the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Estimating Emissions of Methane Consistent with Atmospheric Measurements of Methane and δ13C of Methane
- 1Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt MD
- 2Earth System Science Interdisciplinary Center, University of Maryland, College Park MD
- 3Cooperative Institute for Research in Environmental Science, University of Colorado, Boulder CO
- 4Global Monitoring Laboratory, National Oceanic and Atmospheric Administration, Boulder CO
- 5Institute for Arctic and Alpine Research, University of Colorado, Boulder CO
- 6Environmental Defense Fund, Berlin, Germany
- 7Ricerca sul Sistema Energetico (RSE S.p.A.), Milano, Italy
- 8Instituto Nacional de Pesquisas Espaciais, São José dos Campos, São Paulo, Brazil
- 9Max Planck Institute for Biogeochemistry, Jena, Germany
- 10AGH University of Science and Technology, Krakow, Poland
- 11National Institute for Environmental Studies, Tsukuba-shi, Ibaraki, Japan
- 12Center for Atmospheric and Oceanic Studies, Tohoku University, Sendai, Japan
- 13Italian National Agency for New Technologies, Energy, and Sustainable Economic Devlopment (ENEA), Rome, Italy
- 14National Institute of Meteorological Sciences, Seogwipo-si, Jeju-do, Korea
- 15Università degli Studi di Urbino, Urbino, Italy
- 16European Commission, Joint Research Center, Ispra, Italy
- 1Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt MD
- 2Earth System Science Interdisciplinary Center, University of Maryland, College Park MD
- 3Cooperative Institute for Research in Environmental Science, University of Colorado, Boulder CO
- 4Global Monitoring Laboratory, National Oceanic and Atmospheric Administration, Boulder CO
- 5Institute for Arctic and Alpine Research, University of Colorado, Boulder CO
- 6Environmental Defense Fund, Berlin, Germany
- 7Ricerca sul Sistema Energetico (RSE S.p.A.), Milano, Italy
- 8Instituto Nacional de Pesquisas Espaciais, São José dos Campos, São Paulo, Brazil
- 9Max Planck Institute for Biogeochemistry, Jena, Germany
- 10AGH University of Science and Technology, Krakow, Poland
- 11National Institute for Environmental Studies, Tsukuba-shi, Ibaraki, Japan
- 12Center for Atmospheric and Oceanic Studies, Tohoku University, Sendai, Japan
- 13Italian National Agency for New Technologies, Energy, and Sustainable Economic Devlopment (ENEA), Rome, Italy
- 14National Institute of Meteorological Sciences, Seogwipo-si, Jeju-do, Korea
- 15Università degli Studi di Urbino, Urbino, Italy
- 16European Commission, Joint Research Center, Ispra, Italy
Abstract. We have constructed an atmospheric inversion framework based on TM5 4DVAR to jointly assimilate measurements of methane and δ13C of methane in order to estimate source-specific methane emissions. Here we present global emission estimates from this framework for the period 1999–2016. We assimilate a newly constructed, multi-agency database of CH4 and δ13CH4 measurements. We find that traditional CH4-only atmospheric inversions are unlikely to estimate emissions consistent with atmospheric δ13CH4 data, and assimilating δ13CH4 data is necessary to deriving emissions consistent with both measurements. Our framework attributes ca. 85 % of the post-2007 growth in atmospheric methane to microbial sources, with about half of that coming from the Tropics between 23.5° N and 23.5° S. This contradicts the attribution of the recent growth in the methane budget of the Global Carbon Project (GCP). We find that the GCP attribution is only consistent with our top-down estimate in the absence of δ13CH4 data. We find that at global and continental scales, δ13CH4 data can separate microbial from fossil methane emissions much better than CH4 data alone can, and at smaller scales this ability is limited by the current δ13CH4 measurement coverage. Finally, we find that the largest uncertainty in using δ13CH4 data to separate different methane source types comes from our knowledge of atmospheric chemistry, specifically the distribution of tropospheric chlorine and the isotopic discrimination of the methane sink.
Sourish Basu et al.
Status: open (until 16 Aug 2022)
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RC1: 'Comment on acp-2022-317', Anonymous Referee #2, 19 Jul 2022
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Comments on Basu et al.
Estimating emissions of methane consistent with atmospheric measurements of methane and d13C of methane.
First, a disclaimer: in the small closely-collaborating global methane community I have published with several of the co-authors in this paper. My review thus needs to be editorially assessed in terms of potential conflict of interest.
General comments
This is a major contribution to a very important global debate. The work is thorough, has no obvious major errors, and is well-presented.
Basu et al. address key challenges to the UN Paris Agreement – our very poor knowledge of the global methane budget, and our lack of understanding why methane is rising so fast.
If the methane burden is not brought under control soon, the Paris Agreement will fail. Conversely, halting methane growth and then reversing it may be the most immediately cost-effective way of reducing greenhouse warming. There are two linked but separate puzzles – 1) determining the overall methane budget, and 2) understanding the causes of recent growth. Thus this paper, which seeks to answer both questions, is globally important if correct.
Hitherto most global methane budget studies have not used isotopes to constrain the global budget. Hence many budget assessments simply do not tally with the direct isotopic measurements in the air and thus must be wrong. Although some regional budgets have used isotopic balance as a powerful constraint, very few global inversions are consistent with the isotopic record.
The recent exception to this lack of isotopic balance is the important paper by Zhang et al. (2022) Anthropogenic emission is the main contributor to the rise of atmospheric methane during 1993–2017." National science review 9.5: nwab200. Zhang et al domake use of the isotopic data. They inverted a two-box model representing the troposphere in each hemisphere. In contrast, Basu et al. use global atmospheric inversion based on TM5 4DVAR to assimilate methane and d13C in methane. As explained in their paper, this is intrinsically a much more powerful method, avoiding the necessary simplifications of box models.
Both studies, Zhang et al and Basu et al., use similar evidence but different analysis. Zhang et al (2022) find that the dominant cause of recent methane growth is anthropogenic inputs. They further comment that “the hypothesis that a large increase in emissions from natural wetlands drove the decrease in atmospheric δ13C-CH4 values cannot be reconciled with current process-based wetland CH4 models”.
In contrast, Basu et al. come to somewhat different conclusions, perhaps challenging current wetland process models, especially in the tropics. Their work is thoughtful and well-crafted, and persuasive. This is an intelligent, important and scientifically very productive debate, that has wide-reaching social implications.
Thus Basu et al. should be published after minor revisions.
Specific comments
Line 7. Tropics – for future studies it would be interesting to include the extra-tropical monsoon regions.
Line 50. Box models. Major comment here is that Zhang et al. (2022) should be discussed and its different methodology assessed explicitly. Basu et al’s analysis is very persuasive, but the differences of methodology between Basu et al and Zhang et al. should be made clear, and the debate should be returned to in the discussion in Section 4.
Line 60. The F parameter (presumably Flux?) is not specified. It would be good to have a small table of parameter identification to identify all the variables.
Line 94. Maybe later in the paper (in section 4) the power of the TM5 4DVAR approach could be re-emphasised. It’s what makes this paper potent.
Line 102. Here we have fluxes termed ‘x’…see line 60.
Line 136. Is Bergamaschi et al 2007 the last word on Termites? If so, maybe more work is needed. Maybe also relook at older work, such as by Zimmerman.
Lines 137 and 142. The ice core work by Petrenko’s team suggests very strongly and persuasively that natural fossil emissions are much smaller than found by Etiope et al 2019. The Petrenko team’s papers (e.g Hmiel et al https://doi.org/10.1038/s41586-020-1991-8) need to be considered explicitly here. It’s OK if Basu et al disagree with Hmiel et al, but they must explain why they refute the pre-industrial ice core results.
Line 157. Presecribed sinks. OK, understand, but might be worth adding a line of discussion on the impact of this decision to prescribe sinks.
Line 165 paragraph. Upland soil sinks especially in the moist aerated tropical woodlands – very little information on them. Might be worth a discussion here or later in paper?- e.g likely climate change response?
Line 195 I tried the doi but had no answer. All I got was advice on making a cake. I quote: It “looks like there aren't many great matches for your search. Try using words that might appear on the page that you’re looking for. For example, 'cake recipes' instead of 'how to make a cake'.”
Figure 3 right – shows how important it is to get isotopic measurements from Africa and S. America and South Asia. The sole site in Africa, in Namibia, samples southern ocean air. This lack of tropical observation should be stressed in the conclusions.
Line 285 There might also be a discussion here of the difference between Basu et al’s findings and the implications of tropical wetland process models mentioned by Zhang et al..
Line 263 C4 signature chosen seems a bit heavy – yes for pure C4 tropical grass fires but many are perhaps a bit lighter as there are lots of bushes etc in the fires also. Note also that tropical wetlands have varying C3/C4 ratios – dominantly C4 in equatorial areas (papyrus) but more C3 reeds in outer tropics. This gradation also shows in land plants – tropical ruminants are typically as much browsers as grazers.
Line 289. Also L 348. Inundation – yes, but temperature also. It’s likely T is a key factor, with an Arrhenius relation to methane flux. But there is very little work on T impact in tropical wetlands. Lots of models simply cite Gedney Q10, based on a few much older studies.
Note that a key factor may be inundation depth – if the plants are rooted or floating. If they are rooted in ooze (e.g. 4m tall papyrus) then they advect a lot of methane from the anaerobic bottom direct up the stalks. But floating plants in oxygenated shallow open waters allow water methanotrophy to eat the ebullition. So flooding increases area of inundation but may reduce emission from the deeper parts. Conversely the T in hot dry years increases emissions provided there is enough water (and wetland responses have a long, 6-12 month lag time after preciptation anomalies, but an immediate response to Temperature change.).
Line 300. Nice to cite Tans 1997. Key paper!
Line 325 – core finding of the paper.
Line 344 – estimate of pyrogenic emissions not changing? That’s a bit surprising?
Line 390 – major finding…shows how important the isotopic data are.
Line 395. It would be interesting to model what I’d call the monsoonal tropics, rather than limit by geometric latitudes – i.e the region within the sweep of the Inter-tropical convergence zone.
Line 466. Pyrogenic. Surprising.
Line 508. Maybe there should be a mention here of Feng et al. (2022)Tropical methane emissions explain large fraction of recent changes in global atmospheric methane growth rate." Nature communications 13.1 : 1-8.
Line 525 The paragraphs where Basu et al. paper discuss ‘GCP’ findings need to be rewritten and the important work of Zhang et al needs to be addressed. There might also be a discussion of the conflict between Basu et al’s findings and the implications of tropical wetland process models (compare Zhang et al., who accept the modelling). Note that many process models share a major weakness in their dependence on a Q10 assumption that is based on very little observational evidence.
Line 560 More generally, perhaps in the discussion section, maybe the paper could discuss the wide range of needs: more tropical isotopic measurements, better information on isotopic signatures (but I note Line 503), better knowledge of tropical soil uptake, better assessment of Cl and OH sink fractionation, and the potential usefulness of D/H isotopic monitoring. It might also help to add here a table 6 of the methane budget findings by source.
Summary.
Basu et al have a very strong and convincing case: This is an excellent paper and a major contribution. Only minor revisions are needed.
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RC2: 'Comment on acp-2022-317', Martin Manning, 21 Jul 2022
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Summary.
This paper is an important extension of earlier work by the same group (Lan et al 2021). It now provides a detailed analysis of inverse modelling using CH4 mole fraction and δ13C data that shows the latter can significantly modify estimates of the methane sources. It also provides a careful comparison with a similar but different analysis of the methane budget that included δ13C data (Thanwerdas et al, 2022). Problems with this type of inverse modelling for CH4 were noted by Houweling, et al, 2017 (Global inverse modeling of CH4 sources and sinks: An overview of methods. ACP, 17, 235-256) as requiring more careful approaches to prior estimates of source parameters and to model spin-up because of much longer isotopic equilibration times. More generally it is known that different approaches to inverse modelling can affect some forms of bias in determining the source – e.g. Biegler, et al. 2011. Large-Scale Inverse Problems and Quantification of Uncertainty, Wiley.
While this paper has not resolved all the issues with inverse modelling to determine CH4 sources, it has made substantial progress. Concerns that most estimates of the CH4 fossil fuel fraction were too low were raised by Schwietzke, et al, 2016 (Upward revision of global fossil fuel methane emissions based on isotope database. Nature, 538, 88-91). Furthermore, long term records of 14CH4 in the southern hemisphere (Lassey et al, 2007, The atmospheric cycling of radiomethane and the “fossil fraction” of the methane source. ACP, 7, 2141-2149) have provided independent evidence that the fraction was about 30%. For this to now also be seen in a careful inverse analysis of the CH4 concentration and δ13C data should lead to a significant upward revision of the CH4 fossil fuel fraction in future IPCC reports.
Suggested changes.
The relationship between this paper and Lan et al, 2021, is important and as they are in different journals it can help to repeat a bit more of what is in Lan et al here. E.g. the range of scenario options that had been considered in Lan et al before this paper adopts C_WL+ as the base for inversion analysis.
In section 3, I see no mention of how stratospheric removal is treated in the TM5 model. As this leads to return of air with some isotopically enriched CH4 back into the troposphere, and that enrichment varying with latitude, it can effect the δ13C analysis.
Section 4.1 “Comparison with Thanwerdas et al 2021” seems to have been added after the rest of the paper had been written. I think it would read better if this section was merged into section 3.6 “Comparison to the GCP budget”.
The rest of section 4 starts with a summary of what has been covered in the paper and then moves on to consider options for future development. After comparing this with some other papers in ACP, I would suggest that it would be better to have section 4 just focused on how the results can be developed with future work, and to move the initial summary, currently at the beginning of section 4, to a section 5 on “Concluding remarks” that summarised some of the key points made in the paper.
Technical Details.
The Courtier et al 1998 reference given here would be better as the peer reviewed version published in Quarterly Journal of the Royal Meteorological Society, 124:1783-1807, 1998. https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/qj.49712455002
Sourish Basu et al.
Model code and software
TM5 4DVAR atmospheric inverse model Sourish Basu, Arjo Segers, and other TM5 model developers https://sourceforge.net/p/tm5/cy3_4dvar/ci/default/tree/
Sourish Basu et al.
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