Introduction
Methane (CH4) is the second most important
anthropogenic greenhouse gas (GHG) after carbon dioxide (CO2) and
accounts for ∼ 20 % (+0.97 Wm-2) of the increase in
total direct radiative forcing since 1750 (Myhre et al., 2013). CH4
is emitted from a range of anthropogenic and natural sources on the Earth's
surface into the atmosphere. The main natural sources of CH4 include
wetlands and termites (Matthews and Fung, 1987; Cao et al., 1998; Sugimoto
et al., 1998). Livestock, rice cultivation, the fossil fuel industry
(production and uses of natural gas, oil, and coal), and landfills are the
major sectors among the anthropogenic sources (Crutzen et al., 1986; Minami
and Neue, 1994; Olivier et al., 2005; Yan et al., 2009). These results also
suggest that the Asian region is an emission hotspot of CH4 due to
the large number of livestock, intense cultivation, coal mining, waste
management, and other anthropogenic
activities (EDGAR2FT, 2013).
With a short atmospheric lifetime of about 10 years (e.g., Patra et al.,
2011a) and having 34 times more potential to trap heat than CO2 on a
mass basis over a 100-year timescale (Gillett and Matthews, 2010; Myhre
et al., 2013), mitigation of CH4 emissions could be the most
important way to limit global warming at inter-decadal timescales (Shindell
et al., 2009). Better knowledge of CH4 distribution and
quantification of its emission flux is indispensable for assessing possible
mitigation strategies. However, sources of CH4 are not yet well
quantified due to sparse ground-based measurements, which results in limited
representation of CH4 flux on a larger scale (Dlugokencky et al.,
2011; Patra et al., 2016). Recent technological advances have made it
possible to detect spatial and temporal variations in atmospheric CH4
from space (Frankenberg et al., 2008; Kuze et al., 2009), which could fill
the gaps left by ground-, aircraft- and ship-based measurements, albeit at
a lower accuracy than the in situ measurements. Further, despite the
satellite observations having an advantage of providing continuous monitoring
over a wide spatial range, the information obtained from passive nadir
sensors that use solar radiation at the short-wavelength infrared (SWIR)
spectral band is limited to columnar dry-air mole fractions of methane
(XCH4). This is an integrated measure of CH4 with
contributions from the different vertical atmospheric layers, i.e., from the
measurement point on the Earth's surface to the top of the atmosphere (up to
about 100 km or more precisely to the satellite orbit).
The South Asia region, consisting of India, Pakistan, Bangladesh, Nepal,
Bhutan, and Sri Lanka, exerts a significant impact on the global CH4
emissions, with regional total emissions of 37±3.7 Tg CH4 of about 500 Tg CH4 global total
emissions during the 2000s (Patra et al., 2013). The Indo-Gangetic Plain
(IGP) located in the foothills of the Himalayas is one of the most polluted
regions in the world, hosts 70 % of coal-fired thermal power plants in
India, and experiences intense agricultural activity (Kar et al., 2010). This
region is of particular interest mainly due to the coexistence of deep
convection and large emission of pollutants (including CH4) from
a variety of natural and anthropogenic sources. Rainfall during the SW
monsoon season causes higher CH4 emissions from the paddy fields and
wetlands (e.g., Matthews and Fung, 1987; Yan et al., 2009; Hayashida et al.,
2013), while the persistent deep convection results in updraft of
CH4-laden air mass from the surface to the upper troposphere during
the same season, which is then confined by anticyclonic winds at this height
(Patra et al., 2011b; Baker et al., 2012; Schuck et al., 2012). Several other
studies have also highlighted the role of convective transport of pollutants
(including CH4) from the surface to the upper troposphere
(400–200 hPa) during the SW monsoon season (July–September) (Park
et al., 2004; Randel et al., 2006; Xiong et al., 2009; Lal et al., 2014;
Chandra et al., 2016). The dynamical system dominated by deep convection and
anticyclone covers mostly the northern Indian region (north of
15∘ N) due to the presence of the Himalayas and the Tibetan Plateau,
while such a complex dynamical system has not been observed over the southern
part of India (south of 15∘ N) (Rao, 1976).
Satellite-based measurements show elevated levels of XCH4 over the
northern part of India (north of 15∘ N) to be particularly high over
the IGP during the SW monsoon season (July–September) and over southern
India (south of 15∘ N) during the early autumn season
(October–December) (Frankenberg et al., 2008, 2011; Hayashida et al., 2013).
Previous studies have linked these high XCH4 levels to the strong
surface CH4 emissions particularly from the rice cultivation over the
Indian region because they showed statistically significant correlations over
certain regions (Hayashida et al., 2013; Kavitha et al., 2016). The
differences in the peak of the XCH4 seasonal cycle over the
northern and southern regions of India are also discussed on the basis of
agricultural practice in India that takes place in two seasons, May–October
and November–April, respectively. However, inferring local emissions
directly from variations in XCH4 is ambiguous, particularly over
the Indian regions under the influence of monsoon meteorology, because
XCH4 involves contributions of CH4 abundances from all
altitudes along the solar light path.
This study attempts for the first time to separate the factors responsible
(emission, transport, and chemistry) for the distributions of columnar
methane (XCH4) over the Asian monsoon region for different altitude
segments. The XCH4 mixing ratios are used for this study as
observed from GOSAT and simulated by JAMSTEC's ACTM. We aim to understand
relative contributions of surface emissions and transport in the formation of
XCH4 seasonal cycles over different parts of India and the
surrounding oceans. This understanding will help us in developing an inverse
modeling system for estimation of CH4 surface emissions using
XCH4 observations and ACTM forward simulation.
Methods
Satellite data
The Greenhouse gases Observing SATellite (GOSAT) (also referred to as Ibuki)
project is developed jointly by the National Institute for Environmental
Studies (NIES), Ministry of the Environment (MOE), and Japan Aerospace
Exploration Agency (JAXA). It has been providing columnar dry-air mole
fractions of the two important greenhouse gases (XCH4 and
XCO2) at near-global coverage since its launch in January 2009. It
is equipped onboard with the Thermal And Near infrared Sensor for carbon
Observation-Fourier Transform Spectrometer (TANSO-FTS) and the Cloud and
Aerosol Imager (TANSO-CAI) (Kuze et al., 2009). To avoid cloud contamination
in the retrieval process, any scene with more than 1 cloudy pixel within the
TANSO-FTS IFOV is excluded. The atmospheric images from CAI are used to
identify the cloudy pixels. As a result of this strict screening, only
limited numbers of XCH4 data are available during the SW monsoon
over South Asia. This study uses the GOSAT SWIR XCH4 (Version
2.21)-Research Announcement product for the period of 2011–2014. The
ground-based FTS measurements of XCH4 by the Total Carbon Column
Observing Network (TCCON) (Wunch et al., 2011) are used extensively to
validate the GOSAT retrievals. Retrieval bias and precision of column
abundance from GOSAT SWIR observations have been estimated as approximately
15–20 ppb and 1 %, respectively, for the NIES product using
TCCON data (Morino et al., 2011; Yoshida et al., 2013).
Model simulations
Model analysis is comprised of simulations from JAMSTEC's atmospheric
general circulation model (AGCM)-based chemistry-transport model (ACTM; Patra
et al., 2009). The AGCM was developed by the Center for Climate System
Research/National Institute for Environmental Studies/Frontier Research
Center for Global Change (CCSR/NIES/FRCGC). It has been part of transport
model intercomparison experiment TransCom-CH4 (Patra et al., 2011a)
and used in inverse modeling of CH4 emissions from in situ
observations (Patra et al., 2016). The ACTM runs at a horizontal resolution
of T42 spectral truncations (∼ 2.8∘×2.8∘) with
67 sigma-pressure vertical levels. The evolution of CH4 at different
longitude (x), latitude (y), and altitude (z) with time in the Earth's
atmosphere depends on the surface emission, chemical loss, and transport,
which can be mathematically represented by the following continuity equation:
dCH4x,y,z,tdt=SCH4x,y,t-LCH4x,y,z,t-∇ϕx,y,z,t,
where CH4 is the methane burden in the atmosphere, SCH4
is the total emissions/sinks of CH4 at the surface, LCH4
is the total loss of CH4 in the atmosphere due to the chemical
reactions, and ∇ϕ is the transport of CH4 due to the
advection, convection, and diffusion.
The meteorological fields of ACTM are nudged with reanalysis data from the
Japan Meteorological Agency, version JRA-25 (Onogi et al., 2007). The model
uses an optimal OH field (Patra et al., 2014) based on a scaled version of
the seasonally varying OH field (Spivakovsky et al., 2000). The a priori
anthropogenic emissions are from the Emission Database for Global Atmospheric
Research (EDGAR) v4.2 FT2010 (http://edgar.jrc.ec.europa.eu). The model
sensitivity for emission is examined by two cases of emission scenarios based
on different combinations of sectoral emissions. The first one is referred to
as the “AGS”, where all emission sectors in EDGAR42FT are kept at
a constant value for 2000, except for emissions from agriculture soils. The
second one is a controlled emission scenario referred to as “CTL”, which is
based on the ensemble of the anthropogenic emissions from EDGAR32FT (as in
Patra et al., 2011a), wetland and biomass burning emissions from Fung
et al. (1991), and rice paddy emission from Yan
et al. (2009). The emission seasonality differs substantially between the CTL
case and the AGS case due to differences in emissions from wetlands, rice
paddies, and biomass burning; other anthropogenic emissions do not contain
seasonal variations (Patra et al., 2016). Further details about the model and
these emission scenarios can be found in the previous studies (Patra et al.,
2009, 2011a, 2016).
Average seasonal distributions (from 2011 to 2014) of XCH4
obtained from GOSAT observations (a1, a2), ACTM simulations
(b1, b2), and CH4 emission consisting of all the natural and
anthropogenic emissions (c1, c2: ACTM_AGS case) over the Indian
region. Optimized emissions are shown from a global inversion of surface
CH4 concentrations (Patra et al., 2016) and multiplied by a constant
factor of 12 for a clear visualization. The ACTM is first sampled at the
location and time of GOSAT observations and then seasonally averaged. The
white spaces in first two columns (a1, a2, b1, b2) are due to the
missing data caused by satellite retrieval limitations under cloud cover.
XCH4 is calculated from the ACTM profile using the following
equations:
XCH4=∑n=160CH4(n)×Δσp(n),
where CH4(n) is the dry-air mole fraction at model mid-point
level, n= number of vertical sigma pressure layers of ACTM (=1–60 with
σp values of 1.0 and 0.005), and Δσp= thickness
of the sigma pressure level. Note here that we have not incorporated
convolution of model profiles with retrieval a priori and averaging kernels.
Because the averaging kernels are nearly constant in the troposphere (Yoshida
et al., 2011), this approximation does not lead to serious errors in
constructing the model XCH4. For both the CTL and AGS cases, we
adjust a constant offset of 20 ppb to the modeled time series, which
should make the a priori correction have a lesser impact on the model
XCH4. Because the focus of this study is seasonal and spatial
variations in XCH4, a constant offset adjustment should not affect
the main conclusions.
Results and discussion
XCH4 over the Indian region: view from GOSAT and ACTM simulations
This section presents an analysis of XCH4 observed by GOSAT from
January 2011 to December 2014 over the Indian region. We characterize the
four seasons specific to the region as winter (January–March), spring
(April–June), summer (July–September) or the SW monsoon, and autumn
(October–December), as commonly used in meteorological studies (e.g., Rao,
1976). To study the seasonal XCH4 pattern in detail depending on
the distinct spatial pattern of surface emissions and XCH4 mixing
ratios shown in Fig. 1, the Indian landmass was partitioned into eight
sub-regions: Northeast India (NEI), Eastern India (EI), Eastern IGP (EIGP),
Western IGP (WIGP), Central India (CI), Arid India (AI), Western India (WI),
Southern Peninsula (SP), and two surrounding oceanic regions, the Arabian Sea
(AS) and the Bay of Bengal (BOB) (Fig. 2a). Regional divisions are made based
on spatial patterns of emission and XCH4 (Fig. 1a1–c2) and our
knowledge of seasonal meteorological conditions. Since general features of
XCH4 simulated by ACTM using emission scenarios AGS and CTL are
similar to each other, the main discussion is carried out using the AGS
scenario only.
(a) The map of the regional divisions (shaded) for the time
series analysis. (b–k) Time series of XCH4 over the
selected regions (shown on the map) as obtained from GOSAT and simulated by
ACTM for two different emission scenarios, namely, ACTM_AGS and ACTM_CTL.
The gaps are due to the missing observational data.
Figure 1a1–a2 show that the XCH4 mixing ratios are lower in
spring and higher in autumn. A strong latitudinal gradient in XCH4
is observed between the Indo-Gangetic Plain (IGP) and the other parts of
India. XCH4 shows the highest value (∼ 1880 ppb) over
the IGP, eastern, and northeastern Indian regions. As seen from Fig. 1b1–b2,
ACTM simulations are able to reproduce the observed latitudinal and seasonal
gradients in XCH4, i.e., higher values during the southwestern
monsoon and autumn seasons and lower values during the winter and spring
seasons over the IGP region. The optimized total CH4 fluxes (AGS and
CTL) show high emissions over the IGP and northeastern Indian regions
(Fig. 1c1–c2). Most elevated levels of XCH4 are often observed
simultaneously with the higher emissions, suggesting a link between the
enhanced XCH4 and high surface emissions in summer. However, this
link is not valid for all locations. For example, over the western and
southern regions of India, XCH4 is higher in autumn than in spring,
though the emissions are higher in spring.
Figure 2b–k show ACTM–GOSAT comparisons of XCH4 time series from
January 2011 to December 2014 over the selected study regions. The simulated
XCH4 data are sampled at the nearest model grid to the available
GOSAT observations and at the satellite overpass time (∼ 13:00 LT) and
then averaged over each study region. Observations are sparse or not
available during the SW monsoon season in some of the regions due to
limitations of GOSAT retrieval under cloud cover. The model captures the
salient features of the seasonal cycles at very high statistical significance
(correlation coefficients, r>0.8; except for northeastern India; Table 1).
The high ACTM-GOSAT correlations for the low-/no-emission regions suggest
that transport and chemistry are accurately modeled in ACTM. Although we do
not have the statistically significant number of observations for the SW
monsoon period, the observed high GOSAT XCH4 are generally well
simulated by ACTM over most of the study regions. Based on these comparisons,
we can assume that model simulations can be used to understand XCH4
variability over the Indian region. Though we showed only the paired GOSAT
and ACTM data that matched in time and location in Fig. 2b–k, we also
confirmed that the correlation is high (r∼0.9) between the monthly
averaged time series of GOSAT and ACTM averaged for the 4 years (2011–2014)
when ACTM is not co-sampled at the GOSAT sampling points (Fig. S1 in the
Supplement). These high correlations ensure representativeness of the data
shown in Fig. 2b–k. Thus, the seasonal evolution of XCH4 using the
ACTM simulations alone is expected to be fairly valid for different altitude
layers (refer to Patra et al., 2011b, for comparison at the aircraft cruising
altitude). Though the model is only validated for XCH4 in this
study, comparisons with surface and independent aircraft CH4
observations have been shown in Patra et al. (2016).
Correlation coefficients (r) between observed and model simulated
seasonal cycles of XCH4. Model simulations are obtained from ACTM
using two different emission scenarios, AGS and CTL.
Site/tracer
ACTM_AGS
ACTM_CTL
Arid India
0.77
0.88
WIGP region
0.86
0.90
EIGP region
0.69
0.88
Northeast India
0.55
0.55
Western India
0.87
0.95
Central India
0.89
0.97
East India
0.78
0.86
Southern Peninsula
0.92
0.91
Arabian Sea
0.86
0.87
Bay of Bengal
0.84
0.86
Seasonal cycle of XCH4 and possible controlling
factors
As mentioned earlier, the persistent deep convection and mean circulation
during the SW monsoon season significantly enhance CH4 in the upper
troposphere (e.g., Xiong et al., 2009; Baker et al., 2012), coinciding with
the period of high surface CH4 emissions due to rice paddy
cultivation and wetlands over the Indian region (Yan et al., 2009; Hayashida
et al., 2013). Although both these emissions and transport processes
contribute greatly to seasonal changes in XCH4, their relative
contributions have not been studied over the monsoon-dominated Indian region.
The bottom panels show the monthly mean climatology of the total
optimized CH4 emissions (a7, b7, c7), estimated after
performing the global inverse analysis (Patra et al., 2016). The second
bottom panels show XCH4 obtained from the GOSAT observations (black
circles in a6, b6, c6) and ACTM simulations (a6, b6, c6)
over the Eastern IGP (column a), Southern Peninsula
(column b) and Arid India (column c) regions. Monthly
climatology is based on the monthly mean values for the period of 2011–2014
for all the values. The error bars in the GOSAT monthly mean values depict
the 1-σ SDs for the corresponding months (a6, b6, c6). The
1-σ values are not plotted for the model simulations to maintain
figure clarity. Simulations are based on two different emission scenarios,
namely, ACTM_CTL (blue lines) and ACTM_AGS (red lines) based on the
different combinations of emissions. The upper five panels show the monthly
climatology of partial columnar methane (denoted by XpCH4)
calculated at five different partial sigma-pressure layers: 1.0–0.8
(a5, b5, c5), 0.8–0.6 (a4, b4, c4), 0.6–0.4 (a3, b3, c3), 0.4–0.2 (a2, b2, c2), and 0.2–0.0 (a1, b1, c1).
Please note that the y scales in the emission plots over the Southern
Peninsula and Arid India (b7, c7) are different from over the EIGP
region (a7).
To understand the role of transport, the atmospheric column is segregated
into five sigma-pressure (σp) layers, starting from the surface
level (σp=1) to the top of the atmosphere (σp=0),
with an equal layer thickness of σp=0.2. Lower Troposphere (LT),
Mid-Troposphere1 (MT1), Mid-Troposphere2 (MT2), Upper Troposphere (UT), and
Upper Atmosphere (UA) denote the layers corresponding to the sigma-pressure
values of 1.0–0.8, 0.8–0.6, 0.6–0.4, 0.4–0.2, and 0.2–0.0. The partial
columnar CH4 is calculated within different σp layers
(denoted by XpCH4) using the same formula for XCH4, as
in Sect. 2.2. The model results are averaged over each sub-region of our
analysis for the XCH4 seasonal cycle. To understand the role of
surface emission in the XCH4 seasonal cycle, the climatology of the
optimized total CH4 flux for each sub-region is compared. Figure 3
shows the monthly mean climatology (average for 2011–2014) of total
CH4 flux, XCH4, and XpCH4 from the model
averaged over three selected regions, EIGP (a1–a7), SP (b1–b7), and AI
(c1–c7). These representative regions have been selected because they show
distinct XCH4 seasonal cycles and the dominant controlling factors
(such as emission, transport, and chemistry). The observed GOSAT
XCH4 values are also shown for a reference;
however, the model results do not
correspond to the location and time of GOSAT observations (as opposed to
those in Fig. 2). The plots for the remaining seven regions are available in
Figs. S2 and S3.
Over the EIGP region, the magnitude and timing of the seasonal peak in
emission differ substantially between the CTL and AGS emission scenarios
(refer to Fig. 3a7). ACTM simulated XCH4 seasonal peak is in
agreement with the peak in emission in June for the AGS case (Fig. 3a6).
However, simulated XCH4 remains nearly constant until September,
although the emission decreases substantially toward winter. In general, the
emission is relatively higher in the monsoon season (July–August–September)
than in other seasons in both cases. However, in the LT, where we expect most
susceptibility to the surface emission, the partial column CH4
indicates very different seasonality from the emissions; XpCH4
(LT) increases toward winter continuously (Fig. 3a5). The partial CH4
columns for the upper troposphere and middle troposphere (Fig. 3a2–a3) show
similar seasonality to the total XCH4 rather than in the LT.
Therefore, this analysis strongly suggests that the emissions from the
surface and the upper tropospheric partial column both contribute to the
formation of the XCH4 seasonal cycle. These results also suggest
the possibility that GOSAT and ACTM XCH4 data can be used for
correcting a priori emission scenarios by inverse modeling.
In contrast to the XCH4 seasonal cycle over EIGP, a notable
difference is observed in the emission and XCH4 seasonal cycle
over the SP region (Fig. 3b). The XCH4 seasonal cycle and emission
seasonal cycle are found to be out of phase with each other and the
differences in emission scenarios are not reflected in XCH4
seasonal variations. Both emission scenarios show the distinct seasonal
pattern: AGS shows annual high emissions from April to September, while CTL
shows an annual high during August–September (Fig. 3b7). The total emissions
over SP are much lower than that of EIGP (note the different y axis scale
for Fig. 3b7) and hence the difference between the XCH4 simulations
from both emission scenarios is comparatively low. The XCH4 shows
almost identical seasonal cycles for both of the emission scenarios, a peak
in October, and prolonged low values during May–September. The seasonal
XpCH4 cycle in the LT layer shows the seasonal pattern similar to
the total XCH4. Inconsistency between emission seasonality and
XCH4 coupled with low emissions strongly suggests that the
XCH4 can be controlled by transport and/or chemistry but not
emissions. Surface winds during May–September over SP are of marine origin,
which effectively flushes the air with low CH4 (see Fig. S4).
Further, the distinct seasonal cycle of chemical loss is observed over the SP
region compared to other study regions; the loss rate starts increasing from
6 ppbday-1 in January to 12 ppbday-1 in April, and
continues to remain high until September (refer to Fig. S5). These pieces of
evidence clearly suggest that the combined effect of transport and chemistry
causes the low XCH4 values for the May–September period over the
SP region. The peaks in the upper layers in October (Fig. 3b1–b4) and
transport from the polluted continental layer in the LT layer (refer to
Fig. S4) could together contribute to the seasonal XCH4 peak over
SP. Based on these findings, we conclude that the XCH4 measurements
do not impose a strong constraint on surface emissions for inverse modeling
over the SP region, suggesting a need for in situ measurements.
Over the Arid India (AI) region, the XCH4 seasonal cycle is
observed to be different from those of the EIGP and SI regions. The simulated
XCH4 (Fig. 3c6) shows extremely weak sensitivity to the surface
emission differences between the AGS and CTL cases (Fig. 3c7). Additionally,
the XpCH4 in the LT layer (Fig. 3c5) does not resemble the phase
of seasonality in surface emissions and simulated/observed XCH4.
The XpCH4 in the LT layer decreases from January to August and
increases until December. On the other hand, a remarkable peak
(∼ 1896 ppb) is observed in XCH4 during August,
followed by a decline afterward (Fig. 3c6). This is an outstanding example of
deceiving linkage between surface emissions and XCH4 in terms of
seasonal variation. An enhancement in the mixing ratios of XpCH4
is observed from May to August only in the MT2 and UT layers (Fig. 3c2–c3)
and from June to August in the UA layer (Fig. 3c1). This analysis infers that
MT2 and UT partial columns mostly contribute to the formation of the
XCH4 seasonal cycle over the AI region.
Next, we quantify the contributions of different partial layers
(XpCH4) in the formation of XCH4 seasonal amplitude
(Fig. 4). As the phase of the XpCH4 seasonal cycle does not always
match with that of XCH4, we have fixed months of peak and trough in
the XCH4 seasonal cycle for this analysis. First, we calculate the
differences of the XpCH4 values at the time of the peak and the
trough of the XCH4 over each region, and then the differences at
different partial layers are divided by the seasonal amplitude of
XCH4 for calculating the contributions from the respective layers
into the seasonal amplitude of XCH4.
Contributions of partial columns in the seasonal amplitude of
XCH4 over selected regions for the AGS case. Differences in the
XpCH4, calculated at the same time as the maxima and minima of the
seasonal XCH4 cycle, are used to calculate the percentage
contributions of respective partial columns in the seasonal amplitude of
XCH4.
Vertical structure of seasonally averaged CH4 transport rate
due to the convection (a1–a4, in ppbday-1) and
CH4 mixing ratios (b1–b4 from AGS scenarios) averaged over
83–93∘ E for the year 2011. Positive and negative transport rate
values represent the accumulation and dissipation of mass, respectively. The
contour lines in the first (a1–a4) and second (b1–b4)
columns depict the average omega velocity (in hPas-1) and
u wind component, respectively, for the same period. The solid contour
lines show the positive values and the dotted lines show negative values.
Positive and negative values of the omega velocity represent downward and
upward motions, respectively. The zero value of u wind indicates that the
wind is either purely southerly or northerly. White spaces in zonal-mean
plots (a1–b4) show the missing data due to orography. The rightmost
column (c1–c4) depicts the maps of averaged CH4 and wind
vectors (in ms-1; arrow) during all four seasons in 2011 at
200 hPa height.
Figure 4 reveals that ∼ 40 % of the seasonal enhancement in the
observed XCH4 can be attributed to the partial pressure layers
below 600 hPa (LT and MT1) for the EIGP region, which is directly
influenced by the surface emissions. About 40 % in seasonal enhancement
comes from layers above 600 hPa. Over the SP region, about 60 %
of the seasonal XCH4 amplitude is attributed to layers below
600 hPa and the remaining 40 % results from the upper layers.
Although the activities in the lower atmosphere (below 600 hPa)
govern most of the seasonal XCH4 cycle over this region, there is
no clear link with seasonal variations in emissions as this region is under
greater influence of changes in monsoon meteorology. These regions are under
the influence of emission signals from the Indian subcontinent during winter,
while in the summer, clean marine air controls CH4 levels (see also
Patra et al., 2009). In contrast to the two regions mentioned above, over the
AI region, the LT and MT1 layers together contribute only about 12 % to
the formation of the XCH4 seasonal cycle amplitude, and the layers
above 600 hPa contribute the remaining 88 %. These findings lead
us to conclude that instead of surface emissions, the high CH4 in the
upper tropospheric layers contributes significantly to the formation of
seasonal peaks in XCH4.
Source of high CH4 in the upper troposphere
The reason for high mixing ratios in the upper troposphere, as discussed in
the previous section, can be explained by vertical transport of high
CH4 emission signals from the surface, because the vertical transport
timescales in the tropical region are much shorter than the chemical lifetime
of CH4, on the order of 1–2 years (Patra et al., 2009).
Figure 5a1–a4 show the latitude–pressure cross sections of the convective
transport rate (in ppb day-1) and vertical velocity (hPas-1)
averaged over 83–93∘ E for the different seasons of 2011 (the ACTM
AGS case). The positive/negative values of the convective transport rate and
vertical velocity in Fig. 5a1–a4 indicate the gain/loss of mass and
downward/upward motions, respectively. Rapid updrafts of CH4, as
indicated by higher negative vertical velocity, by deep convection during the
monsoon season are aided by the regional topography of the IGP region (north
of 20∘ N and east of 79∘ E in the Indian region). These
updrafts lift CH4-rich air into the upper tropospheric region
(Fig. 5b3). The CH4 concentrations at the surface level decreased
rapidly at an average rate of ∼ 10 ppbday-1 during the SW
monsoon season, and accumulate in the upper troposphere at a similar rate
over the IGP region (Fig. 5a3). During the winter, spring, and autumn seasons
surface CH4 decreased at an average rate of 2, 8, and
7 ppbday-1, respectively. CH4 levels accumulate in the
middle and upper troposphere at an average rate of 6 ppbday-1
during the spring and autumn seasons, while during the winter season no
significant accumulation has been observed at this height over the IGP region
(Fig. 5a1, a2, and a4). Overall these transport processes repeat every year
with a certain degree of interannual variation, as can be seen for the years
from 2011 to 2014. The interannual variations are likely to have been caused
by the early/late onset and retreat of the SW monsoon as well as the
weak/strong monsoon activity over the years.
The horizontal cross sections of CH4 at 200 hPa are shown
with wind vectors in Fig. 5c1–c4 for understanding the spatial extent of
uplifted CH4-rich air over the whole South Asian region. The uplifted
CH4-rich air mass is trapped in the upper troposphere
(∼ 200 hPa) when encountered by the anticyclonic winds during
the SW monsoon season. This leads to a widespread CH4 enhancement
covering a large part of South Asia, and the CH4-rich air leaked
predominantly along the southern side of the sub-tropical westerly jet over
to East Asia (Fig. 5c3; see also Umezawa et al., 2012). As a result of this,
the high CH4 air masses at the upper troposphere are not limited to
the regions of intense surface emissions as discussed earlier. After the SW
monsoon season, the strong westerly jet breaks the upper tropospheric
anticyclone and the CH4-rich air mass shifts over southern India
during the autumn season (Fig. 5c4). In this way, the convective updraft of
high-CH4 air mass, followed by horizontal spreading of the air mass
over the larger area by anticyclonic circulation, controls the redistribution
of CH4 in the upper troposphere over the northern part of India
during the SW monsoon season, and over the Southern Peninsula during the
early autumn season.
Conclusions
The seasonal variations in dry-air mole fractions of methane
(XCH4) measured by the Greenhouse gases Observation SATellite
(GOSAT) are analyzed over India and the surrounding seas using
JAMSTEC's atmospheric
chemistry-transport model (ACTM). The region of interest (the Indian
landmass) is divided into eight sub-regions, namely, Northeast India (NEI),
Eastern India (EI), Eastern IGP (EIGP), Western IGP (WIGP), Central India
(CI), Arid India (AI), Western India (WI), Southern Peninsula (SP), and two
surrounding oceanic regions, the Arabian Sea (AS) and the Bay of Bengal
(BOB). The ACTM simulations are conducted using a couple of surface fluxes
optimized by the inverse analysis as described in Patra et al. (2016). We
have shown that the distinct spatial and temporal variations of
XCH4 observed by GOSAT are governed not only by the heterogeneity
in surface emissions, but also by complex atmospheric transport mechanisms
caused by the seasonally varying Asian monsoon. The seasonal XCH4
patterns often show a fair correlation between emissions and XCH4
over the regions residing in the northern half of India (north of
15∘ N: NEI, EI, EIGP, WIGP, CI, WI, AI), which would imply
XCH4 levels are closely associated with the distribution of
emissions on the Earth's surface. However, detailed analysis of transport and
emission using ACTM over these regions (except for AI) reveals that about
40 % of seasonal enhancement in the observed XCH4 can be
attributed to the lower tropospheric layer (below 600 hPa). The lower
tropospheric layers are affected either by the surface emissions, e.g., in
the northern India regions or seasonal changes in horizontal winds due to
monsoon for the SP region. Up to 40 % of the seasonal CH4
enhancement is found to come from the uplifted air mass into the
600–200 hPa height layer over northern regions in India. In
contrast, over the semi-arid AI region, as much as ∼ 88 % of
contributions to the XCH4 seasonal cycle amplitude came from the
height above 600 hPa, and only ∼ 12 % are contributed by
the atmosphere below 600 hPa. The primary cause of the higher
contributions from above 600 hPa over the northern Indian region is
the characteristic of air mass transport mechanisms in the Asian monsoon
region. The persistent deep convection during the southwestern monsoon season
(June–August) causes strong updrafts of CH4-rich air mass from the
surface to upper tropospheric heights (∼ 200 hPa), which is
then confined by anticyclonic winds at this height. The anticyclonic
confinement of surface emission over a wider South Asia region leads to
strong contribution of the upper troposphere in formation of the
XCH4 peak over most regions in northern India, including the
semi-arid regions with extremely low CH4 emissions. In contrast to
these regions, over the SP region, the major contributions (about 60 %)
to XCH4 seasonal amplitude come from the lower atmosphere
(∼ 1000–600 hPa). Both transport and chemistry dominate in the
lower troposphere over the SP region and thus the formation of the
XCH4 seasonal cycle is not consistent with the seasonal cycle of
local emissions. As the upper level anticyclone does not cover the southern
Indian region during the active phase of southwestern monsoon, no enhancement
in XCH4 is observed over the Southern Peninsula region.
This study shows that ACTM simulations are capturing the GOSAT observed
seasonal and spatial XCH4 variability well, and results provide
an improved understanding of emissions, chemistry, and transport of
CH4 over one of the strongest global monsoonal regions.