Methane vertical profiles over the Indian subcontinent derived from the GOSAT/TANSO-FTS thermal infrared sensor

We examined CH4 variability over different regions of India and the surrounding oceanic regions derived from thermal infrared (TIR) band observations by the Thermal And Near-infrared Sensor for carbon Observation-Fourier Transform 10 Spectrometer (TANSO-FTS) onboard the Greenhouse gases Observation SATellite (GOSAT) and simulated by the updated MIROC4.0-based Atmospheric Chemistry Tracer Model (MIROC4-ACTM) for the period 2009-2014. This study attempts to understand the sensitivity of the vertical profile retrievals at different layers of the troposphere and lower stratosphere, arising from the averaging kernels and a priori assumptions. We stress that this is of particular importance when the satellite derived products are analysed using a different ACTMs from that is used as retrieval a priori. A comparison of modeled and retrieved 15 CH4 vertical profiles shows the 22 vertical levels of GOSAT/TANSO-FTS TIR retrievals provide critical information about transport from the top of the boundary layer to upper troposphere and lower stratosphere in a consistent manner. The mean model-GOSAT TIR CH4 mismatch is within 50 ppb, excepting 150 hPa and upward, where the sensitivity of GOSAT/TANSOFTS TIR observations becomes very low. Convolution of the modeled profiles with GOSAT/TANSO-FTS TIR averaging kernels reduce the mismatch to below uncertainty. Distinct seasonal variations of CH4 have been observed at the upper 20 atmospheric boundary layer (800 hPa), free troposphere (500 hPa), and upper troposphere (200 hPa) levels over northern and southern regions of India corresponding to the southwesterly monsoon (July–September) and post-monsoon (October– December) seasons. Analysis of the transport and emission contributions to CH4 suggests that the CH4 seasonal cycle over the Indian subcontinent is governed by both the heterogeneous distributions of surface emissions and the influence of the global monsoon divergent wind circulations. GOSAT/TANSO-FTS TIR observations provide additional information about CH4 25 observations in this region compared to what is known from in situ data, which is important for improving the accuracy of emission flux optimization. Based on two emission sensitivity simulations, we suggest that the emissions of CH4 from the India region is 51.2 ± 1.6 Tg yr1 during the period of 2009-2014. https://doi.org/10.5194/amt-2020-101 Preprint. Discussion started: 13 July 2020 c © Author(s) 2020. CC BY 4.0 License.


Introduction
The Asian Summer Monsoon Anticyclone (ASMA) is a dominant circulation in the Upper Troposphere and Lower 30 Stratosphere (UTLS) in the Northern Hemisphere summer, which extends from Southeast Asia to the Middle East [Webster et al., 1998, Fleitmann et al., 2007. The Asian monsoons may be classified into a few sub-systems, such as the South Asian Monsoon, which affects the Indian subcontinent and surrounding regions. The monsoon is associated with persistent strong convection over India and the Bay of Bengal, elevated surface heating over the Tibetan Plateau, and orographic uplifting at the southern/south-western slopes of the Himalayas, which contribute to overall ascension of boundary layer air to the upper 35 troposphere (up to 200 -100 hPa) [Fu et al., 2006]. The deep convection and associated circulation patterns of the monsoon provides an important pathway for polluted boundary layer air to reach UTLS Park, 2006, Randel et al., 2010].
Then atmospheric compounds can be advected over other regions, or further uplifted in the stratosphere [Xiong et al., 2009, Patra et al., 2011, Garny and Randel, 2016. Due to the influence of deep convection and long-range transport, the chemical tracers such as CH4, CO, and ozone show sometimes extreme values [Park et al., 2004[Park et al., , 2008. 40 The South Asia region, consisting of India, Pakistan, Bangladesh, Nepal, Bhutan, and Sri Lanka, play an import part in the global CH4 budget, as the regional total emission cover 8% of about 500 Tg CH4 global total emissions during the 2000s . The recent economic growth of India has led to a significant increase in industrial emissions [Akimoto, 2003, Ohara et al., 2007, Janssens-Maenhout et al., 2019, especial in the Indo-Gangetic Plain (IGP) encompassing northern regions of India, which is one of the most densely populated region on the globe [Kar et al., 2010]. However, there are little or no long-45 term measurements of CH4 and the other greenhouse gases to evaluate the inventory emissions [Ganesan et al., 2017, Lin et al., 2018, Chandra et al., 2019.
Global observations from satellite instruments can complement and extend the information available from the surface in situ and aircraft measurements to improve our knowledge of the processes controlling emission and distribution of methane, to monitor its variability on different scales. The shortwave infrared (SWIR) and thermal infrared (TIR) bands are available 50 for measurements of CH4 from space.
Observations in the SWIR, such as those from SCIAMACHY on Envisat [Buchwitz et al., 2005, Frankenberg et al., 2011 and TANSO Fourier transform spectrometer (FTS) on GOSAT [Butz et al., 2010, Parker et al., 2011, Yoshida et al., 2013, provide information on column-averaged methane, in cloud-free conditions. SWIR measurements are limited to daytime and predominantly over land. On the other hand, TIR CH4 observations provide much greater geographical and temporal coverage, 55 and more importantly the measurements of vertical profiles allow a better understanding of the CH4 cycle over a region. The sensitivity of TIR CH4 observations is stronger in the mid to upper troposphere and relatively low near the surface, as its spectral signatures depend on thermal contrast between the atmosphere and surface [Saitoh et al., 2016, de Lange andLandgraf, 2018].
aboard Metop-A and Metop-B, launched in respectively 2006[Razavi et al., 2009, Crevoisier et al., 2009, Xiong et al., 2013, Siddans et al., 2017; TANSO-FTS, aboard GOSAT, launched in 2009[Kuze et al., 2009, Yokota et al., 2009, Saitoh et al., 2012, 2016; Cross-track Infrared Sounder (CrIS), aboard Suomi-NPP, launched in 2011 [Han et al., 2013]; and TANSO-FTS-2, aboard GOSAT-2, launched in 2018 [Matsunaga et al., 2019]. 65 The columnar dry-air mole fractions of methane (XCH4) retrieved over Indian regions from SCIAMACHY shows large spatio-temporal variation closely associated with the distribution of sources like livestock population, wetland, biomass burning, oil and gas production [Kavitha and Nair, 2016]. The seasonal variation of XCH4 is controlled by agricultural activities, mainly rice cultivation as revealed by NDVI analysis [Hayashida et al., 2013]. Along with the heterogeneity in surface emissions variations of XCH4 governed by complex atmospheric transport mechanisms during the southwestern 70 monsoon season in July-September and northeastern monsoon season in October-December as observed by GOSAT. Chandra et al. [2017] have highlighted the difficulty in interpreting the emissions from the surface by columnar CH4 measurements from SWIR spectra, without using an atmospheric chemistry-transport model. At the same time Ricaud et al. [2014] investigated the space-time variations in tropospheric CH4 over the Mediterranean Basin regions using a wide variety of datasets including GOSAT/TANSO-FTS TIR observations. 75 This study attempts to analyze the vertical distributions of CH4 over the Asian monsoon region. We used CH4 mixing ratios observed from GOSAT/TANSO-FTS TIR (hereafter referred as "GOSAT-TIR") and simulated by the Model for Interdisciplinary Research on Climate (MIROC, version 4.0) [Watanabe et al., 2008] based atmospheric chemistry transport models (ACTM) [Patra et al., 2018] referred to as "MIROC4-ACTM". We aim to understand relative contributions of surface emissions and transport in the formation of CH4 seasonal cycles over different parts of India and the surrounding oceans.The 80 paper is structured as follows. In Section 2, we briefly describe the spaceborne instrument GOSAT-TIR and vertical profiles retrievals of CH4, the MIROC4-ACTM simulation setup, the study domain and data processing. The meteorology and climatology of CH4 inferred from the different data sets over the study domain, variability of CH4 vertical profiles and the impact of the Asian Monsoon Anticyclone to the distribution of the tropospheric CH4 are discussed in Section 3. Major conclusions are given in Section 4. 85

GOSAT data
GOSAT is the first satellite dedicated to global observations of greenhouse gases CO2 and CH4 from space [Yokota et al., 2009]. After the launch on 23 January 2009, GOSAT has performed observations on a 666 km sun-synchronous orbit with a 3-day revisit cycle, a 12-day operation cycle, and the local solar time of 13:00 ± 15 min. 90 The Thermal and Near-infrared Sensor for Carbon Observation Fourier Transform Spectrometer (TANSO-FTS) on board GOSAT detects short-wavelength infrared (SWIR) light reflected from the earth's surface, along with the thermal infrared (TIR) radiation emitted from the ground and atmosphere [Kuze et al., 2009[Kuze et al., , 2012. As a result, from these spectral bands, https://doi.org/10.5194/amt-2020-101 Preprint. Discussion started: 13 July 2020 c Author(s) 2020. CC BY 4.0 License.
GOSAT/TANSO-FTS can simultaneously observe CH4 column averaged dry-air mole fractions and CH4 profiles in the same field of view, corresponding to a nadir footprint diameter of 10.5 km. The a priori profiles used in the CH4 retrieval are provided 95 by the National Institute for Environmental Studies (NIES) transport model [Saeki et al., 2013]. Temperature and water vapor profiles necessary for the retrieval are provided by the Japan Meteorological Agency Grid Point Values (JMA-GPV) dataset.
The first retrieval version of the GOSAT-TIR CH4 product (V00.01) and its validation analysis showed the total column values of CH4 (XCH4) based on the GOSAT-TIR CH4 profiles agreed within 0.5% of the aircraft XCH4 values over the tropical ocean [Saitoh et al., 2012]. Holl et al. [2016] did comparisons among CH4 data from ACE-FTS, ground based FTS, and the 100 current released version of GOSAT-TIR ( ) in the Canadian high Arctic, although GOSAT-TIR CH4 measurement information content is too low for a true profile retrieval because of the low thermal contrast and the low signal-to-noise ratio there. Global comparisons with AIRS retrievals reveal good agreement at 300-600 hPa, where both AIRS and GOSAT-TIR CH4 have peak sensitivities [Zou et al., 2016]. Mean mismatch in CH4 (GOSAT-AIRS) were 10.3-31.8 and -16.2±25.7 ppbv for the levels of 300 and 600 hPa, respectively. Comparison of the XCH4 shows that GOSAT-TIR agrees with AIRS to within 1% in the mid-105 latitude regions of the Southern Hemisphere and in the tropics. However, disagreement increases to ∼1-2% in the mid to high latitudes [Zou et al., 2016]. Olsen et al. [2017]  In the overlapping altitude ranges of the three satellite data products there is a small, but consistent, positive bias of around 20 ppbv, or 1% in GOSAT-TIR CH4 data. In the upper troposphere, good agreement between TANSO-FTS and NDACC was found, without a bias. In a more recent comparison, the average bias in CH4 profile retrieved from GOSAT-TIR spectra with a spectral correction scheme is less than 2% over the full altitude range, when compared with data from the Monitoring Atmospheric Composition and Climate (MACC) scaled to the total column measurements of the Total Carbon Column 115 Observing Network (TCCON) [de Lange and Landgraf, 2018].
This study uses the GOSAT-TIR CH4 product, which is released for the period from April 23, 2009, through May 24, 2014.
The number of vertical grid layers of the GOSAT-TIR CH4 product is 22 from the surface to 0.1 hPa.

MIROC4-ACTM simulations
The measurements are compared to results of simulations by the MIROC4-ACTM chemical tracer simulation [Watanabe 120 et al., 2008, Patra et al., 2018. The MIROC4-ACTM runs at a horizontal resolution of T42 spectral truncations (≈2.8 ×2.8) with 67 sigma-pressure vertical levels. The MIROC4-ACTM simulated horizontal winds (U and V) and temperature (T) are nudged to the Japan Meteorological Agency reanalysis fields (JRA-55) at all the vertical levels [Kobayashi et al., 2015]. The model uses an optimal OH field based on a scaled version of the seasonally varying OH field [Patra et al., 2014].
Two simulations were performed using combinations of inverted fluxes based on the following a priori emission scenarios 125 prepared on a monthly basis by combining the emissions from all anthropogenic and natural sectors, and by subtracting the surface sinks due to bacterial consumption in the soil [Chandra et al., 2020]: 1. FluxCao: EDGAR + GFED + other + VISIT wetland (Cao scheme [Cao et al., 1996]).

Data processing
The MIROC4-ACTM data were collocated to the GOSAT-TIR observation points. The criteria for the collocation are the nearest model grid cell in space, and the nearest hour in time. For vertical profile comparison, the MIROC4-ACTM data were 140 interpolated on the retrieval pressure levels of the GOSAT-TIR product, i.e. from 67 to 22 levels.

Averaging kernels and the retrieval sensitivity
The averaging kernels (AK) are defined to provide a simple characterization of the relationship between the retrieval and the true state. The retrieval sensitivity can be obtained from the sum of the columns of the averaging kernel matrix, which is also referred to as "the area of the averaging kernel" [Rodgers, 2000]. 145 Along with "raw" model simulation results (ACTM Cao,WH ) we analysed (ACTM Cao,WH AK ) profiles convoluted with retrieval a priori and the GOSAT-TIR CH4 averaging kernel matrix using the following vector equation [Rodgers, 2000, Saitoh et al., 2012: Here, A is an averaging kernel matrix, Xapriori represent a vector of a priori vertical profile, ACTM Cao,WH and ACTM Cao,WH AK 150 are vectors of "raw" and convoluted model simulated profiles, respectively.

Study domain
This work follows the setup described by [Chandra et al., 2017] and uses 10 regions (

Atmospheric conditions controlling the spatial distribution of methane
Key-components of the climatology in the Indian Ocean and the surrounding areas are the annual migration of the 165 Intertropical Convergence Zone (ITCZ) and seasonal development of the monsoon winds [Findlater, 1969, Webster et al., 1998, Fleitmann et al., 2007. In boreal spring the ITCZ migrates northward across the Indian Ocean and reaches its northernmost position at approximately 35 N during summer. A strong pressure gradient between the low-pressure zone over the Tibetan Plateau and a high-pressure zone over the Southern Indian Ocean generates a strong near surface monsoonal airflow from July to September ( Fig. 2c1-c3). 170 In autumn the ITCZ then retreats southward and reaches its southernmost position at approximately 25 S in January. The reversed pressure gradient during the winter months generates the moderate and dry northeast monsoon (Fig. 2d1-d3). Using Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Terra and Aqua satellites Devasthale and Fueglistaler [2010] showed that a significant fraction of high opaque clouds reaches and penetrates the tropical tropopause layer during https://doi.org/10.5194/amt-2020-101 Preprint. Discussion started: 13 July 2020 c Author(s) 2020. CC BY 4.0 License. https://doi.org/10.5194/amt-2020-101 Preprint. Discussion started: 13 July 2020 c Author(s) 2020. CC BY 4.0 License.
They suggested that very deep convection over the Tibetan plateau is comparatively weak, and may play only a secondary role in troposphere-to-stratosphere transport.

GOSAT-TIR CH4 profile properties
The observation by GOSAT-TIR band enables us to analyse the vertical structure of atmospheric CH4. This band has 190 relatively high spectral resolution of ~0.2 cm -1 and provides CH4 vertical profiles in 22 layers. The degrees of freedom of signal for CH4 observation by GOSAT-TIR band (V1 algorithm version) is around 1 over low-latitude part of India. Figure 3 suggests that the GOSAT-TIR spectra are sensitive to the CH4 concentrations in the height range of 900 hPa to 30 hPa. The spectra sensitivity does not change significantly between the different part of our analysis region, as seen from Fig. 3. https://doi.org/10.5194/amt-2020-101 Preprint. Discussion started: 13 July 2020 c Author(s) 2020. CC BY 4.0 License.
Through analysis of the AK profiles of the GOSAT-TIR V1 CH4 products, Zou et al. [AMT, 2016] show the sensitivity of GOSAT-TIR measurements gradually increases from the surface up and reaches a maximum at the levels of 300-600 hPa in the high latitudes and 200-600 hPa in the tropics. While below 800 hPa the sensitivity is small reflecting the major limitation of TIR in measuring the change of CH4 in the lower troposphere. Figure 3 shows typical GOSAT-TIR CH4 profile with retrieval 205 errors and a priori profile (left panels) and its corresponding AK profiles (right panels) for the Southern, Arid, and Northeast regions during the monsoon season. At the pressure levels of 500-150 hP where GOSAT-TIR measurements have sensitivity judging from their AK profiles, there are some differences between the retrieved and a priori CH4 profiles and they are beyond the retrieval errors, which means they should be significant differences.

CH4 over India observed by GOSAT-TIR and simulated by MIROC4-ACTM 210
In this section we analyzed CH4 distributions from GOSAT-TIR and MIROC4-ACTM at the levels of the constant pressure Due to a lack of GOSAT-TIR CH4 data in cloudy scenes and the influence of the complex orography of the studied area, the number of points used for averaging in each grid cell varies with height over land (Fig. 4d1-d3). This is especially noticeable for the northern regions of India, since a significant part of Tibet and the Himalayas are above the level of 800 hPa (Fig. 4d1).
Northern India also has large sources of CH4 with different types of emission. These two factors cause large standard deviations 220 (STD) in CH4 (Fig. 4e1-43). For South India and the marine regions, the STD values are much lower compared to those over the land.
In the middle and upper troposphere, the perturbations from the heterogeneity of the emissions are smoothed out, the density of observation points increases, therefore, the averaging errors decrease. At a height of 200 hPa, the average STD for GOSAT-TIR is approximately 25 ppb. 225 The density of observation points decreases with the onset of the monsoon season ( Fig. 5d1-d3), however, it remains sufficient to detect significant changes in CH4 concentrations even considering the relatively large STD values there. A significant increase in concentration values is noticeable primarily in the middle and upper parts of the atmosphere (Fig. 5a1-a3), which is due to the repeatedly confirmed effect of convective transport from surface sources upward. After reaching a level near the tropopause, the increased concentrations are distributed by three jets: the lateral (the cross-equatorial circulation) 230 and transverse (flows between the arid regions of north Africa and the Near East and south Asia) monsoons, and the Walker Circulation is extended across the Pacific Ocean [Webster et al., 1998]. The CH4 concentration in the eastern jets is higher, since it is formed over more northern areas with larger emission. The influence of the third component (the cross-equatorial circulation) is more noticeable in the post-monsoon period (Fig. S2).

240
GOSAT-TIR CH4 retrievals are constrained to the a priori CH4 data (panels b1-b3 of Fig. 3-4) especially in lower pressure levels due to the relatively low signal-to-noise ratio of the TIR spectra at CH4 absorption bands [Saitoh et al., 2012, Zou et al., 2016. Nevertheless, the GOSAT-TIR CH4 product shows vivid differences in CH4 from the a priori values even in the lower https://doi.org/10.5194/amt-2020-101 Preprint. Discussion started: 13 July 2020 c Author(s) 2020. CC BY 4.0 License.
part of the atmosphere, where sensitivity is weak (panels c1-c3 of Fig. 3-4). This implies an additional signal of CH4 concentration could be captured by the GOSAT-TIR measurements. 245 As explained in Section 2.2, MIROC4-ACTM simulations were performed with two flux combinations reflecting different approaches for estimation of the wetland CH4 emission. In general, the WH scheme fluxes are about 5-10% larger the Cao, 250 https://doi.org/10.5194/amt-2020-101 Preprint. Discussion started: 13 July 2020 c Author(s) 2020. CC BY 4.0 License. excepting the WIGP, EIGP, and NEI regions of India and Bangladesh where the maximum difference reaches 20-40% (Fig.   6). Besides, there are small hot spots in Southeast Asia (e.g. Mekong River Delta).
Since in the pre-and post-monsoon seasons (AMJ and OND) the excess concentration due to additional emission is locked in the boundary layer (as seen from MOPITT CO) [Kar et al., 2010], we can detect only a slight increase in concentration at the levels selected for the analysis. CH4 simulated using both emission schemes are consistent with the GOSAT-TIR retrieval 255 with averaged mismatch within ±2%, the heterogeneity of which is apparently caused by transport regimes (see Fig. 7 for AMJ). By analogy to the CH4 distribution from GOSAT-TIR the increased scatter found in modeled CH4 over IGP, wherein the enhanced values extend up to the level of 200 hPa (see supplement Fig. S4).
During the monsoon, the difference between emission scenarios becomes significant, as additional CH4 mass is carried to the middle and upper atmosphere (Fig. 8). The larger mismatch in comparison with GOSAT-TIR ( Fig. 8b1-b3) emphasizes 260 the redundancy of CH4 emission of the WH scheme.

CH4 vertical profiles
This section aiming to investigate the CH4 tropospheric profile time and space variations above the Indian regions and to attribute the altitude CH4 variability to the regional emission strength and different synoptic and global scale depending on the season. Figure 9 depicts seasonal mean CH4 vertical profiles observed by GOSAT-TIR and simulated by the model for premonsoon (April-June) and monsoon (July-September) of 2011. The variation of GOSAT-TIR sensitivity are taking into 275 account by the implementation of the averaging kernel the modelled data sets (ACTM AK Cao and ACTM AK WH).
By using the a priori and retrieved CH4 profiles with retrieval error and AK profile (Fig. 3), we found that differences between a priori and retrieved CH4 profiles are larger than its retrieval error, so the differences are valid to be discussed. The 285 variabilities shown in Figure 9 are larger than GOSAT-TIR retrieval errors, so GOSAT-TIR and model show good agreements within both errors (natural variabilities and retrieval random errors).
The vertical CH4 profiles have a characteristic curved shape with double peak. The first peak near the surface is associated with emissions from local sources, the second one at the level of 150-200 hPa is caused by the vertical updraft . Reflecting the increase of CH4 surface fluxes intensity (Fig. 6), the vertical gradient between the near-290 surface and upper troposphere levels increases in the direction from South-West (marine regions (Fig. 9a,c) have slightly lower concentrations in the boundary layer since the sea is a weak source) to the North-Eastern (where EIGP, WIGP, and Northeast Indian stand out in significant sources due to various natural and anthropogenic sources (Fig. 9h,i,j)). atmospheric masses with a low CH4 coming from the Indian Ocean [Findlater, 1969]. These regions do not have significant sources of CH4, and therefore, concentration in the vertical profiles increase with height due to transport from other regions.
The third southern region (Bay of Bengal; Fig. 9c) has similar properties, but at the same time, it is under the influence of 305 transport from neighboring regions (i.e. East India, EIGP), as evidenced by a large spread near the surface.
The use of AK is taking into account the relatively low vertical resolution of satellite measurements and the change in the sensitivity of the retrieval by smoothing along the a priori profile and reduces the spread at the levels where the sensitivity of satellite sensors is weak. Convolution of modelled profiles with GOSAT-TIR CH4 averaging kernels (Eq. 1) smooths the model profiles to fit the GOSAT-TIR vertical resolution and reduce their mismatch. Fig. 10 shows GOSAT-TIR AK has significant 310 smoothing, approaching the MIROC4-ACTM model profiles to a priori so much that the difference between the calculations for the Cao and WH emission scenarios becomes barely distinguishable. This is especially vivid above the level of 150 hPa, where the sensitivity of GOSAT-TIR there drops sharply and the satellite retrievals and the AK convolved model profiles strongly follow the a priori profiles.

The choice of an a priori profile (usually provided by model calculations) is an important point in retrieval problems. The 315
TransCom-CH4 experiment [Patra et al., 2011] showed a significant scatter between the participated models, including the NIES model later selected for calculating GOSAT-TIR a priori profile. In our study, a significant difference in the methane profile gradient, its seasonal variability (winter and summer) between a priori and the MIROC4-ACTM model was revealed in UTLS zone (levels of 150-20 hPa). Apparently, the difference in modelling the tropopause region and the tracer transport into the lower stratosphere is a key factor. Here should be noted that MIROC4-ACTM uses a more modern reanalysis to 320 calculate the meteorological parameter, and the vertical resolution (67 sigma-pressure levels) is quite higher than that of the NIES (47 sigma levels). Even more important, the stratospheric part of the NIES model was adjusted to observed age of air for CO2 and long-term satellite observations from HALOE for CH4 [Saeki et al. 2013]. This emphasizes the uncertainty in modelling transport processes near the tropopause derived by different methods.
From the moment GOSAT was launched, the calculation of a prior profiles is carried out according to the same scheme. 325 This is important for the long-term consistency of the GOSAT-TIR CH4 product but does not take into account the significant improvements (for example, updated OH fields, reanalysis, convective parameterization) implemented for MIROC4-ACTM.
This emphasizes the need to use custom a priori profiles in retrieval, which requires greater transparency of technical information from satellite projects. This problem is less noticeable, but no less relevant for satellite CH4 receivers operating in the SWIR band aiming to obtain the total column CH4. 330

CH4 time-altitude variation
The monsoon anticyclone shows substantial intra-seasonal oscillations, which are connected to variable forcing from transient deep convection over the Indian subcontinent and the Bay of Bengal. This variability is typically associated with active/break cycles of the monsoon with timescales of ∼10-20 days. Significant correlations exist between outgoing longwave radiation (OLR; Fig. 2a2-d2) and circulation within the monsoon region, such that the entire balanced anticyclone varies in 335 https://doi.org/10.5194/amt-2020-101 Preprint. Discussion started: 13 July 2020 c Author(s) 2020. CC BY 4.0 License. concert with convective heating: enhanced convection leads to warmer tropospheric temperatures, stronger anticyclonic circulation, and colder lower stratospheric (and tropopause) temperatures [Randel and Park, 2006]. This causes a significant heterogeneity of the flux transported upward and CH4 concentration in the upper layers during ASMA (Fig. 11).

340
and right panels respectively) for considered regions. Note that the profiles are shown for the tropospheric altitudes as the GOSAT-TIR retrieval system is not sensitive to the stratospheric altitudes (See Fig. 9 and the associated text). https://doi.org/10.5194/amt-2020-101 Preprint. Discussion started: 13 July 2020 c Author(s) 2020. CC BY 4.0 License.
The IGP region experiences intense agricultural activity, and use of traditional biofuels. In the winter months the IGP is often enveloped by thick fog and haze (Gautam et al., 2007). The prevailing winds at low altitudes (surface to ∼850 hPa) are northerly to northwesterly with low wind speeds (<5 m/s) and the eastern parts of the IGP are impacted by a localized area of 345 strong subsidence in winter [Dey and Di Girolamo, 2010]. These conditions tend to trap the pollution at low altitudes [Kar et al., 2010].

Seasonal variation of CH4
The Prophet time-series analysis and forecasting model [Taylor and Letham, 2018]  Compared to traditional exponential smoothing, Prophet can easily handle temporal patterns with multiple periods and has no requirements regarding the regularity of measurement spacing. The model has a robust performance in the presence of missing data and trend shifts and typically handles outliers well while working with time-series that have several seasons of historical data with strong seasonal patterns. The Prophet allows the use of all data points for the study period, thereby increasing 355 accuracy and reducing sensitivity to random outliers [Belikov et al. 2019]. Though the GOSAT-TIR and MIROC4-ACTM mismatch in trend is almost negligible, the difference in the simulation of the amplitude and phase of the seasonal variation can be significant (Fig. 12).
Seasonal changes are controlled primarily by meteorological parameters, so the most noticeable effect is determined by the summer monsoon. During this season enhanced transport redistributes CH4 along all layers of the troposphere. The minimum 360 CH4 seasonal variation is found in the lower troposphere (800 hPa), while the maximum occurs in the upper part ( Fig. 12e-f).
The amplitude of seasonal changes is determined by the net amount of the sources; therefore, it increases from south to north from marine regions to the most densely populated areas. The figure 12 shows that a significant difference between the Cao and WH fluxes is evident in the three northern regions (WIGP, EIGP and NEI). During summer their differences can reach almost 50%. This inequality determines the difference between seasonal variability not only for these regions, but also 365 for the nearest neighbours. Especially noticeable for AI and WI, where intrinsic fluxes are much small.
With the onset of autumn, the deep convective transport is suppressed, therefore under the influence of the Hadley cell circulation the slow outflow of air masses is started in the opposite south-west direction. This moment is characterized by the peak of concentration at 800 hPa, which slowly moves from the northern regions (over EIGP in October) to the southern (over Arabian Sea in the late November). 370 India occupies a large region of South Asia, where a fewer observations limit the chance to reduce the uncertainty in the greenhouse gases (including methane) flux. Used in this work the Cao and WH flux combinations for the South Asia region for the period 2009-2014 account 65.7 ± 2.1 and 82.4 ± 2.8 Tg yr -1 respectively. In order to identify which emission scenario is more realistic, we compared the monthly mean methane concentrations averaged over the region's surface area Fig. 13. For https://doi.org/10.5194/amt-2020-101 Preprint. Discussion started: 13 July 2020 c Author(s) 2020. CC BY 4.0 License. all considered levels, ACTMWH is superior to ACTMCao. However, a comparison with GOSAT-TIR may lead to a slightly 375 different results depending on the level selected for comparison. significant inter-seasonal variability, which can be greatly influenced due to the large spread (large STD values) of individual samplings (Fig. 4-5 panels d1-d3)). Another important factor is the GOSAT-TIR retrieval a priori profile derived from NIES TM with the coarse vertical grid and simplified scheme for modeling of the boundary layer height, which shows strong diurnal and seasonal variations [Kavitha et al., 2018]. In UTLS significant seasonal fluctuations also occur (Fig. 13a). 385 https://doi.org/10.5194/amt-2020-101 Preprint. Discussion started: 13 July 2020 c Author(s) 2020. CC BY 4.0 License.
The strong summer peak in the MIROC4-ACTM CH4 is associated with excessive vertical transport, which, apparently was not completely resolved upon the transition to the new (MIROC-4.0) meteorology. Moreover, it remains unclear what causes significant drops in concentration in the winter period. In the middle troposphere (Fig. 13b) a good consistency in phase 395 is found and the ACTMCao to ACTMWH concentration mismatch is strongly associated with the flux difference. Relying on this comparison results, we can suggest the Cao flux combination as more reliable emission estimation. This confirm the assessment made by Patra et al. [2016], indicating that the EDGAR inventory (version 4.2FT2010) overestimated the South Asia regional emission by 10-15 Tg yr -1 . A significant part of the extra fluxes is concentrated in a few relatively small regions in the Northen India ( fig. 6). However, our best estimate emission of 51.2 ± 1.6 Tg yr -1 over the India is much greater than 400 those estimated by [Ganesan et al., 2017], combined in situ data of different time coverage and SWIR CH4 retrievals in trajectory-based modelling framework.

Conclusions
Vertical profile observations of CH4 from GOSAT-TIR at 22 pressure layers and simulations by MIROC4-ACTM, sampled at the location and time of the satellite overpass, were analyzed over India and surrounding oceanic regions for the period 405 2009-2014. The area of our analysis is subdivided in to several land and ocean regions. The main highlights of the present study are summarized below: 1. GOSAT-TIR observations provide data coverage and density suitable to study detailed horizontal features of CH4 at the top of the atmospheric boundary layer (excepting high mountain regions), free troposphere, and upper troposphere.
While [Chandra et al., 2017] mainly used the model simulations to understand the vertical transport (after validating 410 the model using GOSAT-SWIR measurements), using GOSAT-TIR measurements we show the seasonal evolution of transport and emissions on the CH4 at different layers of the troposphere using both the model and measurements.
2. The GOSAT-TIR product shows vivid differences in CH4 from the a priori values even in the lower part of the troposphere, where sensitivity of the TANSO-FTS sensor is relatively weak compared to the middle and upper troposphere. This implies an additional signal of CH4 concentration signal was captured by the TIR observations. 415 3. Distinct seasonal variations of CH4 have been observed at the different levels of the troposphere over northern and southern regions of India corresponding to the southwest monsoon (July-September) and early autumn (October-December) seasons. The major contrast between monsoon, and pre-and post-monsoon profiles of CH4 over Indian regions are noticed near the boundary layer levels. This is mainly caused by seasonal change in local emission strength. Unlike the work by [Guha et al., 2018], we found a strong difference between seasons in the middle and 420 upper troposphere caused by variability in atmospheric circulation and vertical convection.
4. Even if no averaging kernel incorporated, the mean MIROC4-ACTM and GOSAT-TIR mismatches are within 50 ppb, except for the level of 150 hPa and upward, where the GOSAT-TIR sensitivity becomes very low. Convolution of the modeled profiles with retrieval a priori and averaging kernels reduce the mismatch to below uncertainty. https://doi.org/10.5194/amt-2020-101 Preprint. Discussion started: 13 July 2020 c Author(s) 2020. CC BY 4.0 License.
However, the influence of the a priori profiles becomes too large with such smoothing. In comparison with AIRS 425 satellite observation [Kavitha and Nair, 2019], finer vertical resolution of GOSAT-TIR allows capturing more detailed features in CH4 vertical profiles. Consequently, we obtained more prominent CH4 patterns related to different regions and seasons.
5. The significant difference in the methane profile gradient, its seasonal variability (winter and summer) between a priori (derived from the NIES TM simulations) and the MIROC4-ACTM model was revealed in UTLS zone (levels 430 of 150-20 hpa). During monsoon season daily variation in a priori profiles is found in the middle troposphere. Thus, additional studies with use of custom a priori profiles in retrieval is of great importance.
6. Although we found the noticeable error in the model data in phase and amplitude at the end of summer-fall period, the performance of MIROC4-ACTM in CH4 transport in the troposphere and the lower stratosphere was improved due to the use of MIROC4.0 as the meteorological model. Furthermore, an additional analysis with aircraft 435 observations is necessary to analyze the GOSAT-TIR and MIROC4-ACTM mismatch found above the level of 150hPa. Our results suggest that the selection of a priori model for satellite data retrieval could play a significant role and should be addressed in the developments of future retrieval systems. 7. Among the two emission scenarios considered above, the Cao scheme seems to be more balanced than WH for individual regions and the whole South Asia during the monsoon season. In the other periods, no strong difference 440 was found. Using the Cao and WH emission combinations, the annual mean emission for the South Asia region is estimated to 65.7 ± 2.1 Tg yr -1 for the period 2009-2014.
Overall, the MIROC4-ACTM simulations of CH4 in the Indian regions compare favorably with the GOSAT-TIR samplings, in terms of seasonality and global variability. Inconsistencies seen in the GOSAT-TIR and MIROC4-ACTM comparisons could provide opportunities for further flux optimization with inverse modeling methods. More insight could be obtained after 445 the extension of the released data period of the GOSAT-TIR CH4 product.

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Acknowledgements. This research was supported by the Environment Research and Technology Development Fund (2-1802) of the Environmental Restoration and Conservation Agency of Japan. We also acknowledge the MODIS mission scientists and associated NASA personnel for the production of the data used in this research effort. Analyses and visualizations used in this paper were partly produced with the Giovanni online data system, developed and maintained by the NASA GES DISC. 465 https://doi.org/10.5194/amt-2020-101 Preprint. Discussion started: 13 July 2020 c Author(s) 2020. CC BY 4.0 License.