Introduction
The atmospheric concentration of methane (CH4) has been
increasing globally since 2007, following a slowdown in growth from 1999 to
2006 (Dlugokencky et al., 2017). The onset of the observed increase in
CH4 coincides with an isotopic trend to lighter CH4, more
depleted in 13C (Nisbet et al., 2014). The
13CH4 : 12CH4 ratio (denoted by the
δ13CH4 value) is controlled by both the isotopic signatures
of the sources and the isotopic fractionation associated with atmospheric
CH4 sinks. Broadly speaking, the emissions types can be categorised
into the relatively light biogenics (∼-62 ‰), heavier fossil
fuels (∼-44 ‰), and the even heavier biomass burning emissions
(∼-22 ‰) (Schwietzke et al., 2016), resulting in a total
isotopic source signature of between -51 ‰ and -53 ‰.
Isotopic fractionation in the atmosphere by the reaction with the hydroxyl
(OH) radical and chlorine (Cl) atoms enriches 13CH4, causing a
background atmospheric δ13CH4 of ∼-47 ‰.
Previous studies have used simple global box models for source and sink
attribution of recent atmospheric CH4 trends, with contradictory
findings. Nisbet et al. (2014, 2016) and Schaefer et al. (2016) suggested
that either increased wetland or agricultural emissions were the likely
cause,
while Rigby et al. (2017) and Turner et al. (2017) found the most likely
explanation to be a decreased global mean OH concentration. The latter two
studies emphasised that the problem is not very well constrained by existing
data and as a result could not discard the hypothesis that OH is not
changing. These approaches are able to isolate the three emissions categories
noted above, and sometimes sink terms. Specific attribution, for example
between wetlands and agricultural emissions changes, requires spatial
representation of both CH4 and δ13CH4. The box model
approach provides little or no information of spatial variation in posterior
emissions estimates, preventing regional attribution. Rice et al. (2016)
performed a 3-D chemical transport model (CTM) inversion using CH4
and isotopologue measurements over the period from 1984 to 2009. They found a
24 Tg yr-1 increase in fugitive fossil fuel emissions between 1984 and
2009, most of which occurred after 2000. The time trend in their inversion
appeared similar to their prior emissions estimates. Although they used a 3-D
CTM the posterior emissions were calculated globally and not regionally.
Furthermore, their study did not focus on the possible role of OH variations
and did not consider inversions after 2009, so only captured 2 years of the
continued post-2007 growth.
Here we perform a synthesis inversion using the TOMCAT 3-D CTM, building on
previous work (Bousquet et al., 2006, 2011; Rigby et al., 2012; Schwietzke et
al., 2016; Rice et al., 2016) and using surface measurements of both
CH4 (Dlugokencky et al., 2017) and δ13CH4 (White et
al., 2017). The synthesis inversion technique uses the forward 3-D CTM to
optimise monthly CH4 emissions over relatively large regions and for
multiple source sectors. This spatial resolution is not present in existing
box model inversions. We investigate regional source contributions and the
roles of tropospheric OH and Cl in the recent growth of CH4. From
this we derive possible source and sink changes between 2003 and 2015 which
best fit the observations.
Results
Synthesis inversion
Inversion results constrained by CH4 and δ13CH4
observations (INV-FULL) show, as expected, improved seasonal and interannual
monthly averaged posterior CH4 and δ13CH4 estimates
when compared with assimilated surface observations (Fig. 1). The correlation
with observations (R) for CH4 increases from an all-site average of
0.72 in the prior to 0.94 in the posterior, and for δ13CH4
increases from 0.52 to 0.87. Similarly, the root-mean-square error (RMSE)
decreases from 38.2 to 9.7 ppb for CH4 and from 0.25 ‰ to
0.09 ‰ for δ13CH4. The prior model captures some of
the initial 2007 CH4 growth but fails to capture the sustained growth
(Fig. 1a). The bias in the prior, relative to both the posterior and
observations, grows throughout the simulation period. This results in a large
bias at the end of the time period, which is evident in the large RMSE values
(Figs. 1 to 4). The prior also shows a slight decrease in
δ13CH4 since 2007, but the magnitude of this is smaller than
observed (Fig. 1b). The renewed growth of CH4 and corresponding
decrease in δ13CH4 in 2007 are well captured in the
inversion.
(a) Observed surface CH4 (ppb, black line) from
2003 to 2015 at six selected NOAA sites and global mean. Also shown are
results from TOMCAT simulations using prior emissions estimates (blue line),
posterior estimates based on a CH4 synthesis inversion (INV-CH4,
green line), and posterior estimates based on a combined CH4 and
δ13CH4 synthesis inversion (INV-FULL, red line).
(b) Same as (a) but for observed and modelled
δ13CH4. Global averages are based on site interpolations onto
180 1∘ latitude bins, which are weighted by surface area.
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Monthly mean XCH4 volume mixing ratio (ppb) from GOSAT
between April 2009 and December 2015 (black line) for five emissions regions.
Also shown are results from TOMCAT simulations with prior (blue) and
posterior (green) emissions estimates, both with GOSAT averaging kernels
applied. Correlation coefficients, RMSE, and growth rates of the model
simulations and GOSAT in each region are shown in the panels.
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Inversion results constrained by CH4 (INV-CH4), and not δ13CH4, also accurately reproduce assimilated CH4 observations
(R=0.93). INV-CH4 also shows some improved agreement with
δ13CH4 observations relative to the prior (R=0.60),
although values are overestimated in earlier years (2003–2008) (Fig. 1b).
TCCON sites (Wunch et al., 2011) used for evaluation of the TOMCAT
simulations.
Site Name
Latitude
Longitude
Altitude
Reference
(∘ N)
(∘ E)
(km)
Ny-Ålesund, Norway
78.9
11.9
0.02
Notholt et al. (2017a)
Sodankylä, Finland
67.4
26.6
0.19
Kivi et al. (2017)
Bremen, Germany
53.1
8.9
0.03
Notholt et al. (2017b)
Garmisch, Germany
47.5
11.1
0.74
Sussmann and Rettinger (2017)
Park Falls, USA
45.9
-90.3
0.44
Wennberg et al. (2017a)
Lamont, USA
36.6
-97.5
0.32
Wennberg et al. (2017b)
Izana, Spain
28.3
-16.5
2.37
Blumenstock et al. (2017)
Darwin, Australia
-12.5
130.9
0.04
Griffith et al. (2017a)
Wollongong, Australia
-34.4
150.9
0.03
Griffith et al. (2017b)
Validation of the model inversion using the independent, non-assimilated
GOSAT data shows improved seasonal and interannual representation of
XCH4 (Fig. 2). The RMSE is reduced in all five regions with values
ranging from 48.6 to 64.6 ppb in the prior to 8.3 to 16.5 ppb in the
posterior, with values typically originating from a negative bias in the
model. The correlation is increased in the inversion with R values ranging
from 0.60 to 0.92 in the prior to 0.94 to 0.96 in the posterior. The trend is
also better captured in the posterior in all five regions, although it is still
underestimated in all regions, and more so in EA (-1.3 ppb yr-1) and AO
(-1.1 ppb yr-1). Both the prior and posterior biases are larger in
the Southern Hemisphere, possibly as a result of slow inter-hemispheric
transport within the model, previously noted in Patra et al. (2011). Also
contributing to this offset is an underestimation of Southern Hemisphere
simulated atmospheric CH4 growth rates in the prior model simulation
(Fig. 3).
We performed further validation using measurements from nine non-assimilated
TCCON sites with data available from at least 2009 (see Table 3). The results
show improved model–data correlation at all nine sites, with an increase in the
all-site mean R value from 0.69 in the prior to 0.87 in the posterior
(Fig. 4). The RMSE is reduced at sites; with an all-site mean decrease from
55.9 ppb in the prior to 18.8 ppb in the posterior, further reductions
would be expected if column observations were used in the inversion. Overall
the inversions are found to improve model–data agreement when validated
against the independent measurements from both GOSAT and TCCON. The resulting
Southern Hemisphere offset in the posterior relative to GOSAT and TCCON
suggests the posterior estimates represent a reasonable but not conclusive
scenario for source–sink attribution. As only surface sites are assimilated,
some inaccuracy in the representation of the total column is not surprising.
(a) Zonally averaged monthly mean XCH4 volume
mixing ratio (ppb) from GOSAT between April 2009 and December 2015 plotted
against the sine of latitude, for which black denotes missing values.
(b, c) Same as (a) but for TOMCAT simulations with prior
and posterior emissions estimates, respectively. GOSAT averaging kernels are
applied to model simulations.
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Observed monthly mean XCH4 volume mixing ratio (ppb)
(black line) at nine TCCON sites. Also shown are results from TOMCAT simulations
with prior (blue) and posterior (green) emissions estimates, both with TCCON
averaging kernels applied. Correlation coefficients and RMSE of the model
simulations compared with TCCON are shown for each site.
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Two surface sites were omitted from the inversion and retained for
independent validation, HAGCOC and Cape Grim. Cape Grim is a baseline
station, ideal for comparing the background signal (Fig. 5). Results at Cape
Grim show improved model performance for both CH4 and δ13CH4, with respective RMSE decreases from 52.0 ppb and 0.2 ‰
in the prior to 12.3 ppb and 0.1 ‰ in the posterior and R value
increases from 0.70 and 0.74 in the prior to 0.95 and 0.76 in the posterior.
HAGCOC measurements are taken at high altitude (4464 m), which potentially
provides insight into the vertical profile of measured species. As with Cape
Grim, HAGCOC shows posterior improvements in both CH4 and
δ13CH4, with respective RMSE decreases from 70.5 ppb and
0.2 ‰ in the prior to 27.1 ppb and 0.08 ‰ in the posterior
and R value increases from 0.41 and 0.46 in the prior to 0.62 and 0.74 in
the posterior.
Prior and posterior comparison
The synthesis inversions, INV-FULL and INV-CH4, provide posterior regional
changes in sources and global changes in OH (Fig. 6). Relative to the prior,
INV-FULL and INV-CH4 show an average OH decrease of 5 % and 4 %,
respectively (Table 1). Results from INV-FULL show that globally agricultural
(-13 %), energy (-8 %), and biomass burning (+7 %) emissions
undergo the largest relative average 2003–2015 posterior change compared to
the prior (Table 1). Relative changes in rice, waste, and wetlands are smaller
(<3 %). The posterior emissions errors are between 5 % and 13 %
compared with the 50 % prior error. Regionally (Fig. 7), 2003–2015
average posterior energy sector emissions are increased, relative to the
prior, by 9 %–33 % in four regions (NA, SA, AM, and AO), which is
offset by a 37 % decrease in EA. Notable posterior agricultural emissions
decreases occur in EA (-36 %) and AO (-14 %). Wetland emissions
are increased beyond the posterior error range in NA (+24 %) and EA
(+44 %) and decreased within the error range in SA (-7 %), AM
(-7 %), and AO (-6 %). In all regions posterior emissions estimates
for biomass burning, waste, and rice are within, or close to, the error range
compared with prior estimates (Table 4).
Regional CH4 emissions based on prior (top) and synthesis
inversion estimates (bottom) between 2003 and 2015. Note the total global
emissions, but not the total regional emissions, include the supplementary
emissions (geological, hydrates, oceans, and termites). Uncertainties are also
shown for posterior emissions; all prior emissions have a 50 %
uncertainty.
Region
Prior annual emissions by sector (Tg CH4 yr-1)
Biomass
Energy
Waste
Rice
Wetlands
Agriculture
Total
burning
N. America
1.9
20.7
10.9
0.5
17.7
10.6
71.3
Eurasia
2.9
87.0
21.7
2.1
15.6
17.8
165.9
S. America
3.2
7.2
5.1
0.5
47.2
17.2
82.3
Africa and Middle E.
7.2
26.6
7.2
0.8
33.2
10.4
85.4
S. Asia and Oceania
5.8
32.0
24.9
29.0
40.1
26.8
177.8
Global
21.1
173.5
69.8
33.0
153.8
82.8
571.0
Posterior annual emissions by sector (Tg CH4 yr-1)
Biomass
Energy
Waste
Rice
Wetlands
Agriculture
Total
burning
N. America
1.2±0.6
22.8±4.0
11.2±2.4
0.5±0.1
22.0±4.0
11.0±2.2
68.7±7.3
Eurasia
2.3±1.0
54.4±11.8
21.7±4.8
2.5±0.6
22.5±3.7
11.2±3.1
114.6±13.9
S. America
3.8±1.1
8.7±1.7
5.1±1.2
0.5±0.1
44.0±10.6
16.6±3.8
78.7±15.1
Africa and Middle E.
8.6±1.9
35.4±6.3
7.2±1.6
0.8±0.2
30.8±7.4
10.2±2.3
93.1±12.5
S. Asia and Oceania
6.6±1.6
38.9±7.2
23.6±5.4
29.0±6.7
37.9±8.6
23.1±5.6
159.1±16.7
Global
22.5±2.9
160.1±15.8
68.9±7.8
33.3±6.8
157.2±16.5
72.1±8.1
537.5±26.5
Globally, for the 2003–2015 period, derived posterior and prior emissions
estimates had average growth rates of 4.1±0.6 Tg yr-2 and 4.0±0.2 Tg yr-2, respectively. When considering only the renewed growth
(2007–2015) the posterior growth rate of 5.7±0.8 Tg yr-2 becomes
noticeably larger than the prior (3.7±0.4 Tg yr-2).
The seasonal range of the prior global wetland emissions
(5.7 Tg month-1) is underestimated compared to the posterior
(13.8 Tg month-1). The seasonal cycle in biomass burning emissions is
largely unchanged between the prior and posterior. The seasonal amplitude in
rice emissions also remains largely unchanged, although the seasonal peak
occurs in August in the prior and July in the posterior (Fig. 6).
Observed surface CH4 (top) and δ13CH4
(bottom) from 2003 to 2015 at two independent NOAA sites (black line). Also
shown are results from TOMCAT simulations using prior emissions estimates
(blue line), and posterior estimates based on a combined CH4 and
δ13CH4 synthesis inversion (INV-FULL, green line). RMSE and
correlation coefficients of the model simulations compared with observations
are shown for each site.
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(a–f) Annual CH4 emissions
(Tg CH4 yr-1) from different sectors for global prior (black
solid line), INV-CH4 (black dashed line), and INV-FULL posterior (black dotted
line) estimates. Regional estimates are also displayed for North America
(blue), Eurasia (green), South America (orange), Africa and the Middle East
(red),
and South Asia and Oceania (purple). (g) Prior and posterior global
OH estimates for the same period. Shaded region denotes posterior error A
for INV-FULL (see Eq. 5 in text).
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Time trends in sources and sinks
Average energy, waste, and wetland emissions are increased after 2007 by
12.9±2.7 % (19.0 Tg yr-1), 5.7±1.6 %
(3.8 Tg yr-1), and 2.6±1.8 % (4.0 Tg yr-1),
respectively, relative to their 2003–2006 posterior values (Table 6).
Regionally, the shift in post-2007 energy sector emissions mainly occurs in
AM (+8.4 Tg yr-1) and AO (+11.1 Tg yr-1). Four out of five
of the regions show a positive post-2007 shift in waste emissions of
0.4–1.4 Tg yr-1, SA is the only region with a slight decrease
(-0.03 Tg yr-1). The small increase in wetland emissions since 2007
derived from the inversion, mainly from EA (3.4 Tg yr-1), agrees well
with bottom-up estimates for wetland emissions trends, for example the
3 % increase found by McNorton et al. (2016a). The posterior shows a
negative shift in posterior biomass burning emissions of 11.8±6.4 %
(-2.9 Tg yr-1) for the 2007–2015 period relative to 2003–2006,
which is in partial agreement with the 3.7 Tg CH4 yr-1
decrease derived by Worden et al. (2017) for the 2008–2014 period relative
to 2001–2007. This shift occurs in all five regions, with the largest
decrease in AO (-1.2 Tg yr-1). Overall the derived increase in
energy sector, waste, and wetland emissions coupled with the decrease in
biomass burning emissions agree well with a recent budget review (Saunois et
al., 2017).
The post-2007 posterior emissions growth occurs mainly in the energy (3.4±1.0 Tg yr-2) and wetland (1.4±1.0 Tg yr-2) sectors. For
the entire period most of the posterior energy sector growth occurred in AM
(1.2 Tg yr-2) and AO (1.5 Tg yr-2), with a smaller proportion
from NA (0.6 Tg yr-2) and SA (0.2 Tg yr-2) (Fig. 7 and
Table 5). The recent EDGAR v4.3.2 inventory (Janssens-Maenhout et al., 2017)
for energy sector emissions shows AM and AO growth of 1.0 and
2.4 Tg yr-2, respectively, for 2003–2012. This is smaller than the
2.2 and 3.1 Tg yr-2 shown by our inversion for the same period. The
majority of prior AM energy sector emissions originate from energy for
buildings in Nigeria and eastern Africa, fuel exploitation from the Middle
East, the Niger Delta, and South Africa, and pipelines in western Africa,
Algeria, and the Middle East. The regional aggregation of fluxes in our
inversion system prevents subregional attribution, as a result we are unable
to diagnose more specific posterior spatial patterns, but our results
suggest,
on a regional scale, emissions are underestimated in both magnitude and
growth rate in the prior. For the AO energy sector, the majority of prior
emissions, and therefore the posterior increases, originate from energy for
buildings in India, China, and South East Asia; fuel exploitation in eastern
China, Japan, India, South East Asia and eastern Australia; refineries in
northern India, eastern China, Japan, and Indonesia; and pipelines in India,
eastern China, eastern Australia, and New Zealand. The growth in emissions in
EA in EDGAR v4.3.2 for 2003–2012 (1.4 Tg yr-2) is not seen in our
inversion for the same region and period (-2.2 Tg yr-2).
Map showing regional annual mean CH4 emissions
(Tg CH4 yr-1) and yearly change in emissions
(Tg CH4 yr-2) calculated as a linear regression between 2003
and 2015 for INV-FULL (thin coloured bars) and prior (thick grey bars)
estimates. Error bars represent 1 standard deviation of the mean posterior
emissions and posterior regression errors. Note that the black borders
indicate the five regions used for the flux partitioning.
![]()
Regional CH4 emissions growth trends based on synthesis
inversion estimates between 2003 and 2015.
Region
Annual emissions growth by sector (Tg CH4 yr-2)
Biomass burning
Energy
Waste
Rice
Wetlands
Agriculture
Total
N. America
-0.06
+0.59
+0.03
+0.00
+0.28
+0.11
+0.95
Eurasia
-0.12
-0.58
+0.13
+0.00
+0.48
-0.08
-0.17
S. America
-0.22
+0.20
+0.01
+0.00
-0.15
+0.09
-0.06
Africa and Middle E.
-0.05
+1.18
+0.06
+0.00
+0.06
+0.07
+1.33
S. Asia and Oceania
+0.25
+1.51
+0.23
-0.10
+0.14
+0.00
+2.03
Global
-0.20
+2.91
+0.46
-0.10
+0.81
+0.20
+4.08
Posterior annual CH4 emissions for the period of near-zero
atmospheric growth (2003–2006) and the renewed growth (2007–2015) based on
three different inversion simulations. Note the total emissions include the
supplementary emissions (geological, hydrates, oceans, and termites).
Simulation
Annual emissions by sector for the 2003–2006 period (Tg CH4 yr-1)
Biomass burning
Energy
Waste
Rice
Wetlands
Agriculture
Total
INV_FULL
24.5
146.9
66.3
34.6
154.4
71.6
518.9
INV_CH4
24.4
156.0
66.3
31.1
160.7
75.9
529.8
INV_FIXED
21.8
143.8
66.1
34.6
155.4
71.1
514.6
Annual emissions by sector for the 2007–2015 period (Tg CH4 yr-1)
Biomass burning
Energy
Waste
Rice
Wetlands
Agriculture
Total
INV_FULL
21.6
165.9
70.1
32.7
158.4
72.3
545.8
INV_CH4
20.9
169.9
69.6
30.0
171.9
78.7
557.7
INV_FIXED
21.8
154.5
68.7
34.0
158.1
70.9
536.1
Difference in annual emissions between 2007–2015 and 2003–2006 (Tg CH4 yr-1)
Biomass burning
Energy
Waste
Rice
Wetlands
Agriculture
Total
INV_FULL
-2.9
+19.0
+3.8
-1.9
+4.0
+0.7
+26.9
INV_CH4
-3.5
+13.9
+3.3
-1.1
+11.2
+2.8
+27.9
INV_FIXED
0.0
+10.7
+2.6
-0.6
+2.7
-0.2
+21.5
During the 2008–2012 period NA energy sector emissions were found to be
11.4 Tg yr-1 (+66 %) higher than the 2003–2015 (excluding
2008–2012) average, resulting in uncertainty in the NA growth rate (Fig. 6).
These findings are also present in INV-CH4, which shows an
11.8 Tg yr-1 increase over the same period. This period of anomalously
high emissions is not present in the prior and therefore is due to the
assimilated observations. These high emissions may be associated with oil or
natural gas extraction (Helmig et al., 2016). During periods of high NA
energy sector emissions, the EA energy sector emissions are reduced and
vice versa, suggesting a possible dipole caused by the inversion. This
suggests increased uncertainty in the derived EA and NA energy sector
emissions, possibly due to a paucity of observations over these regions.
Posterior wetland emissions estimates show a growth of 0.8 Tg yr-2 for
the 2003–2015 period, which increases to 1.4 Tg yr-2 for the
2007–2015 period. The majority of this growth occurs in EA
(+0.5 Tg yr-2). The four remaining emissions sectors all have a
global annual change of less than ±0.5 Tg yr-2.
For the posterior time series, OH concentrations in INV-FULL and INV-CH4 are
relatively constant throughout the period of 2007–2015 (Fig. 6), but relative to
their 2003–2006 concentrations these values are smaller by 1.8±0.4 % and 0.3±0.5 %, respectively. The larger drop after 2007 in
INV-FULL OH concentration, relative to INV-CH4, highlights the importance of
including δ13CH4 in the inversion. A decrease in OH as a
contributor to the renewed growth agrees well with previous simple global box
models (Rigby et al., 2017; Turner et al., 2017). The OH shift found here is
smaller in magnitude than the -8 % shift between 2004 and 2014 derived
by Rigby et al. (2017) and the -7 % shift between 2003 and 2016 derived
by Turner et al. (2017). The posterior OH error is reduced from the prior
estimate of 2 % to 1.8 %, which, although a reduction, is similar to
the modelled post-2007 OH decrease. The decrease in OH contributes to a
decrease in δ13CH4 and an increase in global CH4.
Section 3.5 details analysis of OH sensitivity.
Source and sink attribution
Analysis performed on our inversion results using the box model approach
described by McNorton et al. (2016b) suggests that ∼30 % of the
sustained CH4 growth after 2007 can be explained by decreased OH,
while ∼60 % and ∼10 % is attributed to an increased energy
sector and wetland emissions (Table 5). The shift in emissions between
2003–2006 and 2007–2015 is broadly consistent for each sector for three
different inversions, INV_FULL, INV_CH4, and INV_FIXED (fixed
annual emissions; see below) (Table 6). We investigated source and sink
contribution to the negative δ13CH4 trend using simple one
box model analysis, outlined in the appendix of McNorton et al. (2016b), and
posterior estimates from INV-FULL. Results show that post-2007 changes in
energy sector (+0.15 ‰), biomass burning (-0.08 ‰),
wetland (-0.05 ‰), waste sector (-0.03 ‰), and
agricultural (-0.01 ‰) emissions, as well as OH
(-0.12 ‰), contributed to the observed trend.
Suite of inversion sensitivity experiments with varying errors on
source and sink estimates.
Source–sink
Sensitivity test error
Control
S1
S2
S3
S4
S5
S6
S7
S8
S9
Wetlands
50 %
50 %
50 %
50 %
10 %
20 %
100 %
100 %
100 %
50 %
Rice
50 %
50 %
50 %
50 %
10 %
20 %
100 %
100 %
100 %
50 %
Agriculture (excluding rice)
50 %
50 %
50 %
50 %
10 %
20 %
100 %
50 %
50 %
50 %
Waste
50 %
50 %
50 %
50 %
10 %
20 %
100 %
50 %
50 %
50 %
Energy
50 %
50 %
50 %
50 %
10 %
20 %
100 %
50 %
50 %
50 %
Biomass burning
50 %
50 %
50 %
50 %
10 %
20 %
100 %
50 %
50 %
50 %
OH
2 %
1 %
3 %
10 %
2 %
2 %
2 %
2 %
3 %
0 %
Sensitivity tests
To test the robustness of the inversion to changes in prior error estimates
we performed nine perturbation experiments (S1–S9). Monthly source errors
were perturbed between 10 % and 100 % and yearly OH errors from
0 % to 10 % (Fig. 8 and Table 7). For small error perturbations, the
inversion results do not change much relative to INV-FULL (Fig. 8 and
Table 8). However, when the emissions errors are reduced from 50 % to
10 % (S4) the posterior energy emissions estimates deviate from the
control (INV-FULL) inversion, with a mean bias of 60.5 Tg yr-1. We
consider these large ranges in posterior estimates to be an unrealistic
representation of interannual variability in energy sector emissions
(Fig. 8), which suggests the model fails to provide reasonable posterior
estimates when the prior emissions error is set too low. For most cases of
increased emissions errors the OH change is similar to the control. However,
for 100 % emissions errors (S6) the agricultural emissions are further
reduced, from 82.8 Tg yr-1 in the prior and 72.1 Tg yr-1 in
INV-FULL to 64.1 Tg yr-1. In this case OH is only reduced by
0.5 % after 2007, relative to 2003–2006, compared to 1.8 % in
INV-FULL. This results in a smaller OH contribution to the post-2007
CH4 growth.
(a–f) Annual mean CH4 emissions
(Tg CH4 yr-1) from different sectors for global prior (black
solid line) and INV-FULL (black dotted line) estimates. (g) Same as
(a–f) but for global mean OH (molecules cm-3). Additional
lines in each panel show sensitivity inversions with different emissions and
OH uncertainties (coloured lines) and an inversion assuming no change in OH
(black dashed line).
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Posterior annual CH4 emissions for the period of near-zero
atmospheric growth (2003–2006) and the renewed growth (2007–2015) based on
a
suite of inversion sensitivity experiments with varying errors on source and
sink estimates. Note the total emissions include the supplementary emissions
(geological, hydrates, oceans, and termites).
Simulation
Annual emissions by sector for the 2003–2006 period (Tg CH4 yr-1)
Biomass burning
Energy
Waste
Rice
Wetlands
Agriculture
Total
Control
24.5
146.9
66.3
34.6
154.4
71.6
518.9
S1
24.9
150.4
66.8
35.0
158.7
72.3
530.4
S2
24.1
143.5
65.9
34.3
150.2
70.9
507.3
S3
23.2
135.2
64.7
33.4
139.9
69.2
479.6
S4
23.7
146.7
67.8
33.3
150.7
80.3
515.2
S5
23.9
146.5
67.3
33.7
152.1
78.5
515.9
S6
25.0
146.6
68.2
36.3
155.2
63.4
524.3
S7
24.7
147.7
66.2
34.7
152.1
72.1
518.5
S8
24.2
143.8
66.0
34.4
145.4
71.9
504.9
S9
25.1
152.0
67.0
35.2
160.7
72.6
535.8
Annual emissions by sector for the 2007–2015 period (Tg CH4 yr-1)
Biomass burning
Energy
Waste
Rice
Wetlands
Agriculture
Total
Control
21.6
165.9
70.1
32.7
158.4
72.3
545.8
S1
22.0
170.5
70.7
33.2
164.0
73.2
560.6
S2
21.2
161.3
69.4
32.3
152.8
71.5
530.9
S3
20.2
149.7
67.8
31.3
138.8
69.3
493.7
S4
19.9
250.3
70.5
33.3
156.9
82.7
626.5
S5
20.4
174.0
70.1
33.6
157.7
80.3
550.7
S6
22.5
169.8
73.5
32.4
158.8
64.3
560.2
S7
21.7
167.0
70.3
30.7
156.1
74.0
545.4
S8
21.1
161.7
69.9
30.3
147.1
73.8
527.4
S9
22.2
172.6
71.0
33.3
166.5
73.6
567.2
For large or small OH errors (S3: 10 %; S1: 1 %) the posterior OH is
decreased by 18 % or 2 %, respectively, compared to the prior OH.
Assuming no change in OH (S9) post-2007 shifts in biomass burning, energy
sector, and wetland emissions relative to 2003–2006 are required to fit
observations in the inversion. In this scenario biomass burning emissions
decrease globally by -11.5±3.8 % (-2.9 Tg yr-1), and in AO
by -16.1±17.9 % (-1.2 Tg yr-1). Energy sector emissions
increase globally by 13.6±2.7 % (+20.6 Tg yr-1), in NA by
42.9±12.9 % (+7.7 Tg yr-1) and in AO by 36.7±5.1 %
(+12 Tg yr-1). Wetland emissions increase globally by 3.6±1.8 % (+5.8 Tg yr-1). The sign and spatial distribution of
these changes are similar to those seen in INV-FULL, although the magnitude in
post-2007 changes is typically increased in S9 (see Sect. 3.2), which is
expected as the necessary increased growth rate is allocated more to
emissions
changes when OH is assumed constant.
The sensitivity analyses highlight that the prior uncertainty can have a
noticeable influence on the posterior estimates. In particular, the posterior
OH is found to be sensitive to the prior error estimate, highlighting the
importance of prior knowledge for future studies. This limits the accuracy of
the magnitude of the posterior estimates. However, the spatial, temporal, and
sector-specific relative post-2007 changes, compared to 2003–2006, remain
broadly consistent among experiments. This shows a limitation in the
comparison between prior and posterior sources–sinks but does not discount
the importance of the results for trend detection between 2003 and 2015.
We performed a synthesis inversion with no prior trend in emissions or OH
(INV-FIXED), using fixed 2003 emissions, to investigate the sensitivity of
the inversion to prescribed prior trend information (Fig. 9). The results
show an annual average CH4 emissions growth of 2.8±0.6 Tg yr-2, the majority of which comes from the energy sector
(1.8±0.6 Tg yr-2) and wetlands (0.7±0.5 Tg yr-2). On
a global scale the sector attribution agrees well with INV-FULL but with a
smaller magnitude in emissions trends. The reduced growth in INV-FIXED is
offset by a higher negative trend in OH concentration
(-0.23 % yr-1), relative to INV_FULL
(-0.14 % yr-1).
(a–f) Annual
CH4 emissions (Tg CH4 yr-1) from different sectors for
INV-FIXED (black solid line) and INV-FULL posterior (black dotted line)
estimates. Regional estimates are also displayed for North America (blue),
Eurasia (green), South America (orange), Africa and the Middle East (red),
and South Asia and Oceania (purple). (g) INV-FIXED (black solid line) and INV-FULL (black dotted
line) posterior global OH estimates (molecules cm-3) for the same
period. Shaded region denotes posterior error A (see Eq. 5 in text).
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In absolute terms OH concentrations are 0.8 % lower in INV-FIXED compared
to INV-FULL, which acts to offset the lower emissions. OH concentrations for
INV-FIXED are 1.8 % lower for the 2007–2015 period, relative to the
2003–2006 period, matching the relative change from INV-FULL. Regionally,
the largest trends are observed over NA (1.2±0.9 Tg yr-2), AM
(0.9±0.3 Tg yr-2), and AO (0.7±0.4 Tg yr-2), with over
half of the growth in each of those regions originating from the energy
sector. Overall INV-FIXED shows good spatial agreement with INV-FULL when
considering sector attribution but the magnitude of emissions increases is
slightly smaller.
Tropospheric Cl only accounts for a small fraction of the total CH4
sink (∼5 % or less) (Kirschke et al., 2013; Hossaini et al., 2016)
but, as the kinetic fractionation of Cl reacting with CH4 is more
than an order of magnitude greater than that of OH, it is plausible that
changes in Cl could contribute to the post-2007 trend in
δ13CH4. In results from an experiment that inverts for CL,
INV-CL (Fig. 10) and the sensitivity set-up with fixed OH show similar
posterior fluxes. This suggests that Cl trends and their effect on
δ13CH4 are unlikely to be an important contributor to the
post-2007 CH4 trends, although it is important to note that whilst
variability was applied to prior emissions and the OH field, for some years,
no variability is applied to the prior Cl field.
(a–f) Annual CH4 emissions
(Tg CH4 yr-1) from different sectors for global prior (black
solid line) and INV-CL (black dotted line) estimates. Regional estimates are
also displayed for North America (blue), Eurasia (green), South America
(orange), Africa and the Middle East (red), and South Asia and Oceania (purple).
(g) Prior and posterior global tropospheric Cl estimates for the
same period. Shaded region denotes posterior error A (see Eq. 5 in text).
![]()
Posterior error
The robustness of the experimental set-up is further investigated using the
posterior error covariance matrix calculated using Eq. (5). By splitting the
inversion into 12-month intervals, emissions later in the year are constrained
by fewer observations, possibly only by observations close to the source. The
influence of this was investigated and the posterior error was found to be on
average 12 % higher for December emissions relative to the January
emissions, which was broadly consistent among regions and sectors.
Relatively small time-independent off-diagonal error correlations are found
among different regions and sectors (Fig. 11). The posterior covariances
produced using Eq. (5) have been normalised using the corresponding posterior
standard deviations to provide posterior correlation values. The largest
negative correlation is between EA and NA energy sector emissions, which
suggest an artificial trade-off of our results with the increasing NA
emissions over 2008–2012 being offset by a decrease in EA emissions over the
same period. Overall the results are well constrained by the inversion.
Typically, the temporal error correlation is also found to be relatively
small, with the exception being the energy sector emissions. Both positive
and negative off-diagonal error correlations are found in posterior energy
estimates at a monthly resolution, possibly relating to the prior temporal
correlation applied; as a result we typically report annual values.
Posterior error correlation matrix for all regions and sectors for
January (a) and July (b) 2015. Posterior error correlation
matrix for 12 months in 2015 for the Eurasian energy sector (c) and
South American wetlands (d). The plots show a subset of the total
posterior error correlation matrix as an example. Values are calculated by
normalising the posterior covariances using the corresponding posterior
standard deviations.
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Conclusions
We have performed a synthesis inversion using a 3-D CTM to
investigate the post-2007 renewed growth in atmospheric CH4 and
decline in δ13CH4. This work adds to the results from other
studies, which were based on a box model approach for source and sink
attribution based on CH4 and δ13CH4 observations
(e.g. Rigby et al., 2017). By using a 3-D CTM we have been able to provide
detailed monthly regional attribution of six different emissions sectors and
global OH changes, evaluating both the trends over the full 2003–2015 period
and shifts that occurred around 2007. We have also been able to validate
these results using independent surface sites and recent XCH4 data
available from GOSAT and TCCON. The sensitivity of the inversion has been
tested for different prior assumptions and uncertainties.
A CH4-only inversion under-constrains the solution with respect to
13CH4 observations, resulting in reduced correlation with
δ13CH4 observations (R=0.60). The agreement of the
simulations with observations improved when additional 13CH4
observations were used to constrain CH4 fluxes, with the correlation
increasing to R=0.87. The prior model based on published emissions does
not capture the CH4 and δ13CH4 trend either at the
assimilated surface site observations or in the non-assimilated GOSAT and
TCCON data. In contrast, our derived posterior emissions inventories capture
both the renewed growth in CH4 and the reduction in
δ13CH4 observed from the assimilated NOAA surface sites from
2007 to 2015 and compare well with independent surface CH4 and
δ13CH4 observations as well as with GOSAT and TCCON-derived
XCH4. The independent validation suggests that, although the
CH4 growth rate is better represented in the posterior, it is still
underestimated. The posterior model agreement with assimilated surface data
and slight bias with validation column data (TCCON and GOSAT) highlight a
potential a posteriori model error in total column CH4
concentrations; however, this bias is small. The magnitude of the
contribution of model transport error to this underestimation is unknown.
Both prior and posterior simulations underestimate Southern Hemisphere
CH4 concentrations, highlighting possible issues with
interhemispheric transport within the model. The lack of independent data
around the end of the CH4 “hiatus” means it is difficult to
evaluate model performance over this period (2007).
Our inversion results suggest that the 2007–2015 growth in CH4 can
be best explained by a 1.8±0.4 % reduction in mean OH, a 12.9±2.7 % increase in energy sector emissions, mainly from AM and AO, and a
2.6±1.8 % increase in wetland emissions, mainly from EA. The
expected increase in atmospheric δ13CH4 caused by increased
energy sector emissions (+0.15 ‰) is offset mainly by the decrease
in OH (-0.12 ‰), small decrease in biomass burning emissions
(-0.08 ‰), and small increase in wetland emissions
(-0.05 ‰).
When δ13CH4 is not assimilated the trend in posterior
emissions is slightly increased after 2007 and the OH decrease is smaller
(-0.3 %). By including the δ13CH4 observations, a larger
post-2007 OH decrease is required (-1.8 %), highlighting the importance
of including δ13CH4 within the inversion.
An alternative scenario, in which OH is assumed constant after 2007, requires a
-11.5±3.8 % decrease in biomass burning emissions and 13.6±2.7 % and 3.6±1.8 % increases in energy sector and wetland
emissions. These results agree with previous studies, which also assumed
constant OH (Nisbet et al., 2016; Schaefer et al., 2016; Worden et
al., 2017). Whilst a reduction in OH is found to be, in part, the most likely
explanation for the renewed CH4 growth, this alternative scenario
with no change in OH provides an alternative explanation for the cause of the
post-2007 CH4 growth.
The inversion results suggest Eurasian energy sector emissions are typically
overestimated by inventories and previous top-down studies, such as the
Global Carbon Budget (Saunois et al., 2016). The reduced EA emissions are
found to be offset by an underestimate in all other regions. We find prior
annual estimates of biomass burning, waste, and rice to be relatively
accurate, whilst agricultural estimates are overestimated. Small changes
occur in the seasonal cycle of rice emissions and the seasonal range is
underestimated in wetland emissions.
Our inversion is found to be robust when small changes are made to
uncertainty errors; however, large uncertainty remains around the accuracy
of prior emissions. Assuming no prior trend in emissions reduces the
required growth rate in both wetland and energy sector emissions, although
they remain the main source contribution to the renewed growth after 2007.
The reduction in the emissions trend is offset by an increased negative trend
in OH concentration. Overall the magnitude of the trends inferred varies
among experiments but there is consistent agreement that both OH decrease
and wetland and energy sector emissions increase contributed to the
post-2007 growth.
Our inversion results represent plausible scenarios for variations in
CH4 sources and sinks, though several caveats exist. The
uncertainties in the sources and sinks are somewhat subjective and we have
not considered source signature and kinetic fractionation uncertainty. We
have assumed that all uncertainties are independent of each other (excluding
energy emissions). We have also not considered variation in other sinks (e.g.
O(1D), soil). The synthesis inversions are performed over coarse
spatial regions and only attribute emissions at the monthly scale; future
studies should utilise increased observations to provide finer spatial and
temporal resolution. The assumption that emissions within a region are
correlated limits more specific spatial attribution of sources. Within a
region it is likely that some posterior emissions are too high, offset by
emissions being too low elsewhere within the domain. The choice of regional
aggregation is likely to influence the synthesis inversion, which may result
in aggregation errors causing biases in the posterior fluxes (Kaminski et
al., 2001). Finally, an important question is whether tropospheric OH has
varied in the way suggested by CH4 inversion studies. The processes
causing variations in OH are complex and remain poorly quantified. Possible
explanations include changes in tropospheric O3 and trends in
tropospheric UV radiation related to global stratospheric O3
recovery. If the reduction in available OH due to increased reactive carbon
gases is no longer being sufficiently offset by increased emissions of
OH-forming nitrogen oxides, then OH concentrations might be in decline
(Lelieveld et al., 2004). For example, Itahashi et al. (2014) showed a
reduction in column NO2 growth associated with the economic downturn
over East Asia between 2008 and 2009, which approximately coincides with the
increased CH4 growth.