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. 2021 Jun;35(6):e2021GB007000.
doi: 10.1029/2021GB007000. Epub 2021 Jun 17.

Improved Constraints on Global Methane Emissions and Sinks Using δ 13C-CH4

X Lan  1   2 , S Basu  3   4 , S Schwietzke  1   5 , L M P Bruhwiler  2 , E J Dlugokencky  2 , S E Michel  6 , O A Sherwood  6   7 , P P Tans  2 , K Thoning  2 , G Etiope  8   9 , Q Zhuang  10 , L Liu  10 , Y Oh  2   10 , J B Miller  2 , G Pétron  1   2 , B H Vaughn  6 , M Crippa  11
Affiliations

Improved Constraints on Global Methane Emissions and Sinks Using δ 13C-CH4

X Lan et al. Global Biogeochem Cycles. 2021 Jun.

Abstract

We study the drivers behind the global atmospheric methane (CH4) increase observed after 2006. Candidate emission and sink scenarios are constructed based on proposed hypotheses in the literature. These scenarios are simulated in the TM5 tracer transport model for 1984-2016 to produce three-dimensional fields of CH4 and δ 13C-CH4, which are compared with observations to test the competing hypotheses in the literature in one common model framework. We find that the fossil fuel (FF) CH4 emission trend from the Emissions Database for Global Atmospheric Research 4.3.2 inventory does not agree with observed δ 13C-CH4. Increased FF CH4 emissions are unlikely to be the dominant driver for the post-2006 global CH4 increase despite the possibility for a small FF emission increase. We also find that a significant decrease in the abundance of hydroxyl radicals (OH) cannot explain the post-2006 global CH4 increase since it does not track the observed decrease in global mean δ 13C-CH4. Different CH4 sinks have different fractionation factors for δ 13C-CH4, thus we can investigate the uncertainty introduced by the reaction of CH4 with tropospheric chlorine (Cl), a CH4 sink whose abundance, spatial distribution, and temporal changes remain uncertain. Our results show that including or excluding tropospheric Cl as a 13 Tg/year CH4 sink in our model changes the magnitude of estimated fossil emissions by ∼20%. We also found that by using different wetland emissions based on a static versus a dynamic wetland area map, the partitioning between FF and microbial sources differs by 20 Tg/year, ∼12% of estimated fossil emissions.

Keywords: atmospheric methane; atmospheric modeling; greenhouse gas; methane budget; source attribution; stable isotope of methane.

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Conflict of interest statement

The authors declare no conflicts of interest relevant to this study.

Figures

Figure 1
Figure 1
Globally averaged atmospheric CH4 (a) and δ 13C‐CH4 (b) from NOAA's Global Greenhouse Gas Reference Network; the blue curves in (a) and (b) are approximately weekly data and the red shaded areas are their uncertainty bounds (note uncertainties are too small to be visible in (a)), and the black curves are annual means. See Section 2.1 for uncertainty calculation. (c) The marine boundary layer sites from this network with CH4 and δ 13C‐CH4 measurements used in this study.
Figure 2
Figure 2
CH4 emission scenarios with hypothesis overview. *The “gap” refers to the differences between bottom‐up and top‐down emission estimates. The symbols “↑” and “↓” indicate positive and negative trends, respectively. See Section 2.4 for description of each scenario.
Figure 3
Figure 3
Country‐level δ 13C‐CH4 source signatures for ONG (2010) and coal emissions (assume time invariant). For grid cells without data, a global flux weighted mean is used. ONG, Oil and Natural Gas.
Figure 4
Figure 4
Modeled global mean CH4 (a) and annual mean latitudinal gradients ((b) for 2006 and (c) for 2012) from different emission scenarios, compared with those from Marine Boundary Layer observations (black). All scenarios show similar performances on global mean CH4 in (a) since they are constructed to be consistent with the atmospheric CH4 global mean growth rates.
Figure 5
Figure 5
Modeled global mean δ 13C‐CH4 (a, b) and their latitude gradients (c, d) from different emission scenarios compared with those from Marine Boundary Layer observations. (b) A zoom‐in view of (a). The shaded area around the observations in (b)–(d) is estimated uncertainty bounds. See Section 2.1 for uncertainty calculation.
Figure 6
Figure 6
Modeled global mean δ 13C‐CH4 (a, b) and annual mean latitudinal gradients (c, d) from different emission scenarios combined with a sink scenario excluding tropospheric Cl. (b) A zoom‐in view of (a). The shaded area around the observations in (b)–(d) is estimated uncertainty bounds. See Section 2.1 for uncertainty calculation.
Figure 7
Figure 7
Modeled global mean δ 13C‐CH4 (a, b) and annual mean latitudinal gradients (c, d) from different emission scenarios combined with a sink scenario using OH fractionation of −5.4‰. (b) A zoom‐in view of (a). The shaded area around the observations in (b)–(d) is estimated uncertainty bounds. See Section 2.1 for uncertainty calculation.
Figure 8
Figure 8
Modeled global Marine Boundary Layer mean CH4 (a) and δ 13C‐CH4 (b, c) seasonal cycles when using a dynamic WL map (scenario C) and a static WL map (scenario Q). In (b) and (c), “_cantrell” refers to the sink scenario using OH fractionation of −5.4‰ (Cantrell et al., 1990), while “_nocltrop” refers to the sink scenario excluding tropospheric Cl. Long‐term trends are first removed before estimating seasonal cycles by a 3‐year running average method.
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