We use 2010–2015 satellite observations of atmospheric methane to improve estimates of methane emissions and their trends, as well as the concentration and trend of tropospheric OH (hydroxyl radical, methane's main sink). We find overestimates of Chinese coal and Middle East oil/gas emissions in the prior estimate. The 2010–2015 growth in methane is attributed to an increase in emissions from India, China, and areas with large tropical wetlands. The contribution from OH is small in comparison.
We use 2010–2015 satellite observations of atmospheric methane to improve estimates of methane...
Global distribution of methane emissions, emission trends, and OH concentrations and trends inferred from an inversion of GOSAT satellite data for 2010–2015
Global distribution of methane emissions, emission trends, and OH concentrations and trends inferred from an inversion of GOSAT satellite data for 2010–2015
Global distribution of methane emissions, emission trends, and OH concentrations and trends inferred from an inversion of GOSAT satellite data for 2010–2015Global distribution of methane emissions, emission trends, and OH concentrations and trends...Joannes D. Maasakkers et al.
Joannes D. Maasakkers1,a,Daniel J. Jacob1,Melissa P. Sulprizio1,Tia R. Scarpelli1,Hannah Nesser1,Jian-Xiong Sheng1,Yuzhong Zhang1,2,Monica Hersher1,A. Anthony Bloom3,Kevin W. Bowman3,4,John R. Worden3,Greet Janssens-Maenhout5,and Robert J. Parker6,7,8Joannes D. Maasakkers et al.Joannes D. Maasakkers1,a,Daniel J. Jacob1,Melissa P. Sulprizio1,Tia R. Scarpelli1,Hannah Nesser1,Jian-Xiong Sheng1,Yuzhong Zhang1,2,Monica Hersher1,A. Anthony Bloom3,Kevin W. Bowman3,4,John R. Worden3,Greet Janssens-Maenhout5,and Robert J. Parker6,7,8
Correspondence: Joannes D. Maasakkers (maasakkers@fas.harvard.edu)
Received: 31 Dec 2018 – Discussion started: 18 Jan 2019 – Revised: 01 May 2019 – Accepted: 09 May 2019 – Published: 12 Jun 2019
Abstract
We use 2010–2015 observations of atmospheric methane columns from
the GOSAT satellite instrument in a global inverse analysis to improve
estimates of methane emissions and their trends over the period, as well as
the global concentration of tropospheric OH (the hydroxyl radical, methane's
main sink) and its trend. Our inversion solves the Bayesian optimization
problem analytically including closed-form characterization of errors. This
allows us to (1) quantify the information content from the inversion towards
optimizing methane emissions and its trends, (2) diagnose error correlations
between constraints on emissions and OH concentrations, and (3) generate a
large ensemble of solutions testing different assumptions in the inversion.
We show how the analytical approach can be used, even when prior error
standard deviation distributions are lognormal. Inversion results show large
overestimates of Chinese coal emissions and Middle East oil and gas emissions in
the EDGAR v4.3.2 inventory but little error in the United States where we use a new
gridded version of the EPA national greenhouse gas inventory as prior
estimate. Oil and gas emissions in the EDGAR v4.3.2 inventory show large
differences with national totals reported to the United Nations Framework
Convention on Climate Change (UNFCCC), and our inversion is generally more
consistent with the UNFCCC data. The observed 2010–2015 growth in
atmospheric methane is attributed mostly to an increase in emissions from
India, China, and areas with large tropical wetlands. The contribution from
OH trends is small in comparison. We find that the inversion provides strong
independent constraints on global methane emissions (546 Tg a−1) and
global mean OH concentrations (atmospheric methane lifetime against oxidation
by tropospheric OH of 10.8±0.4 years), indicating that satellite
observations of atmospheric methane could provide a proxy for OH
concentrations in the future.
Methane is an important greenhouse gas with a particularly strong decadal
climate impact (Stocker et al., 2013). The atmospheric methane
concentration has increased by a factor of 2.5 since pre-industrial times
(Hartmann et al., 2013). This increase is not well understood but is
most likely to be mainly driven by anthropogenic activities including the
oil and gas industry, coal mining, livestock, landfills, wastewater treatment,
biomass burning, and rice cultivation
(Dlugokencky et al., 2011; Kirschke et al., 2013;
Saunois et al., 2016). Wetlands are the main natural source and
could be affected by climate change (Kirschke et al., 2013). Atmospheric
methane has a lifetime of 9.1±0.9 years (Prather et al., 2012), with a
dominant sink from oxidation by the hydroxyl radical (OH) that is also
subject to interannual variability and trends (Holmes et al., 2013). The
methane burden rose by ∼ 12 ppb a−1 in the late 1980s and by
∼ 6 ppb a−1 in the 1990s, plateaued in the early 2000s
(∼ 0.5 ppb a−1), and has resumed increasing at
∼ 7 ppb a−1 since 2007
(https://www.esrl.noaa.gov/gmd/ccgg/trends_ch4/, last access: 27 April 2019), for reasons that remain unclear
(Turner et al., 2017). Inverse analyses can help interpret these trends
by combining atmospheric methane observations with a chemical transport model
(CTM) to infer the distribution of methane emissions most likely to explain
the observations
(Houweling et al., 2017; Saunois et al., 2016; Jacob et al., 2016). Here we use
global 2010–2015 methane observations from the GOSAT satellite in an
analytical inverse analysis with closed-form error characterization to better
quantify methane sources and interpret the recent trend, including changes in
both methane emissions and OH concentrations.
A number of explanations have been proposed for the renewed growth of
atmospheric methane concentrations since 2007. A parallel increase in ethane
has been proposed as evidence for an increase in oil and gas emissions
(Hausmann et al., 2016; Franco et al., 2016). A trend towards isotopically lighter
methane has been attributed to an increase in microbial sources such as
livestock and wetlands
(Schaefer et al., 2016; Schwietzke et al., 2016; Nisbet et al., 2016; McNorton et al., 2016; Thompson et al., 2018).
Worden et al. (2017) suggest that a decrease in open fire emissions may
mask the isotopic signature of increasing fossil fuel emissions. Observations
of methyl chloroform, a proxy for global OH concentrations, suggest that a
decrease in the methane sink may be implicated in the renewed growth
(Turner et al., 2017; Rigby et al., 2017; McNorton et al., 2018).
Turner et al. (2017) find from a global two-box model analysis that the
surface record of methane observations is too sparse to arbitrate between
methane emissions and OH concentrations as drivers for the methane increase,
though Naus et al. (2019) pointed out that there are inherent biases in the
two-box modeling approach.
GOSAT was launched in 2009 and measures atmospheric methane columns with high
precision (0.7 %) by solar backscatter in the shortwave infrared (SWIR)
(Butz et al., 2011; Buchwitz et al., 2015; Kuze et al., 2016). A number of
inverse analyses have used the GOSAT data to improve estimates of methane
emissions (Monteil et al., 2013; Cressot et al., 2014; Alexe et al., 2015; Turner et al., 2015; Pandey et al., 2016, 2017; Miller et al., 2019).
Here we use the GOSAT data to optimize
not only global emissions but also their 2010–2015 trends together with OH
concentrations and their trends. The independent optimization of OH and
emissions in the inversion is based on the different signatures of those two
terms on the methane concentration field (Zhang et al., 2018). We use an
analytical inverse method with closed-form error characterization of the
solution, rather than the adjoint approaches used in previous inverse studies
that do not provide rigorous characterization of errors. This allows us in
particular to diagnose the error correlation between the independent
constraints on methane emissions and OH concentrations and their trends. It
also allows us to readily conduct inversions for an ensemble of cases once
the Jacobian matrix for the problem has been constructed.
2 Data and methods
We use the GEOS-Chem CTM
(http://acmg.seas.harvard.edu/geos/, last access: 27 April 2019) as forward model to simulate the distribution of atmospheric
methane and its response to trends. Model results are fit statistically to
the GOSAT data by Bayesian optimization, including regularization from prior
knowledge of methane emissions and OH concentrations. The
January 2010–December 2015 GOSAT methane column data are arranged in an
observation vector y, and the
inversion optimizes a state vector x including global methane
emissions on the GEOS-Chem grid, 2010–2015
linear trends of emissions on that same grid, and global mean OH
concentrations for individual years (we will also present results from an
inversion optimizing a linear OH trend over the 2010–2015 period). The
optimal solution is obtained by minimizing a Bayesian
cost function that balances the information from the observations (weighed by
the observational error covariance matrix SO) and the
prior knowledge xa (weighed by the prior error covariance
matrix Sa) (Rodgers, 2000). Below we describe
the different elements and steps in the inversion.
2.1 GOSAT observations
The TANSO-FTS instrument on board the Greenhouse Gases Observing Satellite
(GOSAT) observes column-averaged dry-air methane mixing ratios by solar
backscatter in the SWIR with near-unit sensitivity down to the surface
(Butz et al., 2011). The satellite is in polar sun-synchronous orbit.
Observations are made at around 13:00 local time for circular pixels of
10 km diameter. In the default observation mode, the pixels are separated by
∼ 250 km along track and cross track, with repeated observation of the
same pixels every 3 days. Denser observations are also made in target mode
over features of interest. GOSAT spectra have shown no significant drift or
degradation of data quality since the beginning of the record
(Kuze et al., 2016). We use the University of Leicester version 7
CO2 proxy retrieval over land
(Parker et al., 2011, 2015) from January 2010 to
December 2015 in order to have even observations of all seasons. The
single-observation precision is 13 ppb, and the relative (regional) bias is
2 ppb compared to ground-based column-averaged dry-air mole factions from
the Total Carbon Column Observing Network (TCCON;
Buchwitz et al., 2015). Other retrievals of GOSAT data are consistent
with the University of Leicester product (Buchwitz et al., 2015).
Figure 1 illustrates the GOSAT data ingested in our inversion,
representing a total of 1 211 468 retrievals. Glint data over the oceans
and data poleward of 60∘ are not included because of seasonal
sampling biases (Turner et al., 2015).
Figure 12010–2015 average of the GOSAT methane dry column mixing ratios
used in our inversion. Data are from the University of Leicester version 7
CO2 proxy retrieval (Parker et al., 2015), excluding glint
observations over the oceans and observations poleward of 60∘. GOSAT
pixels are of 10 km circular diameter and are inflated here to 0.5∘ for
visibility. The red stripes are an averaging artifact as these retrievals are
from towards the end of the 2010–2015 time period when methane was higher.
The inversion requires prior estimates and error statistics for all
components of the state vector including methane emissions on the GEOS-Chem grid (1009 ice-free land-containing grid cells
with prior emissions larger than Mg km−2 a−1,
covering 99 % of global emissions), 2010–2015 linear emission trends on
the same grid, and global mean OH concentrations for individual years
2009–2015 (2009 is only used for initialization), for a total of 2025 state
vector elements.
Figure 2Prior estimates of methane emissions from wetlands, livestock, coal
mining, oil and gas, wastewater and landfills, and other sources. Values are
2010–2015 averages and are shown on the
GEOS-Chem grid used for the inversion. Global totals for each source type are
given in Table 1.
Table 1 gives our global prior inventory with the contributions from
different source types, and Fig. 2 shows the spatial
distributions. Monthly wetland emissions for individual years are from the
WetCHARTS v1.0 extended ensemble mean (Bloom et al., 2017). For
anthropogenic emissions we use the EDGAR v4.3.2 global emission inventory for
2012 (https://edgar.jrc.ec.europa.eu/, last access: 1 December 2017; Janssens-Maenhout et al., 2019) as worldwide default, including
additional information from EDGAR to subset the “fuel exploitation”
emissions category into oil and gas and coal mining. Over the continental United States, we
replace EDGAR v4.3.2 with a gridded version of the US EPA greenhouse gas
inventory (Maasakkers et al., 2016). In Canada and Mexico, we use the
oil and gas emissions from Sheng et al. (2017). Anthropogenic emissions are
assumed as aseasonal for lack of better prior information except for manure
management and rice cultivation. Seasonal scaling of manure management
emissions is done using the temperature dependence of
Maasakkers et al. (2016). Seasonal scaling of rice cultivation emissions
is based on Zhang et al. (2016). Daily global open fire emissions are
from QFED (Darmenov and da Silva, 2013). Termite emissions are from
Fung et al. (1991). Emissions from geological macroseeps (oil and gas seeps
and mud volcanoes) are based on Etiope (2015) and
Kvenvolden and Rogers (2005). For areal seepage, we use the sedimentary basins
(microseepage) and potential geothermal seepage maps from
Kvenvolden and Rogers (2005) with the emission factor previously used by
Lyon et al. (2015). Over the United States, we use the sedimentary basin map
from the Energy Information Administration (EIA, 2016) and basin-specific
emission factors from Etiope and Klusman (2010). While global geological
emissions have previously been estimated to be over 50 Tg a−1(Kirschke et al., 2013), Petrenko et al. (2017) showed that based on
ice core measurements they should be no higher than 15 Tg a−1.
Table 1Prior global estimates of methane sources and sinks (mean 2010–2015
values).
* Including fossil fuel combustion, industrial processes, and agricultural field burning.
Construction of the prior error covariance matrix Sa
requires estimates of error variances for the prior emissions on the
grid. For wetland emissions, we use the
standard deviation of annual emissions from
the WetCHARTs ensemble members (Bloom et al., 2017). The error variance
averages 58 % on the grid level. For US anthropogenic emissions and
oil and gas emissions in Canada and Mexico, we use the scale-dependent error
variances from Maasakkers et al. (2016). For lack of better information,
we assume 50 % error standard deviation for EDGAR v4.3.2 emissions
(Turner et al., 2015) and 100 % for non-wetland natural emissions.
The diagonal terms of Sa are then constructed by adding
the error variances of individual source types for grid cells in quadrature, capping total errors at 50 %. We
assume no error spatial covariance on the grid
so that Sa is diagonal. This is a reasonable assumption
for anthropogenic emissions (Maasakkers et al., 2016), though errors on
wetland emissions may still be correlated on that scale
(Bloom et al., 2017).
Our state vector in the inversion includes linear emission trends for
grid cells over the 2010–2015 period,
superimposed on interannual variability in the case of wetlands and fires.
Our global prior estimate of mean methane emissions for the 2010–2015 period
exceeds the sinks by 13 Tg a−1 (Table 1), which drives a
5 ppb a−1 increase in methane concentrations over that period, even in
the absence of an emission trend. Therefore our prior estimate of linear
emission trends for individual grid cells is
zero, with an absolute error standard deviation of 10 % of the local
prior emissions over the 2010–2015 time period (1.7 % a−1). This
error standard deviation is based on trend estimates for North America
inferred from GOSAT data (Turner et al., 2016; Sheng et al., 2018a).
The prior estimate of the global tropospheric OH concentration is based on a
GEOS-Chem full-chemistry simulation (Wecht et al., 2014) that yields a
methane lifetime of 10.6 years, consistent with
the best estimate inferred from the methyl chloroform proxy (Prather et al., 2012)
and the 9.7±1.5 years estimate from the ACCMIP model ensemble
(Naik et al., 2013). Here and elsewhere, is defined
as the ratio between the total mass of atmospheric methane (including the
stratosphere) and the annual loss rate from oxidation by OH below the
tropopause. The uncertainty in the methane lifetime is about 10 %
(Prather et al., 2012), but the uncertainty on OH interannual variability is less,
about 3 % (Holmes et al., 2013). We assume a 3 % error standard
deviation in the global annual mean OH concentration for our standard
inversion but also conduct a sensitivity study with 10 % error standard
deviation. We further conduct an inversion taking the OH trend over the
2010–2015 period as linear and assuming in that case error standard
deviations of 10 % for the mean global OH concentration and
5 % a−1 (absolute) for the linear trend. Scaling of global OH
concentrations in the inversion is done without modifying the spatial or
seasonal OH distribution. Zhang et al. (2018) found that inversions of
atmospheric methane data using the 3-D GEOS-Chem OH fields give consistent
results with inversions using other global OH distributions from the ACCMIP
model ensemble (Naik et al., 2013).
2.3 Forward model
We use the GEOS-Chem CTM v11-01 at grid
resolution (Wecht et al., 2014; Turner et al., 2015) as forward model for
the inversion. The model is driven with 2009–2015 MERRA-2 meteorological
fields (Bosilovich et al., 2016) from the NASA Global Modeling and Assimilation Office
(GMAO). Atmospheric methane concentrations are initialized on January 2009
using the previous GOSAT inversion results of Turner et al. (2015),
shown in that work to be unbiased compared to surface and aircraft background
data including for the tropospheric meridional gradient.
The loss from oxidation by tropospheric OH is computed with archived 3-D
monthly fields of OH concentrations from a GEOS-Chem full-chemistry
simulation as described by Wecht et al. (2014). Local tropopause
information is from the MERRA-2 data. The global loss rate for individual
years is optimized in the inversion by uniform scaling of the OH
concentrations. Other minor loss terms include stratospheric oxidation
computed with archived monthly loss frequencies from the NASA Global Modeling
Initiative model (Murray et al., 2012), tropospheric oxidation by Cl
atoms computed using archived Cl concentration fields from
Sherwen et al. (2016) and the reaction rate constant from
Allan et al. (2007), and soil uptake as described by
Fung et al. (1991) with temperature-based seasonality based on
Ridgwell et al. (1999). The loss from oxidation by Cl totals
9 Tg a−1, intermediate between the 12–13 Tg a−1 estimated by
Hossaini et al. (2016) using the TOMCAT chemical transport model and
5.3 Tg a−1 estimated by Wang et al. (2019) in a GEOS-Chem
simulation with full accounting of tropospheric chlorine. These minor sinks
are not optimized in the inversion.
The GEOS-Chem simulation of GOSAT methane columns features a
latitude-dependent background bias that needs to be corrected
(Turner et al., 2015). This bias likely reflects a model overestimate
of methane in the extratropical stratosphere (Saad et al., 2016), which
is common across global models due to excessive meridional transport in the
stratosphere (Patra et al., 2011) and was first seen in a SCIAMACHY
inversion using the TM5 chemical transport model (Bergamaschi et al., 2007).
Stanevich (2018) found a significant difference in methane columns simulated
by GEOS-Chem at compared to resolution, but we find that this difference is mainly in the
stratosphere (Appendix A). We remove the background bias by applying the
latitudinal correction based on background grid cells from
Turner et al. (2015), recomputed with the University of Leicester v7
GOSAT proxy retrieval (Parker et al., 2015) and the MERRA-2
meteorological fields. The mean model–GOSAT difference in column mean
mixing ratio for background grid cells is
fitted to a second-order polynomial of latitude:
where θ is the latitude in degrees, and ξ is the model correction
in ppb. This correction is similar to Turner et al. (2015), who used
. A seasonal
bias remains after application of this correction, and we fix it by removing
the zonal monthly mean concentration differences averaged over rolling
12∘ latitudinal bands. This seasonal bias may be due to errors in the
seasonality of emissions or atmospheric transport
(Saad et al., 2016; Bader et al., 2017; Stanevich, 2018). We find that the seasonal
correction does not affect the inversion results significantly, as shown in
Appendix B, where we optimize emissions for individual seasons separately
without applying a seasonal correction.
2.4 Observational error covariance matrix
The observational error covariance matrix SO includes
contributions from random instrument and forward model errors. We construct
it applying the residual error method of Heald et al. (2004) using the
2010–2015 time series of local methane column differences for individual grid cells between the GEOS-Chem model with prior
estimates (emissions and OH concentrations) and the GOSAT observations after
background bias correction. The mean difference is to be corrected in the inversion, while the residual error is taken as the observational error. Statistics of
Δ′ define the observational error variance (diagonal of the
observational error covariance matrix). The same method was previously used
in the satellite-based methane inversions by Wecht et al. (2014) and
Turner et al. (2015). The resulting observational error standard
deviation averages 13 ppb. The mean instrument error standard deviation is
11 ppb (Parker et al., 2015), implying that most of the observational
error is generally from the instrument rather than from the forward model.
This would indeed be expected for the random error of individual
measurements. For a given measurement, if the local error standard deviation
computed by the residual error method is smaller than the reported
measurement precision, then we use the latter instead; this is the case for
10 % of retrievals. All observational error standard deviations are set
to be at least 10 ppb (this threshold affects 8 % of retrievals).
SO is taken to be diagonal for lack of better information,
but the general effect of error correlation in the observations is accounted
for in the inversion by a regularization factor (Sect. 2.5).
2.5 Inversion procedure
We perform inversions with two different specifications of prior error
variance statistics: normal and lognormal. Assumption of normally
distributed errors enables a linear optimization problem with an analytical
solution including closed-form error characterization
(Rodgers, 2000). Assumption of lognormal errors may be more
appropriate for modeling the high tail of the probability density function
(Zavala-Araiza et al., 2015) and also has the advantage of enforcing positive
solutions (Miller et al., 2014), but the optimization problem is then nonlinear.
By comparing the two approaches we can evaluate consistency in results.
Both inversions minimize the Bayesian cost function J(x)(Rodgers, 2000):
where x is the state vector, xa is the prior
estimate, Sa is the prior error covariance matrix,
F(x) is the simulation of observations y by the
GEOS-Chem model, SO is the observational error covariance
matrix, and γ is a regularization factor (Brasseur and Jacob, 2017). The
variances in SO are underestimated because of correlation
in the observational error that is missing in the diagonal formulation of
SO and is difficult to quantify. We use γ to scale
the original diagonal SO to get an optimal covariance
matrix to be used in the inversion. Zhang et al. (2018) showed in an observing
system simulation experiment (OSSE) for inversion of methane satellite data
that a regularization factor γ=0.05 adjusts the variances optimally
and prevents overfitting. This was done by calculating the likelihood at
for a range of values of γ. Diagnosis of overfit
and optimization of γ is readily done in an OSSE such as in
Zhang et al. (2018) where the “true” solution is known. Here we find that using
γ=1 (as in the pure Bayesian statement of the optimization problem)
produces checkerboard patterns in the solution that are likely spurious. We
choose γ=0.05 consistent with Zhang et al. (2018) for our base inversion
as providing the best balance between prior and observational terms in the
posterior value of the cost function. We examine the sensitivity to the
choice of γ by conducting a sensitivity inversion with γ=0.1.
Further balancing of the cost function is needed because the global OH
concentration and its interannual variability are represented by only seven state
vector elements, while the emissions on the
grid are represented by 1009 elements. To provide equal weight to OH and
emissions for explaining global methane trends, we increase the weight of the
OH terms in the cost function (through the OH components of
Sa) by the ratio of the number of state vector elements
1009∕7 so that from a cost-function perspective, a change in OH and global
methane emissions is equally expensive. The sensitivity inversion assuming
10 % prior error standard deviation on OH instead of 3 % is
equivalent to decreasing this weighting by a factor of 11.
The GEOS-Chem forward model y=F(x) relating methane
column concentrations y to the state vector x is essentially
linear. There is a small nonlinearity from the optimization of OH
concentrations because changes in the methane concentrations affect the loss
rate (Houweling et al., 2017), which we neglect because changes in methane
concentrations are small, and methane is well mixed globally. We therefore
express the forward model as , where is the Jacobian matrix
of the model, and c is an initialization constant (January 2009
concentrations taken from Turner et al., 2015). Replacing
F(x)=Kx in Eq. (2) and subtracting
the initialization constant c from the observations, the minimization
problem has an analytical
solution for the optimal posterior solution (Rodgers, 2000):
The posterior error covariance matrix describing the
error statistics of is given by
and the averaging kernel matrix () defining the sensitivity of the solution to the true state
is given by
The trace of the averaging kernel matrix defines the degrees of freedom for
signal (DOFS) of the inversion, that is the number of pieces of information
on the state vector that can be gained from the observing system.
The analytical solution as described by Eqs. (3)–(5)
requires the explicit construction of the Jacobian matrix K
characterizing the GEOS-Chem model. We do this column by column, with
GEOS-Chem simulations perturbing each element of the state
vector independently. This is readily achievable, even for 2025 state vector elements as a
massively parallel computation. Sparse matrix algebra is used where possible
in solving Eqs. (3)–(5), taking advantage of the diagonal
structure of the error covariance matrices.
The analytical solution to the Bayesian optimization problem requires
assumption of Gaussian errors, but this allows for the possibility of
negative values of state vector elements. Negative emissions could
conceivably be attributed to locally strong soil uptake or oxidation by Cl
atoms but may also be unphysical (Miller et al., 2014). We can enforce positivity
in the Bayesian solution by optimizing for ln (x) instead of
x, with normal Gaussian errors specified for ln (x)
(corresponding to lognormal errors for x). The model is then
nonlinear, so that the solution and the corresponding error statistics must
be found iteratively with an updated Jacobian matrix at each iteration N. This
recomputation is immediate using the previously derived Jacobian matrix
K for the linear problem, since the individual scalar elements
of K′ are related to those of
K by . Thus only a simple scaling of the linear Jacobian matrix is
required at each iteration. This conversion to log space is done only for the
emissions component of x. Emission trends and global OH
concentrations are still optimized with normal error distributions, and no
scaling is applied to those rows of the Jacobian.
Optimizing emissions in log space means that the best posterior estimate is
for the median of emissions instead of the mean. The mean and the median of
the lognormal distribution are not equal, so results cannot be summed
over grid squares to provide a best estimate of the mean. For this reason,
analysis of aggregate and global emissions and sinks will be done with the
inversion using normal errors.
The iterative solution for the inverse problem with lognormal errors is
obtained with the Levenberg–Marquardt method (Rodgers, 2000) for
each iteration N:
where , the initial guess is the prior
estimate, and κ is a coefficient for the iterative approach to the
solution that is set to 100 to start and is gradually decreased as the
solution is approached. The prior error covariance matrix
(diagonal elements ) defining error variances
for ln xa is derived from the previously described prior
error covariance matrix Sa (diagonal elements sA) by
scaling the error variances for the individual elements:
2.6 Error correlations between global estimates of sources and sinks
Inversion results for the spatial distributions of emissions and trends on
the grid are mainly informed by local and regional
patterns of methane concentration. However, implied inversion results for the
global methane emission and its trend may be significantly correlated with
those for the global tropospheric OH concentration and its trend. Some
separation is expected because sources of methane have a different
spatial and seasonal imprint on the global methane distribution than the OH sink
(Zhang et al., 2018), but it is important to quantify the error correlation, i.e.,
the extent to which adjustments to the global methane emission and its trend
may be aliased by adjustments to the global OH concentration and its trend.
To do this we reduce the dimensionality of the inverse analysis by collapsing
global emissions and trends into one state vector element each. Following
Calisesi et al. (2005), if the state vector can be transformed using a summation
matrix W as
then the averaging kernel matrix of the reduced system
(Ared) is given by
where is the
generalized pseudo-inverse of W. Our original state vector
x in this case includes mean 2010–2015 emissions and their linear
trends on the grid and the global mean
tropospheric OH concentration for 2010–2015 and its linear trend. Again, the
minor sinks in Table 1 are not optimized and are maintained instead at their
prior values. We apply the summation matrix W to the emission
terms and thus reduce the state vector to four elements defining the global
methane budget (global mean emission, global mean OH concentration, global
emission trend, global OH trend). The off-diagonal terms of the reduced
averaging kernel matrix Ared then measure the extent to
which differences relative to the true state are aliased between sources and
sinks in the optimization of this global budget. The advantage of this
summation approach, as compared to a global inversion including just four
elements, is that the distribution of methane emissions and its trends is
still optimized.
Figure 3Comparisons of observed methane concentrations to the GEOS-Chem
forward model using either prior or posterior (optimized) estimates of
2010–2015 emissions and OH concentrations. Panels (a) and (b) show differences
between the model and GOSAT observations for 2010–2015 means on the grid. Panels (c) and (d) show the monthly time series of
the differences averaged over latitude bands. Panels (e–g) show
independent 2010–2015 comparisons to global observations from NOAA surface
stations, HIPPO aircraft meridional cross sections over the Pacific (2010 and
2011, with the model sampled along the flight tracks), and TCCON.
Reduced major axis (RMA) regressions are as shown along with the 1 : 1 line
(in grey). HIPPO observations are averaged over GEOS-Chem grid cells. The
NOAA surface stations and HIPPO aircraft measure local methane dry-air mole
fractions, while TCCON measures column-averaged dry-air mole
factions. We apply the same latitudinal and seasonal corrections to TCCON
that we applied to GOSAT.
We conduct an ensemble of inversions to characterize the sensitivity of the
solution to different assumptions made in the formulation of the inverse
problem. Our base inversion optimizes annual mean emissions with normal error
distributions and seasonal background correction to the GOSAT–model
difference as discussed above. To test whether choices in the regularization
and cost-function construction affect our conclusions, we also conduct
inversions with (1) lognormal error distributions for emissions, (2) a
regularization factor γ of 0.1 instead of 0.05, (3) no seasonal
background correction to the model–GOSAT difference, (4) a 10 % error
standard deviation on the global OH concentration instead of 3 %,
(5) optimization of a linear trend in global OH concentration instead of
year-to-year variability, assuming 10 % error standard deviation for mean
OH and 5 % for the 2010–2015 trend, (6) no interannual variability in
prior emission estimates, and (7, 8) seasonally resolved emission
optimization including seasonal correction and not including seasonal correction (see Appendix B). All
inversions produce consistent results, and we will focus our main presentation
on the base inversion, bringing in the sensitivity inversions to illustrate
the spread of results and to address specific issues.
Before presenting results from the inversion, we compare the posterior
solution to observations to show that the inversion accomplishes its task of
providing an improved forward model fit to observations.
Figure 3a–d show the improvement in
the GEOS-Chem comparison to the GOSAT data when using posterior vs. prior
emissions, emission trends, and OH concentrations. As expected for a
successful inversion, the posterior values provide a better fit to the
observations. The inversion corrects prior underestimates over tropical
regions and an overestimate over China. It also fits the observed 2010–2015
trend in methane concentrations and its latitudinal distribution, while the
prior model underestimated the growth rate, especially in 2014–2015. It does
not fully correct the prior bias in the Arctic because GOSAT observations
north of 60∘ N are not used in the inversion.
Figure 3 also shows independent evaluation of the
inversion results with background observations from the NOAA cooperative
flask sampling network
(https://esrl.noaa.gov/gmd/ccgg/flask.php, last access: 16 February 2018), the HIPPO aircraft campaigns across the Pacific and
Atlantic (legs III–V; https://hippo.ornl.gov/, last access: 27 April 2019; Wofsy, 2011), and the Total Carbon
Column Observing Network (TCCON;
https://tccondata.org/, last access: 27 April 2019;
Wunch et al., 2011). These observations are mainly of the
seasonal/latitudinal methane background and are not used in the inversion.
The background is already well simulated in the prior estimate, and the
posterior simulation does not degrade this agreement.
Figure 4Optimization of the global distribution of mean 2010–2015 methane
emissions using GOSAT observations. Prior emissions are in (a) (see
breakdown in Fig. 2). Panel (b) shows averaging kernel
sensitivities for the base inversion (diagonal elements of the averaging
kernel matrix), with the degrees of freedom for signal (DOFS; trace of the
averaging kernel matrix) in the legend. Panels (c, d) show the posterior
emissions from the base inversion and the associated ratios between posterior
and prior emissions. Grey grid cells (for example in North Africa and
Australia) indicate small negative posterior emissions. Panels (e, f) show
the same but for the inversion assuming lognormal prior errors, which does
not allow for negative posterior emissions.
3.1 Spatial distribution and source attribution of methane emissions
Figure 4 shows the global distribution of mean 2010–2015
posterior emissions from the base inversion and from the sensitivity
inversion assuming lognormal errors in the prior emission estimates.
Correction patterns are very similar between the two inversions. Small
negative emissions are found in the base inversion for 6 of the 1009
optimized grid cells. The inversion assuming lognormal errors does not allow
these negative emissions. Downward corrections tend to be smaller in the
inversion assuming lognormal errors, while positive corrections are larger
and more concentrated in a few grid cells, as would be expected from the
different shapes of the error standard deviation distributions.
Figure 4b shows the diagonal terms of the
averaging kernel matrix for the base inversion (averaging kernel
sensitivities), measuring the ability of the observations to constrain the
inversion. The trace of the averaging kernel matrix (DOFS = 128) measures
the number of independent pieces of information constrained by the inversion.
A Bayesian inversion without correcting for overfit (γ=1 in
Eq. 3) would erroneously produce much higher DOFS. We find that the
inversion provides strong constraints on the
grid for source regions in East Asia, central Africa, and South America.
Averaging kernel sensitivities are generally weaker over North America and in
Europe, indicating that the inversion provides more diffuse spatial
information in these regions.
We find that the EDGAR v4.3.2 inventory prominently overestimates
anthropogenic emissions over eastern China, likely from coal production, and
around the Persian Gulf, likely from oil and gas production. The finding of an
EDGAR overestimate in China is consistent with previous global inversions of
GOSAT data using EDGAR v4.1, v4.2, and v4.2FT2010 as prior estimates
(Monteil et al., 2013; Thompson et al., 2015; Alexe et al., 2015; Turner et al., 2015; Pandey et al., 2016)
and a regional inversion using EDGAR v4.3.2 (Miller et al., 2019). The
overestimate of coal mining emissions may be because standard IPCC emission
factors used by EDGAR v4.2 were too high for Chinese coal mines, and recovery
of coal mine methane is not sufficiently taken into account
(Peng et al., 2016). Emission factors were decreased in EDGAR v4.3.2
(Janssens-Maenhout et al., 2019), but we still find an overestimate. We find that EDGAR
underestimates emissions over Japan and Southeast Asia, where rice
cultivation is the largest anthropogenic source, but there are also large
wetland emissions. There are also large corrections in wetland areas of
central Africa, South America, and North America.
We do not find large correction factors over the United States, except for the
southeastern coast which is likely due to an overestimate of methane
emissions from coastal wetlands in the prior WetCHARTs inventory. This
overestimate of US coastal wetland emissions in WetCHARTs is consistent with
a previous inversion of aircraft observations over the Southeast United States by
Sheng et al. (2018b) and may be explained by low soil organic carbon in
these ecosystems (Holmquist et al., 2018) and/or the overestimated
impacts of partial wetland land-cover classes predominant in the southeastern
United States (Lehner and Döll, 2004; Bloom et al., 2017). Previous inversions found
factor of 2 underestimates of EDGAR v4.2 emissions of the South Central United States (Miller et al., 2013; Turner et al., 2015), but we do not find such an
underestimate here and attribute this to our use of the gridded version of
the US EPA inventory as prior estimate (Maasakkers et al., 2016). EDGAR
v4.2 allocated oil and gas emissions mainly according to population, which
greatly underestimates emissions in oil and gas production regions in the
South Central United States (Maasakkers et al., 2016).
Figure 5Global methane emissions by source type in the prior estimate for
the inversion (Table 1, “other” includes fossil fuel combustion, industrial
processes, and agricultural field burning) and in the posterior estimate.
Values are 2010–2015 means. The attribution to source types in the posterior
estimate is done by assuming that the relative contributions of different
source types in individual grid cells are
correct in the prior estimate. Posterior estimates are from the base
inversion, and error bars show the ranges of results from the inversion
ensemble.
Improved estimates of global methane emissions for the individual source
types of Table 1 can be inferred from our results by assuming that the
relative contributions from different source types in a given grid cell are correct in the prior inventory. The global
posterior estimate for a given source type is then obtained by applying the
posterior / prior ratios from Fig. 4
to the distribution of source types in Fig. 2. Results in
Fig. 5 indicate little change to 2010–2015 average emissions
compared to the global prior inventory by source type, even though there are
large regional reallocations. Coal mining emissions decrease by 29 %,
mainly due to China, and rice cultivation and livestock increase by 15 %
and 8 % respectively, mainly driven by the tropics.
There has been particular interest in quantifying emissions from oil and gas
exploitation because of the potential for large reductions of these emissions
through simple control measures (Zavala-Araiza et al., 2015; Alvarez et al., 2018).
The EDGAR v4.3.2 national oil and gas emission totals can differ greatly from the
national (spatially unresolved) totals reported by individual countries to
the United Nations Framework Convention on Climate Change (UNFCCC, 2017).
This is shown in Fig. 6 with national oil and gas emissions from the
top 10 countries in either the EDGAR v4.3.2 or UNFCCC inventories. We can
estimate national oil and gas emission totals from our inversion by again
assuming that the relative contributions of oil and gas to total emissions in
individual grid cells are correct and by
further mapping the correction factors to the
EDGAR emission grid. The emission-weighted
scaling factor is then used with the national oil and gas totals reported by
EDGAR. Russia is the largest national source, but the inversion is limited in
its ability to constrain oil and gas emissions there because a third of these
emissions are north of 60∘ N in EDGAR v4.3.2 (Fig. 2).
Figure 6National estimates of methane emissions from the oil and gas industry
for countries in the top 10 of either the EDGAR v4.3.2 or UNFCCC
inventories. Values reported by individual countries to the UNFCCC for 2012
(Annex I countries) or the closest year (non-Annex I countries: Nigeria
(1994), Venezuela (1999), Algeria (2000), Iran (2000), India (2010), Saudi
Arabia (2010), and China (2012)) are compared to 2012 emissions from EDGAR
v4.3.2 national oil and gas totals and to the posterior values from our base
inversion as described in the text. Black lines are ranges for the ensemble
of inversions. A large part of Russian emissions are too far north to be
effectively constrained by the inversion.
Figure 72010–2015 methane emission trends and global tropospheric OH trends
as optimized by the inversion of GOSAT data and corresponding averaging
kernel sensitivities (diagonal terms of the average kernel matrix). The
degrees of information for signal or DOFS (trace of the averaging kernel
matrix) is shown inset. Panel (c) gives the global attribution of the
emission trends to individual source types, with ranges from the inversion
ensemble. Shaded sections of the bars indicate the contribution from the
tropics (24∘ S–24∘ N). The vertical bars in the OH trend
panel are the posterior error standard deviations from the base inversion.
The 2010–2015 decreasing trend in OH concentrations is not statistically
significant (95 % confidence level).
Results in Fig. 6 show that the inversion generally pushes the
prior EDGAR v4.3.2 estimates of oil and gas emissions toward the UNFCCC values.
One would expect the UNFCCC national reports to provide better estimates than
EDGAR v4.3.2 because of their use of local information (Scarpelli et al., 2018) as
compared to the more generic estimates used by EDGAR on the basis of IPCC
Tier 1 methodology (IPCC, 2006). Thus we find that EDGARv4.3.2 greatly
underestimates emissions in Uzbekistan, which are high because of leaky
infrastructure (Scarpelli et al., 2018). For Iran, Algeria, Nigeria, Saudi Arabia,
and Qatar we find much lower emissions than EDGAR v4.3.2 that are more consistent
with the UNFCCC data. For China we are in better agreement with EDGAR v4.3.2
than with the UNFCCC estimate, which relies on anomalously low emission
factors (Larsen et al., 2015). In Venezuela we find higher emissions
than both EDGAR v4.3.2 and UNFCCC. The latest available report from
Venezuela to the UNFCCC dates back to 1999.
3.2 Spatial distribution and source attribution of methane emission trends
Figure 7 shows base inversion results for the linear emission
trends on the grid for 2010–2015 and the
associated averaging kernel sensitivities. Also shown in panel d is the 2010–2015 time series of posterior OH concentrations with error
standard deviations from the posterior error covariance matrix. We find no
significant OH trend over the period, although uncertainties are large. The
information on the spatial distribution of emission trends originates from
local and regional gradients of atmospheric methane observed by GOSAT, and we
find from the posterior error covariance matrix of the inversion that it is
not correlated with information on OH concentrations. Thus the large
posterior uncertainty in global OH concentrations does not induce any
significant correlated error in the spatial distribution of emission trends.
This may be expected in view of the long lifetime of methane relative to the
relevant timescales for atmospheric transport.
The GOSAT data provide seven independent pieces of information (DOFS) on the
spatial distribution of the emission trend. Again, a Bayesian inversion
without correcting for overfit (γ=1) would erroneously indicate much
higher DOFS. We find increasing emissions in the tropics and little change at
higher latitudes. There are well-defined anthropogenic positive trends over
China, India, and the Persian Gulf. Trends in China are in areas with
dominant emissions from coal mining but also significant contributions from
livestock and waste. In an inversion of surface observations,
Thompson et al. (2015) previously found an increasing trend over China for
2000–2011, which they attributed to coal mining. Miller et al. (2019)
found that this trend continued up to 2015 using GOSAT in a regional
inversion. Trends over India are in areas of rice production but may also
reflect waste management and livestock. The trend over India is 0.4
(0.3–0.5) Tg a−1 (range of the inversion ensemble), consistent with
the 2010–2015 trend of 0.7±0.5 Tg a−1 from a regional GOSAT
inversion by Miller et al. (2019). Ganesan et al. (2017) found a
nonsignificant trend (0.2±0.7 Tg a−1) over India for 2010–2015
using an ensemble of GOSAT, commercial aircraft (CARIBIC), and surface
station methane data, but our estimate is not incompatible with their range.
EDGAR v4.3.2 predicts a 0.4 Tg a−1 increase in anthropogenic emissions
from India between 2010 and 2012, mainly from livestock, coal, and waste based
on increasing activity data (this trend is not included in our prior estimate). The
trend over the United States is less well defined and not well constrained but suggests
an increase over the eastern part of the country where multiple source types
could contribute (Sheng et al., 2018a, b).
Figure 7c shows the attribution of the
global increasing trend in emissions to individual source types, following
the same assumption that was used in Fig. 5 to attribute
emissions to source types. We further separate tropical and extratropical
contributions. Boreal wetland trends cannot be constrained by our inversion
effectively (no observations north of 60∘ N). 43 % of the
5 Tg a−1 global emission trend found in the inversion for 2010–2015
is driven by wetlands (mainly tropical), 16 % by livestock, and 11 %
by oil and gas. No source type shows a global decrease. Our source attribution of
the methane trend is consistent with isotopic evidence, suggesting that the
increase in methane over the past decade has been driven by biogenic sources
outside the Arctic (Nisbet et al., 2016; Schwietzke et al., 2016; Schaefer et al., 2016),
including tropical wetlands (McNorton et al., 2016). Worden et al. (2017)
previously found a decrease in biomass burning from 2001–2007 to 2008–2014
but no significant change for the 2010–2015 period investigated here. Their
argument that a decrease in the biomass burning emissions would have masked
the effect of an increase in fossil fuel emissions on the isotope signature
of methane would not apply for our time period.
Table 2Global 2010–2015 methane budgeta.
a From the inversion optimizing (1) mean 2010–2015
methane emissions on the grid, (2) linear
methane emission trends on that same grid, (3) global mean 2010–2015
tropospheric OH concentration, and (4) linear trend in global OH
concentrations. Expected values and error standard deviations are shown. The
prior estimates are described in Sect. 2.2. The posterior global
emission and its trend are obtained by summing the contributions from all
grid cells, and the error standard deviations
are computed accounting for posterior error correlation. Minor methane sinks
totaling 61 Tg a−1 are not optimized in the inversion.
b Methane lifetime against oxidation by tropospheric OH, computed
as the ratio between the total atmospheric mass of methane (including the
stratosphere) and the annual loss rate from oxidation by OH in the
troposphere.
The previous sections showed that our inversion of the GOSAT data is able to
provide relatively fine information on the spatial distribution of methane
emissions (DOFS = 128) as well as some information on the spatial
distribution of 2010–2015 emission trends (DOFS = 7). This information
on the spatial distribution originates from local and regional gradients of
atmospheric methane observed by GOSAT. We now examine to what extent error
correlations may limit our ability to independently quantify the global
emission of methane, the global tropospheric OH concentrations, and their
respective trends.
Figure 8Constraints on the global 2010–2015 methane budget from our
inversion of GOSAT data. The lines show the rows of the averaging kernel
matrix Ared (Eq. 9) for the reduced four-element
state vector consisting of the 2010–2015 mean emission, the linear emission
trend, the 2010–2015 mean tropospheric OH concentration, and the linear OH
trend.
Figure 9Joint probability density functions (pdfs) for the global methane
budget as constrained by the 2010–2015 GOSAT data. Panel (a) shows the
joint pdfs of the 2010–2015 global mean methane emission and methane
lifetime against oxidation by tropospheric OH. Panel (b) shows the joint
pdfs of the 2010–2015 global emission trend and the OH trend. Contours show
confidence ranges from 0.1 to 0.9. The error correlation coefficients are
shown inset. The tilt of the ellipse indicates the extent of error
correlation.
To analyze the constraints from the inversion on the global budget of
methane, we collapse the inversion to the reduced four-member global state
vector of 2010–2015 mean values described in Sect. 2.6 (global
methane emission, global emission trend, global tropospheric OH
concentration, global OH trend). We use normal errors for all state vector
elements (using lognormal errors could bias the mean). Table 2 compares the
prior and posterior values for this global budget. The uncertainty in global
emissions and trends is likely underestimated because of the lack of prior
error covariance assumed between the 1009 grid cells. The global mean
tropospheric OH concentration is expressed in terms of the corresponding
methane lifetime . Figure 8 shows
the averaging kernel rows for this reduced global state vector
(Ared in Sect. 2.6), measuring the
sensitivity of the inversion results to the true values (diagonal terms) and
the aliasing due to error correlations (off-diagonal terms). We find that the
mean 2010–2015 global methane emission and OH concentration are strongly and
independently constrained, with averaging kernel sensitivities near unity and
little error correlation. On the other hand, there is strong negative error
correlation between emission trends and OH trends, and the OH trend can only
be weakly constrained. This is illustrated further in Fig. 9
with the joint probability density function (pdf) plots of the posterior
estimates, where the confidence levels measure the probability of a given
value, and the tilts of the ellipses measure the error correlations.
A major implication of being able to constrain the global
methane emission and the global OH concentration independently is that satellite
observations of atmospheric methane can provide an independent proxy for
quantifying the global mean tropospheric OH concentration. Our posterior
estimate of the methane lifetime is 10.8±0.4 years. It is strongly constrained by the inversion, as shown by the
averaging kernel sensitivity near unity, and is thus largely independent of
the prior estimate of 10.6±1.1 years. So far the main method for
estimating global OH has been through the methyl chloroform budget
(Prather et al., 2012), but this is becoming problematic as methyl chloroform
concentrations decrease, and previously minor potential sources like ocean
outgassing may become significant
(Wennberg et al., 2004; Liang et al., 2017). Satellite observations of
methane could provide an alternative. Our inversion confirms the best
estimate of global OH from the methyl chloroform budget (Prather et al., 2012) but
reduces its uncertainty from 10 % to 4 %. The magnitude of reduction
may be overoptimistic because of the idealized treatment of error statistics,
the assumption that the global 3-D OH distribution in the forward model is
correct, and the assumption that the minor sinks (Table 1) are correct.
Zhang et al. (2018) present a more thorough error analysis of this potential of
methane satellite observations as proxy for global OH concentrations.
We find on the other hand that there is large error correlation between our
estimates of global 2010–2015 emission trends and OH trends and limited
ability to constrain the OH trend. We find that most of the increase in
methane is explained by increasing emissions. Our posterior estimates for the
2010–2015 trends are % a−1 (4.6±0.2 Tg a−1) for emissions and % a−1 ( Tg a−1) for OH. The joint pdf in Fig. 9
illustrates the error correlation between the two. Other factors driving the
2010–2015 atmospheric methane trend are the initial imbalance in the 2010
budget, which we can derive from the posterior estimates of the mean
2010–2015 budget imbalance and trends, and the interannual variability of
wetlands emissions as represented by WetCHARTS. Figure 10
shows the contributions of these different terms to the observed 2010–2015
methane growth. 2010 was a relatively high year for tropical wetlands
emissions according to WetCHARTS, which acts to dampen the overall trend. We
can state with some confidence that increasing tropical emissions
(Fig. 7) made an important contribution to the 2010–2015
methane trend, but any conclusion about the effect of an OH trend is highly
uncertain, including in its sign. Our 2010–2015 growth rate averages
6.8 ppb a−1, compared to 7.3 ppb a−1 in the NOAA record
(https://www.esrl.noaa.gov/gmd/ccgg/trends_ch4/, last access: 27 April 2019). The increase in the NOAA record is higher because of
especially strong growth in 2014 (12.8 ppb), which is not fully captured by
the linearized optimization used here. In our base inversion, this anomaly is
explained by a reduced sink from OH.
Figure 10Attribution of the 2010–2015 increase in the atmospheric burden of
methane. The grey bars show the trend imposed by the 2010 imbalance between
sources and sinks combined with the interannual variability (IAV) of the
prior estimate (mainly from wetlands). This trend decreases over the
2010–2015 period because the methane sink rises in response to the
increasing methane concentration and also because wetland emissions in 2010
are higher than in other years. Purple and orange show the contributions of
the 2010–2015 methane emission trends and OH trends. The apportionment of
the emission trend by source region and source type is shown in
Fig. 7. The OH trend has high uncertainty as discussed in the
text.
We used 2010–2015 observations of atmospheric methane columns from the GOSAT
satellite instrument in a global inverse analysis to optimize a state vector
including (1) mean 2010–2015 methane emissions on a 4 grid, (2) 2010–2015 emission trends on that same grid, and
(3) global mean tropospheric OH concentrations for individual years. Our work
aimed to improve current understanding of global methane sources and the
renewed growth in atmospheric methane over the past decade and to examine if
satellite observations can independently constrain methane emissions and
tropospheric OH, the main methane sink.
Our inversion used the GEOS-Chem chemical transport model as forward model to
relate the state vector elements (1)–(3) to atmospheric methane columns. We
fitted the model to the GOSAT observations by analytical solution of the
Bayesian problem, including construction of the full Jacobian matrix. The
analytical solution provides closed-form characterization of errors and of
the information content in the solution. This is critical for diagnosing the
ability of the GOSAT observations to constrain emission trends and to achieve
separate constraints on emissions and OH concentrations. It also allows us to
easily generate an ensemble of inversions testing different assumptions.
An analytical solution of the inverse problem generally requires normal prior
error distributions, but we show here that it can be readily extended to
lognormal prior error distributions by using a simple scaling of the
original Jacobian matrix.
Our optimization of mean 2010–2015 methane emissions on the grid achieves 128 degrees of information for signal
(DOFS), with strong constraints in source regions. The EDGAR v4.3.2
anthropogenic emission inventory taken as default anthropogenic prior
estimate in the inversion is too high in China (coal emissions) and in the
Middle East (oil and gas emissions). Oil and gas national totals in EDGAR v4.3.2 can
differ greatly from the values reported by individual countries to the United
Nations Framework Convention on Climate Change (UNFCCC), and our inversion
results are generally more consistent with the UNFCCC estimates. We find
little correction to anthropogenic US emissions when a new gridded version of
the US EPA greenhouse gas inventory is used as the anthropogenic prior estimate.
Previous inverse studies that relied on the EDGAR v4.2 inventory as prior estimate found large underestimates of US emissions, but this may reflect errors in
the spatial distribution of EDGAR v4.2 oil and gas emissions.
Optimization of methane emission trends over the 2010–2015 period yields DOFS of 7 on the grid, meaning that only
strong source regions can be constrained. We find that the 2010–2015
increasing trend in atmospheric methane is mostly due to increasing emissions
rather than decreasing OH concentrations. Most of the increase is in tropical
wetlands, India, and China. Trends in North America and Europe are small. Our
findings are consistent with isotopic constraints pointing to tropical
biogenic sources as responsible for the renewed growth of methane over the
past decade.
We further examined the ability of the GOSAT data to constrain the global
methane emission and its trend over the 2010–2015 period independently of the global OH concentration and its trend. For this purpose we considered a
reduced four-component state vector consisting of (1) the global mean methane
emission for 2010–2015, (2) the global emission trend over that period,
(3) the global mean OH concentration for 2010–2015, and (4) the global OH trend
over that period. (1) and (2) were obtained by collapsing the inverse
solutions for emissions on the grid, so that
the distributions of emissions and their trends are still optimized. Results
show that the global methane emission (546±2 Tg a−1) can be
constrained independently of the global OH concentration (atmospheric
methane lifetime against oxidation by tropospheric OH of 10.8±0.4 years), with little error correlation. This is because methane emissions
and loss have different and separable signatures on atmospheric methane
columns. An important implication is that satellite observations of
atmospheric methane can serve as a useful proxy for the global OH
concentration. In contrast, we find that errors on the 2010–2015 OH trends
are strongly correlated with the stronger signal from emission trends.
Satellite observations of atmospheric methane are expected to vastly improve
in the near future with the launch of the TROPOMI instrument in October 2017,
the advent of geostationary observations from the GeoCARB instrument to be
launched in the early 2020s, and other instruments measuring methane on local
to global scales (Jacob et al., 2016). Our work with the relatively
sparse GOSAT data suggests that this future constellation of satellites will
enable the mapping of emissions at fine scales. Satellite observations of
methane could also provide an effective means for monitoring OH
concentrations, replacing methyl chloroform whose ability to serve as an OH
proxy is declining.
Appendix A: Comparison of forward model simulations at 4∘× 5∘
and 2∘× 2.5∘ resolutions
Stanevich (2018) pointed out significant global meridional biases in the
GEOS-Chem simulation of methane columns at
resolution relative to , and they argued that
was much better for use in global inversions
of methane sources. However, we find that most of the difference between the
two resolutions is in the stratosphere, which we correct following
Eq. (1). Figure A1 illustrates this point with the
differences between the two resolutions averaged over latitudinal bands.
Values are 2010–2015 means for the full column and for the tropospheric
column only. There are large high-latitude biases for the total column, but
these are mainly in the stratosphere. The tropospheric bias is less than
5 ppb at all latitudes. Results for individual seasons are similar.
Buchwitz et al. (2015) consider that biases below 10 ppb are
acceptable in methane inversions.
Figure A1Difference between methane column concentrations simulated by
GEOS-Chem at versus . Values are 2010–2015 averages over latitudinal bands for
total atmospheric columns and tropospheric columns.
Appendix B: Sensitivity to seasonal bias in prior emission estimates
The GEOS-Chem forward model simulation using prior emission estimates shows a
seasonal background bias relative to GOSAT observations, for which we applied
a latitude-dependent correction (Sect. 2.3). This
correction could mask a bias in the seasonality of prior emissions. We
conducted an additional inversion in which we did not apply this seasonal
correction and instead optimized emissions for individual seasons with no
prior error correlation between seasons. This brings the total size of the
state vector up to 5052, which challenges the power of the GOSAT observations
to provide independent constraints. As shown in Fig. B1, the
effective posterior / prior ratios found by summing the seasonal emissions are
very similar to the ones from the base inversion. This indicates that the
global pattern of scaling factors is not driven by corrections made to
improve the seasonal agreement between the model and GOSAT. The effective
scaling factors are smaller in magnitude and smoother than the previous
results because fewer observations are available per state vector element,
resulting in smoothing error (Turner and Jacob, 2015).
Figure B1Results from the seasonal inversion, showing effective posterior / prior scaling factors in the top panel and the seasonal scaling factors in the four
bottom panels.
JDM and DJJ designed the study. JDM performed the analysis.
JDM, MPS, HN, and MH performed simulations and supporting simulations and analysis.
JDM, DJJ, MPS, TRS, HN, JXS, YZ, MH, AAB, KWB, and JRW discussed the results.
AAB provided the WetCHARTS emissions and supporting data. GJM provided guidance
and supporting data on the EDGAR v4.3.2 emissions. RJP provided the GOSAT data
and supporting guidance. JDM and DJJ wrote the paper, and all authors provided input on the paper for revision before submission.
Competing interests
The authors declare that they have no conflict of
interest.
Acknowledgements
We thank the anonymous reviewers and Luke Western for their thorough comments. Robert J.Parker thanks the Japanese Aerospace Exploration Agency, National Institute for Environmental Studies, and the Ministry of Environment for the GOSAT data and their continuous support as part of the Joint Research Agreement. This research used the ALICE High Performance Computing Facility at the University of Leicester for the GOSAT retrievals.
Financial support
This research was funded by the Carbon Monitoring System program of the NASA Earth Science Division. Part of this research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. Kevin W. Bowman acknowledges support from the NASA Carbon Monitoring System (NNH16ZDA001N-CMS). Robert J. Parker is funded by the UK National Centre for Earth Observation (NCEO grant no. nceo020005) and the ESA Greenhouse Gas Climate Change Initiative (GHG-CCI).
Review statement
This paper was edited by Andreas Hofzumahaus and reviewed by
two anonymous referees.
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We use 2010–2015 satellite observations of atmospheric methane to improve estimates of methane emissions and their trends, as well as the concentration and trend of tropospheric OH (hydroxyl radical, methane's main sink). We find overestimates of Chinese coal and Middle East oil/gas emissions in the prior estimate. The 2010–2015 growth in methane is attributed to an increase in emissions from India, China, and areas with large tropical wetlands. The contribution from OH is small in comparison.
We use 2010–2015 satellite observations of atmospheric methane to improve estimates of methane...