Ghats mountain barrier during wintertime. A weak near-easterly winds (4–6 m s−1) and greater wintertime atmospheric stability could typically block the flow (Whiteman, 2000)

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Fig. 5. Same as described in Fig. 4, but for the de-trended (a–d) atmospheric CO2 mole concentrations (ppm) and (e–h) spatial variance of CO2 (ppm).

in the upwind side which could trap the polluted air masses creating longer residence times in the vicinity of SNG (Fig. 3b). However, strong summertime monsoon winds (10 to 15 m s−1) typically facilitate the monsoon flow go past the mountain barrier which makes the transport signatures at SNG and CRI uniquely similar, and furthermore even at a far-inland location at NGP as well (figure not shown). Looking at the de-trended means and variances of CO2 (Fig. 5), the spatial variability is more pronounced during the post-monsoon months

(October to November) over the Indian subcontinent, in particular, in the northern parts of the Western Ghats Mountains. The concentration gains during MAM (March to May) along the west coast of India are about 3 ppm, while the losses associated with the regional sinks during SON (September to November) are as high as 5 ppm. The concentration gains in the northern parts of India and over the IGP region are consistent with the transport mechanisms as described in the previous section. Although further investigation may be necessary, it is reasonable

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Fig. 6. (a) Scatter plot of de-trended atmospheric CO2 mole fractions/concentration valid for the observations at SNG (2010–2013) and CRI (1993–2012) and the climatological mean standard deviation for (b) CRI and (c) SNG. (d) NDVI climatological mean (2000–2010) at Sinhagad (SNG), Cape Rama (CRI), and Nagpur (NGP).

to attribute the CO2 accumulation in the north-eastern parts of India to influences from the intra-seasonal variabilities associated with the active and break spells in the ISM (Annamalai and Slingo, 2001; Rajeevan et al., 2008; Krishnan et al., 2000; Vellore et al., 2014). The concentration gains in the IGP region and north-eastern parts of India are potentially from a wet mixture of continental and marine fluxes from the Bay of Bengal. Smaller concentration gains associated with more rains in the southern peninsular Indian region during winter monsoons are also consistent with the previous explanation on the increasing vegetation cover in the vicinity of SNG. The associated variance between summer and winter monsoons is more significant over the IGP region and in the north-eastern parts of India. Also noticeable is that the changing variances across the northern part of the Western Ghats during summer monsoon and post-summer monsoon months are somewhat uniform; however, there is no significant variability seen in the southern part of the Western Ghats. Looking at CT model simulated

variability at Hanle (HLE), an air sampling site over the mountainous Himalayas (Fig. 2), the variability is greater during the summer monsoon period than pre (before June)- and post-monsoon (later than September) seasons (figure not shown). Tiwari et al. (2013b) showed that the mountainous location HLE, at an elevation of 4500 m asl, is predominantly influenced by the upper level westerly winds from the mid-latitudes than from the monsoonal wind response, and therefore there is no significant variability seen at HLE. 4. Summary and conclusions This study addresses the spatial variability of CO2 over the Indian subcontinent and the impact of summertime and wintertime Indian monsoon circulations on Sinhagad (SNG) and Cape Rama (CRI) air sampling sites using the ground-based observations and transport modeling (FLEXPART and Carbon Tracker) tools. In response to seasonal reversal

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wind patterns associated with Indian monsoons, the regional source contributions to the sites primarily arise from the horizontal flow in conduits within the planetary boundary layer (PBL). The Lagrangian back-trajectory analysis suggests that both continental and marine tracer transport mechanisms influence the Indian subcontinent. The receptor sites over the subcontinent show a narrow region of upwind source sensitivity during July and the extent of regional sensitivity increases going from north to south sectors of the subcontinent. The SNG site is more susceptible to regional (short-range) terrestrial fluxes predominantly from the IGP region in January and to the long-range transport of CO2 in July with wider upwind source regions from the Arabian Sea. The observations indicated larger variances at SNG in post-summer monsoon months (September to October) than at CRI, and the larger variances are likely due to the effects of crop harvesting and associated biomass burning in the vicinity of the receptor sites. The Western Ghats mountain range appears to play a crucial role in the seasonal variability at the SNG and CRI sites with respect to continental trapping of pollutants due to strong/weak monsoonal winds and atmospheric stability. The spatial variability is more (less) pronounced during October to November over the Indian subcontinent in the northern (southern) parts of the Western Ghats mountains. The increasing concentration in the northern parts of India and over the IGP region is consistent with the monsoonal transport mechanisms. The Lagrangian back-trajectory results from this study suggest that the strength of summer/winter monsoons defined in terms of precipitation appear to show less influence on the transport mechanisms in a larger sense. However, some recent climate studies indicate a weakening of summertime monsoon circulations and increasing droughts in a global warming environment (Krishnan, 2013; Krishnan et al., 2013; Swapna et al., 2013), and therefore, further investigation is needed into the seasonal transport mechanisms from the perspective of stronger/weaker monsoon circulation mechanisms as well as the impact from the drought affected vegetation changes. The CO2 spatial variability, which is apparently inclined to monsoon circulations, suggests that the seasonal prevalence of both stronger and weaker transport mechanisms over the Indian subcontinent can produce strong diversities in the source-sink mechanisms. Although this study provides the support for the upwind tracer transport mechanisms to the receptor locations over the Indian subcontinent, driven by the monsoon meteorology, it also highlights the current lack of understanding of various sink mechanisms associated with the larger temporal variability shown in the observations. This demonstrates that more spatio-temporal observations over the subcontinent are required to further understand the chemistry associated with GHG sinks, and to have a better understanding of source-sink coupled mechanisms.

Conflict of interest Authors have no conflict of interest.

Acknowledgement The authors thank Professor B. N. Goswami and Dr. R. Krishnan, Indian Institute of Tropical Meteorology (IITM), for their enthusiastic support throughout the course of this study. They further extend their gratitude to the National Oceanic and Atmospheric Administration/ Earth System Research Laboratory (NOAA/ESRL), Boulder, Colorado, USA, for the FLEXPART model computational support for this study and Drs. John Miller and Pieter Tans (NOAA/ESRL) for the motivational discussions from time to time. We also thank NCEP/NCAR for the reanalysis meteorological data sets. The authors thank the anonymous reviewers for their valuable suggestions which substantially improved the manuscript.

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