National quantifications of methane emissions from fuel exploitation using high resolution inversions of satellite observations

Domain: earth_science
Published: 2023-08-16
Editorial outcome: minor_revision

Abstract
Reducing methane emissions from fossil fuel exploitation (oil, gas, coal) is an important target for climate policy, but current national emission inventories submitted to the United Nations Framework Convention on Climate Change (UNFCCC) are highly uncertain. Here we use 22 months (May 2018-Feb 2020) of satellite observations from the TROPOMI instrument to better quantify national emissions worldwide by inverse analysis at up to 50 km resolution. We find global emissions of 62.7 ± 11.5 (2σ) Tg a−1 for oil-gas and 32.7 ± 5.2 Tg a−1 for coal. Oil-gas emissions are 30% higher than the global total from UNFCCC reports, mainly due to under-reporting by the four largest emitters including the US, Russia, Venezuela, and Turkmenistan. Eight countries have methane emission intensities from the oil-gas sector exceeding 5% of their gas production (20% for Venezuela, Iraq, and Angola), and lowering these intensities to the global average level of 2.4% would reduce global oil-gas emissions by 11 Tg a−1 or 18%.

Introduction
Methane (CH4) is the second most important anthropogenic greenhouse gas after CO2 and is responsible for 0.6 °C global warming since preindustrial times1. Under the Paris Agreement, individual countries must set goals for mitigating their anthropogenic methane emissions relative to current baselines. The Global Methane Pledge signed by over 110 countries commits to reducing collective methane emissions by 30% by 20302. Emission from fossil fuel exploitation (oil, gas, and coal) is an important mitigation target because it is estimated to account for about one-third of the global anthropogenic total and could be cost-effective to control3–6. National emission inventories submitted by individual countries to the United Nations Convention on Climate Change (UNFCCC) under the Paris Agreement follow bottom-up approaches in which emission factors are applied to activity data, sometimes with additional facility-specific information. But these national inventories are typically uncertain by a factor of two or more6 and often by more than an order of magnitude for the oil-gas sector5. This uncertainty hinders the setting and tracking of mitigation goals for methane emissions.

Top-down approaches apply inverse methods to infer emissions from measurements of atmospheric methane. They use prior information from the bottom-up inventories and provide an independent way of improving these inventories. Many previous top-down studies have exploited methane observations from surface sites, aircraft, and the Greenhouse gases Observing SATellite (GOSAT) satellite7–16 but these observations are very sparse. Satellite observations from the Tropospheric Monitoring Instrument (TROPOMI) launched in October 2017 provide considerably higher global data density, with continuous daily mapping of methane columns at 7 km × 5.5 km nadir resolution17. The TROPOMI observations have been applied to detect large point sources18–20 and quantify emissions from a few source regions21–24. A global inversion showed artifacts in early versions of the data25 that have since largely been corrected17.

Here, we conduct a global ensemble of regional inversions of TROPOMI data to quantify emissions from fossil fuel exploitation worldwide at up to 50-km resolution, validate the results with field measurements across the globe, and further report improved inventory estimates for all countries in support of the Paris Agreement. Furthermore, we use our inversion framework to assess the sensitivity of the results to different bottom-up inventories, satellite data density, and choices of inversion parameters, from where we define the conditions under which TROPOMI can provide significant information to assist national bottom-up estimates.

Results
Correction factors to UNFCCC inventories and evaluation with field campaign data
We tile the world with 15 domains that account for 96% of global emissions from fossil fuel exploitation in 2019 according to the Global Fuel Emissions Inventory version 2 (GFEIv2), which allocates national emissions reported to the UNFCCC on a 0.1°×0.1° grid5. Figure 1a shows these 15 domains and the gridded UNFCCC emissions. A few countries have not reported their emissions to the UNFCCC since 2000, notably Iraq and Libya, and for these GFEIv2 uses standard IPCC Tier 1 methods5. We conduct inversions of the TROPOMI observations in each domain from May 2018 to February 2020, using the GEOS-Chem model (https://doi.org/10.5281/zenodo.3634864) for simulation of atmospheric transport, to quantify emissions with a resolution of up to 0.5°×0.625° (~50 km) for grid cells with significant fuel emissions (>1 Gg a−1). We apply weight to the gridded bottom-up prior emission estimates for fuel and other sectors with the TROPOMI observations using analytical Bayesian optimization to solve for the maximum-likelihood posterior estimates of methane emissions on the 0.5°×0.625° grid (Eq. 1), and from there we aggregate emissions to the regional and country scales. Each grid cell includes emissions from nonfuel sectors as given by the respective prior inventories. Here, we attribute the posterior corrections to the different sectors in the grid cell on the basis of their error-weighted contributions to the prior emissions23,24, and sectors with higher prior uncertainty are subject to larger relative corrections (Supplementary Note 1). This error weighting is particularly important for grid cells containing wetland emissions, which are particularly uncertain. We derive the global emissions from fossil fuel exploitation by summing posterior estimates for all 15 domains and adding the GFEIv2 inventory for the 2.8 Tg a−1 of emissions outside of the domains (4% of global GFEIv2 emissions). We conduct an ensemble of inversions with different prior estimates (GFEIv2, GFEIv1, Emissions Database for Global Atmospheric Research version 6 or EDGARv6) and different assumptions on error statistics to quantify the uncertainty in our results. More details are provided in the Methods section.

Figure 1b shows the spatial distribution of optimized correction factors from fossil fuel exploitation relative to the gridded UNFCCC inventories. Corrections are upward in most large fuel production basins including in North America (the US, Canada, and Mexico), Venezuela, Russia, Central Asia (Turkmenistan and Kazakhstan), Africa (Algeria, Egypt), Middle East (Saudi Arabia, Iran), and Australia. Notable countries with downward corrections include Nigeria, Libya, and China.

Figure 2 compares our top-down emission estimates in large fossil fuel exploitation basins to 24 independent estimates of emissions from field campaigns, including in situ and remote sensing observations across the globe (Supplementary Table 1-2 for more details). The field campaigns were carried out over a range of time periods between 2011 and 2020. The correlation coefficient between our TROPOMI-derived posterior estimates and the field campaign estimates is 0.95 with no systematic bias. Results further indicate that TROPOMI can successfully detect emissions as low as 0.2 Tg a−1 on the basin scale.

Global emission estimates
Our optimized estimates of global methane emissions from TROPOMI data are 62.7 ± 11.5 Tg a−1 for the oil-gas sector and 32.7 ± 5.2 Tg a−1 for the coal sector, yielding a total fossil fuel emission of 95.4 ± 12.9 Tg a−1 for the period of May 2018 to February 2020. Here and elsewhere, uncertainties are reported as two error standard deviations (2σ) from Monte Carlo analysis of our inversion ensemble using posterior error covariance matrices. These global estimates have low sensitivity to the priors used (Supplementary Fig. 1), implying that they are mainly determined by satellite observations. We evaluate our posterior emission estimates by implementing them in GEOS-Chem and using them to simulate column-averaged methane mixing ratios for comparison with TROPOMI and GOSAT, and surface concentrations for comparison with surface measurements from the National Oceanic and Atmospheric Administration (NOAA) network26. Results show consistent improvements in the model-observation bias relative to using prior emissions (Supplementary Fig. 2-5).

Figure 3 compares our global emission estimates to previous literature in the context of their reporting periods going back to the 1980s and separating oil-gas from coal when available. Bottom-up estimates tend to show increases over the period, reflecting increasing production. The GFEIv2 inventory (based on 2019 UNFCCC reports) is much lower than other bottom-up estimates, largely driven by downward revision of Russian oil emissions reported to the UNFCCC5. Top-down fossil fuel estimates show large variability (80–110 Tg a−1) but tend to be lower by 20 Tg a−1 than bottom-up inventories (110–130 Tg a−1, except GFEIv2), and comparison across top-down studies does not suggest a global increase of emissions over the 2010–2020 period (Fig. 3a).

Our satellite-based global estimate of oil-gas emissions (62.7 ± 11.5 Tg a−1) for 2018–2020 is in the range of previous top-down studies (59–70 Tg a−1) and 30% higher than GFEIv2 (48 Tg a−1) (Fig. 3b), while our estimate of coal emissions (32.7 ± 5.2 Tg a−1) is higher than previous top-down studies (20-30 Tg a−1) and in agreement with GFEIv2 (33 Tg a−1) (Fig. 3c) (details in Supplementary Table 3). The low coal emissions in previous top-down studies could stem from incorrect spatial distribution of Chinese emissions in older bottom-up inventories27,28. When combining oil-gas and coal emissions together, our top-down estimate (95.4 ± 12.9 Tg a−1) is at the lower 20th percentile of the distribution of all previous estimates for 2010–2021 (80–140 Tg a−1). The next section attributes these differences to individual countries.

National emission estimates and comparison to UNFCCC reports
Figure 4 shows our posterior estimates of oil-gas and coal methane emissions from the top emitting countries and compares to the UNFCCC reports. National data for all individual 93 countries with total fuel emissions larger than 1 Gg a−1 are compiled in Supplementary Table 4. Posterior estimates of the top-20 oil-gas and coal emitting countries using different prior inventories can be found in Supplementary Table 5-6. Also shown in Fig. 4a is the methane intensity, defined as the national oil-gas emissions per unit of total gas production4 (assuming 90% of methane content as in Alvarez et al.4), and the coal emission factors per unit production as defined by IPCC29. Emission factors for the individual oil and gas countries are given in Supplementary Fig. 6. Overall, our higher global oil-gas emission estimate relative to the UNFCCC is largely driven by underestimates of emissions from the US, Russia, Venezuela, and Turkmenistan in the national reports. Of these top emitters, Venezuela, Turkmenistan, Uzbekistan, Angola, Iraq, Ukraine, Nigeria and Mexico have methane intensities of 5-25% from the oil-gas sector (>20% for Venezuela, Iraq, and Angola). Lowering the emission intensity of these 8 countries to the global average level of 2.4% would reduce oil-gas methane emissions by 11 Tg a−1 or 18% globally, implying a high margin for emission mitigation. China’s oil-gas emission (2.7 ± 1.1 Tg a−1) is twice higher than the UNFCCC inventory, consistent with Chen et al.28, and these corrections are largest in the north (Fig. 1 and Supplementary Fig. 7) where a number of ultra-emitting point sources from the oil industry have been detected from TROPOMI data20.

Our posterior estimate for Russia’s oil-gas emissions is 9.4 Tg a−1 but with large uncertainty (95% confidence level, 4.5-16.6 Tg a−1) that reflects both the low observation density of TROPOMI at high latitudes (Supplementary Fig. 8-9) and a wide range in the prior inventories (Supplementary Fig. 10). Russia previously applied the IPCC 2006 emission factor for developing countries in its UNFCCC reports and this was used in GFEIv1, but its latest UNFCCC 2019 report used in GFEIv2 applies the emission factor for developed countries, resulting in a downward revision of its national oil-gas emissions by a factor of 5 (from 20.5 to 2.1 Tg a−1 for oil and from 4.5 to 2.0 Tg a−1 for gas)5. Overall, our results suggest that the most recent UNFCCC 2019 report is more accurate but needs to be corrected upward by a factor of two. Lauvaux et al.20 found a large number of ultra-emitting point sources from the oil-gas sector in Russia including in particular from pipelines. Future satellite instruments may be more effective at observing high latitudes and Russian point sources30.

Venezuela (posterior estimate of 4.0 ± 3.0 Tg a−1) and Nigeria (1.5 ± 1.2 Tg a−1) are the two largest oil-gas emitters in the tropics from our TROPOMI analysis, again with high uncertainty because of low TROPOMI observation density related to extensive cloudiness. Spatial collocation of oil-gas basins with wetlands in Nigeria further increases the difficulty of separating oil-gas emissions (Supplementary Fig. 11). Nigeria’s emissions reported to UNFCCC increased from 0.4 (GFEIv1 for 2016) to 3.3 Tg a−1 (GFEIv2 for 2019) after adopting an emission factor at the upper limit of the IPCC (2006) recommendations31. Our inversion implies that the more recent report should be reduced by 40-50%. The need for upward corrections of Venezuela’s emissions has been previously reported in inversions of GOSAT data12,15. Lu et al.15 estimated oil-gas emissions for Venezuela of 7.7 Tg a−1 for 2010-2017. Our lower value (4.0 Tg a−1) may be related to declining oil production over the 2016-2019 period as a result of intensified economic sanctions5 (Supplementary Fig. 12).

Our posterior estimate of Turkmenistan’s oil-gas emissions (3.6 ± 1.3 Tg a−1) is 2.4 times higher than its UNFCCC report (1.5 Tg a−1), and makes it the 4th largest oil-gas emitter in the world (Fig. 4). This could be due to a large population of high-emitting point sources not accounted for in the bottom-up estimates. We find that the upward corrections are largest in the southern production basins (Supplementary Fig. 13), consistent with the previous high emitters identifications32–34.

Figure 4b also displays the top 20 coal-emitting countries in our posterior estimates. Unlike for oil-gas emissions, we find in general good agreement with the UNFCCC inventories. This is likely because the country-scale emission factors from coal production have much lower variability than for oil-gas (Fig. 4) and emissions originate from a relatively small number of facilities. China’s coal-based methane emission is 18.9 ± 3.3 Tg a−1, slightly but not significantly lower than the UNFCCC inventory estimate. Our estimate for China is comparable to the range of 16.2-18.0 Tg a−1 from recent satellite-based estimates (Supplementary Table 7)28. Indonesia is the second largest coal producer in the world but its submitted emission to the UNFCCC is only 0.2 Tg a−15; our posterior estimate is 0.7 Tg a−1 but with very large uncertainty (0.1-2.7 Tg a−1) because of extensive cloudiness and retrieval difficulties associated with coastlines (Supplementary Fig. 8). The largest relative correction factors are for Australia and Kazakhstan, where we find emissions to be respectively 1.8 and 4.8 times higher than in the UNFCCC reports. Our result for Australia is consistent with a recent finding that emissions from three large coal mines are seven times larger than the bottom-up estimates19.

We find that the ability of TROPOMI to quantify fuel emissions for a given country varies greatly depending on the country, as illustrated in Fig. 4 and Supplementary Fig. 14. This variability is determined by the TROPOMI data density, the magnitude of national emissions, and the prior uncertainty in these emissions which we estimate on the basis of the range of the bottom-up inventories used in our inversion ensemble (GFEI v2, GFEI v1, EDGARv6). For the top fuel-emitting countries (>1 Tg a−1), the posterior/prior relative uncertainty reduction for total fuel emissions is 40% on average with a range from 10% to 70%, suggesting the effective role of TROPOMI in improving country-scale emissions. After combining inversions from the full ensemble (see Methods for more details), our results show that TROPOMI can constrain the emissions with a relative posterior uncertainty <30% (2σ) for most large mid-latitude emitters, including China, the US, Turkmenistan, Kazakhstan, India, and Canada. It has more difficulty in Russia and the tropics (Supplementary Fig. 14), where satellite observation density is relatively low and oil/gas fields are often collocated with wetlands, so that inversion results have limited information content and are sensitive to the choice of prior inventory (Supplementary Table 5-6).

Methods
Satellite observations
We use the TROPOMI methane product version 2.02 from the Netherlands Institute for Space Research17 for the period May 2018-February 2020. TROPOMI is aboard the Sentinel 5 Precursor (S5-P) satellite and has a ~13:30 local overpass time. It provides daily global coverage of methane columns at a spatial resolution of 7 km × 7 km (7 km × 5.5 km after August 2019)38,39. The methane column dry mixing ratio (XCH4) is retrieved from the sunlight backscattered by the Earth’s surface in the shortwave infrared (SWIR) at 2305-2385 nm with near-unit sensitivity down to the surface under clear-sky conditions. The retrieval success rate is limited by cloud cover and by heterogeneous or dark surfaces, and is 3% globally over land17. We only use recommended high-quality XCH4 measurements with the following criteria: (1) qa_value ≥ 0.5, (2) blended albedo ≤ 0.85, and (3) surface altitudes ≤ 2 km. The blended albedo, combining the surface albedo in the NIR and SWIR, can be used to filter scenes covered by snow40. The total number of TROPOMI observations is 1.4 × 108 for May 2018-February 2020, with large variability in observation density across the globe30.

Gridded national bottom-up inventories
We use the Global Fuel Exploitation Inventory version 2 (GFEIv25) in 2019 as the baseline prior inventory for fossil fuel methane emissions. GFEIv2 is based on the national inventories reported to the UNFCCC for oil, gas, and coal, spatially allocated to 0.1°×0.1° resolution. We divide the globe into 15 inversion domains that can account for 96% of fossil fuel methane emissions based on this inventory. We also consider the GFEI version 1 inventory41 for the year 2016, based on earlier UNFCCC reports, and the EDGARv6 inventory42 for the year 2018 to evaluate the sensitivity of our results to the prior estimates (Supplementary Fig. 17). Such sensitivity arises from differences in both the magnitude and the spatial distribution of emissions among inventories, especially in regions like Russia, the Middle East, Venezuela, and Nigeria (Supplementary Fig. 18). Prior anthropogenic emissions from other sources including livestock, waste, and rice cultivation are from EDGAR v4.3.243. Wetland emissions are taken from the mean of the nine high-performance members of the WetCHARTs v1.3.1 inventory ensemble36. Other natural sources include open-fire emissions from the Global Fire Emissions Database version 4s (GFED4s)44, termite emissions from Fung et al.45, and geological seepage emissions from46 with global scaling to 2 Tg a−147. Details of sectorial contributions can be found in Supplementary Table 8.

Forward model
We use the GEOS-Chem 12.7.0 chemical transport model (https://doi.org/10.5281/zenodo.3634864) as the forward model to simulate the sensitivity of atmospheric methane to emissions. GEOS-Chem is driven by MERRA2 reanalysis meteorological fields with 0.5°×0.625° horizontal resolution within the regional domains of Fig. 1, nested within a global simulation at 4°×5° resolution. Following Shen et al.23, we ensure that model boundary conditions are consistent with local TROPOMI observations by scaling the GEOS-Chem vertical fields in each boundary grid square both temporally and spatially to match smoothed TROPOMI column observations. Methane sinks including atmospheric oxidation and soil absorption are included in our forward model but are not optimized because they are slow and relatively smooth.

Construction of the state vector
The state vector for the inversion in individual regions consists of the spatially resolved emissions to be optimized on the 0.5°×0.625° model grid, along with boundary conditions for each quadrant. Our inversion mainly targets grid cells with high fossil fuel emissions, thus we can aggregate grid cells in areas of less interest, an approach often used by previous studies23,24,48. Following Shen et al.24, our state vector includes native 0.5°×0.625° grid cells where prior fuel emissions exceed 1 Gg a−1 and aggregates grid cells elsewhere. Altogether, the state vector consists of 5651 elements globally. The inversion optimizes total methane emissions for the state vector grid cells including contributions from all sectors. We follow Shen et al.23 to attribute the posterior correction factors and variances in each grid cell to individual sectors based on the error-weighted contributions from these sectors in the prior inventory.

Atmospheric inversion
We perform the inversion analysis following our previous work in North America23,24. The optimized methane emissions are obtained by minimizing a Bayesian cost function that balances the information from observations and the prior emissions, weighed by the corresponding error covariances49. For this we assemble gridded emissions and boundary conditions into a state vector x, and the May 2018-Feburary 2020 TROPOMI methane column data into an observation vector y. Applying Bayes’ theorem and assuming Gaussian errors leads to an optimized maximum-likelihood estimate for x by minimizing the cost function J given by

J(x) = (x−xA)T SA−1 (x−xA) + γ(y−Kx)T SO−1 (y−Kx) (1)

Here xA is the vector of prior emissions and boundary conditions, K=∂y/∂x is the Jacobian matrix describing the sensitivity of methane columns to the perturbation of every element in x, and SA and SO are covariance matrices for prior and observational errors, both taken to be diagonal. For SA, we assume 50% error standard deviations for emission elements in the baseline inversion and 5 ppb error standard deviation for the boundary conditions. For SO, we apply the residual error method50,51 by calculating the residual standard deviation between observations and GEOS-Chem simulations that use the prior estimates xA. The regularization term γ is designed to account for unresolved observational error covariances in the inversion and thus avoid “overfit” to observations52. Following Lu et al.15, we choose γ such that (x^−xA)T SA−1 (x^−xA) ≈ n where n is the number of state vector elements and is the expected value of the chi-squared distribution for a diagonal matrix. This yields γ in the range 0.01-0.2 for different inversion domains. Uncertainties in the specifications of SA and γ, and in the assumption of Gaussian errors, are addressed with the inversion ensemble described below.

Minimization of Eq. 1 at ∇x J(x)=0 yields the optimal estimate x^ for the state vector, the corresponding posterior error covariance matrix S^, and the averaging kernel matrix A as follows

x^ = xA + (γKTSO−1K + SA−1)−1 γKTSO−1 (y−KxA) (2)

S^−1 = γKTSO−1K + SA−1 (3)

A = I − S^ SA−1 (4)

where I is the identity matrix. The averaging kernel matrix A defines the sensitivity of the solution to the true state. The trace of A quantifies the degrees of freedom for signal (DOFS), representing the number of independent pieces of information that can be effectively constrained in the inversion53. We construct the Jacobian matrix explicitly column-by-column by conducting 22-month sensitivity GEOS-Chem simulations perturbing individual state vector elements. This enables analytical solution for x^ in Eq. 2 and closed-form expressions for S^ and A following Eq. 3 and 4. Our inversion can constrain 568 pieces of independent information in the global spatial distribution of methane emissions (Supplementary Fig. 19).

Running a series of 15 regional inversions to cover the important source domains for methane (Fig. 1) is much less computationally expensive than a global inversion, and this allows for much higher spatial resolution. In addition, use of regional GEOS-Chem model simulations with TROPOMI boundary conditions regularizes the inversion by avoiding bias in initializing the model, accounting for the methane sink, and simulating stratospheric transport48. It also mitigates the effect of any large-scale regional biases in the TROPOMI data. The 15 inversion domains cover 96% of global oil-gas emissions, 82% of all anthropogenic emissions, and 51% of natural emissions according to our prior emission inventories (Supplementary Table 8). To calculate the world’s total emissions, we sum posterior emissions for all 15 domains and retain prior emissions for the rest of the world. Our posterior estimate for global anthropogenic emission is 363 Tg a−1, consistent with a global inversion of GOSAT and TROPOMI observations by Qu et al.25.

Inversion ensemble and uncertainty analysis
The posterior error covariance matrix S^ represents the uncertainty within our choices of inversion parameters, but there is uncertainty in these parameters. Here we take advantage of the construction of the Jacobian matrix K to obtain a large ensemble of analytical solutions (x^, S^) by varying the parameters and prior estimates in the inversion. The ensemble has 39 members, including (1) use of either GFEIv2, GFEIv1, or EDGARv6 as prior bottom-up inventories, (2) regularization factors γ varied by a factor of 0.5 and 2 from the baseline values; (3) error standard deviation of 50% and 75% on the prior estimate; (4) lognormal error statistics (geometric standard deviation of 2) for the prior estimate. We then use the Monte Carlo method to estimate the posterior error statistics from the ensemble according to (x^, S^) for each ensemble member. We report error statistics on the inversion results as two standard deviations (2σ), corresponding to the 95% confidence level.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This study uses 22 months of TROPOMI satellite observations to quantify national fossil fuel emissions worldwide by inverse analysis techniques at finer (~50 km) resolution. This is of interest to the scientific community to understand the CH4 budget and the policymakers in defining the baseline emissions to mitigate climate change. I have no expertise in some statistical methods (inverting XCH4 from satellite) underpinning several calculations and therefore cannot evaluate them. Considering the pervasive amount of work behind the manuscript, the quality of the scientific approach and conclusive elements brought by the authors is significant. However, some extra effort is needed to improve the clarity of the current presentation and some missing details. Please find details of these points.\n(1) How did you calculate the global emissions? Did you sum all the inverted emissions over 15 boxes? But as you mentioned that these boxes contain 96% of global emissions. How did you include 4% in estimating the global emissions?\n(2) The details on the inversion are not very clear. Did you separately run the inversion at 15 boxes individually or simultaneously? Did you use a nested grid over each box? Why didn’t you perform a single global inversion at each grid? How will the results be changed if you perform a single global inversion at each grid? How many sources did you optimize? A summary of other prior and optimized sources is good to report. What is the resolution of your forward model?\n(3) What was the approach to select the best posterior estimate (Line 64-66)?\n(4) How did you use the EDGARv6 inventory emissions for sensitivity for May 2018-Feb, 2020, when inventory emissions are available until 2018? Did you extrapolate the EDGARv6 inventory emissions? A clarification of these details is worth including.\n(5) Some of the highest correction (e.g., over Russia) is collocated with the high wetland emissions. Further, the number of samplings in a different month (high solar zenith angle) differs over the higher latitude region (e.g., Russia). How robust are total emissions over the high latitude countries for uneven sampling in different seasons and collocated potent emission sources? A spatial plot of the total number of samplings in each season (for 2019 and 2020) may be good to show in the supplement.\n(6) Most of the campaigns shown in Figure 2 are mainly in USA and Canada. More validation work is needed, especially the spatial allocation of the corrected correction in emissions. Checking the optimized model simulations with the meridional gradient (for 2019 and 2020) based on surface CH4 observation (also the satellite observation) will be a nice check.\nTechnical Details:\nTitle: Good to replace “fuel exploitation” with fossil fuel exploitation”\nLine 58-59: “…. which allocates….as of September 2021.” The message is not very clear. Consider rewriting.\nLine 68. Use EDGARv6 consistently here and in other places.", "reviewer_2": "GENERAL COMMENTS\nThe manuscript by Shen et al. uses 2018-2020 data from the TROPOMI methane product and column observations to perform atmospheric inversion analyses using the GEOS-Chem 12.7.0 chemical transport model. Results are then compared to fossil fuel exploration basins, bottom-up, top-down, and isotopic emission inventories for the oil, gas, and coal industries. The study finds that emissions from the oil and gas industry are 30% higher compared to global total emissions from UNFCCC reports. This difference is mainly driven by underestimation by UNFCCC reports of the four largest emitting countries in the world: U.S., Russia, Venezuela, and Turkmenistan.\nThe manuscript is well written, organized, and with high-quality analyses. The results are based on widely used top-down and bottom-up data, established optimization methods, and robust statistical data analyses. The authors made a considerable effort to compare their results with a vast compendium of field campaigns and emission inventories. This manuscript will be a valuable contribution to scientific and policy purposes after revisions.\nSPECIFIC COMMENTS\nLine 67. Include a brief explanation of how error-weighted contributions were estimated.\nLines 87-88. Given that Fig S1 shows up to ~10% differences among posterior totals depending on the prior used, I suggest changing the phrase to: “global estimates have low sensitivity to the priors used”.\nAdditionally, Fig. 4 shows large differences between UNFCCC reports depending on the country analyzed. Can a similar comparison of priors and posteriors be done for the top emitting countries? Such analysis would provide a disaggregated perspective of the sensitivity to the priors used.\nNational emission estimates and comparison to UNFCCC reports section. Fig.4 shows Mexico as the country with the highest coal methane emission factor, what is the uncertainty associated with the TROPOMI observations?\nMaterials and Methods general comments\nLines 140-141. The aggregation of grid cells based on prior emissions can be a good strategy for regional studies (like the ones cited here) where emission sources are well known. However, for a global scale study, such a strategy can mask important emission sources that are not spatially allocated correctly. This is especially true when using EDGAR as a prior inventory since multiple studies have shown important differences in spatial distribution between it and local inventories.\nLines 253-254. Why were lognormal error statistics performed only on GFEIv2 and not the other ones?\nThe inability of TROPOMI to detect offshore emissions is never mentioned in the methods section. What is the contribution to country-wide and global totals of unaccounted offshore oil and gas emissions?\nMINOR EDITS\nLine 68. Add the version of EDGAR used.\nLines 96-97. Add citations to previous top-down studies.", "reviewer_3": "Review of Shen et al., “Worldwide quantification of national methane emissions from fuel exploitation using high-resolution inversions of satellite data”, for Nature Communications.\nThis work performs regional inversion analyses of 22 months of TROPOMI total-column methane measurements at a very high (50 km) spatial resolution to quantify coal and oil+gas fossil fuel emissions of methane to the atmosphere. This is critically important and valuable work, given the recent importance assigned to methane as a greenhouse gas, given that we have more power now to cut down our methane emissions than our CO2 emissions in the short term. The fact that they report results (separately for oil+gas and coal) for roughly 100 countries/regions is nothing short of amazing, especially given some of the small areas of these countries. They also compare the oil+gas emissions to total production in order to quantify the “methane intensity” of each country, that is, the so-called “methane leakage rate”. Therefore, after addressing the following comments and concerns, I suggest this work be published with all due speed.\n\"What are the noteworthy results?\"\nTheir primary results are their unprecedented disaggregation of country-scale fossil fuel-based methane emissions over the time period of study. This is very noteworthy and of both high scientific and political/policy-based importance.\n\"Will the work be of significance to the field and related fields?\"\nYes.\n\"How does it compare to the established literature?\"\nI am not a methane emissions researcher (currently), so it is difficult for me to fully answer this question. Based on the manuscript, it seems their results for various regions (Russia, China, etc) are generally consistent with previous findings.\n\"If the work is not original, please provide relevant references.\"\nIt certainly appears to be original.\n\"Does the work support the conclusions and claims, or is additional evidence needed?\"\nUsually, but not everywhere, as discussed in my comments below.\n\"Are there any flaws in the data analysis, interpretation and conclusions? Do these prohibit publication or require revision?\"\nMajor flaw is the relatively incomplete error analysis as I discuss more fully below. This requires some level of revision.\n\"Is the methodology sound? Does the work meet the expected standards in your field?\"\nYes.\n\"Is there enough detail provided in the methods for the work to be reproduced?\"\nIt appears so, but I am not an inversion expert so I am not sure.\nHigh-Level Comments:\nMy central critique of this work is the relatively incomplete nature of the error analysis discussion. Given the relatively high scientific and political importance of their findings, having appropriate and valid error bars on their results is critical. First, it appears based on the “Materials and Methods” discussion that the uncertainty due to wetland emissions is not included in their posterior emissions estimate. It seems to be it must be, as TROPOMI only sees the total CH4 emissions, even in a relatively small area. For instance, what if the entire ensemble of WetCHARTS estimates had been included, instead of just the mean? For many countries/areas this may not have been a significant contributor to the uncertainty, but certainly in some areas (especially in the tropics) it would be. The authors must discuss this potentially important source of uncertainty and why it is justified to completely ignore it.\nSecond, what is the contribution of atmospheric transport uncertainty? I realize they are doing regional inversions but still, given that they are trying to disaggregate coal, oil+gas, livestock, agricultural, and wetland emissions, I would think that atmospheric transport uncertainty could play a large role in their posterior emissions estimates. Is it feasible to perform these runs with a different transport model and compare the differences? If not, the authors MUST be clear from the outset the potential role of atmospheric transport uncertainty in potentially invalidating some of their conclusions.\nFinally, I suggest including additional error bars in some of their figures, as discussed below.\nSpecific comments:\n• I don’t love the title. “Fuel exploitation” is not a common phrase. Suggest “fossil fuel exploitation” to be more specific.\n• Figure 2: Please consider using a log-log scale (rather than linear) to better show the agreement at the lower emission rates.\n• Figure 3: Please include vertical error bars for the various top-down measurements. These are a critical part of the measurement and are typically displayed on these plots, so we can get a sense of how well the different estimates agree to within their uncertainties, and how your uncertainty (rather than just absolute value of emissions) compares. If older Top-Down uncertainty estimates are artificially low because they do not include certain contributors to uncertainty, it is important for your to add this to your discussion.\n• Figure 4: I like that you included emissions uncertainties on the TROPOMI estimates. But it is also very important to include them on the methane intensities (top plot) and emission factors (bottom plot). Please do this. (If not possible for some reason, please state why not in the text, and speculate on what your uncertainties on these values might be.)\n• L117: Discussion of the large uncertainty on Russia’s oil-gas estimate . Your data density for Russia is not much different for the U.S. it appears. It appears that the driving factor is the large disparity between GFEI v1 as compared to v2 and EDGARv6. Please comment on ways to potentially reduce this uncertainty, given the extremely important implications for these results?\n• L128 area. I notice you do not comment on the ~factor of 2 discrepancy between GFEIv2 and TROPOMI for Nigeria’s emissions. Please add a sentence on this, whether you think that any statements on this discrepancy are unwarranted given the relatively large uncertainty on your estimate, or if your value agrees better with GFEIv2 vs. EDGAR and possibly why.\n• L131: regarding your comment on the “lower value (4.1 Tg a-1) may be related to…” you should caveat this by saying “, though it is difficult to assess trends in their emissions given our the large uncertainty.” Also, do the bottom-up inventories imply declining production in Venezuela during this time period, consistent with your statement? If so, say so!\n• L133: “Is three times higher” → “Is 2.5 times higher” (3900/1500=2.6).\n• L140: “China’s methane emission is” → “China’s coal-based methane emissions are”.\n• L141: “, lower by 10% than the UNFCCC inventory estimate” → “, consistent with the UNFCCC inventory estimate within our uncertainty.” I don’t think the claim that China’s emissions are 10% lower is warranted given your large (17%) uncertainty.\nGrammatical comments:\n• L37: “which can provide” → “can provide”\n• L63: “We weigh” → “We weight”\n• L69: “More details are in” → “More details are provided in”\n• L114: China’s oil-gas emissions () are two times higher than the…\n• L155: → “…can constrain the emissions with a relative posterior uncertainty of better than 30% (2σ)…”", "reviewer_4": "The authors use satellite observations of methane to estimate global methane emissions from fossil fuel extraction. Overall, I think the results of this manuscript are important, novel, and worthy of publication in Nature Communications. I also believe the manuscript is clear and well-written. I have included several suggestions for the manuscript below.\n* The manuscript and SI do not include any model-data comparisons. I think that including model-data comparisons, particularly against independent CH4 observations, is an important step for evaluating the inverse model. A handful of recent studies have found substantial differences between CH4 emissions estimated using TROPOMI observations and GOSAT observations (see below), potentially due to errors in the TROPOMI CH4 observations. These findings make model-data comparisons against independent observations all the more important. There are numerous independent datasets that could be used in this study for evaluation -- GOSAT observations, in situ observations from the NOAA Greenhouse Gas Reference Network, and observations from some of the campaigns cited in Fig. 2.\n* A recent study by Liang et al. (https://acp.copernicus.org/preprints/acp-2022-508/) find differences of ~30% between inverse estimates of CH4 for India and China that use TROPOMI observations versus GOSAT observations. Furthermore, they argue that emissions estimated using GOSAT observations are more consistent with surface observations. Liang et al. also use the updated Lorente et al. (2021) data product. By contrast, line 44 of the present manuscript argues that biases in the TROPOMI observations have largely been corrected. How do you reconcile the results of Liang et al. with the present study? Should those results imply larger uncertainty bounds on the reported CH4 emissions estimated from TROPOMI?\n* Materials and Methods section: How does the inverse model treat temporal variability in the emissions? The inverse model optimizes the overall magnitude and spatial patterns in the emissions, but does the inverse model optimize seasonal or year-to-year variability in the emissions? In other words, does the inverse model assume that the temporal distribution of emissions is known or fixed to the temporal distribution of the prior?\n* Lines 18, 49, and 161: I find the wording in these lines somewhat misleading. The inverse model, as described in the Materials and Methods section, is used to estimate emissions at 50km resolution in selected grid boxes of the model domain. By contrast, the authors aggregate model grid boxes into coarser regions for other areas of the model domain. Hence, I think it's fair to say that the inverse model is used to estimate emissions at spatial resolutions up to 50km resolution, but the emissions are not optimized at that resolution everywhere.\nLines 36-38: This sentence is grammatically complex, and I think simplifying or breaking up the sentence would improve readability.\n* Lines 46-47: I also find these lines to be somewhat misleading, and I recommend deleting this sentence from the manuscript. It is true that existing, global inverse modeling studies often estimate emissions at coarse spatial resolutions. With that said, the present study also makes compromises on resolution to improve the computational feasibility of the inverse model. For example, the emissions in the present study are estimated at coarser, aggregate resolutions in grid boxes where fossil fuel emissions are less than 1 Gg a-1. In addition, my impression is that the inverse model in the present study does not correct temporal variability in the emissions. Overall, the state vector in this study has 5651 elements, fewer elements than many global GHG inverse modeling studies that use satellite data.\nLine 76: Independent estimates of what?"}, "author_response": "Response to referee comments on “Worldwide quantification of national methane emissions from fossil fuel exploitation using high-resolution inversions of satellite data”\nWe thank the four referees for their careful reading of the manuscript and valuable comments. This document is organized as follows: the Referee’s comments are in italics, our responses are in plain text, and all the revisions in the manuscript are shown in blue. Blue text here denotes text written in direct response to the Referee’s comments. The line numbers in this document refer to the updated manuscript with tracked changes.\nOverall, we have made the following changes to improve our manuscript.\n1. We have evaluated our posterior emission estimates by implementing them in GEOS-Chem and using them to simulate column-averaged methane mixing ratios for comparison with TROPOMI and GOSAT, and surface concentrations for comparison with in-situ observations in NOAA. Results show consistent improvements in the model-observation bias relative to using prior emissions. Please see Fig. S2-S5 and related texts for more details.\n2. We have tested the effects of using different wetland priors on posterior estimates of fossil fuel methane emissions. Overall, we find the effects are larger (0.7 Tg a-1 or 17%) in Venezuela but tiny elsewhere. Please see Fig. S15 for more details.\n4. We have added one paragraph (at the end of the Section ‘national emission estimates and comparison to UNFCCC reports’) to discuss the potential shortcomings of our method.\n3. We have included many new figures and tables in supplementary materials to provide more details of our work. Please check.\nReviewer #1\nThis study uses 22 months of TROPOMI satellite observations to quantify national fossil fuel emissions worldwide by inverse analysis techniques at finer (~50 km) resolution. This is of interest to the scientific community to understand the CH budget and the policymakers in defining the baseline emissions to mitigate climate change. I have no expertise in some statistical methods (inverting XCH from satellite) underpinning several calculations and therefore cannot evaluate them. Considering the pervasive amount of work behind the manuscript, the quality of the scientific approach and conclusive elements brought by the authors is significant. However, some extra effort is needed to improve the clarity of the current presentation and some missing details. Please find details of these points.\nResponse. We thank the referee for making these valuable comments. Please see our following response.\n(1) How did you calculate the global emissions? Did you sum all the inverted emissions over 15 boxes? But as you mentioned that these boxes contain 96% of global emissions. How did you include 4% in estimating the global emissions?\nResponse. Thanks for pointing this out. Now we make it clear in the main text how we calculate global emissions.\nLine 92. We derive the global emissions from fossil fuel exploitation by summing posterior estimates for all 15 domains and adding the GFEI v2 inventory for the 2.8 Tg a-1 of emissions outside of the domains (4% of global GFEI v2 emissions).\nLine 619. To calculate the world's total emissions, we sum posterior emissions for all 15 domains and retain prior emissions for the rest of the world.\n(2) The details on the inversion are not very clear. Did you separately run the inversion at 15 boxes individually or simultaneously? Did you use a nested grid over each box? Why didn’t you perform a single global inversion at each grid? How will the results be changed if you perform a single global inversion at each grid? How many sources did you optimize? A summary of other prior and optimized sources is good to report. What is the resolution of your forward model?\nResponse: The referee is right that we separately run the inversion at 15 boxes individually, which can save a lot of computational cost than running global inversions at a high resolution (~50km). For example, a global 2-year run at 50km resolution can take 15-20 days using 8 CPU cores, compared to <1 day in a regional domain like Europe. And we use nested simulations. Now we make it clear in the text.\nLine 490. Running a series of 15 regional inversions to cover the important source domains for methane (Fig. 1) is much less computationally expensive than a global inversion, allowing for much higher spatial resolution.\nLine 432. GEOS-Chem is driven by MERRA2 reanalysis meteorological fields with 0.5°×0.625° horizontal resolution within the regional domains of Fig. 1, nested within a global simulation at 4o×5o resolution.\nTheoretically, the results should be consistent if we perform a single global inversion at each grid because the Jacobian sensitivity won’t change. Meanwhile, regional inversions have the advantage of avoiding the potential influence of regional bias in satellite data. Now we say this in text:\nLine 491. In addition, use of regional GEOS-Chem model simulations with TROPOMI boundary conditions regularizes the inversion by avoiding bias in initializing the model, accounting for the methane sink, or simulating stratospheric transport. It also mitigates the effect of any large-scale regional biases in the TROPOMI data.\nWe optimize all sources inside our 15 inversion domains. Now we report all the emission changes of other prior and optimized sources (Table S8).\nLine 494. The 15 inversion domains cover 96% of global oil-gas emissions, 82% of all anthropogenic emissions, and 51% of natural emissions according to our prior emission inventories (Table S8). To calculate the world's total emissions, we sum posterior emissions for all 15 domains and retain prior emissions for the rest of the world. Our posterior estimate for global anthropogenic emission is 363 Tg a-1, consistent with a global inversion of GOSAT and TROPOMI observations by Qu et al.25 .\nTable S8. Posterior estimates of all sectors and comparison with another global TROPOIN inversion work.\nThis study Qu et al. 30\nPercentage of prior\nPrior TROPOMI TROPOMI-GOSAT\nemission covered by our 15 Posterior estimates\nestimate inversion joint inversion\ninversion domains\nTotal sources 516 71% 556 556 570\nAnthropogenic 325 82% 363 336 363\nOil and Gas 48 96% 61 53 56\nCoal 33 96% 33 NA NA\nLivestock 115 71% 127 126 139\nRice 37 86% 42 NA NA\nWastewater 38 80% 42 44 44\nLandfill 29 87% 31 27 31\nOther\nanthropogenic 25 83% 27 23 26\nsources\nNatural 191 51% 193 220 207\nWetlands 162 50% 165 195 183\nTermites 12 56% 13 12 12\nOpen fires 15 61% 13 11 10\nSeeps 2 74% 2 2 2\n(3) What was the approach to select the best posterior estimate (Line 64-66)?\nResponse. Thanks for pointing out this. The best posterior estimate is the maximum likelihood estimate based on the Bayesian cost function. We realize the word ‘best’ here may be misleading; thus, we use “the maximum-likelihood posterior estimates” instead. And we refer to Eq. 1 in the main text to reduce confusion.\nLine 85. We weight the gridded bottom-up prior emission estimates for fuel and other sectors with the TROPOMI observations using analytical Bayesian optimization to solve for the maximum-likelihood posterior estimates of methane emissions on the 0.5°×0.625° grid (Equation 1).\nLine 456. Applying Bayes’ theorem and assuming Gaussian errors leads to an optimized maximum-likelihood estimate for x by minimizing the cost function J given by\n𝑱(𝒙)=(𝒙−𝒙 )𝑻𝑺#𝟏(𝒙−𝒙 )+g (𝒚−𝑲𝒙)𝑻𝑺#𝟏(𝒚−𝑲𝒙) (1)\n𝑨 𝑨 𝑨 𝑶\n(4) How did you use the EDGARv6 inventory emissions for sensitivity for May 2018-Feb, 2020, when inventory emissions are available until 2018? Did you extrapolate the EDGARv6 inventory emissions? A clarification of these details is worth including.\nResponse. Thanks for pointing this out. Now we make it clear in the text.\nLine 419. We also consider the GFEI version 1 inventory 40 for the year 2016, based on earlier UNFCCC reports, and the EDGAR v6 inventory 41 for the year 2018 to evaluate the sensitivity of our results to the prior estimates.\nLine 416. We use the Global Fuel Exploitation Inventory version 2 (GFEI v2 5) in 2019 as the baseline prior inventory for fossil fuel methane emissions.\n(5) Some of the highest correction (e.g., over Russia) is collocated with the high wetland emissions. Further, the number of samplings in a different month (high solar zenith angle) differs over the higher latitude region (e.g., Russia). How robust are total emissions over the high latitude countries for uneven sampling in different seasons and collocated potent emission sources? A spatial plot of the total number of samplings in each season (for 2019 and 2020) may be good to show in the supplement.\nResponse. Thanks for making this good point. The referee is correct that wetlands could potentially influence our results. As mentioned in our original manuscript, we used the 9 highest-performance members from WetCHARTs v1.3.1 as the prior inventory. As a sensitivity test, we used all 18 members from WetCHARTs to conduct another set of inversions. Now we discuss it in text as follows.\nLine 312. An additional limitation in quantifying oil-gas emissions at high latitudes and in the tropics is the uncertainty in wetland emissions. Whereas oil-gas fields at northern mid-latitudes are usually in arid regions, oil-gas fields at high latitudes and in the tropics are often co-located with wetlands and separating the two emission sectors can be difficult. Our prior estimate of wetland emissions is obtained by averaging the 9 highest-performance members of the WetCHARTs v1.3.1 inventory ensemble38,39, for a global emission of 149 Tg a-1 in 2019. We conducted another set of inversions using the ensemble average of all 18 members of WetCHARTs v1.3.1 with prior wetland emissions that are 10% higher globally, 20-30% higher in the tropics, and 15% lower in central Russia (Fig. S15). The resultant changes in posterior fossil fuel emissions are 0.7 Tg in Venezuela and <0.2 Tg a-1 in other countries (Fig. S15).\nEffectsofwetlandemissionsonposteriormethaneemissionsfromfossilfuelexploitation\nFig. S15. Effects of wetland inventories on posterior fossil fuel emissions. (a) Difference in wetland methane emissions using the 9 highest-performance members and 18 all members in WetCHARTs v1.3.13,5. The five inversion domains with the highest relative difference in wetland methane emissions are shown as black rectangles. (b) The 5 top countries with the highest absolute changes of posterior methane emissions from fossil fuel exploitation if using all 18 WetCHARTs members.\nAbout the uneven sampling in different seasons, we now report the spatial plot of the total number of samplings and corresponding seasonal correction factors in the high latitudes. We have made the following revisions:\nLine 321. There are other poorly accounted sources of uncertainty in our analysis. TROPOMI observations are affected by regional bias in some parts of the world17,25,37 and the biases not removed by our quality flags (see Methods) would propagate to our inversion results. High latitudes and tropics have large seasonal variations in observation density that would affect inversion results if fossil fuel emissions\n1-s2-mcselucelom2101\n(a)WetCHARTswetlandmethaneemissions(highperformance–allensembles)\n+15% 1.5\n1.0\n0.5\n+30%\n+21% +20% 0.0\n+18% −0.5\n−1.0\n−1.5\n(b)Δmethaneemissionsfromfossilfuelexploitationinthetop5countries\n(highperformance–allensembles)\nVenezuela Russia India Nigeria Coted’Ivoire\nPosteriorOGandCoal\nemissionsusinghigh\n4.2 11.3 1.9 3.4 0.27\nperformanceWetCHART\nmembers(Tga-1)\nΔemissionsifusingall\nWetCHARTmembers -0.7 0.3 -0.2 -0.15 -0.15\n(Tga-1)\nwere seasonally variable (we assume that they are not). Independent inversions for different seasons show near-zero posterior corrections in the wintertime at high latitudes because of the low observation density (Fig. S8-S9, S16, Text S2).\nText S2. Seasonal posterior correction factors and effects of data density at high latitudes\nWe also calculated posterior emissions from fossil fuel exploitation using TROPOMI observation in different seasons at northern high-latitudes, where the observation density varies considerably across one year (Fig. S8-S9). In these high-latitude countries, posterior corrections are nearly zero in winter because of scarce observations and low averaging-kernel sensitivities (Fig.S16). This result also demonstrates that seasonal corrections are partly influenced by satellite data availability, which does not necessarily reflect the temporal variability in emissions in regions with uneven observation density. In this study, we do not try to optimize for higher temporal variability of emissions because uneven and inadequate seasonal sampling frequencies are present in most regions of the world (Fig. S8-S9).\nFigure S8. (a) TROPOMI data density (counts km-2 a-1) from May 2018 to February 2020, mapped to 0. 5°×0.625° horizontal resolution. (b) Same as (a) but only for gridcells with fossil fuel methane emissions greater than 1 Gg a-1.\n1-a2-mkstnuoc\nTROPOMIobservationdensity(2018-2020)\nTROPOMI obs freq\n3.0\n2.5\n2.0\n1.5\n1.0\n0.5\n0.0\nTROPOMI obs freq in high fossil fuel emission gridcells\n1.0\n0.8\n0.6\n0.4\n0.2\n0.0\n1-a2-mkstnuoc\nFraction of TROPOMI observations in different seasons\nDJF MAM\n% %\n100 100\n80 80\n60 60\n40 40\n20 20\n0 0\nJJA SON\n% %\n100 100\n80 80\n60 60\n40 40\n2.0\n20 20\n1.5\n0 0\nFigure S9. Fraction of TROPOMI observation in different seasons (DJF, MAM, JJA 1a.0nd SON), mapped to 0.\n5°×0.625° horizontal resolution. Gridcells with zero observation in each season are shown as white.\n0.5\nSeasonalcorrectionfactorsinhighlatitudesfromTROPOMIinversion\n0.0\nDJF 3\n2\n−0.5\n1\n−1.0\n0\n−1\nMAM 3\n2\n1\n0\n−1\nJJA 3\n2\n1\n0\n−1\nSON 3\n2\n1\n0\n−1\nFig. S16. Posterior correction factors in different seasons at high latitudes north of 30°N. This test uses GFEIv2 as the prior inventory. Please note that the seasonal corrections here are largely determined by the sampling frequency in different seasons (Fig. S8-S9), which do not necessarily reflect the temporal variability in emissions.\nSee Text S2 for discussion.\n1−a\ngG\n(a) Gridded UNFCCC inventory from fossil fuel exploitation in 2019\n(b) Correction factors relative to the UNFCCC inventory\n33\n22\n11\n00\n−1\n(6) Most of the campaigns shown in Figure 2 are mainly in USA and Canada. More validation work is needed, especially the spatial allocation of the corrected correction in emissions. Checking the optimized model simulations with the meridional gradient (for 2019 and 2020) based on surface CH observation (also the 4\nsatellite observation) will be a nice check.\nResponse. We thank the referee for making this good point. The referee is correct that most campaigns are in the US and Canada. To our knowledge, the campaign data in Fig 2 are all we can collect from recent publications. Now we acknowledge this shortcoming in the main text.\nLine 328. Our evaluation of inversion results with field campaigns in Fig. 2 does not reveal obvious biases but these campaigns are limited to North America and Europe.\nAnd we conduct more validation work in this revised manuscript.\nLine 143. We evaluate our posterior emission estimates by implementing them in GEOS-Chem and using them to simulate column-averaged methane mixing ratios for comparison with TROPOMI and GOSAT, and surface concentrations for comparison with surface measurements from the National Oceanic and Atmospheric Administration (NOAA) network26. Results show consistent improvements in the model-observation bias relative to using prior emissions (Fig. S2-S5).\nBiasofcolumn-averagemethanemixingratiointhepriorandposteriorrunrelativetoTROPOMI\nPrior run − TROPOMI Posterior run − TROPOMI\nppb ppb\n20 20\n10 10\n0 0\n−10 −10\nmean diff. = −2.0 ppb mean diff. = −1.1 ppb\n−20 −20\nRMSE = 6.5 ppb RMSE = 4.9 ppb\nFig. S2. Bias of column-averaged methane mixing ratio in the prior and posterior GEOS-Chem simulations relative to TROPOMI from May 2018 to February 2022. To be consistent with our inversion setups, we run the GEOS-Chem model in 15 inversion domains using prior and posterior inventories at the 0.5°×0.625° resolution, and then aggregate the simulation results. The mean bias and root mean square errors (RMSE) are shown inset.\nBiasofcolumn-averagemethanemixingratiointhepriorandposteriorrunrelativetoGOSAT\nPrior run − GOSAT Posterior run − GOSAT\nppb ppb\n20 20\n10 10\n0 0\n−10 −10\nmean diff. = −8.3 ppb mean diff. = −6.7 ppb\n−20 −20\nRMSE = 12.2 ppb RMSE = 10.2 ppb\nFig. S3. Bias of column-averaged methane mixing ratio in the prior and posterior GEOS-Chem simulations relative to GOSAT from May 2018 to February 2022. To be consistent with our inversion setups, we run the GEOS-Chem model in 15 inversion domains using prior and posterior inventories at the 0.5°×0.625° resolution, and then aggregate the simulation results. The mean bias and root mean square errors (RMSE) are shown inset.\n−50 0 50\nFig. S4. Latitudinal column-averaged methane mixing ratio between GOSAT, TROPOI, GEOS-Chem posterior runs.\nGEOS-Chem simulations are performed in 15 inversion domains with boundary conditions calibrated to match TROPOMI (same as our inversion setups). Thus, GEOS-Chem’s latitudinal variability closely resembles TROPOMI’s.\n0091\n0581\n0081\n)vbpp(\nenahteM\nLatitudinal XCH4 in GEOS−Chem, TROPOMI and GOSAT\nGOSAT\nTROPOMI\nGEOS−Chem posterior\nThe biasofmethaneconcentrationsinthepriorandposteriorrun\nrelativetoin-situobservations\nGC prior runs − surface observations\nppb\n40\n-27ppb\n20\n-21ppb 0\n-2.3ppb\n-7.9ppb −20\n−40\nGlobal:-12±27ppb\nGC posterior runs − surface observations ppb\n40\n-13ppb\n20\n0\n-1.9ppb\n+9.0ppb\n−20\n-1.0ppb\n−40\nGlobal:+3±24ppb\nFig. S5. Bias of surface methane concentrations in the prior and posterior GEOS-Chem simulations relative to NOAA in-situ observations inside our 15 inversion domains from May 2018 to February 2022 (ObsPack, Schuldt et al.4). The mean bias and root mean square errors (RMSE) are shown inset.\nTechnical Details:\nTitle: Good to replace “fuel exploitation” with fossil fuel exploitation”\nResponse. Thanks, now we use ‘fossil fuel exploitation’ in the main text.\nLine 58-59: “…. which allocates….as of September 2021.” The message is not very clear. Consider rewriting.\nResponse. Thanks. The information of the year here is redundant. Now we say:\nLine 78. We tile the world with 15 domains that account for 96% of global emissions from fossil fuel exploitation in 2019 according to the Global Fuel Emissions Inventory version 2 (GFEI v2), which allocates national emissions reported to the UNFCCC on a 0.1°×0.1° grid5.\nLine 68. Use EDGARv6 consistently here and in other places.\nResponse. Fixed, thanks!\nReviewer #2\nGENERAL COMMENTS\nThe manuscript by Shen et al. uses 2018-2020 data from the TROPOMI methane product and column observations to perform atmospheric inversion analyses using the GEOS-Chem 12.7.0 chemical transport model.\nResults are then compared to fossil fuel exploration basins, bottom-up, top-down, and isotopic emission inventories for the oil, gas, and coal industries. The study finds that emissions from the oil and gas industry are 30% higher compared to global total emissions from UNFCCC reports. This difference is mainly driven by underestimation by UNFCCC reports of the four largest emitting countries in the world: U.S., Russia, Venezuela, and Turkmenistan.\nThe manuscript is well written, organized, and with high-quality analyses. The results are based on widely used top-down and bottom-up data, established optimization methods, and robust statistical data analyses. The authors made a considerable effort to compare their results with a vast compendium of field campaigns and emission inventories. This manuscript will be a valuable contribution to scientific and policy purposes after revisions.\nResponse. We thank the referee for making these valuable comments. Please see our following responses.\nSPECIFIC COMMENTS\nLine 67. Include a brief explanation of how error-weighted contributions were estimated.\nResponse. Thanks, now we add this brief explanation to the text.\nLine 90. and sectors with higher prior uncertainty are subject to larger relative corrections (Text S1).\nLines 87-88. Given that Fig S1 shows up to ~10% differences among posterior totals depending on the prior used, I suggest changing the phrase to: “global estimates have low sensitivity to the priors used”.\nResponse. That is corrected, thanks.\nAdditionally, Fig. 4 shows large differences between UNFCCC reports depending on the country analyzed. Can a similar comparison of priors and posteriors be done for the top emitting countries? Such analysis would provide a disaggregated perspective of the sensitivity to the priors used.\nResponse. We now have new Tables S5-S6 to provide these details. Please check. Now we say this in the main text.\nLine 191. Posterior estimates of the top-20 oil-gas and coal emitting countries using different prior inventories can be found in Table S5-S6.\nLine 275. After combining inversions from the full ensemble (see Methods for more details), our results show that TROPOMI can constrain the emissions with a relative posterior uncertainty <30% (2σ) for most large mid-latitude emitters including China, the US, Turkmenistan, Kazakhstan, India, and Canada. It has more difficulty in Russia and the tropics (Fig. S14), where satellite observation density is relatively low and oil/gas fields are often collocated with wetlands, so that inversion results have limited information content and are sensitive to the choice of prior inventory (Table S5-S6).\nTable S5. Posterior estimates of oil-gas methane emissions (2018-2020) using different priors (GFEIv2, GFEIv1 and EDGARv6) for top emitting countries.\nCountries using GFEIv2 (UNFCCC) using GFEIv1 using EDGARv6\nAveraged\nPrior Posterior Prior Posterior Prior Posterior\nposterior\n(Tg a-1) (Tg a-1) (Tg a-1) (Tg a-1) (Tg a-1) (Tg a-1)\n(Tg a-1)\nUnited States# 8.1 12.6 8.3 9.6 12.6\nRussia 4.1 5.6 24.9 15.0 6.9 7.7 9.4\nVenezuela 1.4 4.1 3.2 6.4 0.9 1.6 4.0\nTurkmenistan 1.5 3.9 1.5 3.8 1.3 3.1 3.6\nIran 3.3 2.2 4.1 2.6 7.4 3.9 2.9\nChina 1.2 2.2 1.1 2.1 3.2 3.8 2.7\nAlgeria 1.1 2.0 1.2 2.1 2.1 2.5 2.2\nCanada# 1.4 2.2 1.6 1.6 2.2\nUzbekistan 1.9 2.1 2.6 2.1 1.2 1.1 1.8\nNigeria 3.3 2.1 0.4 0.5 3.7 1.9 1.5\nIraq 2.9 1.7 0.1 0.2 4.6 2.0 1.3\nSaudi Arabia 0.6 0.8 0.6 1.1 4.7 2.8 1.5\nMexico† 0.6 1.3 0.6 0.9 1.3\nUkraine 1.3 1.3 1.1 0.9 0.9 1.1 1.1\nMalaysia 1.0 0.9 0.9 0.9 1.3 1.2 1.0\nAngola 1.0 0.8 1.1 1.0 1.2 0.9 0.9\nOman 0.7 0.9 0.1 0.3 1.1 0.9 0.7\nIndia 0.9 0.7 1.0 0.8 0.7 0.5 0.6\nKazakhstan 0.2 0.5 0.3 0.6 0.9 0.9 0.7\nEgypt 0.4 0.5 0.4 0.5 1.1 1.1 0.7\n#Here the prior inventory is based on GFEIv2, which has improved spatial distribution relative to GFEIv1 and EDGARv6. We also scale the prior in the Permian basin to match EDF’s inventory20 (extrapolated from site-scale emission rates) in sensitivity experiments, following the same setups in Shen et al.36. Thus, we do not use GFEIv1 and EDGARv6 as priors.\n†Here we use GFEIv2 as the prior and scale Mexico’s offshore emissions by a factor of 0.1 to match field campaign results37, following the same setups in Shen et al.1 . So we do not use GFEIv1 and EDGARv6 as priors.\nTable S6. Posterior estimates of coal-based methane emissions (2018-2020) using different priors (GFEIv2, GFEIv1 and EDGARv6) for top emitting countries.\nusing GFEIv2 (UNFCCC) using GFEIv1 using EDGARv6 Averaged\nCountries Prior Posterior Prior Posterior Prior Posterior posterior\n(Tg a-1) (Tg a-1) (Tg a-1) (Tg a-1) (Tg a-1) (Tg a-1) (Tg a-1)\nChina 21.1 20.2 18.5 18.2 19.9 18.2 18.9\nUnited States# 2.1 2.6 2.9 1.6 2.6\nRussia 2.7 2.0 2.5 1.8 3.1 2.0 2.0\nAustralia 0.9 1.6 1.0 1.7 0.9 1.6 1.6\nKazakhstan 0.3 1.2 0.9 1.4 0.7 1.4 1.3\nIndia 0.9 1.1 0.8 1.0 1.2 1.4 1.2\nColombia 0.3 0.7 0.3 0.6 0.2 0.4 0.6\nSouth Africa 1.2 0.7 0.4 0.5 1.2 0.7 0.6\nIndonesia 0.20 0.19 0.14 0.13 4.51 1.93 0.75\nUkraine 0.48 0.30 0.65 0.36 0.16 0.24 0.30\nMexico† 0.23 0.22 0.22 0.04 0.22\nPoland 0.66 0.30 0.79 0.29 0.44 0.16 0.25\nTurkey 0.27 0.27 0.22 0.21 0.12 0.12 0.20\nViet Nam 0.11 0.13 0.09 0.09 0.50 0.60 0.27\nRomania 0.22 0.24 0.24 0.20 0.03 0.03 0.16\nVenezuela 0.04 0.17 0.02 0.04 0.03 0.11 0.10\nMozambique 0.18 0.18 0.02 0.03 0.18 0.17 0.13\nNigeria 0.00 0.00 0.25 0.43 0.00 0.00 0.14\nGermany 0.01 0.01 0.10 0.14 0.14 0.18 0.11\nPakistan 0.04 0.06 0.06 0.09 0.04 0.05 0.07\n#Here the prior inventory is based on GFEIv2, which has improved spatial distribution relative to GFEIv1 and EDGARv6. We also scale the prior in the Permian basin to match EDF’s inventory20 (extrapolated from site-scale emission rates) in sensitivity experiments, following the same setups in Shen et al.36. Thus, we do not use GFEIv1 and EDGARv6 as priors.\n†Here we use GFEIv2 as the prior and scale Mexico’s offshore emissions by a factor of 0.1 to match field campaign results37, following the same setups in Shen et al.1 . So we do not use GFEIv1 and EDGARv6 as priors.\nNational emission estimates and comparison to UNFCCC reports section. Fig.4 shows Mexico as the country with the highest coal methane emission factor, what is the uncertainty associated with the TROPOMI observations?\nResponse. We now have updated Fig. 4 to show the uncertainty associated with the emission intensity.\nNational emission estimates from TROPOMI inversion and UNFCCC reports\nFig. 4. National methane emissions from the oil-gas and coal sectors estimated by inversion of TROPOMI observations and compared to the UNFCCC reports. The TROPOMI observations are for May 2018 – February 2020, and the UNFCCC reports are for 2019 (Annex I countries) or most recent (other countries), as compiled by the GFEI v2 inventory of Scarpelli et al. 5. Iraq has not reported to the UNFCCC since 2000 and its emission is estimated in GFEI v2 using IPCC emission factors. The top 20 emitting countries are shown here; data for the 93 countries with total fuel emissions larger than 1 Gg a-1 are in Table S2. Vertical bars indicate the 95% confidence levels from the inversion ensemble. The circles represent the methane intensity from oil-gas production (top panel), defined as the total oil-gas emission per unit of gas produced (assuming 90% methane content for gas) 4,29, and the coal emission factor (bottom panel), defined following IPCC 30 as the total coal emission per unit coal produced. The empty rectangles denote the 95% confidence levels of oil-gas emission intensities and coal-based emission factors. Note break in left ordinate axis of bottom panel, as Chinese coal emissions are much higher than for any other country.\nMaterials and Methods general comments\n)1−a\ngT(\nnoissime\nHC\n4\n51\n01\n5\n(a) Oil and natural gas emissions\nGFEI v2 (UNFCCC)\nTROPOMI estimates\nMethane intensity\nUnited States Ru V s e s n T ia e u z rk u m el e a nistan Iran Chin A a lger C ia a U n z a b d e S a k a i u st d a i n Arabia Nigeria Ira M q exic U o kra M ine alaysia Angola O man K E a g z y a p k t hstan India\n)1−a\ngT(\nnoissime\nHC 4\n42\n22\n02\n81\n2\n614\n06\n05\n04\n03\n02\n01\n0\n)%( \nytisnetni\nenahteM\n(b) Coal emissions\nGFEI v2 (UNFCCC)\nTROPOMI estimates\nEmission factor\nUn C ite h d in a States Russ A i u a K st a ra z l a ia khstan I I n n d d ia o S n o e u s th ia Afr C ic o a lo mbi U a kraine Mexi V co ietna m Poland Turk R e o y mani M a N o ig z e a r m ia biq G ue er m V a e n n y ezue P la akistan\n80.0\n60.0\n40.0\n20.0\n00.0\nrotcaf\nnoissime\nenahtem\nlaoC\n]noitcudorp\nsnot\n0001\nrep\ngG[\nLines 140-141. The aggregation of grid cells based on prior emissions can be a good strategy for regional studies (like the ones cited here) where emission sources are well known. However, for a global scale study, such a strategy can mask important emission sources that are not spatially allocated correctly. This is especially true when using EDGAR as a prior inventory since multiple studies have shown important differences in spatial distribution between it and local inventories.\nResponse. We thank the referee for this comment. Now we discuss it in the main text and have a new figure in the SI to support the discussion.\nLine 421. Such sensitivity arises from differences in both the magnitude and the spatial distribution of emissions among inventories, especially in regions like Russia, the Middle East, Venezuela, and Nigeria (Fig. S18).\nLine 277. It has more difficulty in Russia and the tropics (Fig. S14), where satellite observation density is relatively low and oil/gas fields are often collocated with wetlands, so that inversion results have limited information content and are sensitive to the choice of prior inventory (Table S5-S6).\nFossilfuelMethaneemissionsfrompriorinventories\nGFEIv2\n50\n40\n30\n20\n10\n50\n40\n30\n20\n10\n50\n40\n30\n20\n10\n1-agG\nLines 253-254. Why were lognormal error statistics performed only on GFEIv2 and not the other ones?\nResponse. Now we apply that to all prior inventories, and we have updated all numbers and related figures.\nFigure S1. Global prior vs. posterior methane emissions of fossil fuel exploitation using different prior inventories. Different colors represent the results using different bottom-up priors and assumptions of errors. Triangles denote the magnitude of the priors, points refer to estimates assuming a lognormal error, and the boxplots denote the distribution of the posteriors (see Methods for more details).\nThe inability of TROPOMI to detect offshore emissions is never mentioned in the methods section. What is the contribution to country-wide and global totals of unaccounted offshore oil and gas emissions?\nResponse. Thanks for this good point. Now we say this in text.\nLine 329. Offshore oil-gas emissions are not directly observed by TROPOMI in the source grid cell (TROPOMI observations over the oceans are limited to the glint mode, which we do not use here)17 and are optimized solely on the basis of their plumes transported across coastlines and over land. Global offshore oil-gas emissions amount to 3.8 Gg a-1 in GFEIv2 (7.9% of total oil-gas emissions) and we find that only 60% of these offshore emissions are effectively optimized in our inversion (averaging kernel sensitivity larger than 0.1).\nMINOR EDITS\nLine 68. Add the version of EDGAR used.\nResponse. Corrected, thanks.\nLines 96-97. Add citations to previous top-down studies.\nResponse. Updated, thanks.\n)1−a\ngT(\nsnoissime\n4HC\n021\n011\n001\n09\n08\nGFEI v2 GFEI v1 EDGAR v6 All\nReviewer #3\nReview of Shen et al., “Worldwide quantification of national methane emissions from fuel exploitation using high-resolution inversions of satellite data”, for Nature Communications.\nThis work performs regional inversion analyses of 22 months of TROPOMI total-column methane measurements at a very high (50 km) spatial resolution to quantify coal and oil+gas fossil fuel emissions of methane to the atmosphere. This is critically important and valuable work, given the recent importance assigned to methane as a greenhouse gas, given that we have more power now to cut down our methane emissions than our CO2 emissions in the short term. The fact that they report results (separately for oil+gas and coal) for roughly 100 countries/regions is nothing short of amazing, especially given some of the small areas of these countries. They also compare the oil+gas emissions to total production in order to quantify the “methane intensity” of each country, that is, the so-called “methane leakage rate”. Therefore, after addressing the following comments and concerns, I suggest this work be published with all due speed.\n\"What are the noteworthy results?\"\nTheir primary results are their unprecedented disaggregation of country-scale fossil fuel-based methane emissions over the time period of study. This is very noteworthy and of both high scientific and political/policy-based importance.\n\"Will the work be of significance to the field and related fields?\"\nYes.\n\"How does it compare to the established literature?\"\nI am not a methane emissions researcher (currently), so it is difficult for me to fully answer this question. Based on the manuscript, it seems their results for various regions (Russia, China, etc) are generally consistent with previous findings.\n\"If the work is not original, please provide relevant references.\"\nIt certainly appears to be original.\n\"Does the work support the conclusions and claims, or is additional evidence needed?\"\nUsually, but not everywhere, as discussed in my comments below.\n\"Are there any flaws in the data analysis, interpretation and conclusions? Do these prohibit publication or require revision?\"\nMajor flaw is the relatively incomplete error analysis as I discuss more fully below. This requires some level of revision.\n\"Is the methodology sound? Does the work meet the expected standards in your field?\"\nYes.\n\"Is there enough detail provided in the methods for the work to be reproduced?\"\nIt appears so, but I am not an inversion expert so I am not sure.\nResponse. We thank the referee for making these valuable comments. Please see our following responses.\nHigh-Level Comments:\nMy central critique of this work is the relatively incomplete nature of the error analysis discussion. Given the relatively high scientific and political importance of their findings, having appropriate and valid error bars on their results is critical. First, it appears based on the “Materials and Methods” discussion that the uncertainty due to wetland emissions is not included in their posterior emissions estimate. It seems to be it must be, as TROPOMI only sees the total CH4 emissions, even in a relatively small area. For instance, what if the entire ensemble of WetCHARTS estimates had been included, instead of just the mean? For many countries/areas this may not have been a significant contributor to the uncertainty, but certainly in some areas (especially in the tropics) it would be. The authors must discuss this potentially important source of uncertainty and why it is justified to completely ignore it.\nResponse. We thank the reviewer for making this good point. Each grid cell includes emissions from non-fossil fuel sources. Thus, we attribute posterior emissions to each sector based on error-weighted contributions from different sectors in the prior inventory, and the uncertainty of wetland emissions is partly taken into accounted.\nLine 89. Here, we attribute the posterior corrections to the different sectors in the grid cell on the basis of their error-weighted contributions to the prior emissions23,24, and sectors with higher prior uncertainty are subject to larger relative corrections (Text S1). This error weighting is particularly important for grid cells containing wetland emissions, which are particularly uncertain.\nFollowing the reviewer’s suggestion, we have included a new set of inversion using all WetCHARTS members as the prior in the analysis.\nLine 312. An additional limitation in quantifying oil-gas emissions at high latitudes and in the tropics is the uncertainty in wetland emissions. Whereas oil-gas fields at northern mid-latitudes are usually in arid regions, oil-gas fields at high latitudes and in the tropics are often co-located with wetlands and separating the two emission sectors can be difficult. Our prior estimate of wetland emissions is obtained by averaging the 9 highest-performance members of the WetCHARTs v1.3.1 inventory ensemble38,39, for a global emission of 149 Tg a-1 in 2019. We conducted another set of inversions using the ensemble average of all 18 members of WetCHARTs v1.3.1 with prior wetland emissions that are 10% higher globally, 20-30% higher in the tropics, and 15% lower in central Russia (Fig. S15). The resultant changes in posterior fossil fuel emissions are 0.7 Tg in Venezuela and <0.2 Tg a-1 in other countries (Fig. S15).\nEffectsofwetlandemissionsonposteriormethaneemissionsfromfossilfuelexploitation\nFig. S15. Effects of wetland inventories on posterior fossil fuel emissions. (a) Difference in wetland methane emissions using the 9 highest-performance members and 18 all members in WetCHARTs v1.3.13,5. The five inversion domains with the highest relative difference in wetland methane emissions are shown as black rectangles. (b) The 5 top countries with the highest absolute changes of posterior methane emissions from fossil fuel exploitation if using all 18 WetCHARTs members.\nSecond, what is the contribution of atmospheric transport uncertainty? I realize they are doing regional inversions but still, given that they are trying to disaggregate coal, oil+gas, livestock, agricultural, and wetland emissions, I would think that atmospheric transport uncertainty could play a large role in their posterior emissions estimates. Is it feasible to perform these runs with a different transport model and compare the differences? If not, the authors MUST be clear from the outset the potential role of atmospheric transport uncertainty in potentially invalidating some of their conclusions.\nResponse. The reviewer makes a good point here that atmospheric transport could be an important source of uncertainty. But using a different transport model to test the difference is too expensive for us. Now we acknowledge this potential shortcoming in the discussion.\nLine 326. GEOS-Chem transport error is treated as random through the observational error covariance matrix, but any systematic transport bias40 would again propagate to inversion results.\nFinally, I suggest including additional error bars in some of their figures, as discussed below.\nSpecific comments:</bold>\n• I don’t love the title. “Fuel exploitation” is not a common phrase. Suggest “fossil fuel exploitation” to be more specific.\nResponse. Corrected thanks.\n• Figure 2: Please consider using a log-log scale (rather than linear) to better show the agreement at the lower emission rates.\n1-s2-mcselucelom2101\n(a)WetCHARTswetlandmethaneemissions(highperformance–allensembles)\n+15% 1.5\n1.0\n0.5\n+30%\n+21% +20% 0.0\n+18% −0.5\n−1.0\n−1.5\n(b)Δmethaneemissionsfromfossilfuelexploitationinthetop5countries\n(highperformance–allensembles)\nVenezuela Russia India Nigeria Coted’Ivoire\nPosteriorOGandCoal\nemissionsusinghigh\n4.2 11.3 1.9 3.4 0.27\nperformanceWetCHART\nmembers(Tga-1)\nΔemissionsifusingall\nWetCHARTmembers -0.7 0.3 -0.2 -0.15 -0.15\n(Tga-1)\nResponse. Corrected. Please check the updated Fig. 2.\n0.01 0.02 0.05 0.1 0.2 0.5 1.0 2.0 5.0\nField campaign (Tg a−1)\nFig. 2. Methane emissions from 24 oil-gas and coal production basins across the globe. Estimates from field campaigns are compared to results from our TROPOMI inversions. For the US and Canada basins, we adjust TROPOMI’s results to campaign years using relative linear trends of observation-derived (surface measurements + GOSAT) basin-scale emissions from Lu et al.57. The 1:1 line is dashed, and the correlation coefficient is shown inset.\nMore details including references for the field campaigns can be found in Table S1-S2. Note the log-log scale.\n• Figure 3: Please include vertical error bars for the various top-down measurements. These are a critical part of the measurement and are typically displayed on these plots, so we can get a sense of how well the different estimates agree to within their uncertainties, and how your uncertainty (rather than just absolute value of emissions) compares. If older Top-Down uncertainty estimates are artificially low because they do not include certain contributors to uncertainty, it is important for your to add this to your discussion.\nResponse. Thanks for making this good point. In Table S3, we now summarize the uncertainty of these top-down estimates. We find that only Sanunois et al. (2016, 2020) and Fraser et al. (2013) reported reasonable uncertainty ranges and other top-down uncertainty estimates are artificially low (as the referee pointed out). We feel it is difficult to display these uncertainties in Fig. 3, so we put this information in the text and refer to it in the main text.\nLine 625. Top-down error estimates are either not reported or unrealistically low (<5%) (except Saunois et al. 3,6 and Fraser et al. 62), which can be found in Table S3 for more details.\nLine 156. while our estimate of coal emissions (32.7 ± 5.2 Tg a-1) is higher than previous top-down studies (20-30 Tg a-1) and in agreement with GFEI v2 (33 Tg a-1) (Fig. 3c) (details in Table S3).\nTable S3. Methane emissions from the oil-gas and coal exploitation in different top-down studies.\nOil and Gas (Tg a-1) Coal (Tg a-1) Fossil fuel (Tg a-1)\nYears of emissions\nTop-down studies Mean Uncertainty Mean Uncertainty Mean Uncertainty\n101 77-126# 2000-2009\nSaunois et al.28 105 77-133# 2003-2012\n112 90-137# 2012\n)1−a\ngT(\nIMOPORT\nTROPOMI vs. field estimates of emissions from fossil fuel exploitation basins\n5.0 S Ib u b r e a n t, b A u u re s n tr , a G lia ermany R=0.95\nGronigen, Netherland\nUpper Silesian Coal Basin, Poland\nAlbert west, Canada\nLloydminster, Canada\nBakken2014, US\n2.0 Bakken2015, US\nBarnett, US\nDelaware, US\nDenver Basin, US\n1.0 Denver−Julesburg, US\nEagle Ford East, US\nEagle Ford West, US\nFayetteville , US\nHaynesville−Bossier, US\n0.5 Haynesville, US\nMarcellus, US\nNE PA, US\nPermian, US\nSan Juan, US\n0.2 S U W int a P , A U , S US\nWestern Arkoma, US\n0.1\n0.05\n0.02\n0.01\n101 71-151# 2000-2009\nSaunois et al.29 111 81-131# 2008-2017\n108 91-121# 2017\nTurner et al.30 67 NA 30 NA 97 NA 2009-2011\nZhang et al.31 59.4 2%* 21 2%* 80.4 2%* 2010-2018\nLu et al.32 70 NA 23 NA 93 NA 2010-2017\nMaasakkers et al.33 67.5 <2%* 27.6 <2%* 95 <2%* 2010-2015\nQu et al.34 54 5%* 26 NA 80 5%* 2019\nFraser et a.35 NA NA 77.8 71.8-83.8 2009\nThis work 62.6 50.9-73.2 32.6 29.3-37.8 95.4 81.5-108.3 2018-2020\n#The uncertainty refers to the min-max range of reported studies.\n*These studies didn’t report the uncertainty specifically for the fossil fuel sector; thus, we use the uncertainty of global anthropogenic emissions here. The uncertainties in these studies are extremely low because they assume the grid-scale posterior estimates are independent with each other; thus, the uncertainty of global emissions inversely scales with the length of state vectors.\n• Figure 4: I like that you included emissions uncertainties on the TROPOMI estimates. But it is also very important to include them on the methane intensities (top plot) and emission factors (bottom plot). Please do this. (If not possible for some reason, please state why not in the text, and speculate on what your uncertainties on these values might be.)\nResponse. We have added the uncertainty for the emission intensity. Please check.\nNational emission estimates from TROPOMI inversion and UNFCCC reports\nFig. 4. National methane emissions from the oil-gas and coal sectors estimated by inversion of TROPOMI observations and compared to the UNFCCC reports. The TROPOMI observations are for May 2018 – February 2020, and the UNFCCC reports are for 2019 (Annex I countries) or most recent (other countries), as compiled by the\n)1−a\ngT(\nnoissime\nHC\n4\n51\n01\n5\n(a) Oil and natural gas emissions\nGFEI v2 (UNFCCC)\nTROPOMI estimates\nMethane intensity\nUnited States Ru V s e s n T ia e u z rk u m el e a nistan Iran Chin A a lger C ia a U n z a b d e S a k a i u st d a i n Arabia Nigeria Ira M q exic U o kra M ine alaysia Angola O man K E a g z y a p k t hstan India\n)1−a\ngT(\nnoissime\nHC 4\n42\n22\n02\n81\n2\n614\n06\n05\n04\n03\n02\n01\n0\n)%( \nytisnetni\nenahteM\n(b) Coal emissions\nGFEI v2 (UNFCCC)\nTROPOMI estimates\nEmission factor\nUn C ite h d in a States Russ A i u a K st a ra z l a ia khstan I I n n d d ia o S n o e u s th ia Afr C ic o a lo mbi U a kraine Mexi V co ietna m Poland Turk R e o y mani M a N o ig z e a r m ia biq G ue er m V a e n n y ezue P la akistan\n80.0\n60.0\n40.0\n20.0\n00.0\nrotcaf\nnoissime\nenahtem\nlaoC\n]noitcudorp\nsnot\n0001\nrep\ngG[\nGFEI v2 inventory of Scarpelli et al. 5. Iraq has not reported to the UNFCCC since 2000 and its emission is estimated in GFEI v2 using IPCC emission factors. The top 20 emitting countries are shown here; data for the 93 countries with total fuel emissions larger than 1 Gg a-1 are in Table S2. Vertical bars indicate the 95% confidence levels from the inversion ensemble. The circles represent the methane intensity from oil-gas production (top panel), defined as the total oil-gas emission per unit of gas produced (assuming 90% methane content for gas) 4,29, and the coal emission factor (bottom panel), defined following IPCC 30 as the total coal emission per unit coal produced. The empty rectangles denote the 95% confidence levels of oil-gas emission intensities and coal-based emission factors. Note break in left ordinate axis of bottom panel, as Chinese coal emissions are much higher than for any other country.\n• L117: Discussion of the large uncertainty on Russia’s oil-gas estimate . Your data density for Russia is not much different for the U.S. it appears. It appears that the driving factor is the large disparity between GFEI v1 as compared to v2 and EDGARv6. Please comment on ways to potentially reduce this uncertainty, given the extremely important implications for these results?\nResponse. Here we have updated the figure about observation data frequency and results show that the US has much higher data density in oil-gas producing regions.\nFigure S8. (a) TROPOMI data density (counts km-2 a-1) from May 2018 to February 2020, mapped to 0. 5°×0.625° horizontal resolution. (b) Same as (a) but only for gridcells with fossil fuel methane emissions greater than 1 Gg a-1.\nWe now discuss like this:\nLine 277. It has more difficulty in Russia and the tropics (Fig. S14), where satellite observation density is relatively low and oil/gas fields are often collocated with wetlands, so that inversion results have limited information content and are sensitive to the choice of prior inventory (Table S5-S6).\nDeploying facility-scale measurements and aircraft campaigns targeting the large oil-gas producing basins should be the most effective way reducing the estimate uncertainty in Russia. However, we cannot argue about this due to the high expense. But according to Jacob et al. (2022), future satellite instrument should be more effective at high latitudes. Now we say this in text.\n1-a2-mkstnuoc\nTROPOMIobservationdensity(2018-2020)\nTROPOMI obs freq\n3.0\n2.5\n2.0\n1.5\n1.0\n0.5\n0.0\nTROPOMI obs freq in high fossil fuel emission gridcells\n1.0\n0.8\n0.6\n0.4\n0.2\n0.0\n1-a2-mkstnuoc\nLine 260. Future satellite instruments may be more effective at observing high latitudes and Russian point sources31.\n• L128 area. I notice you do not comment on the ~factor of 2 discrepancy between GFEIv2 and TROPOMI for Nigeria’s emissions. Please add a sentence on this, whether you think that any statements on this discrepancy are unwarranted given the relatively large uncertainty on your estimate, or if your value agrees better with GFEIv2 vs. EDGAR and possibly why.\nResponse. Thanks, now we say this in the main text.\nLine 217. Nigeria’s emissions reported to UNFCCC increased from 0.4 (GFEI v1 for 2016) to 3.3 Tg a-1 (GFEIv2 for 2019) after adopting an emission factor at the upper limit of the IPCC (2006) recommendations32. Our inversion implies that the more recent report should be reduced by 40-50%.\n• L131: regarding your comment on the “lower value (4.1 Tg a-1) may be related to…” you should caveat this by saying “, though it is difficult to assess trends in their emissions given our the large uncertainty.” Also, do the bottom-up inventories imply declining production in Venezuela during this time period, consistent with your statement? If so, say so!\nResponse. Yes, both EIA oil production and the bottom-up inventory show consistent declining trends in Venezuela from 2000-2019. Now we say this in text:\nLine 250. Our lower value (4.0 Tg a-1) may be related to declining oil production over the 2016-2019 period as a result of intensified economic sanctions 33 (Fig. S12).\n2010 2012 2014 2016 2018 2020 2022\nFig. S12. Oil production (https://www.eia.gov/international/data/country/VEN) and fossil fuel (oil + natural gas) methane emissions from GFEIv2 in Venezuela from 2010 to 2019.\n• L133: “Is three times higher” → “Is 2.5 times higher” (3900/1500=2.6).\nResponse. Fixed, thanks.\n• L140: “China’s methane emission is” → “China’s coal-based methane emissions are”.\nResponse. Fixed, thanks.\n• L141: “, lower by 10% than the UNFCCC inventory estimate” → “, consistent with the UNFCCC inventory estimate within our uncertainty.” I don’t think the claim that China’s emissions are 10% lower is warranted given your large (17%) uncertainty.\nResponse. Thanks for pointing out this. Now we say this in the main text.\n0052\n0002\n0051\n0001\n005\n)d/bM(\nnoitcudorp\nliO\n4\n3\n2\n1\n0\n)1−a\ngT(\nsnoissime\nenahtem\nGO\nOil production and methane emissions in Venezuela (2010−2019)\nLine 260. China’s coal-based methane emission is 18.9 ± 3.3 Tg a-1, slightly but not significantly lower than the UNFCCC inventory estimate.\nGrammatical comments:\n• L37: “which can provide” → “can provide”\n• L63: “We weigh” → “We weight”\n• L69: “More details are in” → “More details are provided in”\n• L114: China’s oil-gas emissions () are two times higher than the…\n• L155: → “…can constrain the emissions with a relative posterior uncertainty of better than 30% (2σ)…”\nResponse. All fixed. Thanks!"}]

Decision letter