the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Multi-observational estimation of regional and sectoral emission contributions to the persistent high growth rate of atmospheric CH4 for 2020–2022
Abstract. An inverse study of atmospheric methane (CH4) estimated regional and sectoral emission contributions to the unprecedented surge of the atmospheric growth rate for 2020–2022. Three inverse analyses, which used only surface observations, surface and aircraft observations, and satellite (GOSAT) observations, consistently suggested notable emission increases in the tropics (15° S–10° N) (10–18 Tg CH4 yr−1) and in northern low-latitudes (10–35° N) (ca. 20 Tg CH4 yr−1), the latter of which likely contributed to the growth rate surge from 2020. The emission increase in the northern low-latitudes is attributed to emissions in South Asia (6–7 Tg CH4 yr−1) and northern Southeast Asia (5 Tg CH4 yr−1), which abruptly increased from 2019 to 2020, and elevated emissions continued until 2022. Meanwhile, the tropical emission increase is dominated by tropical South America (5–7 Tg CH4 yr−1) and central Africa (3–6 Tg CH4 yr−1), but they were continuously increasing before 2019. Agreement was found in sectoral estimates in the tropics and northern low-latitudes, suggesting that biogenic emissions from wetlands, agriculture, and waste are the largest contributors. High-precision surface and aircraft observations imposed constraints that were comparable to or 1.5 times stronger than GOSAT constraints on the flux estimates in South and Southeast Asia. Furthermore, a sensitivity inversion test suggested that the effect of the probable reduction of OH radicals in 2020 might be limited in the Asian regions. These results highlight the importance of biogenic emissions in Asian regions for the persistent high growth rate observed during 2020–2022.
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RC1: 'Comment on egusphere-2024-2457', Anonymous Referee #1, 22 Oct 2024
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This manuscript presents an inverse modelling system for estimating global methane emissions. It is used to study the causes of the accelerated methane increase during 2020-2022. Compared with previous studies, a larger set of surface and aircraft measurements is used, extending the data coverage in south and east Asia, in addition to the use of alternative GOSAT retrieval datasets. The results highlight the role of increasing Asian emissions in the global growth rate enhancement during this period, attributed mostly to increases in agricultural emissions. The sensitivity of inversion-estimated emissions to the observational datasets is an important – although not unexpected – finding. This study points to a trade-off between African and Asian emission increases depending on the data that are used, which makes an important contribution the scientific understanding of the causes of the global emission increase.
The manuscript is well written. Provided that the few points raised below are sufficiently well addressed I see no reason to uphold publication.
GENERAL COMMENTS
The method section describes how posterior uncertainties are quantified. However, besides posterior flux covariances and uncertainty reduction very little use is made of posterior uncertainties. How do posterior uncertainties compare with the differences that are found between the different inversions? How about the significance of the most important flux deviations from the prior that are used to explain the 2020-2022 growth rate anomaly? Some of the plots miss error bars.
Increases in emissions over Africa and southeast Asia are discussed, which have been attributed increases in natural wetlands and agriculture. However, it is not clear to which extend these increases are in the a priori fluxes already. A priori emission estimates in zonal bands are presented that give some indication, but it is unclear whether those differences are representative for what is found for the regions that are used in the sectorial bar graphs.
The sensitivity to observational datasets and their spatial coverage raises the question whether the size of regional observational constraints could drive the differences in the outcomes of different inversions. If Asian data are added, the importance of Asian emissions increases, if data over Tropical Africa are added (i.e. proxy-method GOSAT retrievals) the importance of African emissions increases. It could be coincidence but might also be a symptom of sampling bias. It would be useful to add a data thinning experiment to distinguish between the extra information on methane emissions that new measurements bring versus the impact of their added observational constraint.
SPECIFIC COMMENTS
line 30: “increase” compared to what? It misses a notion of the extent to which this is expected or not given the a priori fluxes.
line 35: Agreement was found between what?
line 119: How do you mean 'derived'? From what?
Equation 1: Parentheses are missing indicating the limits of the sum over i processes (that is only for a part of the equation, but it is unclear which part). Why are some processes corrected using delta-alpha and others using delta-f? This treatment makes an important but unexplained difference. Does delta-f cover grid boxes for which the corresponding f has zero emissions?
Sect. 2.2: What spatial and temporal error covariances are assumed of the 1x1 degree a priori monthly and annual fluxes?
line 163: How large are the wetlands, rice, soil uncertainties derived from VISIT?
line 172: How about the temporal coverage of data that have been used? Is the sampling network changing over time?
line 187: 'deemed to be comparable' within what accuracy?
Equation 2: How does balancing of data constraints work out for the observational weights of surface, aircraft and GOSAT data? How do the corresponding terms in the cost function compare?
Line 307: How does the inversions performance evaluation in Figure 2 distinguish between data that are or are not used in the inversion?
Figure 6: uncertainty reductions are a % reduction?
Appendix A: Using the method that is presented reduces the likelihood of negative emissions but does not prevent that negative emissions might happen. To which extent is this still the case?
Appendix B: line 644: It is mentioned that GOSAT retrievals are biased, but this need not be the case. There could also be an inconsistency between modelled surface and total column mixing ratios due to a transport model problem. I doubt that comparisons between GOSAT and TCCON show this bias. Past studies that used GOSAT struggled with this too, but concluded that the problem was probably more a model problem than a retrieval problem.
Appendix C: The description of the method is clear, but it would be helpful add a map of what the resulting OH reduction looks like?
TECHNICAL CORRECTIONS
line 599: “for several reasons” instead of “through several reasons”
Citation: https://doi.org/10.5194/egusphere-2024-2457-RC1
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