# What controls the atmospheric methane seasonal variability over India?

Tania Guha, Yogesh Tiwari, Vinu Valsala, Xin Lin, Michel Ramonet, Anoop

Mahajan, Amey Datye, K. Ravi Kumar

► To cite this version:

Tania Guha, Yogesh Tiwari, Vinu Valsala, Xin Lin, Michel Ramonet, et al. What controls the atmospheric methane seasonal variability over India?. Atmospheric Environment, 2018, 175, pp.83-91. 10.1016/j.atmosenv.2017.11.042. hal-02414692

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What controls the atmospheric methane
seasonal variability over India?

<table><tr><td>Item Type</td><td>Article</td></tr><tr><td>Authors</td><td>Guha, Tania; Tiwari, Yogesh K.; Valsala, Vinu; Lin, Xin; Ramonet, Michel; Mahajan, Anoop; Datye, Amey; Kumar, K. Ravi</td></tr><tr><td>Citation</td><td>Guha T, Tiwari YK, Valsala V, Lin X, Ramonet M, et al. (2017) What controls the atmospheric methane seasonal variability over India? Atmospheric Environment. Available: http://dx.doi.org/10.1016/j.atmosenv.2017.11.042.</td></tr><tr><td>Eprint version</td><td>Post-print</td></tr><tr><td>DOI</td><td>10.1016/j.atmosenv.2017.11.042</td></tr><tr><td>Publisher</td><td>Elsevier BV</td></tr><tr><td>Journal</td><td>Atmospheric Environment</td></tr><tr><td>Rights</td><td>NOTICE: this is the author's version of a work that was accepted for publication in Atmospheric Environment. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Atmospheric Environment, [, , (2017-11-28)] DOI: 10.1016/j.atmosenv.2017.11.042. © 2017. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/</td></tr><tr><td>Download date</td><td>2025-06-19 16:20:04</td></tr><tr><td>Link to Item</td><td>http://hdl.handle.net/10754/626251</td></tr></table>
Accepted Manuscript

What controls the atmospheric methane seasonal variability over India?

Tania Guha, Yogesh K. Tiwari, Vinu Valsala, Xin Lin, Michel Ramonet, Anoop
Mahajan, Amey Datye, K. Ravi Kumar

PII: S1352-2310(17)30803-8

DOI: 10.1016/j.atmosenv.2017.11.042

Reference: AEA 15699

To appear in: *Atmospheric Environment*

Received Date: 13 May 2017

Revised Date: 26 October 2017

Accepted Date: 26 November 2017

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Please cite this article as: Guha, T., Tiwari, Y.K., Valsala, V., Lin, X., Ramonet, M., Mahajan, A., Datye, A., Kumar, K.R., What controls the atmospheric methane seasonal variability over India?, *Atmospheric Environment* (2017), doi: 10.1016/j.atmosenv.2017.11.042.

This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
The weaker CH₄ concentrations above Sinhagad (SNG) and Cape Rama (CRI) India during summer monsoon months JJAS may characterize a southern hemisphere origin. In order to verify this we compared the SNG and CRI CH₄ with that of the southern hemispheric values taken from Seychelles at Mahe Island (SEY). Noticeably the SEY CH₄ has similar seasonal variability as that of SNG and CRI, but with weaker amplitude probably because of its location over the open ocean. The in-phase seasonality between SNG, CRI and SEY thus suggest a possible southern hemisphere origin of CH₄ that is observed over the Indian peninsula during JJAS.

<figure><img src="image_4.png" /><figcaption>Figure: Model (LMDz) simulated CH₄ concentration compared with observations a) SNG (Sinhagad) and SEY (Seychelles), and b) CRI (Cape Rama) and SEY (Seychelles).</figcaption></figure>
What controls the atmospheric methane seasonal variability over India?

Tania Guha¹, Yogesh K. Tiwari¹*, Vinu Valsala¹, Xin Lin², Michel Ramonet², Anoop Mahajan¹,
Amey Datye¹, K. Ravi Kumar¹,³

¹Indian Institute of Tropical Meteorology, Pune 411008, India

²Laboratoire des Sciences du Climat et de l'Environnement, Paris, France

³Now at, King Abdullah University of Science and Technology, Thuwal 23955, Saudi Arabia

*Corresponding author: yktiwari@tropmet.res.in
26 **Abstract**

27 Atmospheric CH₄ observations from two ground-based stations within Indian 28 subcontinent, namely, Sinhagad (SNG) and Cape Rama station (CRI) showed a strong 29 seasonality with a minima (~1800±20 ppb) during southwest monsoon (SWM; i.e. June- 30 September, JJAS) and a maxima (2000±30 ppb) during northeast monsoon (NEM i.e. December- 31 February, DJF) with a peak-to-peak seasonality close to 200 ppb. The Indian summer (winter) 32 monsoon is characterized with strong southwesterly (northeasterly) winds of oceanic origin at the 33 surface level and strong easterly (westerly) jet streams aloft. The monsoon dynamics has 34 pronounced impact on CH₄ variability over India and is analyzed with winds, Lagrangian 35 trajectories, and 3-dimensional distributions of CH₄ simulated by a general circulation model. 36 The model simulations suggest a consistent annual vertical structure (mean and sub-seasonal 37 uncertainty) of CH₄ over India with a stark contrast in concentration from summer to winter at 38 surface levels (below 750 mb) in confirmation with what is identified by the ground-based 39 observations. During SWM (NEM) the air with comparatively lower (higher) CH₄ concentrations 40 from southern (northern) hemisphere reduces the CH₄ over India by 1814±26 ppb (enhances by 41 1950±51 ppb). The contribution of local fluxes to this seasonality appears to be albeit weak as 42 the synthesized CH₄ fluxes (from EDGAR dataset) of the Indian peninsula itself show a peak in 43 summer and a dip in winter. Similar property of CH₄ is also common to nearby oceanic region 44 (i.e. over Arabian Sea, 1765±10 ppb during summer) suggesting the role of monsoon dynamics 45 as the controlling factor. Further the mixing and convection carries the CH₄ to the upper 46 atmosphere and advect inward or outward aloft according to the seasonal monsoon dynamics.

47 **Keywords:** Atmospheric CH₄ observations; Seasonal variation over India; CH₄ simulations
1. Introduction

The atmospheric methane (CH₄) is of chief concern as it is the second largest contributor to the global warming after carbon dioxide (CO₂), with a warming potential estimated to be ~0.48±0.05 Wm⁻² (Fifth Assessment Report, IPCC, 2013). In addition to its global warming potential the CH₄ also reduces the oxidation chemistry of the atmosphere via its reaction with OH radicals (Kirschke et al., 2013). Atmospheric CH₄ was steadily increasing (12 ± 6ppb yr⁻¹) in the 1980's. Thereafter the growth rate was slow in the 1990's, but again by 2007, rose to a growth rate of 8ppb yr⁻¹ (Dlugokencky et al., 2009).

The atmospheric CH₄ has both natural and anthropogenic origins. The wetland systems act as primary natural source of CH₄ (Cao et al., 1998) while rice paddies (Purkait et al., 2005; Khalil et al., 2008), livestock (Crutzen et al., 1986; Naqvi and Seijan, 2011), landfills (Chakraborty et al., 2011), combustion of fossil fuel, agricultural waste, biofuel (Howarth et al., 2011. Lelieveld et al., 1998), etc. acts as the dominant anthropogenic sources. The major source of CH₄ for Indian region is enteric fermentation of cattle, rice cultivation, waste water treatment etc (EDGAR HTAP database 2000-2010; http://edgar.jrc.ec.europa.eu/htap_v2/). The major sink of CH₄ in the atmosphere is via its reaction with OH radicals within the troposphere (Kirschke et al., 2013; Patra et al., 2011, Patra et al., 2014; Ghosh et al., 2015). Although the possible sources and sinks of CH₄ are well understood the accuracy of its global budget has large uncertainty especially at regional scales (Patra et al., 2016). Prima facie this difficulty is due to the distinct sources and sinks of CH₄ which vary widely with the seasons. The predicament is also escalated by the shortage of observations that capture the complex variations of CH₄ fluxes within a region (Patra et al., 2016).
Based on both top-down and bottom up approach, the annual flux of CH4 was estimated to be 37.9±3.6 and 38.5±5.2 TgCyr⁻¹ from South Asian Region (Bangladesh, Bhutan, India, Nepal, Pakistan and Sri Lanka) (Patra et al., 2013). The discrepancy between estimated using both top-down and bottom up approach can be solved by expanding the atmospheric observation stations in South Asia (Patra et al., 2013). The atmospheric CH₄ variability over Indian subcontinent can be attributed to two major processes. The first is related to the CH₄ source itself. In India, the major anthropogenic sources of CH₄ includes livestock, rice paddies etc. An average CH₄ flux from Indian livestock was estimated to be 74.4 kg/hectare (Chhabra et al., 2013). Rice paddies are the major crop in India and its production increases following the southwest monsoon (hereafter SWM; from June-September; JJAS) due to the enhanced rain. This potentially increases the CH₄ emissions (Khalil et al., 2008). The second is atmospheric transport related; during SWM the lateral transport of air could bring inter-hemispheric CH₄ over the Indian region. The monsoon related deep convection over India could alleviate the surface CH₄ by transporting it to the mid/upper troposphere. The deep convection during SWM is an important factor in the trace gas dynamics over the Indian subcontinent (Ravi et al., 2013; Tiwari et al., 2014; Tania et al., 2015; Revadekar et al., 2016). This convection, in fact, is associated with a large scale upper level anticyclone over Tibetan plateau which forms at the beginning of SWM, propagates westward during latter part of the season and disappears by October-November (Krishnamurti and Balme, 1976; Hoskins and Rodwell, 1995; Hsu et al., 1999, Randel et al., 2010). Studies show that the Tibetan anticyclone could trap pollutants in mid/upper troposphere and mitigate them westward by the strong easterly jet streams (Randel et al., 2010; Schuck et al., 2010; Fadnavis et al., 2014). A similar dynamics to CH₄ can be expected at the upper atmosphere but its relation to surface observed CH₄ over India is not well understood.
Atmospheric Infrared Sounder (AIRS) satellite retrievals and model simulations showed an enrichment of CH₄ at mid-troposphere with a plume-like structure over South Asian region during JJAS (Xiong et al., 2009; Schuck et al., 2010). Arguably this enhancement could be due to the deep convective transport of surface CH₄ during SWM as detectable by data from trans-flight CH₄ measurement programs (CARIBIC; Schuck et al., 2009, 2010, 2012, CONTRAIL; Machida et al., 2008). The trans-flight measurements showed an enhancement of CH₄ concentrations at an altitude of 8-12km over a vast region of 50°E-80°E and south of 40°N. Schuck et al., (2010, 2012) showed that the CH₄ data from ground based monitoring station at Mauna Loa, Hawaii Islands is also comparable to that of the upper atmospheric (CARIBIC) observations. On contrary a station that is located over India (Cape Rama; 18.35° N, 73.75° E) showed a diminished CH₄ during SWM plausibly because the former station is out of the core Indian monsoon zone. This aspect, however, is not well explored in previous studies.

Aside from few observations of upper atmospheric CH₄, its surface monitoring over Indian subcontinent is sparse in order to decipher any relation between CH₄ variability and monsoon (Bhattacharya et al., 2009; Ganesan et al., 2013, 2015; Lin et al., 2015; Sreenivas et al., 2015). The seasonality of CH₄ found to be varying differently over different parts of India (Lin et al., 2015). For example a station at Darjeeling (Eastern Himalaya) captures episodes of higher CH₄ concentrations throughout the year (Ganesan et al., 2013). On the other hand during SWM the CH₄ over a station at Hanle (situated at northwestern Himalaya) captures higher values while stations at Pondichery and Andaman Islands (situated at southeast coast of peninsular India and Bay of Bengal, respectively) capture comparatively lower values (Lin et al., 2015). Such
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complex spatiotemporal variability is attributed to seasonally varying CH₄ sources and monsoonal winds (Ganesan et al., 2013, 2015; Lin et al., 2015).

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The column averaged CH₄ concentration (XCH₄) from SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) showed a spatiotemporal variation over Indian region (Kavitha and Nair, 2016a). The northern region captured seasonal peak in monsoon and post monsoon, whereas the peak was shifted to the end of October-December for the Southern peninsula region. This regional heterogeneity of XCH₄ is closely related with the livestock population, rice cultivation, wetland emissions etc. (Kavitha and Nair, 2016a). Upper tropospheric (~8 – 10km) CH₄ from CARIBIC Aircraft measurement also showed seasonal high in monsoon, post monsoon and low in winter (Kavitha and Nair, 2016b). In comparison to both the column averaged and upper air CH₄ observation, near-surface observation showed contrasting seasonality where CH₄ peaks in winter and reaches its low in pre-monsoon and monsoon (Kavitha and Nair, 2016b). During monsoon convective activity uplifts CH₄ from boundary layer to upper troposphere and thus reduces the concentration at the surface. Convection, vertical transport and boundary layer height variation are identified as the possible factors for creating the contrast between near-surface and upper air observation of CH₄ (Kavitha and Nair, 2016b). Occasional high or low CH₄ concentrations over distinct parts of the atmosphere over Indian subcontinent were reported in several of the previous studies. However a linkage between them and its association with underlying monsoonal dynamics is largely unknown. In the current study we attempt to link the surface and upper air variability in atmospheric CH₄ to get a 3-dimensional distribution of it over Indian subcontinent
This work, for the first time, highlight the extreme contrasting origins of CH4 arriving
over a small subcontinent like India by the dramatic changes in the persisting seasonal
atmospheric circulation. The contemporary observations of greenhouse gases are very limited
and sparse in regions like India. Therefore one has to rely on models and satellite data to study
the variability of greenhouse gases in such regions. In this view, the performance of the model is
ultimately important. In this paper with the available observations we validated the model. The
model can be a very good tool considering the accuracy it achieved when compared to two
surface stations and airplane observations over India. None of the previous studies attempted
and made a case that the model indeed are reliable, though with given uncertainty, and can be
utilized for further study about CH4 variability over India.

## 2. Methods

### 2.1. Surface CH₄ observations

The present study is based on the observation of atmospheric CH₄ concentration over 3 different sites located in the western India and the Southern Hemisphere (Figure 1). Along with the site locations the figure shows NCEP reanalysis of wind at surface (1000mb) and at upper level (200mbar) for January and July month representing two seasons. During July (representative for JJAS months), strong westerly / southwesterly wind prevails at the surface whereas at the upper troposphere (~200mb) strong easterly jet stream predominates; whereas in January (representative for DJF months) wind at the surface is northeasterly whereas at the upper troposphere strong westerly jet stream prevails. There are two greenhouse gas monitoring sites in western India; 1) Sinhagad (hereafter referred to as SNG) situated 200 km east of the Arabian Sea (18.35° N, 73.75° E) and located over the Western Ghats mountains at an elevation of 1600
m (asl); 2) Cape Rama (hereafter referred to as CRI), is a coastal site at Goa (15.08° N, 73.83° E, elevation = 50 m asl; Figure 1). Routine air samples at SNG site were collected from a 10m meteorological tower at a weekly interval from January, 2012 till December, 2013. The site has less vegetation coverage and the prevailing wind speed during the sampling time (i.e. noon) is comparatively low (of the day) (0.5 - 1m s⁻¹) for air sample to have an effect by the local sources and vegetation (Tiwari et al., 2014). Details of the air sampling method is given in Tiwari and Ravi Kumar, 2011, published in World Meteorological Organization (WMO) report No. 194 (Ravi Kumar et al., 2013).

Air samples collected from SNG site were brought back to Gas Chromatography (GC) lab at Indian Institute of Tropical Meteorology (IITM) for the analysis. The CH₄ concentration in the collected air samples was determined using Flame Ionization Detector (FID) detector of Thermo Trace Gas GC (Trace Gas Ultra, model no. KAV00338). The detail analytical procedure of the chromatographic run is given in Ravi Kumar et al., (2013). The measurement of CH₄ concentration in the air sample was calibrated to WMO standards. The GC analysis was calibrated for CH₄ measurement using International Standards provided by the WMO Central Calibration Laboratory (CCL) at the National Oceanic and Atmospheric Administration (NOAA)/Earth System Research Laboratory (ESRL)/Global Monitoring Division (GMD), Boulder, Colorado, USA. To understand the reproducibility in measurement, paired air samples were collected from SNG site and analyzed in the GC. The paired reproducibility was found to ±5ppb.

At CRI site, maintained by Commonwealth Scientific and Industrial Research Organization (CSIRO) Atmospheric Research GASLAB (Global Atmospheric Sampling
Laboratory), air sampling was going on since February, 1993 till January, 2013 with a few years of sampling gap during October, 2002 to July, 2009 (Francey et al., 1996; Bhattacharya et al., 2009). The observation obtained during July, 2009 till January, 2013 from CRI was considered here. Both the SNG and CRI site observations were compared with another station location in Northern Indian ocean (i.e. ~500km south of equator) for the sake of linking the large-scale monsoon dynamics and CH₄ variability over Indian subcontinent. The stations we chose are Seychelles at Mahe Island (4.67°S; 55.17°E; elevation=7m asl) (mentioned hereafter as SEY) maintained by NOAA/ESRL (WDCGG). The SEY site was operational since May, 1983 till today (WDCGG). In order to compare this site data with SNG and CRI, the observation obtained for the period of July, 2009 till December, 2013 was considered in the current study.

Both the SNG and CRI stations receive the seasonal reversal of wind pattern at surface level associated with both summer and winter monsoons. It is the primary meteorological feature of these sites (Tiwari et al., 2014). The study was focused mainly on SWM (hereafter referred to as JJAS) and winter monsoon i.e. December-February (hereafter referred to as DJF).

## 2.2. Model Simulations

The Indian subcontinent and its surrounding area are largely controlled by the monsoon meteorology both in terms of dynamics and composition of lower and upper troposphere trace gases. Thus in order to understand the effect of the Indian monsoon on the dynamics and dispersion of the atmospheric CH₄ at the surface and at the upper troposphere, the chemistry transport model LMDz (Lin et al., 2017) is utilized to simulate the CH₄ concentrations in the atmosphere. LMDz is general circulation model which is used here with a horizontal resolution of 0.5 x 0.66 latitude by longitude spanning 50–130°E and 0–55°N centered over India. Model
has 19 sigma coordinate layers in the vertical. The model solves the full dynamic equation for winds and then nudged towards European Centre for Medium-Range Weather Forecasts (ECMWF) analyzed meteorology with a relaxation time of 6 hours, in order to force transport to reproduce the observed large scale advection (Bousquet et al., 2005). Model uses advection scheme of Hourdin and Armengaud, 1999 and deep convection is parameterized using Tiedtke, 1989. Surface fluxes used to simulate the model are from different sources as anthropogenic emissions from EDGAR v4.2 FT2010 product (http://edgar.jrc.ec.europa.eu); wetland emissions from Kaplan et al., 2006; biomass burning emissions from GFED v3.0 product (http://www.globalfiredata.org/data.html); termite emissions from Sanderson, 1996; ocean emissions from Lambert and Schmidt, 1993; and soil uptake from Ridgwell et al., 1999. Model simulations are validated using various observations over the globe (Lin et al., 2016). Further details of the model and surface fluxes are described in Lin et. al., 2016.

Second model used in this study is Lagrangian particle dispersion model Flexpart. FLEXPART is an atmospheric transport model primarily designed to investigate the long-range and meso-scale dispersion of pollutants from point sources (Stohl et al., 2005, 2009). This model calculates the advection of multiple tracer particles either backward or forward in time using mean 3D winds and stochastic motions from the turbulence fluxes. It introduces discrete inert particles at each receptor location and moves them backward/forward in time in the model domain using prescribed meteorological field. Three hourly meteorological field data from the National Center for Environmental Prediction Global Forecasting System (NCEP) (http://www.nco.ncep.noaa.gov/pmb/products/gfs/) archived at 0.5° × 0.5° grid resolution is used as inputs to FLEXPART simulations. In this study, backward simulations from both SNG and
CRI sites were made every 3hours interval and are used to understand long range transport of air masses at surface monitoring sites. The starting period of the back-trajectory analysis was fixed on the 15th of the month. Twenty thousand particles are released from a 10 m height at the receptor location at 12:00 h UTC (measurement site) and FLEXPART calculates a 4D (space and time) response function (sensitivity) to emission input. 15-day backtrajectory was initiated at mid of the month during July and January. 15 days back flow is a representative of the characterize transport for the respective month. Detailed model simulations over surface sites in India are given in Tiwari et al., 2013, 2014.

## 2.3. Aircraft CH₄ observations

The CARIBIC system (http://www.caribic-atmospheric.com), loaded onto Lufthansa Airlines measures CO₂, CH₄, O₃, CO etc. and aerosol at an altitude of around 8 - 12.5 km (Schuk et al., 2009, 2012). Air samples are collected onboard using the Triggered Retrospective Air Collector (Brenninkmeijer et al., 2007). Post-flight concentration analysis of the collected trace gases are carried out using Gas Chromatography-Flame Ionization Detector (GC-FID) system at Max Planck Institute of Chemistry, Germany and the precision obtained for CH₄ measurement is 0.17% (Schuck et al., 2009). The air sampling along Frankfurt (FRA) to Chennai (MAA) route was initiated in April 2008 (Schuck et al., 2012), and continued till March 2012.

# 3. Result and Discussions

## 3.1. Seasonal variation of CH₄

The atmospheric CH₄ at SNG and CRI has a seasonality of ~200 ppb (Figure 2a, b). CH₄ concentration reaches its minima during the summer monsoon (i.e. during JJAS); whereas it
reaches its maxima during the winter monsoon (i.e. during DJF). During JJAS the CH$_4$ concentrations at SNG was minimum about 1821±21 ppb and 1818±21 ppb for years 2012 and 2013, respectively; whereas at CRI the minimum is about 1781±18 ppb, 1792±11 ppb and 1795±22 ppb years 2010, 2011 and 2012, respectively. On the other hand during DJF at SNG a maximum is observed about 1882±54 ppb and 2015±32 ppb for years 2012 and 2013, respectively; whereas at CRI the maximum is about 1910±30 ppb, 1922±28 ppb and 1927±20 ppb for years 2010, 2011 and 2012, respectively (see also Table 1). Here the uncertainty depicts the standard deviation of daily CH$_4$ with respect to the monthly seasonal average. In comparison to SNG the CRI is located at the coast and the site is comparably free from vegetation and local anthropogenic influences. Thus CRI captures lower magnitude of CH$_4$ concentration in comparison with SNG (Tiwari et al., 2014).

The lower CH$_4$ concentrations above SNG and CRI during JJAS may characterize a southern hemisphere origin. In order to verify this we compared the SNG and CRI CH$_4$ with that of the observations taken from SEY (see method for details). Noticeably the SEY CH$_4$ has similar seasonal variability as that of SNG and CRI, but with weaker amplitude probably because of its location over the open ocean. The in-phase seasonality between SNG, CRI and SEY thus suggest a possible southern hemisphere origin of CH$_4$ that is observed over the Indian peninsula during JJAS. The contribution of local fluxes to this seasonality appears to be insignificant as the synthesized CH$_4$ fluxes (EDGAR) of the Indian peninsula itself show a peak in summer and a dip in winter (Lin et al., 2017). Nevertheless, during the JJAS, the CH$_4$ at SNG, CRI, SEY all collectively have similar concentrations implying that the air-mass originated from southern hemisphere is visiting the sites.
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Backward trajectories of particles initialized from SNG and CRI in summer monsoon season indicates transport of air mass from the southern hemisphere to the Indian subcontinent. The trajectories are also passing through the station SEY (Figure 3). Therefore the southern hemispheric CH$_4$ arrive SNG and CRI during summer monsoon explaining the similar concentration of CH$_4$ at all three sites examined here. During the winter monsoon, on the other hand, the trajectories indicate continental origin of CH$_4$ at SNG and CRI (see also Tiwari et al, 2014).

## 3.2. CH$_4$ variability from model simulations

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The LMDZ simulated CH$_4$ concentration at SNG and CRI found to match exceptionally well with the ground-based observations (Figure 2a, 2b). To quantitatively understand the consistency between observation and model simulations the root mean square difference (RMSD) between them was calculated for each season. The RMSD was 68.7 (9.6) ppb during summer monsoon and 114.3 (33.8) ppb during winter monsoon for SNG and CRI, respectively. Both the ground-based observation and model was found to be highly consistent with each other with a low RMSD for JJAS compared to DJF. The LMDZ simulated CH$_4$ at upper level also showed close match with CARIBIC observations (Figure 4). The CARIBIC flight track (Figure 4a) facilitates the understanding of model offset over both the Arabian Sea and the Indian subcontinent. LMDZ model simulated CH$_4$ was sub-sampled along the CARIBIC flight track for individual days. Model offset was calculated by subtracting the CARIBIC observations from sub-sampled model simulated CH$_4$. The spatial pattern of model offset along longitude showed large variation between different months in winter over ocean sector whereas near to Indian subcontinent and nearby region, the consistent offset with an average of 12±4ppb was found for
winter of 2011 (Figure 4b). The landing of CARIBIC flight over Chennai, India provided the
vertical profile of model offset over Indian subcontinent. In vertical profile model offset was
found to be 7±4ppb (Figure 4c). In summary, the model offset indicates that during winter model
is over-estimating CH₄ concentration for Indian subcontinent and its surrounding region. The
validation of LMDz model with ground based, and aircraft based observation confirms that the
model simulations are faithful with respect to the available observations of CH₄ from Indian
subcontinent analyzed here.

The consistency between observation and model allowed us to portray a 3-dimentional
structure of atmospheric CH₄ distribution during JJAS and DJF over Indian subcontinent and its
surrounding region. During southwest monsoon the transport of southern hemispheric clean air at
surface causes lower concentration. Upon arrival at the continent it gets mixed with the surface
emitted CH₄. Then the deep convection over continent carries the ground level higher emission to
the upper atmosphere. At the upper atmosphere there is a presence of strong easterly jet stream
which carries the higher CH₄ concentration from continent to the nearby ocean i.e. over Arabian
Sea. Thus there was higher CH₄ over Arabian Sea during southwest monsoon. During winter, the
observation over continent is largely affected by the local emission at the surface. Then effect of
surface emission decreases with increasing altitude. The decrease is more prominent over land
than over ocean.

3.2.1 Vertical profile of atmospheric CH₄ over ocean and continent

The mean vertical profile of CH₄ from a point over the central India (78°E, 20°N) and
over a nearby oceanic region (i.e. over Arabian Sea, 65°E, 17°N) was constructed based on the
model outputs. The mean was calculated as monthly mean of 9-years (2006 till 2014) of model
simulations. An uncertainty to this mean at each level was also calculated as the standard
deviations of daily CH₄ data for individual months from all years. In order make sure the
standard deviation represent for the synoptic variability we have removed first two harmonic
from each year before calculating the spread.

Major contrast between winter and summer profiles of CH₄ over Indian subcontinents are
noticed below 750 mb (Figure 5). During winter the surface CH₄ over the continent was relative
higher (1950±51 ppb) compared to the upper atmosphere (1814±26; above 750 mb). During
summer, on the other hand, CH₄ appears to be well mixed over the continent possibly due to the
summer time convections. This property is also observed over the nearby oceanic region but
further lower concentrations of CH₄ at surface level (1765±10 ppb) as compared to the land
possibly due to the transport of more ‘fresh’ air from the southern hemisphere to the Arabian
Sea.

Above 750 mb, the concentration of CH₄ over continent as well as over the ocean appears
to be similar throughout the year with slight deviation between summer and winter. This mean
structure varies slightly by uncertainty contributed by the synoptic variability. Such fine scale
variability could either be due to variability in atmospheric circulation or due to variability in
fluxes. However there is no precise estimate of CH₄ fluxes at higher resolution to ascertain the
role of transport versus fluxes in the uncertainty of the mean shown here.

3.2.2 Spatial distribution of atmospheric CH₄
In above section, vertical profiles over the land and the ocean provide the glimpses of structure of CH₄ over India as seen from winter and summer. To elaborate a three dimensional view of this vertical distribution of CH₄ we examined zonal average of LMDz simulated CH₄ concentration from 60E-75E and displayed as a meridional section from 59S-22N. The sections are produced for both winter (Figure 6a) and summer (Figure 6b) separately. The inter-hemispheric gradient of CH₄ is well maintained at about equator during winter. The contours show that over the continent the surface CH₄ is somewhat ‘trapped’ in the lower atmosphere and subsidence from aloft, a typical feature of winter monsoon circulation.

A conspicuous nature of the CH₄ vertical profiles over the continent during summer monsoon is the vertical homogenization of concentrations by the convection (Figure 6b). The upper atmosphere experiences an intrusion of CH₄ concentrations from land to ocean. This is typical feature of a Hadley cell circulation established during JJAS over Indian sub-continent.

## 4. Conclusions

CH₄ observations over Indian subcontinent show interesting differences from other tropical sites (eg. Mauna Loa, Hawaii Islands, Shuck et al., 2010). Over Indian subcontinent the CH₄ concentrations appear to be influenced dominantly by the transport and convection rather than the fluxes which itself show a peak in summer following the rain and agricultural activities and a dip in winter (Lin et al., 2016). The summer convection, winter subsidence and lateral transport by monsoonal reversal all contribute to explain the CH₄ variability over India.
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It appears that there is a difference in seasonality between the surface and upper atmospheric CH₄ over Indian subcontinent. The surface observations show that there is a dip in CH₄ during JJAS and a hike in DJF due to the lateral transport. On the other hand at the upper atmosphere the JJAS CH₄ is high compared to the DJF (Schuck et al., 2010, Xiong et al., 2009). The satellite observations are in fact capturing this seasonality in the upper atmosphere because the convection carries the surface fluxes to the upper atmosphere during JJAS. While the surface observations are influenced by the lateral transport and air-mass visiting from the southern hemisphere.

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Atmospheric CH₄ observation at ground level showed a strong seasonality with minima in southwest monsoon and maxima in winter. Although the removal of CH₄ by higher OH radical is expected to play a role in southwest monsoon, the effect in the transport of southern hemisphere ‘clean’ air on the CH₄ variability is not ignorable. In fact the visit of ‘clean’ oceanic air and sink due to elevated OH radicals may contribute to the dilution of CH₄ over surface during JJAS.

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We also quantified the effect of OH on CH₄ loss through oxidation processes during monsoon and winter. OH oxidation is the main loss process for CH₄ in the atmosphere (k = 1.85 x 10⁻¹² e⁻¹⁶⁹⁰/T); Atkinson et al., 2004). In the winter season, considering the background OH concentrations above the Indian subcontinent, the CH₄ lifetime computes to about approximately 1430 days. However, due to an increase OH concentrations in the monsoon season, the CH₄ lifetime is about 782 days, which is about half as much as during winter. To cause a reduction of about 130 ppbv (Figure 2) from average winter concentrations to average monsoon concentrations, it would take about 54 days during the monsoon season. This result indicates that
due to the long lifetime of CH₄ against oxidation, the large seasonal difference would not be controlled by OH oxidation, but rather is indicative of transport of different air masses.

The CO₂ concentrations in the atmosphere over Indian subcontinent are rather influenced by the net ecosystem exchange and the signals related to transport are obscured (Ravi et al., 2016). On the other hand the influence of transport and convection may be separable from the CH₄ variability in comparison with those induced by the surface fluxes. The CH₄ could advect in and out of the Indian subcontinent by the intermittent variability of monsoonal circulation within a season (Goswami et al., 2006). This should be examined in order to see whether the CH₄ fluctuations by monsoonal dynamics feedback any radiative value to the convection over India (Goswami et al., 2006).

Although the Indian monsoon and convections over Indian and adjacent Asian regions are well understood and in this context it may not be surprising to know that the associated CH₄ dynamics also behaving to the tune of the monsoon. However, there are important scientific questions exist behind this scenario as follows. CH₄ is much more active (20 times or more) than CO₂ in the radiative heat budget. Therefore the role of the CH₄ variability in the local monsoon radiation balance needs to be understood. Without background knowledge in CH₄ variation during monsoon one cannot achieve this target. This paper is the first and important step towards this goal. The results should be viewed with this spirit that the atmospheric structure of CH₄ during winter and summer can be extremely different over India irrespective of its sources, though uncertainty looms over this.
393 Acknowledgements

394 The authors would like to express thanks to CARIBIC project for providing upper troposphere
395 methane data in Frankfurt to Chennai flight. We express our gratitude to Carl A. M.
396 Brenninkmeijer and Armin Rauthe-Schoch from Max Planck Institute for Mainz Germany, for
397 their helpful comments and discussion. We would like to thank Ray Langenfeld and Marcel
398 Vanderschot from CSIRO Australia for sharing Cape Rama data. We are thankful to the
399 Ministry of Earth Sciences Govt. of India for supporting research through Indian Institute of
400 Tropical Meteorology Pune India.

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<table><caption>Table 1: Seasonal average of Atmospheric CH4 concentration at Sinhagad (SNG) and Cape Rama (CRI) during JJAS and DJF of 2010 - 2013</caption><thead><tr><th rowspan="2"></th><th colspan="2">SNG</th><th colspan="2">CRI</th></tr><tr><th>JJAS</th><th>DJF</th><th>JJAS</th><th>DJF</th></tr></thead><tbody><tr><th>2010</th><td>-</td><td>-</td><td>1781±18</td><td>1910±30</td></tr><tr><th>2011</th><td>-</td><td>-</td><td>1792±11</td><td>1922±28</td></tr><tr><th>2012</th><td>1821±21</td><td>1882±54</td><td>1795±22</td><td>1927±20</td></tr><tr><th>2013</th><td>1818±21</td><td>2015±32</td><td>-</td><td>-</td></tr></tbody></table>

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<figure><img src="image_5.png" /><figcaption>Figure 1: NOAA NCEP-derived monthly mean u-wind at the surface (1000 mb) and at upper troposphere (200 mb) during July (left panel) and January (right panel). Arrows indicate wind direction. Stations SNG (Singahad, India), CRI (Cape Rama, India), and SEY (Seychelles) are marked in black rectangles.</figcaption></figure>

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<figure><img src="image_6.png" /><figcaption>Figure 2: Measured and LMDz model simulated CH₄ concentration at a) SNG (Sinhagad) and SEY (Seychelles), and b) CRI (Cape Rama) and SEY (Seychelles).</figcaption></figure>

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<figure><img src="image_7.png" /><figcaption>Figure 3: Lagrangian back-trajectories of particles simulated by FLEXPART starting from station SNG (Sinhagad) and CRI (Cape Rama) during south west monsoon month July (left panel) and winter month January (right panel). Colors indicate position of particles in the vertical (m).</figcaption></figure>

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<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /><figcaption>Figure 4: (a) CARIBIC flight track from Frankfurt to Chennai on 16th Jan., 2012, rectangular boxes show oceanic sector and continental sector; (b) Longitudinal variation of CH4 concentration (model minus observed) over oceanic sector, vertical black line represents continental boundary; and (c) Vertical profile of CH4 concentration (model minus observed) over continental sector.</figcaption></figure>
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<figure><img src="image_10.png" /><figcaption>Figure 5: Model simulated vertical profile of CH₄ over (a) Arabian Sea and (b) Indian subcontinent during summer (Jul, Aug) and winter (Dec, Jan) months.</figcaption></figure>

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<figure><img src="image_11.png" /><figcaption>Figure 6: Vertical profile of model simulated CH4 concentration averaged over longitude bands 60°E-80°E and during 2006 to 2013 a) Jan and b) Jul. Contour lines indicates model CH4 concentration values.</figcaption></figure>
* A Summer minima and winter maxima of CH4 at surface is observed over India

* A seasonality of ~200 ppb is linked with the atmospheric circulation and convection

* Seasonality of CH4 is confined to below 750 mb with uniform profile of CH4 aloft

* The low-level convergence, uplift & upper level divergence shapes CH4 over India