ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus GmbHGöttingen, Germany10.5194/acp-15-7049-2015Estimating global and North American methane emissions with high spatial resolution using GOSAT satellite dataTurnerA. J.aturner@fas.harvard.eduhttps://orcid.org/0000-0003-1406-7372JacobD. J.WechtK. J.MaasakkersJ. D.LundgrenE.AndrewsA. E.BiraudS. C.https://orcid.org/0000-0001-7697-933XBoeschH.BowmanK. W.https://orcid.org/0000-0002-8659-1117DeutscherN. M.DubeyM. K.https://orcid.org/0000-0002-3492-790XGriffithD. W. T.https://orcid.org/0000-0002-7986-1924HaseF.KuzeA.https://orcid.org/0000-0001-5415-3377NotholtJ.OhyamaH.https://orcid.org/0000-0003-2109-9874ParkerR.https://orcid.org/0000-0002-0801-0831PayneV. H.SussmannR.SweeneyC.https://orcid.org/0000-0002-4517-0797VelazcoV. A.https://orcid.org/0000-0002-1376-438XWarnekeT.WennbergP. O.https://orcid.org/0000-0002-6126-3854WunchD.https://orcid.org/0000-0002-4924-0377School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, USADepartment of Earth and Planetary Sciences, Harvard University, Cambridge, Massachusetts, USANOAA Earth System Research Laboratory, Boulder, Colorado, USALawrence Berkeley National Laboratory, Berkeley, California, USAEarth Observation Science Group, Department of Physics and Astronomy, University of Leicester, Leicester, UKNational Centre for Earth Observation, University of Leicester, Leicester, UKJet Propulsion Laboratory/California Institute of Technology, Pasadena, California, USACentre for Atmospheric Chemistry, University of Wollongong, NSW, AustraliaInstitute of Environmental Physics, University of Bremen, Bremen, GermanyLos Alamos National Laboratory, Los Alamos, New Mexico, USAKarlsruhe Institute of Technology, IMK-ASF, Karlsruhe, GermanyJapan Aerospace Exploration Agency, Tsukuba, Ibaraki, JapanSolar-Terrestrial Environment Laboratory, Nagoya University, Nagoya, Aichi, JapanKarlsruhe Institute of Technology, IMK-IFU, Garmisch-Partenkirchen, GermanyCooperative Institute for Research in Environmental Sciences, University of Colorado at Boulder, Boulder, Colorado, USACalifornia Institute of Technology, Pasadena, California, USAA. J. Turner (aturner@fas.harvard.edu)30June201515127049706904December201418February201515June201517June2015This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://acp.copernicus.org/articles/15/7049/2015/acp-15-7049-2015.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/15/7049/2015/acp-15-7049-2015.pdf
We use 2009–2011 space-borne methane observations from the Greenhouse Gases
Observing SATellite (GOSAT) to estimate global and North American methane
emissions with 4∘× 5∘ and up to
50 km× 50 km spatial resolution, respectively.
GEOS-Chem and GOSAT data are first evaluated with atmospheric methane
observations from surface and tower networks (NOAA/ESRL, TCCON) and aircraft
(NOAA/ESRL, HIPPO), using the GEOS-Chem chemical transport model as a
platform to facilitate comparison of GOSAT with in situ data. This
identifies a high-latitude bias between the GOSAT data and GEOS-Chem that we
correct via quadratic regression. Our global adjoint-based inversion yields a
total methane source of 539 Tga-1 with some important regional
corrections to the EDGARv4.2 inventory used as a prior. Results serve as
dynamic boundary conditions for an analytical inversion of North American
methane emissions using radial basis functions to achieve high resolution of
large sources and provide error characterization. We infer a US anthropogenic
methane source of 40.2–42.7 Tga-1, as compared to
24.9–27.0 Tga-1 in the EDGAR and EPA bottom-up inventories, and
30.0–44.5 Tga-1 in recent inverse studies. Our estimate is
supported by independent surface and aircraft data and by previous inverse
studies for California. We find that the emissions are highest in the
southern–central US, the Central Valley of California, and Florida wetlands;
large isolated point sources such as the US Four Corners also contribute.
Using prior information on source locations, we attribute 29–44 % of US
anthropogenic methane emissions to livestock, 22–31 % to oil/gas, 20 %
to landfills/wastewater, and 11–15 % to coal. Wetlands contribute an
additional 9.0–10.1 Tga-1.
Introduction
Methane (CH4) emissions have contributed 0.97 W m-2 in global
radiative forcing of climate since pre-industrial times, second only to
CO2 with 1.7 W m-2. As a short-lived climate forcing
agent (lifetime ∼10 years), methane may provide a lever for slowing
near-term climate change . Major anthropogenic
sources include natural gas and petroleum production and use, coal mining,
waste (landfills and wastewater treatment), livestock, and rice cultivation.
Wetlands are the largest natural source. The present-day global emission of
methane is 550 ± 60 Tga-1, constrained by knowledge of the
global tropospheric hydroxyl radical (OH) concentration from the
methylchloroform budget . However, allocation by source
types and regions is very uncertain . Here we use GOSAT
space-borne observations for 2009–2011 to improve our understanding of
global and North American methane emissions using a high-resolution inversion
technique .
The US Environmental Protection Agency (EPA) produces national emission
inventories for anthropogenic methane, with a total of
27.0 Tga-1 in 2012 including 34 % from livestock, 29 % from
oil/gas extraction and use, 21 % from waste, and 11 % from coal
mining . Inverse studies using observations of atmospheric
methane concentrations suggest that the EPA inventory may be too low by up to
a factor of 2, although they differ as to the magnitude and cause of the
underestimate .
There is strong national and international interest in regulating methane
emissions , particularly in the
context of increasing natural gas exploitation and fracking, but uncertainty
in the emission inventory makes regulation problematic.
Space-borne observations of atmospheric methane concentrations in the
shortwave infrared (SWIR) are a unique resource for constraining methane
emissions because of the dense and continuous data that they provide. SWIR
instruments measure column concentrations with near-uniform vertical
sensitivity down to the surface. Data are available from the SCIAMACHY
instrument for 2003–2012 and from the
TANSO-FTS instrument aboard GOSAT for 2009–present (Butz et al., 2011;
Parker et al., 2011; hereafter we refer to the instrument as “GOSAT”).
GOSAT has higher precision and pixel resolution than SCIAMACHY (0.6 % and
10 km× 10 km vs. 1.5 % and
30 km× 60 km), but the observations are not as
dense. The GOSAT retrievals are in good agreement with surface-based column
measurements .
Previous inversions of methane emissions using satellite data have mainly
focused on the global scale, optimizing emissions with coarse spatial
resolution .
This limits the interpretation of the results because emissions from
different source types have large spatial overlap . Spatial
overlap is reduced at higher resolution; thus, optimizing emissions at high
spatial resolution can help improve source attribution.
used SCIAMACHY data for July–August 2004 in a higher-resolution
(∼100km× 100 km) inversion of methane
emissions in North America, but they were unable to achieve such a resolution
using GOSAT because of the sparser data .
Here we use three years (2009–2011) of GOSAT data to constrain global and
North American methane emissions with high spatial resolution, exploiting
both the longer record and a new analytical inversion method where the state
vector of emissions is defined optimally from a set of radial basis
functions . We begin by evaluating the GOSAT retrievals
with surface, aircraft, and total column observations using the GEOS-Chem
chemical transport model (CTM; described in the Appendix) as an
intercomparison platform. This identifies a high-latitude bias between GOSAT
and GEOS-Chem that we correct. We then use GOSAT observations to constrain
global methane sources with the GEOS-Chem model and its adjoint at
4∘× 5∘ resolution, and apply the results as
boundary conditions to optimize North American methane sources with up to
50 km× 50 km resolution and error characterization.
GOSAT observations and bias correction
GOSAT was launched in January 2009 by the Japan Aerospace
Exploration Agency (JAXA). Methane abundance is determined by analysis of the
spectrum of backscattered solar radiances in the SWIR near 1.6 µm.
Data are available from April 2009 on. GOSAT is in Sun-synchronous low earth
orbit with an Equator overpass of 12:45–13:15 LT. The instrument observes
five cross-track nadir pixels (three cross-track pixels after August 2010)
with a footprint diameter of 10.5 km, a cross-track spacing of about
100 km, an along-track spacing of 90–280 km, and a 3-day
revisit time. We use the version 4 proxy methane retrievals from
that pass all quality flags
(http://www.leos.le.ac.uk/GHG/data/styled/index.html). The retrievals
provide a weighted column average dry-mole fraction of CH4,
XCH4, with averaging kernels to describe the vertical weighting.
The averaging kernels show near-uniform vertical sensitivity in the
troposphere and decreasing sensitivity above the
tropopause see. The estimated single-retrieval precision is
scene-dependent and averages 13.3 ppb or 0.8 % .
Figure shows the mean methane concentrations for
June 2009–December 2011 observed by GOSAT and used in this work. There are
590 675 global observations including 74 687 for the North American window
of our high-resolution inversion. The GOSAT sampling strategy of consistently
revisiting the same locations provides a high density of observations over
the sampled locations, but the coverage is not continuous (gray areas in
Fig. ). There are data over oceans from Sun glint
retrievals but not in the product used here.
Methane concentrations are highest over East Asia where rice, livestock, and
fossil fuels contribute large sources. They are also high over the eastern
US. Low concentrations over elevated terrain (Tibetan Plateau, western US)
reflect in part a larger relative contribution of the stratosphere to the
column-average mixing ratio. We see from Fig. that relevant
spatial differences in methane mixing ratio for our inversion are of the
order of 10 ppb. With a mean single-scene instrument precision of
13.3 ppb, reducible by temporal or spatial averaging, GOSAT cannot
resolve day-to-day variability of emissions, but can strongly constrain
a multi-year average.
Mean GOSAT observations of the weighted column-average methane
dry-mole fraction (XCH4) for June 2009–December 2011, globally
and for North America. The data are version 4 proxy methane retrievals from
that pass all quality flags
(http://www.leos.le.ac.uk/GHG/data/styled/index.html).
Previous studies have validated the GOSAT data with surface-based FTIR
methane column abundances from the Total Carbon Column Observing
Network TCCON;. These studies have generally found GOSAT
retrievals to be within their stated precisions .
pointed out that a full validation of the GOSAT retrievals
would require a more extensive validation network than is available from
TCCON. Satellite observations by solar backscatter tend to be subject to
high-latitude biases because of large solar zenith angles, resulting in
longer path lengths and higher interference with clouds.
did not include a latitudinal bias correction in their inverse analysis of
GOSAT data, but used a bias correction based on the
geometric air mass factor and added a latitudinal bias
correction that was fitted as part of the inversion.
The retrieval uses CO2 as a proxy for the light path to
minimize common spectral artifacts from aerosol scattering and instrument
effects :
XCH4=XCH4∗XCO2∗XCO2,
where XCH4∗ and XCO2∗ are the dry-air mole
fractions retrieved from GOSAT under the assumption of a non-scattering
atmosphere and XCO2 is the column-average dry-air mole fraction
of CO2, estimated from the LMDZ global CTM with
3.75∘× 2.5∘ spatial resolution. This could lead to
localized bias in areas of concentrated CO2 sources. We determined the
extent of the bias by replacing XCO2 in Eq. ()
with (sparser) XCO2 data from a full-physics GOSAT retrieval.
This indicates a 14 ppb low bias in Los Angeles but much weaker
biases on regional scales.
2009–2011 methane emissionsa.
Source typeContiguous US North America Global PriorPosteriorbPriorPosteriorbPriorPosteriorTotal31.451.3–52.563.388.5–91.3537539Wetlands5.99.0–10.120.422.9–23.7164169Livestock8.912.6–17.014.520.0–25.7111116Oil/gas5.48.7–13.410.815.5–22.36967Wastec5.58.0–8.59.713.4–14.56065Coal4.04.7–6.54.35.0–6.84730Rice0.40.8–0.90.50.9–1.03845Open fires0.10.11.00.91716Otherd1.11.6–1.72.23.0–3.33132Naturale7.59.8–11.125.025.1–26.2176181Anthropogenicf25.040.2–42.741.962.3–66.2361358
a Emissions are in Tga-1. Prior
emissions are mainly from EDGARv4.2 for anthropogenic sources and
for wetlands (see Appendix). b Range
from two inversions with different assumptions for prior error (see text).
c Including landfills and waste water.
d Including fuel combustion, termites, and soil absorption.
e Including wetlands, open fires, termites, and soil absorption. f Including livestock, oil/gas, waste, coal, rice,
and fuel combustion.
Here we examined the consistency of GOSAT with a large body of independent
surface and aircraft measurements of methane concentrations by using the
GEOS-Chem CTM with prior methane emissions (Table and
Figs. and ) as an intercomparison
platform. Table gives summary comparison statistics and
more details are in the Appendix
(Figs. –). Global
comparisons with HIPPO II-V aircraft profiles across the
Pacific http://hippo.ornl.gov;, the NOAA cooperative
flask sampling network (http://www.esrl.noaa.gov/gmd/ccgg/flask.php),
and the TCCON network http://tccon.ornl.gov, GGG2014
version;
show that GEOS-Chem accurately simulates the global features of the methane
distribution including the meridional gradient in different seasons, with no
significant bias across multiple years and seasons
(Figs. –). One
would then expect similar agreement in the comparison of GEOS-Chem to GOSAT.
Comparing GEOS-Chem at 4∘× 5∘ over North America
with TCCON, the NOAA/ESRL Global Greenhouse Gas Reference Network (surface
flasks, tall tower network, and vertical profiles from the aircraft program)
http://www.esrl.noaa.gov/gmd/ccgg/flask.php;
shows weaker correlations (R2= 0.40–0.60) and the reduced-major-axis
regression slopes (0.67–0.75) suggest a ∼30 % prior underestimate of
North American emissions. Reduction of this bias will provide an independent
check on our inversion results.
GEOS-Chem comparison to 2009–2011 suborbital methane
observationsa.
a GEOS-Chem at 4∘× 5∘
resolution globally and 1/2∘× 2/3∘ resolution for
North America, using either prior emissions (Table and
Figs. and ) or
posterior emissions optimized with the inversion. Further details on the
comparisons are in
Figs. –. NOAA
observations are from the NOAA/ESRL Greenhouse Gas Reference Network.
References for the observations are given in the text. b Slope
(in ppb ppb-1) is from a reduced-major-axis (RMA) regression.
c Mean bias is the mean difference (in ppb) between model and
observations.
Figure compares the GOSAT methane observations
(XCH4) to GEOS-Chem values sampled at the location and time of
the observations, and with local averaging kernels applied. There is
a latitudinal background pattern in the difference between GEOS-Chem and
GOSAT. The bias becomes significant at latitudes poleward of 50∘.
Since GEOS-Chem is unbiased in its simulation of the tropospheric meridional
gradient relative to the surface and aircraft data
(Table , Fig. ), we
attribute the high-latitude bias to errors in either the GOSAT retrieval or
GEOS-Chem stratospheric methane. Bias corrections that are a function of
latitude or air mass factor (solar zenith angle) should be able to correct
for this. However, a bias in the GOSAT data would be expected to correlate
better with the air mass factor, while a bias in the model stratosphere may
correlate better with latitude. We find latitude to be a better bias
predictor based on the Bayesian information criterion (quadratic regression
in Fig. c). This suggests a potential bias in the
GEOS-Chem simulation of methane in the polar stratosphere, which warrants
further investigation with observations such as TCCON partial
columns . In any case, we remove the bias using the
quadratic regression and Fig. d shows the resulting
mean differences between GEOS-Chem and GOSAT after this bias correction. The
differences point to errors in the GEOS-Chem prior emissions that we will
correct in the inversion.
Comparison of the GOSAT observations from Fig. to the
GEOS-Chem model with prior emissions. (a and
b) show the mean bias and residual
standard deviation for the model–satellite difference. (c) shows
the model–satellite difference as a function of latitude for individual
observations along with the data density (contours), and a quadratic
regression (red line; x in degrees latitude, y in ppb) as an estimate of
the bias. The regression excludes grid squares with residual standard
deviation in excess of 20 ppb as model bias in prior emissions could
dominate the difference. (d) is the same as (a) but using
the bias correction from (c).
Global inversion of methane emissions
We use the bias-corrected GOSAT data to infer global methane emissions at
4∘× 5∘ resolution with an adjoint-based
four-dimensional variational data assimilation
system . The system minimizes a cost
function (J) with Gaussian errors,
J(x)=12(y-Kx)TSO-1(y-Kx)+12(xa-x)TSa-1(xa-x).
Here xa is the vector of prior emissions (see
Table and Fig. ), y is the
vector of GOSAT observations, K=∂y/∂x is the Jacobian matrix of the GEOS-Chem
methane simulation used as a forward model, and Sa and
SO are the prior and observational error covariance
matrices, respectively.
The state vector consists of scaling factors for emissions at
4∘× 5∘ resolution for June 2009–December 2011. The
prior emissions are mainly from the EDGARv4.2 inventory for anthropogenic
sources , and for wetlands.
Table gives a summary and further details are in the
Appendix. The error covariance matrices are taken to be diagonal, implying no
error correlation on the 4∘× 5∘ grid. We assume
50 % error variance on the prior for 4∘× 5∘ grid
cells as in .
Observational error variances are estimated following by
using residual standard deviations of the differences between observations
and the GEOS-Chem simulation with prior emissions, as shown in
Fig. b. As shown by , this residual
error provides an estimate of the total observational error needed for the
inversion, summing the contributions from instrument retrieval,
representation, and model transport errors. We find that the resulting
observational error variances are lower than the local retrieval error
variances reported by for 58 % of the observations, and in
those cases we use the latter instead. The implication is that the
error estimates may be too high but provide a conservative
estimate of the observational error.
The GEOS-Chem forward model and its adjoint are as described by
. We optimize methane emissions from 1 June 2009 to
1 January 2012. The forward model is initialized on 1 January 2009 with
concentrations from . There is no significant global bias
in the simulation, as discussed above. The 5-month spin-up allows for the
establishment of gradients driven by synoptic motions and effectively removes
the influence of the initial conditions.
Figure shows the prior and posterior 2009–2011
emissions. We evaluated the posterior emissions in a GEOS-Chem forward
simulation by comparison with the global independent observational data sets
of Table . The prior simulation showed high correlation
and little bias. The posterior simulation shows similar results. The increase
in mean bias relative to the TCCON data is not significant. As pointed out
above, the global data sets mainly test the global emissions and large-scale
meridional gradients. Since we used them previously to justify a bias
correction in the comparison between GEOS-Chem and GOSAT, they do not provide
a true independent test of the inversion results. Nevertheless, we see that
the inversion does not degrade the successful simulation of the background
meridional gradient in the prior GEOS-Chem simulation.
Optimization of methane emissions for 2009–2011 at
4∘× 5∘ horizontal resolution using GOSAT
observations. The panels show prior emissions, posterior emissions, and the
ratio between the two.
Methane emissions in North America in 2009–2011. The left panels
show the prior and posterior emissions and the bottom right panel shows the
scaling factors. The top right panel shows the diagonal elements of the
averaging kernel matrix for the methane emission inversion. The degrees of
freedom for signal (DOFS) is the trace of the averaging kernel
matrix.
The total posterior methane emission is 539 Tga-1, unchanged
from the prior (537 Tga-1). This source is within the
548-22+21Tga-1 range of current estimates reported by
and . However, we find large regional
differences compared to the prior. Emissions from China are revised downward
by 50 % from 29.2 to 14.7 Tga-1, consistent with
, who find that EDGARv4.2 Chinese coal emissions are too
large. This overestimate in Chinese methane emissions is also seen by
, who assimilated the 2000–2010 NOAA surface observations
into CarbonTracker using an ensemble Kalman filter. Emissions in India are
also too high, while emissions in Southeast Asia are too low. Emissions from
wetlands in central Africa are too high. Emissions in northern South America
are too low. Corrections in North America are discussed in the next section.
We inferred the contributions from different source types to our posterior
emissions by assuming that the prior inventory correctly partitions the
methane by source type (see Appendix and Table ) in each
4∘× 5∘ grid cell. This does not assume that the
global distribution of source types is correct in the prior, only that the
local identification of dominant sources is. We find only modest changes to
the global partitioning by source types, with the exception of coal and rice,
partly reflecting regional offsets. For example, wetland emissions increase
globally by only 5 Tga-1 but decrease by 24 Tga-1
in the African wetlands, while increasing by 10 Tga-1 in
northern South America.
North American inversion of methane emissions
We optimize methane emissions over North America by using the nested
GEOS-Chem simulation at 1/2∘× 2/3∘ horizontal
resolution (∼50km× 50 km) over the North
American window in Fig. . Time-dependent boundary conditions for
this nested simulation are from the global model at
4∘× 5∘ horizontal resolution using the posterior
emissions derived above. We only solve for the spatial distribution of
emissions, assuming that the prior temporal distribution is correct
(aseasonal except for wetlands and open fires; see Appendix).
Following , the dimension of the emissions state vector
for the nested North American inversion is optimally reduced from the native
1/2∘× 2/3∘ resolution (n=7366) in order to
(1) improve the observational constraints on individual state vector elements
and (2) enable an analytical inversion with full error characterization. This
is done by aggregating similar state vector elements with a Gaussian mixture
model . We find that an optimal reduction with negligibly
small aggregation error can be achieved using 369 radial basis functions
(RBFs) with Gaussian kernels. The RBFs are constructed from estimation of the
factors driving error correlations between the native-resolution state vector
elements including spatial proximity, correction from one iteration of an
adjoint-based inversion at 1/2∘× 2/3∘ resolution,
and prior source type distributions. Including the correction from the
adjoint-based inversion allows us to account for sources not included in the
prior. Each 1/2∘× 2/3∘ native-resolution grid
square is projected onto an aggregated state vector using the RBFs. This
preserves native resolution where needed (in particular for large point
sources) and aggregates large regions where emissions are uniformly small.
Our optimal estimate of North American emissions was obtained by analytical
solution to Eq. () cf., using the Jacobian
matrix K constructed column by column for the aggregated state
vector. This analytical approach provides the posterior covariance matrix
S^ and averaging kernel matrix A as part of the
solution and thus fully characterizes the errors and information content of
the inversion results.
The observational error covariance matrix is assumed diagonal with terms
specified as the larger of the residual error variance and the retrieval
error variance, same as for the global inversion. The prior error covariance
matrix is assumed diagonal because the radial basis functions are designed to
capture spatial correlations in the emissions. We assume 100 % error on
emissions at the native 1/2∘× 2/3∘ resolution. For
RBFs encompassing larger spatial regions, we assume that the error is reduced
following the central limit theorem:
Sa,{i,i}=sa∑jwi,j,
where Sa,{i,i} is the ith diagonal of Sa,
sa is the prior uncertainty at the native resolution (100 %), and
the summation is for the weights of the ith RBF over all
1/2∘× 2/3∘ grid squares (index j). This error
reduction assumes that the errors on the native-resolution grid cells are
independent and identically distributed, which may be overly optimistic. We
examined the sensitivity to this assumption by conducting an alternate
inversion with a relative error of 30 % for all state vector elements,
similar to the approach taken by using a hierarchial
clustering method for the state vector.
Figure shows the prior and posterior 2009–2011 emissions.
Total posterior emissions in North America (Table ) are
44 % higher than the prior, with large increases in the southern–central
US and weak decreases for the Canadian wetlands. Contiguous US emissions are
52 Tga-1, 70 % higher than the prior. The broad correction
patterns are consistent with the coarse global results in
Fig. that used a completely different inversion method.
Our sensitivity inversion assuming 30 % prior error on all state vector
elements yields the same North American and contiguous US totals to within
3 %.
We evaluated the posterior emissions in a GEOS-Chem simulation over North
America by comparison to the independent observations from
Table . We find great improvement in the ability of the
model to reproduce these observations, as illustrated by the scatterplots of
Fig. . The reduced-major-axis (RMA) regression slopes
improve from 0.72 to 1.03 for the NOAA/ESRL tall tower network, from 0.75 to
0.94 for the NOAA/ESRL aircraft profiles, and from 0.67 to 1.01 for the NOAA
surface flasks.
Evaluation of the GOSAT inversion of methane emissions for North
America with independent data sets. The scatterplots show comparisons of
GEOS-Chem (1/2∘× 2/3∘ resolution) methane
concentrations with observations from the NOAA/ESRL tall tower network (red),
NOAA/ESRL aircraft program (blue), and the NOAA/ESRL surface flask network
(orange), using prior emissions (top) and posterior emissions (bottom). The
1 : 1 lines (dashed) and reduced-major-axis (RMA, solid) linear regressions
are also shown. RMA regression parameters are shown inset and correspond to
the statistics of Table .
Another independent evaluation of our posterior emissions is the estimate for
California. California's methane emissions have been extensively studied with
aircraft and ground-based observations over the past few years in order to
address statewide greenhouse gas regulation targets
.
Figure shows that our posterior emissions are 20 % higher
than the EDGARv4.2 prior inventory for the state of California and 50 %
lower for the Southern California Air Basin (SoCAB). Other studies
constrained with dense aircraft and ground-based observations are consistent
with ours. Our estimate for SoCAB could be biased low due to an underestimate
of local CO2 in the GOSAT retrieval (see Sect. ).
previously found that GOSAT observations were not
sufficiently dense to constrain methane emissions in California. However,
they only used a 2-month record and tried to constrain emissions at
1/2∘× 2/3∘ resolution, incurring large smoothing
error. By using a longer time record and an optimally defined state vector,
we achieve much better success.
Methane emissions for the state of California (top) and for the
Southern California Air Basin (SoCAB; bottom). Our posterior emissions (this
work) are compared to prior emissions (EDGARv4.2) and to previous inverse
estimates constrained by surface and aircraft observations. SoCAB is defined
following as the domain 33.5–34.5∘ N,
117–119∘ W.
Figure (top right panel) shows the averaging kernel
sensitivities for the North American methane emission inversion, defined as
the diagonals of the averaging kernel matrix. The inversion has 39 degrees of
freedom for signal (DOFs), meaning that we can exactly constrain 39 pieces of
information in the distribution of methane emissions. This information is
spread over the continent and mixed with prior constraints as described by
the averaging kernel matrix. We can use the averaging kernel sensitivities in
Fig. to determine which regions are most responsive to the
inversion. These include California, the Canadian wetlands, and the
southeastern and central US. Large isolated point sources such as the US Four
Corners (a large source of coalbed methane at the corner of Arizona, New
Mexico, Colorado, and Utah) are also strongly sensitive to the inversion.
We see from Fig. that the prior underestimate of North
American methane emissions is largely due to the central US, the Canadian Oil
Sands, central Mexico, California, and Florida. Various large point sources
such as the US Four Corners also contribute. We also find regions where the
prior is too high, including the Hudson Bay Lowlands, SoCAB, and parts of
Appalachia. This suggests that oil/gas and livestock emissions are higher
than given in EDGARv4.2, while coal emissions are lower. The overestimate in
SoCAB is likely because EDGARv4.2 uses urban and rural population as
a spatial proxy for landfills and waste water . The
underestimate in Florida is most likely due to wetland sources.
As with the global inversion, we infer the contributions from different
methane source types by assuming that the prior inventory correctly
attributes the source types in a given 1/2∘× 2/3∘
grid cell. Again, this does not assume that the prior distribution is
correct, only that the identification of locally dominant sources is correct.
Results are shown in Fig. . We see that the increase
relative to the prior is mainly driven by anthropogenic sources. This can be
compared to the US EPA anthropogenic inventory , which is
based on more detailed bottom-up information than EDGARv4.2 but is only
available as a national total. We find an anthropogenic source for the
contiguous US of 40.2–42.7 Tga-1, as compared to
27.0 Tga-1 in the US EPA inventory. The largest differences are
for the oil/gas and livestock sectors. Depending on the assumptions made
regarding the prior error, oil/gas emissions from our inversion are
13–74 % higher than the EPA estimate and contribute 17–26 % of
contiguous US methane emissions. Livestock emissions are 36–85 % higher
than the EPA estimate and contribute 24–33 % of contiguous US methane
emissions. Waste and coal emissions are also higher in our posterior estimate
than in the EPA inventory.
Methane emissions in the contiguous US. The left panel shows our
best estimates of total and anthropogenic emissions (this work) compared to
the prior (EDGARv4.2 for anthropogenic sources, for
wetlands) and the previous inverse studies of and
. The right panel partitions US anthropogenic emissions by
source types and compares our results (this work) to EDGARv4.2 and to the
2012 EPA inventory . Error bars on sectoral emissions for our
results are defined by the sensitivity inversion with 30 % prior
uncertainty for all state vector elements.
Comparison to previous inverse studies
Several past inverse analyses have estimated methane emissions in the
contiguous US with differing conclusions, in particular the work of
and . used in situ
observations for 2007–2008 from ground stations and aircraft. They found the
EPA inventory to be underestimated by a factor of 1.5 nationally, with the
largest underestimates in fossil fuel source regions. This is in contrast to
, who used July–August 2004 observations from SCIAMACHY.
They found that the EPA inventory was underestimated by only 10 %, with the
major discrepancy being livestock emissions underestimated by 40 %.
Our continental-scale inversion yields a total US methane emission of
52.4 Tga-1 and an anthropogenic source of
42.8 Tga-1. The general spatial pattern of the posterior
emissions is similar to those of and , but
the total methane emissions found here are more similar to ,
who found US total and anthropogenic emissions of 47.2 and
44.5 Tga-1. The corresponding values obtained by
are 38.8 and 30.0 Tga-1, significantly lower.
Our work finds a larger natural methane source in the contiguous US than
, who used a fixed prior wetland source of
2.7 Tga-1 that was subtracted from the measurements. Our prior
and posterior emissions are 5.9 and 9.0–10.1 Tga-1,
respectively, mostly located in Louisiana and Florida and more consistent
with . Quantifying the wetlands source is important because
it subtracts from the anthropogenic source estimate inferred from the
inversion. In particular, our anthropogenic source of methane in the
contiguous US would be larger than that of if we had not
corrected for the larger wetland source.
found the Four Corners to be the largest single methane source
in the continental US (0.59 Tga-1) on the basis of SCIAMACHY
observations and TCCON observations, with a magnitude 3.5 times larger than
EDGARv4.2 and 1.8 times larger than reported by the US EPA Greenhouse Gas
Reporting Program . This is in contrast to ,
who found the US Four Corners to be overestimated in EDGARv4.2 but only had
weak constraints for that region. Our work finds methane emissions from the
US Four Corners to be 0.45–1.39 Tga-1 and 3–9 times larger
than in the EDGARv4.2 inventory, consistent with the finding of
.
attributed most of the underestimate in the US EPA methane
inventory to fossil fuel, while attributed it to
livestock. We find in our inversion that the source attribution is highly
dependent on the specification of the prior error covariance matrix, as shown
in Fig. . Our standard inversion that adjusts the prior
error for the RBF weights (Eq. ) attributes 31 % of US
anthropogenic emissions to oil/gas and 29 % to livestock, so that most of
the EPA underestimate is for oil/gas. However, an inversion without this
prior error adjustment (error bars in Fig. ) finds the
underestimate to be mainly from livestock. This is because the RBFs
associated with livestock emissions tend to cover larger areas of correlated
emissions than the point sources associated with oil/gas. An inversion with
equal error weighting for different state vector elements will tend to favor
correction of the larger elements associated with livestock. With current
prior knowledge it is thus difficult to conclusively attribute the US EPA
underestimate to oil/gas or livestock emissions. This limitation could be
addressed by a better prior knowledge of the spatial distribution of source
types or by the use of correlative information (e.g., observations of ethane
originating from oil/gas) in the inversion.
Conclusions
We used 31 months of GOSAT satellite observations of methane columns
(June 2009–December 2011) to constrain methane emissions at high spatial
resolution in North America with an inversion based on the GEOS-Chem chemical
transport model. We first conducted a global adjoint-based inversion at
4∘× 5∘ resolution and used the resulting optimized
fluxes as dynamic boundary conditions for a nested inversion with resolution
up to 50 km× 50 km over North America.
We began by evaluating the GOSAT observations with a large ensemble of
aircraft and surface data (HIPPO, NOAA/ESRL surface flasks, NOAA/ESRL
aircraft, TCCON), using GEOS-Chem as an intercomparison platform. This
revealed a high-latitude bias in the GEOS-Chem polar stratospheric methane
(or possibly in the GOSAT data) that we corrected for the purpose of the
inversion. The aircraft and surface data were subsequently used as an
independent check of our inversion results.
Our global GOSAT inversion finds a total methane source of
539 Tga-1 with 39 % from wetlands, 22 % from livestock,
12 % from oil/gas, 12 % from waste, 8 % from rice, and 6 % from coal.
Comparison to the EDGARv4.2 inventory used as a prior for the inversion
indicates that Chinese coal emissions are a factor of 2 too large, consistent
with the findings of and . We find
large regional corrections to the EDGARv4.2 inventory including
a 10 Tga-1 increase in the wetland emissions in South America
and a 10 Tga-1 increase in rice emissions in Southeast Asia.
Our North American continental-scale inversion used an emission state vector
optimally defined with radial basis functions (RBFs) to enable analytical
inversion with full error characterization while minimizing aggregation
error . In this manner we could resolve large point
sources at a resolution of up to 50 km× 50 km while
aggregating regions with weak emissions. Our posterior anthropogenic methane
source for the contiguous US is 40.2–42.7 Tga-1, compared to
25.0 Tga-1 in EDGARv4.2 and 27.9 Tga-1 in the US
EPA national inventory. Differences are particularly large in the
southern–central US. Our posterior inventory is more consistent with
independent surface and aircraft data and with previous studies in
California. On the basis of prior emission patterns, we attribute 22–31 %
of US anthropogenic methane emissions to oil/gas, 29–44 % to livestock,
20 % to waste, and 11–15 % to coal. There is in addition
a 9.0–10.1 Tga-1 wetlands source.
Our work confirms previous studies pointing to a large underestimate in the
US EPA methane inventory. This underestimate is attributable to oil/gas and
livestock emissions, but quantitative separation between the two is difficult
because of spatial overlap and limitations of the observing system and prior estimates. We find
that either oil/gas or livestock emissions dominate the correction to prior
emissions depending on assumptions regarding prior errors. This limitation
could be addressed in the future through better specification of the prior
source distribution using high-resolution information on activity rates, and
through the use of correlated variables in the inversion.
GEOS-Chem description and evaluation with independent data
We use the v9-01-02 GEOS-Chem methane
simulation http://acmg.seas.harvard.edu/geos/index.html;
driven by Goddard Earth Observing System (GEOS-5) assimilated meteorological
data for 2009–2011 from the NASA Modeling and Assimilation Office (GMAO).
The GEOS-5 data have a native horizontal resolution of
1/2∘× 2/3∘ with 72 pressure levels and 6 h
temporal resolution (3 h for surface variables and mixing depths).
The results presented here are from nested simulations at the native
1/2∘× 2/3∘ resolution over North America
(10–70∘ N, 40–140∘ W) and global simulations at
4∘× 5∘ resolution. GEOS-Chem has been extensively
evaluated in the past (e.g., ;
L. ) including three recent studies
that used GEOS-5 meteorology over North
America . These show a good simulation of
regional transport with no apparent biases. Dynamic boundary conditions for
the nested simulations are obtained from the global simulations. Methane loss
is mainly by reaction with the OH radical. We use a 3-D archive of monthly
average OH concentrations from . The resulting atmospheric
lifetime of methane is 8.9 years, consistent with the observational
constraint of 9.1 ± 0.9 years .
Prior 2009–2011 emissions for the GEOS-Chem methane simulation are from the
EDGARv4.2 anthropogenic methane inventory , the wetland model
from as implemented by , the GFED3
biomass burning inventory , a termite inventory and soil
absorption from , and a biofuel inventory from
. Wetlands emissions vary with local temperature, inundation,
and snow cover. Open fire emissions are specified with 8 h temporal
resolution. Other emissions are assumed aseasonal. Table
lists global, North American, and contiguous US emissions.
Figures and show the spatial
distributions of the global and North American prior emissions for the five
largest source types.
We evaluated GEOS-Chem with surface-based (NOAA/ESRL, TCCON), tower
(NOAA/ESRL), and aircraft (HIPPO, NOAA/ESRL) observations of methane
concentrations for 2009–2011, both as indirect validation of the GOSAT data
and as an independent check on our inversion results. See the main text for
references for these observations. We convolve GEOS-Chem with the TCCON
averaging kernels and priors before comparison with TCCON observations.
Figure uses observations from the NOAA
cooperative flask network and from the HIPPO data across the Pacific to
evaluate the global burden and latitudinal gradient in GEOS-Chem.
Figure uses observations from the NOAA/ESRL
Global Greenhouse Gas Reference Network and the TCCON column network for
a more specific evaluation of the model over North America.
Figure shows corresponding scatterplots and
Table gives summary statistics. Discussion of the
results is given in the text.
Prior 2009–2011 methane emissions used in the GEOS-Chem global
simulation at 4∘× 5∘ resolution, and contributions
from the top five sources.
Same as Fig. but for North America with
1/2∘× 2/3∘ resolution.
Global evaluation of the GEOS-Chem methane simulation at
4∘× 5∘ resolution (using prior emissions) with
observations from the NOAA/ESRL surface flask network (top left panel colored
by latitude) and HIPPO aircraft deployments. The central panel shows 3-month
running medians for 2009–2011 of the difference between GEOS-Chem and the
flask data in different latitudinal bands. The gray line is for all of the
observations. Error bars for the running medians are offset from the lines
for clarity. Latitudinal profiles across the Pacific for the four HIPPO
deployments over the period are also shown: in those panels the symbols
represent the pressure-weighted tropospheric average concentrations and the
vertical bars are the standard deviation. Stratospheric air is excluded based
on an ozone–CO concentration ratio larger than 1.25 . Bottom
right panel shows the HIPPO flight tracks.
North American evaluation of the GEOS-Chem methane simulation at
4∘× 5∘ resolution (using prior emissions) with
TCCON (top), NOAA/ESRL aircraft program (middle), and NOAA/ESRL surface flask
network (bottom) observations over North America. Left panels show 3-month
running medians for 2009–2011 of the difference between GEOS-Chem and the
observations in different latitudinal bands and for all the data (gray line).
Right panels show the location of the observations. All values are in
ppb.
Scatterplot comparison of GEOS-Chem at
4∘× 5∘ resolution to independent observations. Left
column uses prior emissions and right column uses posterior emissions.
Individual points show comparisons for individual observations, averaged over
the GEOS-Chem grid resolution in the case of the aircraft data. The 1:1
(dashed) and reduced-major-axis (RMA, solid) regression lines are shown.
Summary statistics are also given in Table . Different
colors correspond to different sites (TCCON), latitudinal bands (flasks), and
deployments (HIPPO) shown in
Fig. .
Acknowledgements
This work was supported by the NASA Carbon Monitoring System and a Department
of Energy (DOE) Computational Science Graduate Fellowship (CSGF) to
A. J. Turner. We also thank the Harvard SEAS Academic Computing center for
access to computing resources. Special thanks to S. C. Wofsy for providing
HIPPO aircraft data, and J. B. Miller and M. Parker for providing NOAA/ESRL
Global Greenhouse Gas Reference Network data. We thank M. L. Fischer and the
CALGEM team at LBNL for their contributions to data collection at tower sites
in central California as supported by the California Energy Commission's
Natural Gas Program through a grant to the US Department of Energy under
contract no. DE-AC02-05CH11231. Part of this work was carried out at the Jet
Propulsion Laboratory, California Institute of Technology, under a contract
with NASA. R. Parker and H. Boesch acknowledge funding from the UK National
Centre for Earth Observation (NCEO) and the ESA Climate Change Initiative
(ESA GHG-CCI). TCCON data at Park Falls, Lamont, and JPL is funded by NASA
grants NNX11AG01G, NAG5-12247 and NNG05-GD07G, and the NASA Orbiting Carbon
Observatory Program. We are grateful to the DOE ARM program for technical
support in Lamont and J. Ayers for technical support in Park Falls. TCCON
data from Bialystok and Bremen is funded by the EU projects InGOS and
ICOS-INWIRE, and by the Senate of Bremen. TCCON data from Darwin is funded by
NASA grants NAG5-12247 and NNG05-GD07G and the Australian Research Council,
DP0879468 and LP0562346. We are grateful to the DOE ARM program for technical
support in Darwin. Garmisch TCCON work has been performed as part of the ESA
GHG-cci project via subcontract with the University of Bremen. In addition,
we acknowledge funding by the EC within the INGOS project. From 2004 to 2011
the Lauder TCCON program was funded by the New Zealand Foundation of Research
Science and Technology contracts CO1X0204, CO1X0703 and CO1X0406. Since 2011,
the program has been funded by NIWA's Atmosphere Research Programme 3
(2011/13 Statement of Corporate Intent). M. K. Dubey thanks LANL-LDRD for
funding 20110081DR for monitoring at Four Corners. We thank B. Henderson
(LANL) for help with retrievals at Four Corners. A part of work at JAXA was
supported by the Environment Research and Technology Development Fund
(A-1102) of the Ministry of the Environment, Japan. Observations collected in
the Southern Great plains were supported by the Office of Biological and
Environmental Research of the US Department of Energy under contract
no. DE-AC02-05CH11231 as part of the Atmospheric Radiation Measurement
Program (ARM), ARM Aerial Facility, and Terrestrial Ecosystem Science
Program. HIPPO aircraft data are available at http://hippo.ornl.gov,
TCCON data are available at http://tccon.ornl.gov, and NOAA/ESRL Global
Greenhouse Gas Reference Network data are available at
http://www.esrl.noaa.gov/gmd/ccgg/flask.php. Edited by: R. Harley
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