Reduced biomass burning emissions reconcile conflicting estimates of the post-2006 atmospheric methane budget

Abstract

Several viable but conflicting explanations have been proposed to explain the recent ~8 p.p.b. per year increase in atmospheric methane after 2006, equivalent to net emissions increase of ~25 Tg CH4 per year. A concurrent increase in atmospheric ethane implicates a fossil source; a concurrent decrease in the heavy isotope content of methane points toward a biogenic source, while other studies propose a decrease in the chemical sink (OH). Here we show that biomass burning emissions of methane decreased by 3.7 (±1.4) Tg CH4 per year from the 2001-2007 to the 2008-2014 time periods using satellite measurements of CO and CH4, nearly twice the decrease expected from prior estimates. After updating both the total and isotopic budgets for atmospheric methane with these revised biomass burning emissions (and assuming no change to the chemical sink), we find that fossil fuels contribute between 12-19 Tg CH4 per year to the recent atmospheric methane increase, thus reconciling the isotopic- and ethane-based results.

Full text

ARTICLE
Reduced biomass burning emissions reconcile
conflicting estimates of the post-2006 atmospheric
methane budget
John R. Worden 1, A. Anthony Bloom1, Sudhanshu Pandey2,3, Zhe Jiang1,4, Helen M. Worden4,
Thomas W. Walker1, Sander Houweling2,3,5 & Thomas Röckmann2
Several viable but conflicting explanations have been proposed to explain the recent ~8 p.p.b.
per year increase in atmospheric methane after 2006, equivalent to net emissions increase of
~25 Tg CH
4
per year. A concurrent increase in atmospheric ethane implicates a fossil source;
a concurrent decrease in the heavy isotope content of methane points toward a biogenic
source, while other studies propose a decrease in the chemical sink (OH). Here we show that
biomass burning emissions of methane decreased by 3.7 (±1.4) Tg CH
4
per year from the
2001–2007 to the 2008–2014 time periods using satellite measurements of CO and CH
4
,
nearly twice the decrease expected from prior estimates. After updating both the total and
isotopic budgets for atmospheric methane with these revised biomass burning emissions
(and assuming no change to the chemical sink), we find that fossil fuels contribute between
12–19 Tg CH
4
per year to the recent atmospheric methane increase, thus reconciling the
isotopic- and ethane-based results.
DOI: 10.1038/s41467-017-02246-0 OPEN
1Jet Propulsion Laboratory, California Institute for Technology, Pasadena, 91109 CA, USA. 2Institute for Marine and Atmospheric Research Utrecht, Utrecht
University, Utrecht, The Netherlands. 3SRON Netherlands Institute for Space Research, Utrecht, The Netherlands. 4National Center for Atmospheric
Research, Boulder, 80301 CO, USA. 5Department of Earth Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands. Correspondence and
requests for materials should be addressed to J.R.W. (email: john.r.worden@jpl.nasa.gov)
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Recent changes in the growth rate of methane1, the second
most important greenhouse gas, and important ozone
precursor2, could be due to changing anthropogenic
emissions in the form of fossil fuel (FF) or agricultural emis-
sions3–8. Alternatively, natural wetland methane fluxes in the
high latitudes or tropics could be increasing in response to var-
iations in temperature, the water cycle, and/or carbon availability
to methanogens9–12, giving a preview of carbon cycle feedbacks to
global warming13. However, determining the relative contribu-
tions of anthropogenic, biogeochemical, and chemical drivers of
methane trends has been extremely challenging and consequently
there is effectively no confidence in projections of future atmo-
spheric methane concentrations. The striking disagreement from
several recent studies explaining the changes to atmospheric
methane since 20065–8is likely due to the assumptions (and
extrapolations) involved in attributing source variability to the
observed changes in atmospheric methane. For example, surface
measurements of CH
4
and its isotopic composition suggest a shift
of methane sources toward increasing tropical biogenic (BG)
sources5,14,15. However, this explanation appears to directly
conflict with observations of increasing FF sources that range
between 5 and 25 Tg CH
4
per year based on ethane/CH
4
ratios6–8
as well as studies based on satellite-based total column methane
measurements16,17. Other studies18,19 show that we cannot rule
out inter-annual variations in the hydroxyl radical (OH) chemical
methane sink as the cause; however, these studies do not directly
show changes in atmospheric OH or provide a mechanistic rea-
son for a change.
Biomass burning (BB) contributes only moderately to atmo-
spheric methane with past estimates ranging from 14 to 26 Tg
CH
4
per year out of the ~550 Tg CH
4
per year budget20,21. The
range of BB CH
4
emissions estimates is in part due to uncer-
tainties in burnt area estimates, combustion factors, and emission
factors22–25 and to large inter-annual variability (IAV) resulting
from substantial regional changes in rainfall due to ENSO26. For
example, larger than normal inter-annual changes in atmospheric
CH
4
in 2006 and likely 1997 can be directly attributed to massive
Indonesian peat fires27,28. Estimates based on burnt area suggest a
decrease of ~2 Tg per year after 2007 (Global Fire Emissions
Database, version 4—GFEDv4s)29 with decreasing burnt area
over Africa likely due to better fire management and agricultural
practices30 as well as reduced emissions over South America and
Indonesia25,31,32. Our study focuses on how changes in biomass
burning BB emissions of methane affect our knowledge of the FF
and BG components of the atmospheric methane budget.
GFED bottom-up estimates for methane emissions from BB
depend on satellite observations of burnt area, vegetation type,
combustion efficiency, and amount of burnt biomass29,33. Top-
down estimates depend on the combination of observationally
constrained total CO flux estimates and in situ or satellite con-
straints on the CH
4
/CO ratio25,28 (Methods). Because the sea-
sonality and location of fires are typically distinct from other
emissions such as biofuels, industry, and transportation, top-
down approaches can robustly distinguish biomass burning
emissions from other sources based on satellite CO concentration
measurements and prior information on burnt-area-based fire
emissions estimates25,28,31,32. Here, we combine bottom-up esti-
mates of fire emissions, based on burnt area measurements, with
the top-down CO emissions estimates31 (Methods), based on the
satellite concentration data and the adjoint of the Goddard Earth
Observing System Chemistry model (GEOS-Chem). This
approach for quantifying CO and CH
4
fire emissions accounts for
published uncertainties in the bottom-up estimates and includes
empirical estimates of the key factors that contribute to uncer-
tainties in emissions inferred from concentration data such as
errors in transport and chemistry, partitioning of CO emissions
on the 5 × 4° GEOS-Chem grid cell to FF, fires, or chemical
sources31,32, and uncertainties in the CH
4
/CO emission factors
and their IAV. We use satellite and in situ measurements of CH
4
/
CO ratios to evaluate fire-based CH
4
/CO values and their asso-
ciated uncertainties (Methods). We then show that biomass
burning emissions of methane decreased by 3.7 (±1.4) Tg CH
4
per year from the 2001–2007 to the 2008–2014 time periods,
nearly twice the decrease expected from prior estimates based on
burnt area measurements. After updating both the total and
isotopic budgets for atmospheric methane with these revised
biomass burning emissions (and assuming no change to the
chemical sink), we find that FFs and BG sources contribute
12–19 Tg CH
4
per year and 12–16 Tg CH
4
per year, respectively,
to the recent atmospheric methane increase, thus reconciling the
isotopic- and ethane-based results.
Results
Trend in CH
4
emissions from fires. Figure 1shows the time
series of CH
4
emissions that were obtained from GFEDv4s and
top-down estimates based on CO emission estimates and
GFED4s-based emission ratios. The CO-based fire CH
4
emissions
estimates amount to 14.8 ±3.8 Tg CH
4
per year for the
2001–2007 time period and 11.1 ±3Tg CH
4
per year for the
2008–2014 time period, with a 3.7 ±1.4 Tg CH
4
per year decrease
between the two time periods. The mean burnt area (a priori)-
based estimate from GFED4s is slightly larger and shows a
slightly smaller decrease (2.3 Tg CH
4
per year) in fire emissions
after 2007 relative to the 2001–2006 time period. The range of
uncertainties (shown as blue error bars in Fig. 1is determined by
the uncertainty in top-down CO emission estimates that are
derived empirically using the approaches discussed in the
Methods). The red shading describes the range of uncertainty
stemming from uncertainties in CH
4
/CO emission factors
(Methods). By assuming temporally constant sector-specificCH
4
/
CO emission factors, we find that mean 2001–2014 emissions
average to 12.9 ±3.3 Tg CH
4
per year, and the decrease averages
to 3.7 ±1.4 Tg CH
4
per year for 2008–2014, relative to
2001–2007. This decrease is largely accounted for by a 2.9 ±1.2
Tg CH
4
per year decrease during 2006–2008, which is primarily
attributable to a biomass burning decrease in Indonesia and
South America25,28,31.
While we account for the IAV in the global CH
4
/CO emission
factors due to varying contributions from individual fire types
(such as savannas or peat fires), the temporal CH
4
/CO variability
due to underlying combustion processes for each fire type is
currently not well characterized. We assess the sensitivity of our
result on decreasing methane BB emissions to larger IAV in
global CH
4
/CO emission factors by randomly perturbing annual
sector-specificCH
4
/CO emission factors (Methods) and examin-
ing how they affect 2001–2014 BB methane emission trends. We
find that the probability of a decrease in methane BB emissions
throughout 2001–2014 is >95% assuming that any unexplained
global annual CH
4
/CO variability is <21% (Fig. 2). There is a 95%
probability that fire methane emissions during 2008–2014
decreased relative to 2001–2007 if the IAV of the global annual
CH
4
/CO ratio is <32%. These perturbations to the CH
4
/CO
emission factors are roughly a factor of three greater than
expected variability from changes in fire-type contributions alone
(global CH
4
/CO IAV 7–8%, Fig. 2). We therefore conclude that
the decrease in biomass burning emissions of methane after 2007
cannot be easily explained by unaccounted inter-annual varia-
tions of the CH
4
/CO due to errors in fire-type contributions.
Furthermore, since coherent sector-specificCH
4
/CO inter-annual
variations comparable to within-sector CH
4
/CO uncertainty (gray
area in Fig. 2) are improbable, unaccounted inter-annual sector-
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specificCH
4
/CO variations cannot easily explain the biomass
burning emission trends.
Wetter years associated with La Nina during the 2008 through
2014 time periods likely contributed to the observed decrease in
fire emissions in South America and Indonesia25,31. It is also
likely that this increased precipitation in these regions affects the
fuel moisture content and in turn the combustion efficiency of the
fires. However, while both CH
4
and CO emission factors, relative
to burnt area or CO
2
, are expected to increase in response to a
reduction in combustion efficiency34,35, there is currently no
established relationship between combustion efficiency and the
CH
4
/CO ratios. To the best of our knowledge, measurements
100%
95%
90%
Probablity (%)
85%
80%
2001–2007 fire CH4 > 2008–2014 fire CH4
Decreasing trend in 2001–2014 fire CH4
Global CH4/CO IAV
Global CH4/CO IAV (prior)
Within-sector CH4/CO uncertainity
75%
0%
0% 10%
6% 20%
11% 30%
16% 40%
Within-sector CH4/CO IAV (%)
Global CH4/CO IAV (%)
21% 50%
27% 60%
32% 70%
37% 80%
43%
Fig. 2 The probability of a decrease in biomass burning methane emissions during 2001–2014. Probability of decrease if the emission factors are within-
sector CH
4
/CO inter-annual variability (black, xaxis) and the corresponding global-scale CH
4
/CO inter-annual variability (light blue, xaxis). The
probability estimates include the propagation of systematic errors in fire CO emission estimates, and sector-specificCH
4
/CO values. For comparison, the
vertical lines show the global CH
4
/CO IAV due to annual changes in relative fire sector contributions. The gray-shaded area shows the within-sector CH
4
/
CO uncertainty
22
Mean
Fire CO uncertainty
CH4/CO uncertainty
GFEDv4
20
18
16
Annual fire CH4 emissions (Tg per year)
14
12
10
8
6
401 02 03 04 05 06 07
Year
08 09 10 11 12 13 14
Fig. 1 Trend of methane emissions from biomass burning. Expected methane emissions from fires based on the Global Fire Emissions Database (black) and
the CO emissions plus CH
4
/CO ratios shown here (red). The range of uncertainties in blue is due to the calculated errors from the CO emissions estimate
and the shaded red describes the range of error from uncertainties in the CH
4
/CO emission factors
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tracking temporal changes in fire CH
4
/CO ratios indicate no
coherent relationship between fire phase and CH
4
/CO variability
on daily timescales34,35 or any significant relationship between
seasonal CH
4
/CO variability and combustion completeness36.
Ultimately, joint constraints on the temporal variability of CH
4
/
CO, e.g., based on further in situ monitoring of fire CH
4
/CO or
Table 1 Isotopic signatures of the three source categories used in our box-model analysis
Source type Previous literature δ13C-CH
4
(‰) Schwietzke et al._201615 δ13C-
CH
4
(‰)
Biogenic −60 ±4.3 −62.3 ±0.7
Fossil fuel (+natural seepages) −39 ±1.7 −44 ±0.7
Biomass burning −24.0 ±2.0 −22.3 ±1.9
The isotopic signatures are reported as means ±1-sigma uncertainty
1830
a
b
c
1820
1810
1800
CH4 (p.p.b.)
13C- CH4 in ‰
(Schwietzke [2016])
13C- CH4 in ‰
(Previous literature)
1790
1780
1770
1760
–46.7
–46.8
–46.9
–47.1
–47.2
–47.3
–47.0
–46.7
–46.8
–46.9
–47.1
–47.2
–47.3
BB-this-study
BB-GFED4s
FF-mf
BG-mf
iso-mf
Measurement
2001
2000
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
–47.0
Fig. 3 Simulated CH
4
and δ13C-CH
4
values. CH
4
mixing ratios (a) and simulated by the box model for values shown in Table 1and listed by model scenario
in Table 2.b,cdescribe the simulated δ13C-CH
4
using the updated values from Schwietzke et al.15 and from prior literature. The biomass burning changes
are prescribed based on the estimates from this study (BB-this-study) and GFED4s (BB-GFED4s). For the BG-mf and FF-mf scenarios, the CH
4
mole
fractions growth is explained by an emission increase of only biogenic or only fossil fuel, respectively (BG-mf and FF-mf overlap in a). The iso-mf scenario
shows the best fit to the isotope and mole fraction data, using an additional source of 24.7 ±1.4 Tg CH
4
per year with an isotopic signature of −56.1 ±1.1‰.
The required adjustments to the methane budgets for fossil fuel and biogenic sources are shown in Figs. 4and 5. The 1-sigma error margins are the
propagated uncertainties of isotopic source signatures and uncertainties of the perturbations. The measurements shown here are the calculated global
average of NOAA-ESRL network measurements
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CH
4
and CO column measurements from upcoming TROPOMI
satellite mission37, could be key to improving the accuracy of fire
methane emission estimates derived from atmospheric CO
constraints.
Balancing the isotopic budget of methane. When accounting for
a 3.7 ±1.4 Tg CH
4
per year decrease in biomass burning emis-
sions between 2001–2007, and 2008–2014, the net change of
other components of the methane budget (e.g., FF, BG sources, or
a change in the OH sink) must have been even stronger than
previously assumed in order to explain the observed increase in
global atmospheric CH
4
levels. Based on our estimated reduction
in biomass burning emissions, we quantify the contribution of
FF-related and BG sources to atmospheric methane increase
using ground-based measurements from the National Oceanic
and Atmospheric Administration Earth System Research
Laboratory (NOAA/ESRL) network (Methods). Relative to FF-
related sources, methane from BG sources is generally depleted in
13C, while methane emitted by biomass burning is relatively
Table 2 Description of CH
4
box-model scenarios
Scenario name Constrained by CH
4
Constrained by δ13C Biomass burning change in
2008
FF and BG change in 2007 OH change
BB-this-study No No This study No change No
BB-GFEDv4s No No GFEDv4s No change No
BG-mf Yes No No change Only BG increase No
FF-mf Yes No No change Only FF increase No
Iso-mf Yes Yes Three BB change scenariosaConstrainedaNo
Iso-mf-OH Yes Yes Three BB change scenariosaConstraineda0–3% reduction
These scenarios are presented in Figs. 3,4,5
aMultiple scenarios are derived based on three BB change scenarios (this study, GFEDv4s, and no change), where BB and BG are constrained based on CH
4
δ13C source signatures and their associated
uncertainties (Figs. 4and 5)
20
Previous literature
Schwietzke et al. (2016)
Fosssil change
in Tg CH
4
per year
Biogenic change
in Tg CH
4
per year
15
10
5
0
–5
30
b
a
25
20
15
10
5No change GFED4s This study
Fig. 4 Change in average annual biogenic and fossil fuel emissions. Change in average annual fossil fuel (a) and biogenic (b) emissions between the
2001–2006 and 2007–2014 periods needed to fit the CH
4
mole fraction for different assumptions about biomass burning emissions and the isotopic
signatures of the methane emission sources. These values are calculated for different proposed changes in biomass burning emissions: GFED4s =2.1 ±1.1
Tg CH
4
per year, this study =3.7 ±1.4 Tg CH
4
per year, and no change =0.0 Tg CH
4
per year. The isotopic signatures assigned to each source type are
shown in Table 1. The error bars are the 1σuncertainties, which are calculated by propagating the uncertainties of the source isotopic signatures, biomass
burning perturbations, and total perturbations needed to fit the growth rate and isotope measurements (see iso-mf scenario in Fig. 3)
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enriched in 13C (Table 1)5,14,15. Recent updates for the isotope
signatures of these emission categories have profoundly changed
the global partitioning between source categories, resulting in a
larger FF contribution to atmospheric methane mole fractions15.
We constrain an atmospheric single box model (Methods) using
isotope signatures to estimate the contributions of BG, FFs, and
biomass burning methane sources to atmospheric methane5,38.
We use both new estimates of isotope signatures15 as well as
previously accepted isotope signatures for our methane source
partitioning estimates (Table 1) in order to verify that these dif-
ferences in the isotopic composition do not affect our
conclusions.
Figure 3shows the box-model results for a range of scenarios
(Table 2) that could explain the observed increase in the CH
4
mole fraction, but would yield different temporal isotope
trajectories, under the assumption of a constant or varying
atmospheric OH sink during this time period (Methods).
Attributing the CH
4
mole fraction increase to either BG (BG-
mf scenario in Table 2) or to FF (FF-mf scenario in Table 2)
emissions leads to δ13C-CH
4
trajectories that do not agree with
the NOAA/ESRL measurements in the post-2007 period. When
optimizing the box-model fluxes in order to fit both the CH
4
and
δ13C-CH
4
time series to the NOAA/ESRL network measurements
(iso-mf scenario), the fits correspond to an additional global
methane source of 24.7 ±1.4 Tg CH
4
per year with average
isotopic signature of −56.1 ±1.1‰; for the iso-mf scenario, this
additional source has been partitioned into contributions from
BG and FF source categories for three scenarios of BB emission
change: no change in biomass burning; the current GFED4s
estimate (−2.1 Tg CH
4
per year); and our CO-based top-down
estimate (−3.7 ±1.4 Tg CH
4
per year).
We find that the larger-than-expected reduction in methane
BB emissions (−3.7 ±1.4 Tg CH
4
per year) leads to a substantial
shift of the global methane source increase from BG to FF
emissions, due to the impact of decreasing 13C-enriched BB
emissions in the CH
4
isotope budget (Fig. 4). For both choices of
isotopic source signatures used in this study, the required increase
in FF emissions is 12–19 Tg CH
4
per year with a corresponding
increase in BG emissions of 12–16 Tg CH
4
per year. As shown in
Fig. 4, FF contributions have to become an increasingly larger
contribution to the overall increase in methane to account for
larger decreases in biomass burning in order to also balance the
isotopic budget. The required FF emission enhancement found
here is substantially larger than in previous literature5, which
showed a contribution of approximately 5.5 Tg CH
4
per year
from FF when assuming BB changes of between 0 and −1.5 Tg
CH
4
per year. In principle, a compensating increase in biofuels
could cancel the decrease in the biomass burning because their
isotopic signatures are similar. However, there are no current
measurements of a concurrent biofuel emissions increase and
20
FF change in Tg CH4 per year
(Schwietzke et al. (2016))
FF change in Tg CH4 per year
(previous literature)
15
10
5
0
–5
20
15
10
5
0
–5 No change GFED4S This study
Sink decrease
–0 %
–1 %
–2 %
–3 %
Fig. 5 Fossil fuel change needed to fit the observed CH
4
growth rate and isotopic composition. Fossil fuel change needed to fit the observed CH
4
growth
rate and isotopic composition assuming a simultaneous change in the CH
4
lifetime due to a change in sink. The results for a constant (0%) sink change
correspond to the iso-mf scenario (red lines in Fig. 3)
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furthermore such an increase is unlikely as it would amount to
25% of the estimated yearly total for the biofuel emissions39.
Recent publications have also shown that we cannot rule out a
decrease in the chemical sink of methane (reaction with OH) as
the cause for the recent increase18,19. To address this possibility,
we have performed additional box-model simulations where the
sink is decreased progressively from 0 to 3%19 (Fig. 5). The largest
effect of assuming changes in the atmospheric OH sink is that the
required global CH
4
source changes accordingly. For example, a
3% sink decrease would require a net source enhancement of ~8
Tg CH
4
per year instead of ~25 Tg CH
4
per year. The isotope
source signature required to match the observed temporal
evolution of δ13C also changes, from −56 to −61‰. Using the
mass balance equation (Eqs. (10)–(12), Methods), the corre-
sponding FF emission contributions have been calculated for the
different BB emissions change scenarios (iso-mf-OH scenario;
Fig. 5). As shown in Fig. 5,wefind that a FF enhancement of
6–12 Tg CH
4
per year is still needed to explain the δ13C
measurements in case of a 3% OH sink decrease; this amount
reflects the total excess of ~8 Tg CH
4
per year, a 1–6Tg CH
4
per
year contribution from BG sources, and the 2.4–5.1 Tg CH
4
per
year decrease from fires. Therefore, our conclusion that an
increase in post-2007 FF emissions is needed to explain the
observed shift in methane emissions5remains valid, even if a
sizeable fraction of the atmospheric methane concentration
increase is due to decreasing atmospheric OH concentrations.
In conclusion, this study provides an updated estimate to
global emissions of methane from fires that are on the low-end of
previous estimates (12.9 ±3.3 Tg CH
4
per year, in contrast to
prior estimates of 14–26 Tg CH
4
per year20,21) for the 2001–2014
time period. We also find that methane emissions from fires
decreased after 2007 by 3.7 ±1.4 Tg CH
4
per year; this decrease is
substantially larger than the GFED4s estimated reduction (2.1 Tg
CH
4
per year). Because fire emissions are isotopically heavier than
those from FF or BG CH
4
sources, the larger-than-expected
decrease in fire emissions requires a substantial re-balancing of
sources to explain both the recent increase in the mole fraction
and isotopic composition of atmospheric methane. We show that
new estimates for biomass burning and revisions to the isotopic
composition of methane sources5,15 lead to a revised estimate of
the FF and BG contributions to the post-2007 atmospheric
methane budget (increase of 12–19 Tg CH
4
year and 12–16 Tg
CH
4
year, respectively), assuming no change in the atmospheric
OH sink of methane; reducing the sink by up to 3% reduces the
FF and BG emissions changes to 6–12 Tg CH
4
year, and 1–6Tg
CH
4
year. Our results therefore reconcile the previously
conflicting findings on the recent changes to atmospheric
methane and its isotopic composition, where isotopic evidence
indicated a BG CH
4
emission increase, while ethane/methane
measurements indicated an increase in FF CH
4
emissions.
Methods
Approach for characterizing CH
4
emissions from fires. Our approach for
quantifying CH
4
emissions from fires using satellite-based CO and CH
4
con-
centration measurements is intended to mitigate and characterize uncertainties due
to (1) errors in transport and chemistry, (2) uncertainties in partitioning CO
emissions on the GEOS-Chem grid back to a priori CO emission types, and (3)
uncertainties in the CH
4
/CO emission factors and their IAV. As discussed in the
following sections, we first quantify monthly CO fluxes and their uncertainties at
monthly timescales on a 5 × 4° (longitude × latitude) grid using measurements of
CO concentrations from the Terra Measurements of Pollution in the Troposphere
(MOPITT) satellite instrument (V6J multi-spectral product40 and the adjoint
version of GEOS-Chem31). CO fluxes are then re-partitioned to the CO emission
types plus uncertainties on each 5 × 4° grid cell using a Bayesian Markov Chain
Monte Carlo approach25,41 that accounts for the a priori and a posteriori uncer-
tainties of the BB emissions and other CO emissions. Estimates of the CH
4
emissions and their uncertainties are then calculated by multiplying BB CO
emissions by the GFED-based estimate of each fire-type contribution, the expected
CH
4
/CO emission factors for all fire types within each grid cell, and the uncer-
tainties of the GFED-recommended emission factors. The emission factor uncer-
tainties are tested with CH
4
and CO measurements from the Aura TES instrument.
0.45
a
b
South America
Southern Africa
Northern Africa
SE Asia
Indonesia
1:1 Line
Prior (+/– 34%)
Prior (+/– 69%)
Weighted mean
Weighted mean SE
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
00 0.05 0.1 0.15 0.2
TES CH4:CO (ppm:ppm)
GEOS-Chem CH4:CO (ppm:ppm)
0.25 0.3 0.35 0.4 0.45
Fig. 6 Comparison of CH
4
/CO ratios from the GEOS-Chem model and Aura TES data. aComparison of CH
4
/CO ratios observed in tropical and sub-
tropical fire plumes from the Aura TES data to those expected from the GEOS-Chem model with GFED-based emission factors. bThe regions
corresponding to symbols in a. The best fit (weighted to the size of the fire emissions) and the corresponding standard error (standard error or the pink-
shaded area in figure) are shown by the red line and shaded area. Fires from different regions are shown as different symbols. The relative size of the fire
emissions is indicated by the relative size of the symbols
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Approach for quantifying CO fluxes. The approach used to quantify CO fluxes
over 15 years using the GEOS-Chem adjoint and Terra MOPITT data is described
in previously published research31. In summary, the inversion approach is to
compare MOPITT data, averaged hourly and on the GEOS-Chem 5 × 4° degree
grid, to the model and modified by prior knowledge of CO emissions based on
published inventories. The prior error for the CO fluxes on each grid is assumed to
50% and is uncorrelated between grid cells31. The error prescribed for each set of
hourly, 5 × 4° degree averaged data is 20%, consistent with the mean uncertainty of
the MOPITT data. Emissions for the prior CO fluxes are also averaged onto the
GEOS-Chem grid. As discussed in previous studies42,43, observations or models
that are coarser than the scales of the actual smoke plumes can have larger
uncertainty because of the effects of sub-grid scale diffusion, transport processes,
and chemistry. However, the emissions from models at different spatial resolutions
that are observationally constrained by satellite concentration data become con-
sistent when averaged over several of the coarser scale model grid cells because the
different model posterior concentrations have to be consistent with the observed
CO concentrations42. The emissions results presented here should therefore be
conservatively interpreted as averages of all fire emissions over a month for
aggregates of the GEOS-Chem grid cells (~2000 km spatial scales).
The approach for calculating CO emissions mitigates and characterizes error in
atmospheric transport and chemistry because they are typically the largest errors
when quantifying CO emissions using concentration data44–46. For example, errors
in the modeled CO fields can be amplified as CO is advected away from a source
region due to the accumulation of transport and chemistry errors. We use a two-
step approach to reduce the impact of these errors: firstly, we assimilate the
MOPITT CO measurements over the ocean so that the modeled CO concentration
fields that are advected over land from the ocean are consistent with the satellite
data42. We then estimate the CO emissions through comparison of model and data
just over continental regions. Effectively this approach accounts for advection of
the observed CO fields over the continents from the oceans while reducing the
sensitivity of emissions from one continent to those from other continents42.
Our inversion approach reduces, but does not remove, chemistry and transport
errors contributions from our CO flux estimates. In order to characterize the
remaining CO flux estimate errors, we produce three different estimates that are,
respectively, based on the MOPITT CO total column, profile, and lower-
troposphere28,46 concentration measurements. The three concentration
measurements have different sensitivities to CO as a function of altitude, and
therefore impose varying effects of transport and chemistry errors onto the model
concentrations28,46 after they are passed through the corresponding instrument
operators described by the a priori and averaging kernels. For example, estimates
based on the total column data will be less sensitive to convection errors because
the total column of the model estimate is effectively the same for all ranges of
convection. However, these estimates will be the most sensitive to errors in
advection and chemistry because the model has to balance these errors with
remotely advected emissions. Total column measurements are also less sensitive to
nearby surface emissions because the total column is representative of air parcels
that originate from hundreds to thousands of kilometers away from the
measurements28. Similarly, estimates based on the profile data will be more
sensitive to emissions near to the measurement site than the total column data but
are also more sensitive to errors in convection in the model. Estimates using the
lower-tropospheric (lowest three to four levels of the MOPITT CO profile) will be
more sensitive to nearby emissions but also more sensitive to errors in
convection28,31,46.
We have increased/decreased confidence in the magnitude and trend of
emissions that are similar/different between these three estimates. For example, the
largest differences between the three estimates occur in India and Indonesia,
regions where there are relatively large emissions and relatively large convective
mass fluxes46, and contributions from remote sources due to strong advection47,48.
The mean of these three estimates is used for estimating the CO fluxes at each grid
box and the variance between the three estimates is used as our uncertainty for
these estimates28,31. To obtain the uncertainty of the fire emissions of CO, we next
need to account for this posterior uncertainty in the CO estimate along with the
partitioning of CO to its different sectors (e.g., biomass burning, FFs, and so on)
and its uncertainties, as discussed next.
Partitioning of posterior CO fluxes to CO emission sectors. In order to parti-
tion CO fluxes estimated on the GEOS-Chem grid cells to their corresponding
emissions, we use a Markov Chain Monte Carlo approach25,41. This approach
quantifies the sectoral CO emissions and their uncertainties on each grid cell such
that the sum of the emissions and their uncertainties statistically represents the
posterior total CO fluxes and its associated uncertainty. In particular, we estimate
emissions for biomass burning (BB, including biofuels), FF, and BG sources. For
each timestep and grid cell, the vector xrepresents the emissions for each CO
sector (x=[BB, FF, BG]); p(x) denotes the prior information on the CO emissions
for each sector; and Fis a scalar denoting the sum of all CO sector emissions (F=Σ
[x]) × p(F|A) denotes the probability distribution of Fgiven atmospheric inversion
constraints (denoted collectively as A), which can be expressed via Bayesian
inference as
pFjAðÞ/pAjFðÞpðFÞ:ð1Þ
As discussed previously, p(F) is prescribed as a Gaussian distribution with mean
equal to the sum of total prior fluxes (BB
0
,FF
0
, and BG
0
) and a standard deviation
of ±50%31. For each grid cell, we model the posterior probability distribution, p(F|
A), based on the flux estimates of the three inversion results, f=[f
1
,f
2
,f
3
], where
pFjAðÞ¼
f±StDevðfÞ:ð2Þ
Similarly, the probability distribution of xgiven atmospheric data A,p(x|A), can be
expressed via Bayesian inference as
pxjAðÞ/pAjxðÞpðxÞ:ð3Þ
The analytical link between p(x|A) and known distributions p(F|A), p(F), and p(x)
is given by joint probability distribution of x,A,F,p(x,A,F), where—through the
probability chain rule:
px;A;FðÞ¼pxjA;FðÞpFjAðÞpðAÞ;ð4Þ
px;A;FðÞ¼pFjA;xðÞpAjxðÞpðxÞ:ð5Þ
Since Fand xare conditionally independent of A, the above can be summarized as
px;A;FðÞ/pxjFðÞpFjAðÞ;ð6Þ
px;A;FðÞ/pFjxðÞpAjxðÞpðxÞ:ð7Þ
Since pxjFðÞ/
pFjxðÞpxðÞ
pFðÞ , the above equations can be expressed as the following
distribution:
pxjAðÞ/
pxðÞpFjAðÞ
pFðÞ :ð8Þ
The distribution of p(x)isdefined as normal, uncorrelated distributions for BB,
FF, and BG, with means FF
0
,BB
0
, and BG
0
. The prior distribution of BB is
constructed based on monthly total CO emissions from the GFEDv4s inventory29,
and uncertainties in the CO emission factor for each fire type (i.e., savannas,
agriculture, forests, and so on). Fire CO emission factors and associated
uncertainties are based on those reported in the product GFED4s readme file
(http://www.globalfiredata.org/data.html). For each 5 × 4° area, we assumed that
the CO emission factor errors from different fire types are uncorrelated. We note
that prior fire CO emission uncertainties are possibly underestimated as the roles of
fuel load and burned area uncertainties are not well known at 5 × 4° scales, and
hence not included in our burned area estimates.
Gridded 5 × 4° monthly uncertaint y estimates are not readily available for FF
and BG sources. We therefore prescribe the FF prior distribution with a mean FF
(or FF
0
) and corresponding uncertainty of ±50%, which is consistent with largest
country-level uncertainty reported by previous estimates49. Similarly, we prescribe
a prior BG distribution of BG
0
±50%. The prior distribution for the CO emissions
used in our analysis31,p(F), is based on GFED version 3, whereas we use GFED4s
for our results described here. Due to computational limitations, we are unable to
repeat the full 15-year CO inversion used in our analysis with GFED4s. However,
the role of the grid cell level GFED version 3 prior is mitigated because the
posterior flux distribution p(F|A) is (a) normalized by the GFED3-based prior CO
emission distribution p(F), and re-weighed by the GFEDv4s-based prior p(x) using
Eq. (8). In addition, the difference between the posterior CO emissions from the
prior are typically comparable or larger to the GFEDv3-GFEDv4s difference. We
would therefore expect the re-partitioning to provide a similar estimate for the
mean CO emissions (within the calculated uncertainties) for the reported time
period and have effectively no impact on our conclusions about the trend estimate.
We use an adaptive Metropolis–Hastings Markov Chain Monte Carlo
(MHMCMC) approach to sample p(x|A)50. Finally, we model the spatial and
temporal error co-variances of BB based on total emission estimates fto match the
mean and standard deviations of retrieved BB emissions. For each monthly 5 × 4°
retrieval of BB, we create three realizations of BB, B=[B
1
,B
2
,B
3
] based on the
three CO emissions estimates. Bis derived based on the three total inverse CO
emissions estimates f=[f
1
,f
2
,f
3
] as follows:
B¼ff

StDevðbÞ
StDevðfÞþb;ð9Þ
where band StDev (b) represents the retrieved mean and standard deviation of BB
within each monthly 5 × 4° grid cell. In this manner, we simultaneously conserve
grid scale BB variances while representing a first-order approximation of the BB
spatial and temporal error covariance structure.
Relating estimated CO fire emissions to CH
4
fire emissions. In order to
quantify fire CH
4
emissions, gridded CO emissions are partitioned into fire types,
fuel load, and burned area extent as reported by GFED4s29 fire emission estimates
as discussed in the last section. CH
4
emissions and their associated uncertainties
(pink-shaded area in Fig. 1) are then derived by multiplying individual fire sector
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CO emissions by the mean and standard deviations of sector-specificCH
4
/CO
emission ratios based on GFEDv4s recommended CH
4
and CO emission factors
and associated uncertainties. The GFED4s CH
4
/CO emission factor is assumed to
be constant but with their reported uncertainties varying between 34 and 69%. As
discussed in the main text, it is possible that this emission factor could change with
different fire phases and combustion efficiency34–36. Since these assumptions are
based on sparse in situ measurements, we further test the GFED4s CH
4
/CO
emission factors, and the corresponding 34–69% uncertainty range, with satellite
measurements of CH
4
and CO in the free troposphere by the Aura tropospheric
emission spectrometer (TES) over tropical fires51 as shown in Fig. 6and Supple-
mentary Figs. 1,2,3,4. Only TES data that are associated with biomass burning in
which CO in the free troposphere is larger than 80 p.p.b. are used because these
data are most likely affected by fire emissions25,28,51. Transport effects in these
comparisons are also mitigated by comparing observed CH
4
/CO ratios from the
satellite data to those corresponding to air parcels modeled by GEOS-Chem after
the model atmospheric concentrations have been convolved with the Aura TES
CH
4
and CO averaging kernels and a priori constraints in order to account for the
vertical resolution and inversion regularization of the Aura TES CH
4
and CO
estimates25,28,51.Wefind that modeled and TES-observed CH
4
/CO ratios are
consistent (shaded red area overlaps the one-to-one dashed line in Fig. 6) if these
ratios are within ±34% of the GFED4s values (Supplementary Figs. 1and 2),
whereas the model-observation agreement are inconsistent for a −69 and +69%
change in CH
4
/CO ratios (Supplementary Figs. 3and 4). Based on these com-
parisons, we conservatively assume the GFED4 CH
4
/CO ratios and their reported
uncertainties ranging from 34 to 69% for our analysis.
Testing a decrease in methane emissions from fires. The global CH
4
/CO ratio
uncertainty (pink-shaded area in Fig. 1) is derived as a function of fire sector CH
4
contributions and their associated CH
4
/CO uncertainties. CH
4
emission uncer-
tainty stemming from CO emission uncertainty (Fig. 1, blue bars), is based on the
three CO BB realizations discussed previously. We find the fire CH
4
/CO emission
factor uncertainties are larger than CO-based emission uncertainties and com-
parable to the resulting trend in CH
4
emissions (Fig. 1). Assuming no inter-annual
CH
4
/CO emission factor variability for each sector throughout 2001–2014, we find
a significant decreasing trend in methane emissions (Fig. 2). To test the sensitivity
of our result to this assumption about yearly variations in the global mean CH
4
/CO
emission factor variability, we statistically evaluate the decreasing BB CH
4
emission
hypothesis under increasing levels of random CH
4
/CO IAV within each fire sector.
The statistical evaluation is performed by randomly sampling one of the three CO
BB emission realizations, their time-invariant CH
4
/CO values for each sector based
on sector-specificCH
4
/CO uncertainty estimates, and their annually varying
within-sector CH
4
/CO anomalies. We find that the probability of a 2001–2014 CH
4
emissions decrease is >95% assuming that sector-specificCH
4
/CO IAV is ≤40% of
the derived CH
4
/CO uncertainty. To our knowledge, CH
4
/CO IAV remains poorly
characterized, and we cannot reject a CH
4
/CO IAV of >40%; however, the cor-
responding global CH
4
/CO variability (~21%) is roughly a factor of 3 greater than
the prior and posterior sector-based global CH
4
/CO IAV. Moreover, a <95%
probability of an emission decrease is only possible when the within-sector CH
4
/
CO IAV is comparable to the sector CH
4
/CO uncertainty (gray-shaded area in
Fig. 2). This analysis demonstrates an increasing 2001–2014 CH
4
fire trend is only
possible if the global fire CH
4
emission variability is largely dominated by random
global-scale CH
4
/CO IAV (i.e., unaccounted by sector-based contributions to
global CH
4
/CO IAV), and globally coherent inter-annual within-sector CH
4
/CO
variability is comparable to sector CH
4
/CO uncertainty; we note that both state-
ments are theoretically possible but exceedingly unlikely, as these levels of CH
4
/CO
IAV would be statistically represented with substantially larger uncertainties in
sector-specificCH
4
/CO estimates.
Impact of decreasing biomass burning for global CH
4
budgets. The impact of
the observed drop in fire emissions on global CH
4
is studied using a single box
model5,38, which simulates the δ13C-CH
4
values and CH
4
mixing ratios, assuming
a well-mixed atmosphere. The model uses the following scalar mass balance
equations:
cðtþΔtÞ¼cðtÞþ eFF þeBG þeBB
½=mk´cðtÞ½´Δt;ð10Þ
13cðtþΔtÞ¼ 13cðtÞþ qFF ´eFF þqBG ´eBG þqBB ´eBB
½=mα´k´13cðtÞ

´Δt;
ð11Þ
δ13 tðÞ¼
13ctðÞ
cðtÞ 13ct
ðÞ
rstd
1
0
@1
A´1000%;ð12Þ
where c(t) and 13c(t) are global mean mixing ratios of CH
4
and 13CH
4
at time t,
respectively. e
FF
,e
BG
, and e
BB
are methane emissions from FF (thermogenic), BG
(microbial), and biomass burning (pyrogenic: biomass and biofuel burning)
sources, respectively. mis a factor of 2.767 Tg CH
4
per p.p.b. used to convert
emissions into atmospheric mole fractions. k¼1
τis the first-order removal rate
coefficient, where τ(=9.1 years) is the atmospheric lifetime and α=ε+ 1, where ε(=
−6.8‰) is sink-weighted isotopic fractionation of the CH
4
in the atmosphere5.q
FF
,
q
BG
, and q
BB
are the 13CH
4
fractions of the corresponding emissions. The model is
numerically discretized to run at daily resolution (Δt=1 d). δ13(t) is the global
mean δ13C-CH
4
value at time t.
We assume that CH
4
mixing ratios are in a steady state between 2000–2006 and
invert global emissions to optimize the agreement between the model and the mole
fraction and isotope measurements from 2007 onwards. Although we report the
decrease in BB emissions for the time periods between 2001–2007 and 2008–2014,
we choose the year 2007 as our start year for the flux inversion because it provides
the most realistic fit in our highly simplified model setup. The isotopic source
signatures5,15 used in the model are listed in Table 1. We perform 1000 Monte
Carlo simulations for each scenario to account for the uncertainties in the isotopic
source signatures and the associated emission adjustments. All the associated
uncertainties are assumed to be normally distributed. For each run, a randomized
isotopic source signature is selected based on the isotopic source signature
uncertainty distribution. For all runs, the pyrogenic contribution (biomass burning
+ biofuel burning) to the total annual methane source is fixed to 35 Tg CH
4
per
year20,21 for the period with no biomass burning perturbation (i.e., 2001–2007).
The FF and BG contributions are adjusted to match the mean values of CH
4
and
δ13C-CH
4
between 2000–2007. Each of the different scenarios shown in Fig. 4is
constrained to fit the observed growth from beginning of 2007 until the end of
2014. The biomass burning perturbation starts in 2008, corresponding to the
transitioning between periods of higher and lower biomass burning in Fig. 1.
Biogenic and FF emission perturbations are introduced in 2007 to fit the
observed CH
4
mole fraction increase. To determine these numbers, we select
NOAA-ESRL sites with both CH
4
mole fraction and isotope measurements. Only
sites with a minimum of 2 years of data between 2001–007 were selected, so that the
corresponding steady state was well defined. From these data, a global mean time
series is derived for CH
4
and δ13C-CH
4
(Supplementary Fig. 6)52. Thereafter, the
mix of sources in the box model is adjusted to find the source composition
compatible with the global mean steady state. Then, the box model is run with
perturbations within a range of realistic emission increases (+10 to 40 Tg CH
4
per
year) and isotopic signatures (–45 to –70‰). We select the emission strength and
isotopic signatures with the minimum root mean square deviation (RMSD) between
model and measurements.
Choice of 2007 as start of the emission perturbation. Biogenic and FF emission
perturbations are introduced in 2007 to fit the observed CH
4
mole fraction
increase. This choice was made after comparing the RMSD between the mea-
surements and the best fit case when starting the optimization in different years
(Supplementary Table 1). 2007 was selected as it resulted in the lowest RMSD.
Supplementary Table 1shows the goodness of fit as measured by the RMSD
between CH
4
mole fractions measurements and optimized box-model simulations
for starting years varying between 2005 and 2008. The corresponding strength of
the optimized methane emission perturbation is also given.
Calculation of global average CH
4
and δ13C-CH
4
. Here we describe the method
used to calculate a global representative time series shown in Fig. 3of the main text.
We use only stations with a sufficient number of measurements for both CH
4
and
δ13C-CH
4
, so that our model is fit to measurements representing the same air
masses. In the first step, zonal averages are taken of measurements in four lati-
tudinal bands: NET (Northern Extra Tropics: 30°N–90°N), NTRO (Northern
Tropics: 0°–30°N), STRO (Southern Tropics: 30°S–0°), and SET (Southern Extra
Tropics: 90°S–30°S). The time series for each station is shown in Supplementary
Fig. 5and for each zonal average in Supplementary Fig. 6. We derive global
representative measurements by averaging the time series for each zone. The zones
are weighted equally in the averaging as they represent the same area and hence
approximately the same air mass. This method avoids the problem that a dis-
proportionally large number of stations in one particularly zone biases the global
mean. Note that in the zonal to global averaging, we use months when zonal means
are available for each zone. For example, we skipped a few months of 2001, as
isotopic values were unavailable for NET. This approach minimizes the influence of
a varying representation of zones due to limitations in measurement availability.
Using this method, we find an optimum fit to the CH
4
and δ13C-CH
4
data when
adding 25.7 ±1.4 Tg CH
4
per year with an isotopic signature of −56.1 ±1.1. A
recent study5performed a similar analysis but obtained a slightly different average
additional source strength of 19.7 Tg CH
4
per year and a range of −56 to −61‰for
the isotopic composition. The disagreement between their values and our best fit
scenario can be explained because they fit their isotope model for the 2006–2014
time period, whereas we start at 2007 (see previous section) and because they use a
different set of NOAA sites to calculate the global mean CH
4
and δ13C-CH
4
time
series.
Data availability. All data used here are publicly available through the Terra
MOPITT website, https://www2.acom.ucar.edu/mopitt and the NASA AVDC
repository, https://eosweb.larc.nasa.gov/project/tes/l2_lite_table. Atmospheric CH
4
mole fraction and δ13C-CH
4
data are publically available through NOAA GMD
website, www.esrl.noaa.gov/gmd/. Isotopic composition of atmospheric methane
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NATURE COMMUNICATIONS |8: 2227 |DOI: 10.1038/s41467-017-02246-0 |www.nature.com/naturecommunications 9
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data are taken from: White, J.W.C., Vaughn, B. H. and Michel, S. E. (2017),
University of Colorado, Institute of Arctic and Alpine Research (INSTAAR), Stable
Isotopic Composition of Atmospheric Methane (13C) from the NOAA ESRL
Carbon Cycle Cooperative Global Air Sampling Network, 1998-2015, Version:
2017-01-20,Path: ftp://aftp.cmdl.noaa.gov/data/trace_gases/ch4c13/flask/. Surface
CH
4
data are taken from:Dlugokencky, E.J. et al. (2017), Atmospheric Methane Dry
Air Mole Fractions from the NOAA ESRL Carbon Cycle Cooperative Global Air
Sampling Network, 1983-2016, Version: 2017-07-28, Path: ftp://aftp.cmdl.noaa.
gov/data/trace_gases/ch4/flask/surface/.
Received: 30 March 2017 Accepted: 15 November 2017
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ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/s41467-017-02246-0
10 NATURE COMMUNICATIONS |8: 2227 |DOI: 10.1038/s41467-017-02246-0 |www.nature.com/naturecommunications
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Acknowledgements
This research was carried out at the Jet Propulsion Laboratory, California Institute of
Technology, under a contract with the National Aeronautics and Space Administration.
The work at Utrecht University is related to the program of the Netherlands Earth
System Science Centre (NESSC), financially supported by the Ministry of Education,
Culture and Science (OCW). The National Center for Atmospheric Research (NCAR) is
sponsored by the National Science Foundation. The MOPITT and TES projects are
supported by the National Aeronautics and Space Administration (NASA) Earth
Observing System (EOS) Program. The MOPITT team also acknowledges support from
the Canadian Space Agency (CSA), the Natural Sciences and Engineering Research
Council (NSERC) and Environment Canada, along with the contributions of COMDEV
and ABB BOMEM. Methane surface data were downloaded from the World Data Centre
for Greenhouse Gases. We are very grateful to all the institutions and individuals who
provide these surface data for researchers to use as these efforts are critical for carbon
cycle science research; the following is hopefully an inclusive list of institutions and
individuals, based on email response, who provide data that we use in this research: (1)
NOAA, Boulder CO/Ed Dlugokencky, Laboratory for Earth Observations and Analyses,
(2) ENEA, Palermo, Italy/Salvatore Piacentino, the CSIRO Flask Network/Paul Krum-
mel, (3) Atmospheric Environment Division, Global Environment and Marine Depart-
ment Japan Meteorological Agency/Atsushi Takizawa, and (4) Canadian Greenhouse Gas
Measurement Program, Environment Canada/Doug Worthy. We would like to thank Dr.
David Schimel of JPL for his helpful comments and feedback. We would like to thank
the Stable Isotope Lab, CU-INSTAAR: James White, Bruce Vaughn, and Sylvia
Michel for use of their data in this analysis.
Author contributions
J.R.W. led the study. A.A.B. characterized how uncertainties in the total CO emissions
and CH
4
/CO ratios affected conclusions about the CH
4
emission trends. Z.J., T.W.W.
and H.M.W. provided CO emissions and supporting analysis using MOPITT data. S.P.,
S.H., and T.R. led the isotopic analysis.
Additional information
Supplementary Information accompanies this paper at https://doi.org/10.1038/s41467-
017-02246-0.
Competing interests: This research was funded through a NASA Carbon Cycle Science
ROSES grant NNH13ZDA001N. The remaining authors declare no competing financial
interests.
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