Worldwide inference of national methane emissions by inversion of satellite observations with UNFCCC prior estimates

Domain: physics
Published: 2025-12-11
Editorial outcome: minor_revision

Abstract
Meeting climate policy goals to reduce methane emissions under the Paris Agreement and the Global Methane Pledge requires nations to set targets and quantify reductions. Individual countries report emissions by sector to the United Nations Framework Convention on Climate Change (UNFCCC) but there are large uncertainties. Here we optimize 2023 national emissions at up to 25 km grid resolution for 161 countries with a globally consistent open-source framework for inverse analysis of Tropospheric Monitoring Instrument (TROPOMI) satellite observations, using UNFCCC reports for prior estimates together with point source information from GHGSat and other satellites. We find global anthropogenic emissions to be 15% higher than UNFCCC reporting (32% for oil-gas), with national emissions more than 50% higher than reporting for a quarter of the countries. Oil-gas emission intensities vary by two orders of magnitude between countries. Sub-Saharan Africa has the highest livestock emission intensity of any region. Hydroelectric reservoirs, generally not included in UNFCCC reporting, contribute 6% of anthropogenic emissions globally. The framework allows updates for subsequent years, enabling monitoring of emission trends and support for improved reporting.

Introduction
Methane is the second most important anthropogenic greenhouse gas (GHG) behind CO2 and has caused 0.6°C of warming since pre-industrial times1. Atmospheric methane concentrations have been rising at an average rate of 0.6% per year for the past decade2 largely due to increasing emissions3. Methane is emitted from multiple anthropogenic sectors, including enteric fermentation and manure from livestock, coal mines, oil and natural gas extraction, landfills and wastewater, and rice agriculture, as well as from natural sources, primarily wetlands4. It has a relatively short atmospheric lifetime of 9 years, compared to more than 100 years for CO2. Reducing anthropogenic methane emissions can slow down climate change in the near term while CO2 reduction technologies are developed and deployed5–7.

Coordinated international efforts relying on national initiatives aim to advance methane reduction goals. Under the Global Methane Pledge, 159 signatory nations plus the European Commission have committed to collectively reduce methane emissions by 30% from year 2020 levels by 2030 (https://www.globalmethanepledge.org). The United Nations Framework Convention on Climate Change (UNFCCC) requires all member states to report national emission inventories through National Communications (NCs) and Biennial Update Reports (BURs), culminating in a Global Stocktake and enabling countries to identify Nationally Determined Contributions (NDCs) for reducing methane emissions. The national inventories use bottom-up methods in which emissions for a sector are estimated by multiplying an activity rate (such as oil production) by an emission factor (methane emitted per unit of oil produced), following IPCC procedures at the discretion of each country8. However, large uncertainties in these national estimates undermine confidence in the NDCs and in the efficacy of collective action to reduce methane emissions9,10. UNFCCC reports may also lag by several years and up to more than a decade, varying between countries. There is aneed for a global system to evaluate and guide improvement in national emission inventories using common, consistent, and near-real-time data.

Satellite observations of atmospheric methane can provide such a global system to evaluate national emission inventories. The TROPOMI satellite instrument, launched in October 2017, provides global daily observations at 5.5 × 7 km2 nadir pixel resolution11. GHGSat and other high-resolution instruments can detect point sources worldwide from targeted observations of atmospheric plumes12. Emissions can be inferred from these satellite observations using knowledge of atmospheric transport and mature inverse methods13,14. This has been done for point sources15,16 and for individual regions17–19. But global inversions have so far been limited to coarse resolution (hundreds of km) because of computational limitations, compromising the ability to separate emissions by country and even more by sector20–23.

Here, we present a globally consistent system for estimating methane emissions in all countries of the world with up to 25-km grid resolution by inversion of TROPOMI observations and including point source information from GHGSat. This is done by tiling the world’s landmasses with eight regional inversions24, thus relaxing computing requirements, giving better control of boundary conditions, and avoiding effects of errors on the chemical loss. TROPOMI observations are from the blended TROPOMI+GOSAT product of Balasus et al.25 that corrects TROPOMI artifacts using the more precise but much sparser Greenhouse Gases Observing Satellite (GOSAT) satellite observations. The inversions use the UNFCCC reports from individual countries, spatially allocated following bottom-up inventories, as prior emission estimates, so that results can be directly interpreted as corrections to these estimates. Prior emissions for the few countries not reporting to the UNFCCC are estimated using IPCC Tier 1 methods. We use the open-source, user-friendly, cloud-based Integrated Methane Inversion (IMI) version 2.026 in all our calculations for transparency, allowing stakeholders to reproduce our results and carry out their own calculations. We apply our system to quantify 2023 emissions for each country and sector, and discuss implications for mitigating methane emissions and for using inverse estimates to aid reporting. Results provide up-to-date observational constraints on emissions from all countries. Our system is set up to allow updates for individual years and thus monitor emission trends.

Results
We estimate 2023 emissions at up to 25-km grid resolution for 161 countries with Bayesian analytical inversions of TROPOMI observations of dry column methane mole fractions (XCH4) using IMI 2.026. The inversion computes optimized (posterior) gridded emissions using error-weighted information from TROPOMI observations and prior emission estimates. Prior anthropogenic emission estimates are from the latest UNFCCC reports for each country, allocated on the 25-km grid using spatial distributions from the Global Fuel Exploitation Inventory (GFEI) v3 and Emissions Database for Global Atmospheric Research (EDGAR) v8 inventories27,28 and a gridded GHGSat inventory of point sources29. Emissions from hydroelectric reservoirs, not included in UNFCCC reports, are added with the ResME inventory30. Prior wetland estimates are monthly fluxes at 0.5° resolution from the Lund Potsdam Jena Earth Observation Simulator (LPJ-EOSIM) driven by MERRA-2 meteorology31. Atmospheric transport is simulated with the GEOS-Chem version 14.4.1 chemical transport model at 25-km resolution (https://doi.org/10.5281/zenodo.12584192). We tile the world with eight regional inversions, shown in Fig. 1, encompassing 99.8% of global anthropogenic emissions according to EDGARv827. Boundary conditions are from a global GEOS-Chem simulation bias-corrected to match TROPOMI+GOSAT XCH426. We optimize emissions at 25-km resolution where the satellite provides high information content, and cluster regions with lower information content while respecting national boundaries. Our baseline inversion assumes log-normal error probability density functions (PDFs) for prior emission estimates with a geometric standard deviation of 2.0, and we construct an inversion ensemble varying inversion parameters to characterize the errors on posterior emissions. The sensitivity of the inversion results to the TROPOMI observations (ability to depart from the prior estimate) is measured by the trace of the averaging kernel matrix. The inversions improve the fit to TROPOMI observations in all regions (Fig. S1; Table S1) and better reproduce methane concentrations versus independent surface observations (Fig. S2). The inversions can resolve emissions from all individual countries except small countries with weak emissions, as shown by analysis of posterior error correlation matrices (Figs. S3–S10). They can also distinguish between major emission sectors (Figs. S3–S10) except for landfills and wastewater treatment, which we combine as waste. Optimization of the methane sink from oxidation by OH is effectively accounted for by correction of boundary conditions. Further details are in the Methods section.

Global and national emission estimates in comparison to UNFCCC reports
Our global posterior emission estimate for 2023 is 598 (475–625) Tg a−1, where parentheses indicate the range from the inversion ensemble. This is higher than the prior estimate of 536 Tg a−1 and is consistent with the Global Carbon Project (GCP) top-down estimate for 2023 of 608 (581–627) Tg a−132. Posterior anthropogenic emissions are 375 (299−392) Tg a−1, 15% larger than prior emissions (326 Tg a−1). They include 313 (247–328) Tg a−1 from UNFCCC-reporting countries for livestock, waste, oil-gas, coal, and rice, 13 (10–13) Tg a−1 from countries not reporting to the UNFCCC (most importantly 9 (8–9) Tg a−1 from Pakistan), 24 (19–25) Tg a−1 from hydroelectric reservoirs, and 26 (21–28) Tg a−1 from minor anthropogenic sources not consistently included in UNFCCC reports including combustion for power generation, combustion for manufacturing, aviation, shipping, rail, road transportation, energy for buildings, chemical production, and iron and steel production. Posterior emissions also include 188 (147–196) Tg a−1 from wetlands, which aligns with the GCP top-down estimate of 175 (151–229) Tg a−1. The regional distribution of wetland emissions is consistent with global coarse-scale TROPOMI inversions for 202333. Figure 1 shows posterior emissions compared to the UNFCCC prior emissions for global sectors. Posterior emissions include 135 (100–144) Tg a−1 from livestock (17% higher than UNFCCC totals for the best estimate), 74 (61–77) Tg a−1 from waste (16% higher), 58 (44–61) Tg a−1 from oil-gas (32% higher), 35 (31–36) Tg from rice (26% higher), and 23 (21–24) Tg a−1 from coal (24% lower).

Figure 2 shows our posterior anthropogenic emission estimates for 161 individual countries and the differences with UNFCCC reports for the latest available years. UNFCCC and posterior emissions by sector for each country are in Supplementary Data S1. We find that national emissions are more than 50% larger than UNFCCC reports in a quarter of all countries and at least 23% smaller in a quarter of all countries. The largest absolute changes are increases in the U.S., Venezuela, India, and Turkmenistan, and decreases in Russia and the Democratic Republic of the Congo. Nations in Africa account for 5 of the top 10 relative increases with factors of 3.4, 3.0, and 1.7 increases in Malawi, Kenya, and Chad, respectively. The Democratic Republic of the Congo (−65%) is an exception that, along with the United Arab Emirates (−63%), has the largest relative decrease among all countries with anthropogenic emissions of at least 0.5 Tg a−1. Venezuela and Turkmenistan increase by factors of 2.5 and 3.2 to 9.0 (4.7–9.6) Tg−1 and 7.2 (4.2–7.7) Tg a−1, respectively, driven primarily by oil-gas emissions.

Many UNFCCC reports from non-Annex I countries predate 2023, but report age (Fig. S11) plays no role in the match between prior and posterior anthropogenic emissions.

Figure 3 shows the top 15 emitting countries from our TROPOMI inversion and the breakdown of anthropogenic emissions by sector. China (59 (49–60) Tg a−1), the U.S. (36 (31–37) Tg a−1), India (28 (24–29) Tg a−1), and Brazil (22 (15–23) Tg a−1) account for 39% of global anthropogenic emissions. Additional contributions from individual countries flatten out so that the top 15 countries account for 61% of global emissions. Among the countries in Fig. 3, the largest corrections from the inversion are in the US (+8.0 Tg a−1), Venezuela (+6.4 Tg a−1), India (+6.0 Tg a−1), and Turkmenistan (+5.4 Tg a−1), and the largest decreases are in Russia (−4.8 Tg a−1) and Indonesia (−0.7 Tg a−1). In the U.S., increases are mainly from oil-gas (13.2 (10.5–13.8) Tg a−1 posterior, up from 8.5 Tg a−1) and livestock (11.8 (10.6–12.2) Tg a−1 posterior, up from 9.1 Tg a−1). In China, decreases to coal (14.0 (13.1–14.6) Tg a−1 posterior, down from 21.2 Tg a−1) offset oil-gas and waste increases. We find that larger relative increases are associated with oil-gas emissions, particularly in Venezuela and Turkmenistan, where the oil-gas sector is 4.8 and 3.8 times larger, respectively, than UNFCCC prior estimates, along with additional increases from livestock in both countries. In Russia, decreases are spread across sectors, while oil-gas emissions are similar to their UNFCCC report. However, the inversion ensemble shows particularly large uncertainty in our results over Russia because of sparse TROPOMI observations at high latitudes. As a result, we cannot rule out an emissions increase in Russia.

Sectoral emissions and intensities for individual countries
Figure 4 shows posterior emissions and emission intensities by sector from our TROPOMI inversion for the top ten emitting countries in each sector, along with the corresponding values from the UNFCCC reports. Emission intensities are computed by dividing posterior emissions by activity levels associated with each sector as defined below.

Livestock: China, the U.S., Brazil, and India are the top emitters from the livestock sector. Worldwide, most of the change to UNFCCC reports comes from lower-emitting countries, with a +2.9 Tg a−1 net change for the top 10 countries and a +17.0 Tg a−1 net change for the remaining countries. The largest increases are in East Africa, where Kenya, Sudan, and Tanzania total +5.5 Tg a−1 over UNFCCC reports, and in the Middle East, particularly Turkmenistan and Iran. In East Africa, the discrepancy may be driven by recent increases in the number of livestock33,34 that would not be reflected in UNFCCC reports that are, on average, a decade old. In Brazil, we find a decrease from the UNFCCC report (−1.6 Tg a−1), in line with joint TROPOMI and GOSAT based estimates35, and a joint inversion of surface and GOSAT observations21.

We estimate the methane intensity of livestock as emissions per unit of animal protein (milk and meat) from major protein-producing livestock. We use country-level FAOSTAT statistics of meat production for cattle, buffalo, chickens, including eggs, goats, mules, pigs, and sheep, and milk production for cattle, sheep, and buffalo, along with the protein content for each protein type36. Across Sub-Saharan Africa (region D in Fig. 1a), the methane intensity of protein production is 8.0 kg CH4 per kg protein, compared to 1.1 and 0.8 kg CH4 per kg protein in North America (region A in Fig. 1) and South and East Asia (region G), respectively. The difference may be due to lower livestock productivity in Sub-Saharan Africa compared to more intensively managed systems in North America and East Asia37. We find a global methane intensity for protein production of 1.4 kg CH4 per kg protein, closely matching bottom-up estimates of 1.3 kg CH4 per kg protein34 but 40% higher than the FAOSTAT estimate of 1.0 kg CH4 per kg protein.

Waste: China and India have the highest combined wastewater and landfill emissions, which we report together as waste, but the lowest per-capita intensity. The largest changes from UNFCCC reports come from the Democratic Republic of the Congo (−68%; −4.0 Tg a−1) and India (+117%; +3.3 Tg a−1). In China, rapidly increasing waste incineration rates that surpass other countries may contribute to lower intensity38, while low per-capita waste production in India relative to other countries leads to lower intensity. Treating wastewater could decrease methane intensity in countries with high intensity and low rates of wastewater treatment, such as Indonesia, the Democratic Republic of the Congo, and Iran, where intensities are >20 kg CH4 per capita and wastewater treatment rates are <40%39,40.

Oil-gas: The U.S. is the largest methane emitter from the oil-gas sector at 13.2 (10.5–13.7) Tg a−1. We find large upward corrections to the UNFCCC prior emissions for the U.S. (+4.7 (2.0–5.3) Tg a−1), Turkmenistan (+3.8 (2.2–3.8) Tg a−1), Venezuela (+2.9 (1.1–3.4) Tg a−1), and China (+2.2 (0.8–2.6) Tg a−1). This underestimate of U.S. emissions has been widely reported41–45. In China, the increase over its 2014 UNFCCC report may be due in part to recent growth in China’s oil-gas emissions46, and has been found by other inversions24,47,48. In Venezuela, large increases from oil-gas emissions over their UNFCCC report are expected17,35. Our finding of increases in Turkmenistan oil-gas emissions provides further evidence for larger than reported oil-gas methane from the country24,49.

In Russia, we find a posterior estimate of 3.6 (2.9–6.5) Tg a−1, in line with Russia’s UNFCCC report of 3.9 Tg a−1, but again with large uncertainty. Other inversions find larger totals (8–11 Tg a−1)9,22,24,48, which may reflect their use of higher prior estimates. Our posterior estimates in the Arctic and subarctic, where TROPOMI observation density is limited, may be affected by the correction to northern boundary conditions. For Russian emissions, we include in our inversion ensemble an additional member with no boundary condition optimization, and consider the results (6.5 Tg a−1) an upper bound on emissions. Spatial overlap with wetlands could also cause uncertainty in Russian oil-gas emissions, but here the effects are minimized by our use of point source observations from GHGSat in the state vector construction.

Figure 5 shows total oil-gas methane emissions per unit of natural gas production (methane intensity) for the 40 top natural gas producing countries, along with total natural gas production and oil-gas sector CH4 emissions. Production and intensity are based on U.S. Energy Information Administration (EIA) worldwide statistics on dry natural gas production for 2023, and assuming 90% methane content of natural gas24,41. Intensity represents the fraction of methane emitted rather than used for fuel, including methane emitted from oil extraction operations. We find that 5 countries meet the Oil and Gas Climate Initiative (OGCI) 0.2% methane intensity target, and among those, Norway and Qatar are the largest producers. If all countries were to follow suit, then emissions from the oil-gas sector would drop by 81% down to 11.0 Tg a−1 and become minimal relative to other sectors. We find intensities of 10% or greater from Venezuela (25 (13–29)%, uncertainty from the inversion ensemble range), Ukraine (15 (14–16)%), Colombia (15 (6–16)%), and Turkmenistan (10 (7–10)%), and lower intensities from the largest producers, which include China (2.3 (1.3–2.6)%), the U.S. (2.0 (1.6–2.1)%), Iran (1.6 (1.3–1.6)%), Canada (1.2 (1.1–1.2)%), and Russia (1.0 (0.8–1.7)%). High intensity from Venezuela matches that of Nathan et al.17 for 2020 using TROPOMI, attributed in part to decreasing production.

Rice: China (10.0 Tg a−1), India (4.9 Tg a−1), Thailand (2.9 Tg a−1), Bangladesh (2.7 Tg a−1), Indonesia (2.1 Tg a−1), and Vietnam (1.9 Tg a−1) dominate methane emissions from rice. Posterior changes to the global rice sector are not large, but there are significant changes from individual countries’ reports. Most notable is Bangladesh, which increases by a factor of 7 from its 0.4 Tg UNFCCC report when we use Global Rice Paddy Inventory (GRPI)50 totals for sectoral allocation. Thailand, India, China, and Cambodia each increase by 0.8 Tg or more, while Pakistan’s emissions are 0.9 Tg lower than reports. Our estimate of global methane intensity from rice, 44 kg of methane emitted per ton of rice produced based on FAOSTAT country-level statistics, is close to the 51 kg CH4 per ton of rice reported in the GRPI inventory. Our estimate for China’s rice emissions is larger than its UNFCCC report (8.9 Tg a−1) and GRPI (8.2 Tg a−1) and matches other studies19. We find enhanced emissions over the Sanjiang Plain in the northeast, confirming recent growth in rice production and emissions from that region51. Among African nations, total emissions are 2.6 Tg a−1, matching a bottom-up estimate which identifies a growing contribution of African rice to global methane emissions52. Larger rice methane intensity in Tanzania may be due to increasing adoption of higher-yielding growing methods not reflected in FAOSTAT estimates. Although India’s total rice production is similar to that of China, and its harvested area is over 60% larger (48 Mha versus 29 Mha)53, its emissions of 4.9 (4.4–4.9) Tg a−1 are 51% lower. Substantially lower emissions factors in India (0.95 kg CH4 ha−1 d−1 versus 2.41 kg CH4 ha−1 d−1 for China) due to a heavier reliance on rainfed lowland harvesting in India54 and a shorter rice growing season50 both contribute to lower rice emissions from India than China.

In Bangladesh, the UNFCCC rice total is low (0.4 Tg a−1) despite the country’s status as the world’s third largest rice producer. Both rice and wetland prior emissions are uniformly distributed throughout the country55 and as a result, the inversion cannot reallocate emissions to rice from wetlands if we rely on prior fractions in the posterior sectoral breakdown; posterior rice emissions remain locked at their unrealistically low levels. We ameliorate this by using GRPI emissions for sectoral allocation in Bangladesh (posterior estimate using its UNFCCC report as prior is marked with a dashed line in Fig. 4) and note that this highlights the critical need for high-quality UNFCCC estimates.

Coal: Global coal mine methane emissions are dominated by emissions from China, which accounts for 60% of global coal emissions and 50% of global coal production. We find an annual total of 14.0 Tg a−1 in China, down from their UNFCCC estimate of 21.2 Tg a−1 with the decrease coming primarily from the Shanxi region and in line with other inversions19,24,47,48. The decrease aligns with an ongoing emissions trend driven by a national shift in coal production to surface mines that have lower emissions, countered in our inversion by a minor increase in southern China, where abandoned mines continue to emit56.

Among other coal-producing countries and comparing to UNFCCC reports, we find a 0.6 Tg a–1 decrease in Russia, a 0.6 Tg a−1 increase from India, and negligible changes to U.S. coal emissions. Our inversion is unable to increase the low coal emissions reported to the UNFCCC by Indonesia, despite its status as the second largest coal producer globally, due to low satellite observation density over the region caused by ocean surfaces and extensive cloud cover24.

Information content from the satellite observations
Averaging kernel sensitivities reported in Figs. 3 and 4 reveal the extent to which the satellite observations inform the posterior estimate. All of the top 15 countries (Fig. 3) have sensitivities greater than 0.5, meaning the posterior estimate is more sensitive to the true emission via the satellite observations than to the prior. Overall, emissions from countries with sensitivities of at least 0.5 represent 89% of posterior anthropogenic emissions. Among individual sectors within countries, sensitivities are high but with exceptions. For example, sensitivities for coal in Mozambique, livestock in Sudan, Argentina, and Tanzania, rice in Cambodia and the Philippines, and oil-gas in China, India, and Malaysia are less than 0.5 due to the difficulty of observation (cloudiness, dark surfaces) and relatively low emissions.

Methods
IMI 2.0
We use the IMI version 2.026 for Bayesian optimization of methane emissions worldwide at up to 25-km grid resolution using TROPOMI satellite observations and prior anthropogenic emission estimates from UNFCCC reports. Details on the state vector are given in the state vector construction subsection. The IMI solves the Bayesian cost function analytically to obtain best posterior estimates of gridded emissions (state vector elements) with closed-form error characterization and the ability to readily generate inversion ensembles by varying inversion parameters64. The IMI is supported as an open-source, user-friendly tool on the Amazon Web Services (AWS) cloud (https://carboninversion.com/) together with the GEOS-Chem chemical transport model (CTM) that serves as a forward model for the inversion. IMI users on the cloud submit their customized inversions by editing a simple configuration file. We apply IMI 2.0 in eight regional inversions, shown in Fig. 1a, with boundary conditions from smoothed TROPOMI observations26. The boundary conditions are also optimized in the inversion. The inversion framework and parameters are consistent across the regional inversions, leading to a globally consistent system.

We use the GEOS-Chem CTM version 14.4.1 (https://doi.org/10.5281/zenodo.12584192) at 0.25° × 0.3125° horizontal resolution to construct the Jacobian matrix relating the gridded methane emissions to the concentrations observed by TROPOMI. GEOS-Chem simulates the transport and losses of methane, including chemical loss in the troposphere and stratosphere and monthly soil absorption from MeMo65, and is driven by NASA GMAO GEOS Forward Processing (GEOS-FP) assimilated meteorological data66.

Analytical inversion
We use IMI v2.0 to minimize the Bayesian cost function J(x):

J(x) = J_A + J_O = (x − x_A)^T S_A^{−1} (x − x_A) + γ(y − Kx)^T S_O^{−1} (y − Kx) (1)

where x is the emissions state vector also including boundary conditions, x_A is the vector of prior estimates, S_A is the error covariance matrix of the prior estimate, y is the vector of satellite observations, K = ∂y/∂x is the Jacobian matrix which describes the linear sensitivity of y to x and is constructed row-by-row by perturbing individual state vector elements in repeated forward model simulations, S_O is the observing system error covariance matrix, and γ ∈ [0, 1] is a regularization parameter that is used to prevent overfitting of the solution to observations by accounting for missing off-diagonal structure in S_O. The following subsections describe the different terms.

TROPOMI Observations
We use 2023 observations of atmospheric methane dry-air column mole fractions (XCH4) from the TROPOMI instrument aboard the sun-synchronous polar-orbiting Sentinel-5 Precursor satellite with an approximate equatorial crossing time of 1:30 pm. The TROPOMI XCH4 operational product from the Space Research Organisation Netherlands (SRON) retrieves in the 2.3 µm band using a full-physics retrieval with 5.5 × 7 km2 nadir pixel resolution and daily global coverage11. Retrievals are limited to clear-sky scenes. We use the blended TROPOMI+GOSAT XCH4 product25 that corrects artifacts in the operational TROPOMI product versions 02.04–02.06 using the more precise but sparser GOSAT satellite observations. We exclude coastal pixels, inland water pixels with poor goodness-of-fit in the operational product (SWIR chi-square value > 20000), and glint observations over the oceans, which are subject to residual artifacts. The blended TROPOMI+GOSAT product includes only observations for which the operational product has a quality assurance value of 1. Satellite observations are averaged into super-observations on the GEOS-Chem 0.25° × 0.3125° grid for individual orbits, and observational error variances are obtained by the residual error method accounting for error correlations in the individual observations averaged into super-observations49. TROPOMI data are unavailable from 10 to 30 August 2023 due to missing VIIRS cloud data.

UNFCCC reports and prior estimates
Prior anthropogenic emission estimates for individual countries are taken from the countries’ UNFCCC reports for livestock, rice, wastewater, landfills, coal, and oil-gas, and are given by sector and country in Supplementary Data S1. Emissions for the few countries not reporting to the UNFCCC (flagged in Supplementary Data S1) are estimated using IPCC Tier 1 methods for oil-gas and coal28 and using EDGARv8 for remaining sectors27. For most countries, we used the UNFCCC portal value available from https://di.unfccc.int/detailed_data_by_party. For countries in South America and Africa, UNFCCC totals by sector are based on countries’ Biennial Update Reports (BURs) and National Communications (NCs), which may be more recent than the portal values. UNFCCC reports do not include emissions from hydroelectric reservoirs, and we add those to the prior national estimates using the ResME bottom-up inventory for individual reservoirs worldwide30.

UNFCCC inventories are reported as national emission totals for individual sectors, and we distribute them on the 0.25° × 0.3125° grid using bottom-up information from other inventories, including the Global Fuel Emissions Inventory (GFEI) version 328 for oil-gas and coal, the GRPI50 for monthly rice, and EDGARv827 for other anthropogenic sectors. These spatial distributions are superseded by GHGI Version 2 Express Extension67 in the U.S., by Scarpelli et al.68 in Canada, and by Scarpelli et al.69 in Mexico. Monthly wetland emissions at 0.5° × 0.5° resolution are from the Lund-Potsdam-Jena Earth Observation Simulator (LPJ-EOSIM) dynamic global vegetation model driven by MERRA-2 meteorology31. Daily biomass burning emissions are from GFED470. Termite emissions are from Fung et al.71.

Spatial redistribution of fossil fuel emissions using GHGSat
We further redistribute emissions from the oil-gas and coal sectors to account for missing point sources by using global gridded emissions constructed from compiled observations of methane plumes by GHGSat satellite instruments (Fig. 6). The gridded GHGSat product29 converts instantaneous point-source observations to annual mean emissions on the 0.25° × 0.3125° grid of our inversion using an emissions persistence model. We incorporate gridded GHGSat emissions in the inversion by adjusting oil-gas and coal emissions according to Eq. 2

x*_a,i = max(G_i, x_a,i) × (Σ_i x_a,i / Σ_i max(G_i, x_a,i)) (2)

For each grid cell i in a given country, x*_a,i is the emission after adjustment by GHGSat, x_a,i is the unadjusted emission from GFEI summing up to UNFCCC national totals28, and G_i is the gridded GHGSat emission rate. This adjustment is applied separately for oil-gas and coal emissions. The adjusted emissions are then used as prior estimates in the inversion. In this way, locations with missing point sources detected by GHGSat are accounted for in the inversion while the country-level totals for oil-gas and coal emissions are maintained.

State vector construction
The inversions optimize individual elements of the state vector x. For each regional inversion domain, the state vector is composed of emissions from individual and clustered 25-km (0.25° × 0.3125°) GEOS-Chem grid cells over land and offshore, and boundary conditions in the cardinal directions (north, south, west, east) along the edges of the domains. The maximum resolution (smallest size) of state vector elements is the 0.25° × 0.3125° resolution of the GEOS-Chem forward model, referred to as the native resolution. To reduce the computational cost of the inversion and maximize information content based on prior emissions and TROPOMI observation density, lower-information grid cells are aggregated from the native resolution into clusters using k-means26. The resolution at which emissions are optimized in the inversion, therefore, varies, with higher resolution where there are large prior emissions and observation density as determined by averaging kernel sensitivities. Figure S12 shows the complete size distribution and emissions content of the state vector for an example region (Europe and North Africa). The distribution has a mode of 25 km and an emission-weighted mean of 93 km (corresponding to a cluster of 14 0.25° × 0.3125° grid cells). In the k-means clustering, we impose that clusters do not cross national boundaries in order to avoid ambiguity in attributing emissions to individual countries.

Before clustering the state vector elements, we identify grid cells that are candidates for remaining at native resolution using locations of 8717 methane plumes observed by point source imaging satellite instruments. These include (1) EMIT, PRISMA, Sentinel-2, Landsat, EnMAP, and GOES as reported by the International Methane Emissions Observatory (IMEO) data platform (https://methanedata.unep.org); (2) TROPOMI used for point source detection72; and (3) EMIT reported by Carbon Mapper (https://data.carbonmapper.org). Locations of plumes are shown in Fig. 6. Candidate grid cells are all grid cells that contain one or more plumes. Once candidate grid cells are identified, we include at native resolution only those grid cells containing plumes with emission rates above a given threshold after averaging together the emission rates of all plumes in the grid cell. For each domain, we test multiple thresholds by building state vectors using thresholds of 0 kg h−1 (no threshold; all point source locations used), 2500 kg h−1, and 10,000 kg h−1, and comparing the estimated information content of each. The final state vector is constructed by choosing the plume rate emission threshold and k-means clustering criteria that yields the highest information content26. In this way, the locations of point sources observed by EMIT, PRISMA, Sentinel-2, Landsat, EnMAP, GOES, and TROPOMI guide the state vector construction, but the posterior emissions do not directly depend on point source rate estimates from these instruments, and the plume observations are not ingested in the inversion. Grid cells chosen to be at native resolution on the basis of plume locations contain 11% of the global anthropogenic emissions in the posterior estimate. Grid cells in which the prior was adjusted by GHGSat emissions contain 11% of oil-gas and coal emissions.

Uncertainty analysis
The analytical optimization in the IMI returns a closed-form posterior error covariance matrix characterizing the error in the posterior solution but this does not capture uncertainty in inversion parameters, notably the prior error PDF used to construct the prior error covariance matrix S_A in Eq. (1). Varying inversion parameters within their expected ranges gives a more conservative estimate of the posterior error (Chen et al.). Here, we estimate uncertainty in this manner. We solve for emissions using either normal or lognormal error PDFs for the prior estimates with geometric standard deviations of 1.5, 2.0, or 2.5 for the lognormal error PDFs and standard deviations of 50, 75, and 100% for the normal error PDFs. For each ensemble member, we choose γ so that J_A/n is unity47. This results in six unique inversions for each region. We report the emissions for our baseline inversion—lognormal error PDF with geometric standard deviation of 2—and use other ensemble members to define the posterior uncertainty range. The baseline inversion is chosen as the one with a global emission total that best matches recent estimates32,33,73.

In the North Asia regional inversion domain (domain F in Fig. 1), emissions are strongly correlated with the inversion’s correction to the boundary condition at the northern edge. Along that boundary, there is low observation density, particularly in boreal winter24, potentially introducing bias into the boundary condition correction and emission results. We therefore include an additional inversion ensemble member in this region with the boundary condition optimization turned off, to test the sensitivity of our results to the boundary correction. Results from this additional inversion are included in the posterior uncertainty range for Russia and discussed in the text.

Attributing emissions to individual countries and sectors
To quantify the total emissions and related information content for individual countries and sectors, we create summation matrices W_n, W_c, W_s, and W_cs to produce reduced state vectors and averaging kernel matrices for individual sectors and for individual countries in each inversion, following Chen et al.47. W_n is a (p × n) matrix where the rows are the p native resolution GEOS-Chem grid cells, and the columns are the n individual state vector elements. W_c is a (c × p) matrix of binary values where rows are the c countries in the inversion and columns are the p model grid cells. W_s is a (s × p) matrix where rows are s emissions sectors and columns are p model grid cells, and values are the annual emissions for each sector in each grid cell. W_cs is a (cs × p) matrix where rows are cs emissions sectors in c countries and columns are p model grid cells. We then calculate weighting matrices for countries and sectors on the state vector dimension, W = W_i W_n where W_i can be W_c, W_s, or W_cs. W is weighted by emissions and normalized so that its rows sum to 1. We then calculate emissions for countries or sectors:

x^_r = W x^

(3)

where x^ is the optimized emissions (posterior estimate), x^_r is the reduced state vector, giving posterior emission totals per country and per sector. We use prior sectoral fractions on the 0.25° × 0.3125° grid for posterior sectoral allocation, meaning we effectively assume that the relative emission correction within a grid cell applies equally to all sectors in that grid cell22,74, but at 25-km resolution, the resulting error is minimized because a single sector is more likely to dominate. We calculate posterior error covariance and averaging kernel matrices for countries or sectors:

S_r = W S_A W^T (4)

A_r = I − S_r S_n^{−1} (5)

where S_r in Eq. 4 is either S_A or S_r, A_r is the reduced averaging kernel matrix, and S_n is the reduced error covariance matrix. Diagonal elements of A_r give the sensitivity, from 0 to 1, of the solution to the true state for individual sectors and countries. An averaging kernel sensitivity near zero means that the solution is primarily informed by the prior estimate, while a value near one means that the solution is determined by the TROPOMI observations with little sensitivity to the prior estimate.

In some cases, inversion domains overlap. Fig. S13 and Fig. 1a show the inversion domains. To avoid errors due to overlap between different regional inversions, each country’s emissions are optimized by a single inversion. For locations where inversion domains overlap, posterior emissions for the country are taken from the primary inversion domain for that country, as shown in Fig. S13.

Evaluation against observations
We evaluate the inversion results against TROPOMI observations and against independent methane observations using the NOAA GLOBALVIEWplus CH4 ObsPack v7.0 database of surface sites worldwide75. Globally, bias against TROPOMI is eliminated using simulations with posterior emissions (prior bias of −1.9 ppb, posterior bias of 0.1 ppb), and RMSE is reduced from 7.7 ppb (prior) to 5.6 ppb (posterior). For the individual regional inversions, RMSE decreases in all cases. Mean bias is reduced in four of eight regions (Europe and North Africa, North Asia, East Asia, and Oceania), and biases in the other regions are small (≤1.1 ppb). Details for each region are in Fig. S1 and Table S1. For comparison against surface observations, we sample the model prior and posterior simulations for each region at the locations and times of the observations for all sites more than 4° away from the domain edge, and limited to the 10–16 local time window to avoid the effect of nighttime stratification. Fig. S2 shows the comparison between modeled and observed concentrations and site locations for the 208 sites used in the evaluation. On average globally, annual root mean square error (RMSE) improves by 9 ppb (29–20 ppb), and bias improves by 8 ppb (−23 to −15 pb). RMSE improves in all regions, and bias improves in all regions except Oceania, where the model already performs well, and the change in the bias is small. The bias in simulating surface observations could reflect model error in vertical mixing76, but also nearby emissions affecting the observations.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "East et al. “Worldwide inference of national methane emissions by inversion of satellite observations and UNFCCC prior\nestimates” provides national CH4 emissions estimates for 161 countries derived from a combination of satellite observations\n(TROPOMI-GOSAT, GHGSat and otehrs) and inversions using a global chemical transport model (IMI v2.0 based on GEOS-\nChem) to show how top-down satellite-based emission estimates can differ from bottom-up reported CH4 emissions.\nOverall, this is a good manuscript describing a significant research contribution, but it needs some more work before it is\nready for publication. Strengths include demonstration of the new version of IMI as well as its efficient application to 8 high-\nresolution continental regions. Unfortunately, as a general rule, uncertainties are not handled very well throughout the\nmanuscript and the methodological description is too brief, with key details unclear or missing. Specific details and requests\nregarding these deficiencies (lack of uncertainties and unclear methods) are elaborated on below. While these issues are\nsignificant to the overall scientific credibility and robustness of the research and the validity of its results, they should be\naddressable by the authors in a revision and resubmission. Some other minor points related to text, wording and\npresentation issues are also specified below.\nSpecific points\nLine 22: “Nations” rather than “National emission inventories” set targets and quantify reductions.\nLine 25: TROPOMI is an undefined acronym.\nLine 30: commas are needed in the “Hydroelectric reservoirs” sentence as already used on line 368.\nLine 34: “Intro” is too informal. “Introduction” is recommended.\nLine 38: Might be worth mentioning that the methane increase over past few decades has not been constant and linear.\nLine 56: Maybe reword with “up to several years”, as the lag can be as short as ~16 months for many Annex 1 countries\n(UNFCCC reporting deadline in May of each year, for one previous year, e.g. May 2025 for complete 2023 annual\nemissions.\nLine 63: It would be clearer to say “Target observations of atmospheric plumes”, since the satellite operators must make a\nconscious decision to observe any given area with an expected plume.\nLine 73: GOSAT acronym is undefined.\nLine 75: Inversions cannot use UNFCCC reports from individual countries since reporting does not provide a gridded\nproduct depicting the spatial distribution. As explained later, the spatial distributions come from GFEI and EDGAR and a\nGHGSat point source inventory. (GFEI and EDGAR acronyms not defined). This fact needs to be alluded to in the\nintroduction in a succinct way, to avoid misleading the reader.\nFigure S1 caption is repetitive and can be simplified.\nTable S2: Posterior biases increase for North America, South America, Sub-Saharan Africa, and Middle East (4 out of 8\nregions), although RMS reduces for all.\nFigure S2: It would be useful to state the number of surface sites (N) on each scatter plot. For Oceania, it looks like there are\nmore different points for each series on the scatter plot than locations on the map.\nFigures S5 and S6: Degree of separation indicated by few high off-diagonal elements for small countries in Europe and\nNorth Africa (Fig S5) and Sub-Saharan Africa (Fig S6) is somewhat surprising.\nFigure S11: It is difficult to determine the year for any specific country with any accuracy from this figure. It might be clearer if\nthe color scale was narrowed since very few countries seem to have the most recent report before 2000 and if so outdated,\nthe exact year might not matter as much. A more useful suggestion would be to add the year to Table S1 either as a new\ncolumn or replacing TRUE and FALSE with the year or “None”.\nLine 131: repeated word “are”\nLine 125-150 and 163-177: All of these emission values should be reported with ensemble ranges or some other form of\nuncertainties.\nFigure 4 is of high quality but it is unclear what is meant by Oil intensity in %, compared with absolute units specified in all\nother cases. For natural gas, I can see how the percent is from the fugitive emissions relative to the total methane assuming\n90% content in natural gas. If so, what about oil?\nLine 246-274: All country Oil and Gas emission values should be presented with uncertainties.\nLine 251: Can the latest year of China’s UNFCCC reporting be specified in the text? (I am aware that it is ambiguously\npresented on Figure S11).\nLine 341: The discussion makes some sweeping statements about satellite inverse estimates, like “satellite observations are\ntoo sparse” but clarification is needed as to whether some of these limitations are related to the existing satellite observation\ncapabilities and whether they could be overcome with better revisit rates, better diurnal sampling, higher spatial resolution,\nlower biases or some other potential future improvement to observing characteristics.\nLine 351: Are the authors referring to “temporal variability”? This is an area where CO2 modelling is ahead of methane since\nscaling for the representation of seasonal, diurnal and even weekly cycles (e.g. Nassar et al. 2013,\nhttps://doi.org/10.1029/2012JD018196) for anthropogenic CO2 emissions in the forward model has become commonplace.\nLine 361: “up to 25 km” is not specific enough. In an abstract or introduction, a statement like this is acceptable, but in the\nmethods section, more exactness is required. What is the full range? Is the range only the result of the change in grid box\narea with latitude (i.e. is the size fixed in degrees?) or is the resolution actually variable?\nLine 366: Should provide an uncertainty on global anthropogenic emission estimate.\nTable S1: Caption for Table S1 could not be found. What is the column labelled ‘A’? Eswatini is erroneously last in the table,\npossibly related to a capitalization issue (e.g. appears as “eSwatini”). Should consider suggestion related to Figure S11\nregarding year of UNFCCC reporting. Recommend simplifying “UNFCCC/prior” to just “Prior”.\nLine 419: “Sentinel-5 Precursor” rather than “Sentinel-5p”\nLine 420: strictly speaking 1:30 pm is the “equator crossing time” but the local overpass time for other latitudes will differ by a\nfinite number of minutes.\nLine 421: There is more than one TROPOMI XCH4 retrieval in use by the community. While the operational retrieval is “full-\nphysics”, this clarification should be stated, although it is not necessary to explain the alternative retrieval methods (e.g.\nWFMD), which are not used.\nLine 422: Is there a version number for the blended TROPOMI+GOSAT data product or simply for the individual input data\nproducts that can be specified? Is the XCH4 data bias-corrected? Are data quality flags used for filtering or any other filters?\nLine 422-423: What about glint over land and smaller bodies of water (e.g. lakes)?\nLine 460-461: It is not optimal wording to refer to x*ai and xai as the prior emission after adjustment and the unadjusted prior.\nIt is recommended to remove the term ‘prior’ in the explanation for this stage, then later say that the adjusted emissions are\nused as a prior.\nLine 468-470: The statement “Yellow closed circles show locations of plumes observed by EMIT, PRISMA, Sentinel-2,\nLandsat, EnMAP, GOES, or TROPOMI used to enforce 25-km native resolution state vector elements” is unclear. What is\nmeant by “enforcing state vector elements”? In general, the point source inventory component could use more details in the\nexplanation of its methodology.", "reviewer_2": "Quantifying national methane inventories is critical for setting and tracking mitigation targets under the Paris Agreement and\nthe Global Methane Pledge. This study combines TROPOMI satellite data with UNFCCC prior inventories and GHGSat\npoint-sources information within a consistent inversion framework to produce 25-km resolution, top-down estimates for 161\ncountries. The disaggregation by country and sector is particularly valuable for identifying high emitters and under-reporting,\nenabling targeted mitigation and strengthening NDC/MRV quantification and accountability.\nI have only several specific comments as follows:\n1. In the sentence “We find global anthropogenic emissions to be 15% higher than UNFCCC reporting (32% for oil-gas), with\nnational emissions more than 50% higher for a quarter of the countries.” Please specify the baseline for the “>50%”\ncomparison (UNFCCC reports, your prior, or another reference)\n2. Line 130, The text mentions minor anthropogenic sources totaling 26 Tg. Please list which categories are included and\nthe reference year.\n3. L141 Delete the duplicated “are”\n4. L145 Please add the unit of Tg\n5. India’s total rice area has been ~48 Mha in recent years (with ~30 Mha irrigated, comparable to China), yet the inferred\nrice emissions are much lower than China’s. However, its emission is much lower than China? Might be, this is resulted from\nthe underestimate of the priors and the limited satellite observations in this region? This should be discussed. Please\ndiscuss potential causes.\n6. Please describe how overlap was handled (masks, de-duplication rules, sectoral prior fractions at 0.25°) and provide\ndiagnostics of sectoral identifiability.\n7. Since the paper emphasizes country- and sector-resolved posteriors, please add a short discussion on practical\nimplementation: how national agencies could use these results to prioritize mitigation (e.g., LDAR for oil/gas, landfill gas\ncapture, AWD in rice), link to MRV/NDC processes, and reduce abatement costs.\n8. L440; The study mixes priors from UNFCCC portal values, BUR/NC reports, EDGAR (v8), GFEI (v3), GRPI, and national\nproducts (GHGI-V2-EE; Scarpelli et al.) with differing definitions and vintages, then downscales national totals to 0.25° using\nproxy datasets—both choices can induce sectoral misallocation and potential double counting. Please provide a\nsupplementary table listing, by country and sector, the prior data source and year and document how overlaps were detected\nand de-duplicated.\n9. Overall, your estimates align well with previous assessments (e.g., the 2023 global methane budget) and are informative\nfor national/sectoral mitigation. Could you also reflect the sensitivity of your national/sectoral estimates to climate-year\nvariability? We note differing wetland CH4 trends between 2019 and 2024 across studies—for example, your “Attributing\n2019–2024 methane growth using TROPOMI satellite observations” suggests declining wetland emissions after 2019,\nwhereas other studies (your refs 20 and 31) report increases. Please provided discussion on seasonal-sensitivity.\nOverall, the findings front this study had strong and policy-relevant contribution. Addressing the points above would improve\nclarity on baselines and strengthen sectoral/national attribution."}, "author_response": "Author responses to comments from Reviewer #1\nResponses in blue. Line numbers refer to the track changes version of the text.\nReviewer #1 (Remarks to the Author):\nEast et al. “Worldwide inference of national methane emissions by inversion of satellite observations and\nUNFCCC prior estimates” provides national CH4 emissions estimates for 161 countries derived from a\ncombination of satellite observations (TROPOMI-GOSAT, GHGSat and others) and inversions using a\nglobal chemical transport model (IMI v2.0 based on GEOS-Chem) to show how top-down satellite-based\nemission estimates can differ from bottom-up reported CH4 emissions. Overall, this is a good manuscript\ndescribing a significant research contribution, but it needs some more work before it is ready for\npublication. Strengths include demonstration of the new version of IMI as well as its efficient application\nto 8 high-resolution continental regions. Unfortunately, as a general rule, uncertainties are not handled\nvery well throughout the manuscript and the methodological description is too brief, with key details\nunclear or missing. Specific details and requests regarding these deficiencies (lack of uncertainties and\nunclear methods) are elaborated on below. While these issues are significant to the overall scientific\ncredibility and robustness of the research and the validity of its results, they should be addressable by the\nauthors in a revision and resubmission. Some other minor points related to text, wording and presentation\nissues are also specified below.\nThank you for the insightful comments. We have carefully considered and responded to each comment.\nWe have expanded our descriptions of the methods where the reviewer noted that details were lacking,\nparticularly in the clustering approach and in the use of point sources. We have also improved our\ntreatment of uncertainty by including the uncertainty ranges for emissions when they are reported\nthroughout the text. Line numbers in our responses refer to the “tracked changes” version of the revised\nmanuscript.\nSpecific points\nLine 22: “Nations” rather than “National emission inventories” set targets and quantify reductions.\nWe made this change.\nLine 25: TROPOMI is an undefined acronym.\nWe have now defined TROPOMI.\nLine 30: commas are needed in the “Hydroelectric reservoirs” sentence as already used on line 368.\nWe made this change.\nLine 34: “Intro” is too informal. “Introduction” is recommended.\nWe made this change.\nLine 38: Might be worth mentioning that the methane increase over past few decades has not been\nconstant and linear.\nWe changed the wording and lines 38-39 now read:\nAtmospheric methane concentrations have been rising at an average rate of 0.6% per year for the past\ndecade2 largely due to increasing emissions3.\nLine 56: Maybe reword with “up to several years”, as the lag can be as short as ~16 months for many\nAnnex 1 countries (UNFCCC reporting deadline in May of each year, for one previous year, e.g. May\n2025 for complete 2023 annual emissions.\nWe made this change in the manuscript.\nLine 63: It would be clearer to say “Target observations of atmospheric plumes”, since the satellite\noperators must make a conscious decision to observe any given area with an expected plume.\nWe changed the wording and lines 63-64 now read:\nGHGSat and other high-resolution instruments can detect point sources worldwide from targeted\nobservations of atmospheric plumes12\nLine 73: GOSAT acronym is undefined.\nWe made this change in the manuscript.\nLine 75: Inversions cannot use UNFCCC reports from individual countries since reporting does not\nprovide a gridded product depicting the spatial distribution. As explained later, the spatial distributions\ncome from GFEI and EDGAR and a GHGSat point source inventory. (GFEI and EDGAR acronyms not\ndefined). This fact needs to be alluded to in the introduction in a succinct way, to avoid misleading the\nreader.\nWe changed the wording and lines 76-78 now read:\nThe inversions use the UNFCCC reports from individual countries, spatially allocated following bottom-\nup inventories, as prior emission estimates so that results can be directly interpreted as corrections to\nthese estimates.\nWe defined GFEI and EDGAR and lines 92-95 now read:\nPrior anthropogenic emission estimates are from the latest UNFCCC reports for each country, allocated\non the 25-km grid using spatial distributions from the Global Fuel Exploitation Inventory (GFEI) v3 and\nEmissions Database for Global Atmospheric Research (EDGAR) v8 inventories27,28 and a gridded\nGHGSat inventory of point sources29.\nFigure S1 caption is repetitive and can be simplified.\nWe simplified the caption to the following:\nFig. S1 | GEOS-Chem model bias compared to TROPOMI XCH . Maps show the union of all eight\n4\nregional domains’ annual mean bias in GEOS-Chem XCH using (a) prior UNFCCC emissions or (b)\n4\nposterior emissions and sampled with the satellite averaging kernel in all cases, versus TROPOMI XCH .\nTable S2: Posterior biases increase for North America, South America, Sub-Saharan Africa, and Middle\nEast (4 out of 8 regions), although RMS reduces for all.\nWe have modified the text to reflect this, lines 615-619 now read:\nGlobally, bias against TROPOMI is eliminated using simulations with posterior emissions (prior bias of -\n1.9 ppb, posterior bias of 0.1 ppb), and RMSE is reduced from 7.7 ppb (prior) to 5.6 ppb (posterior). For\nthe individual regional inversions, RMSE decreases in all cases. Mean bias is reduced in four of eight\nregions (Europe and North Africa, North Asia, East Asia, and Oceania), and biases in the other regions\nare small (<1.1 ppb). Details for each region are in Fig. S1 and Table S2.\nFigure S2: It would be useful to state the number of surface sites (N) on each scatter plot. For Oceania, it\nlooks like there are more different points for each series on the scatter plot than locations on the map.\nWe have added number of unique annual datasets (N) in each map in Fig. S2. Modified caption now\nreads:\nFig. S2 | Evaluation of inversion results with independent surface observations. The figure compares\nannual average modeled and observed CH4 concentrations from surface flask and in-situ sites in the\nNOAA GLOBALVIEWplus CH4 ObsPack v7.0 database. Annual mean bias, root mean square errors\n(RMSE), and site locations for each region are given in the figures. “N” in site maps denotes number of\nunique annual datasets, some of which may be co-located.\nFigures S5 and S6: Degree of separation indicated by few high off-diagonal elements for small countries\nin Europe and North Africa (Fig S5) and Sub-Saharan Africa (Fig S6) is somewhat surprising.\nWe attribute this to two factors. First, the higher resolution of the inversion compared to previous work.\nSecond, in our state vector clustering algorithm, we enforce country borders, meaning that individual state\nvector elements do not cross country borders. Grid cells that overlap borders are assigned to the country\nwhich makes up the majority of that grid cell. Fig S12 shows the full state vector lengthscale distribution\nfor a region (Europe and North Africa) along with a state vector map for context.\nFigure S11: It is difficult to determine the year for any specific country with any accuracy from this\nfigure. It might be clearer if the color scale was narrowed since very few countries seem to have the most\nrecent report before 2000 and if so outdated, the exact year might not matter as much. A more useful\nsuggestion would be to add the year to Table S1 either as a new column or replacing TRUE and FALSE\nwith the year or “None”.\nWe narrowed the range of the colorbar and updated the caption. We also use a new colorbar with a wider\nperceptual range to better distinguish values for individual countries. Table S1 now includes a new\ncolumn with the report year. Modified figure and caption are included below:\nFig. S11 | Year of UNFCCC report. Report year is also given in Table S1. Countries in grey do not\nreport to UNFCCC. Most recent report year of countries in cyan is before 2000. Year of report is the tick\nmark on the left edge of the color boundary. Greenland’s emissions are reported by Denmark.\nLine 131: repeated word “are”\nTypo fixed\nLine 125-150 and 163-177: All of these emission values should be reported with ensemble ranges or\nsome other form of uncertainties.\nEnsemble ranges are now reported with emission values.\nFigure 4 is of high quality but it is unclear what is meant by Oil intensity in %, compared with absolute\nunits specified in all other cases. For natural gas, I can see how the percent is from the fugitive emissions\nrelative to the total methane assuming 90% content in natural gas. If so, what about oil?\nWe modified the caption to give more detail. The caption now reads:\nOil-gas intensity is defined as total oil-gas emissions per unit of gas production, with production data\nfrom U.S. Energy Information Administration (EIA) worldwide statistics on dry natural gas production\nfor 2023 (https://www.eia.gov/international/) and assuming 90% methane content of natural gas24,35. It\nrepresents the fraction of methane emitted rather than used for fuel, including methane emitted from oil\nextraction operations.\nWe also added a similar sentence in the text on lines 280-281:\nIntensity represents the fraction of methane emitted rather than used for fuel, including methane emitted\nfrom oil extraction operations.\nLine 246-274: All country Oil and Gas emission values should be presented with uncertainties.\nEnsemble ranges are now reported with oil-gas emission values and intensity values.\nLine 251: Can the latest year of China’s UNFCCC reporting be specified in the text? (I am aware that it is\nambiguously presented on Figure S11).\nWe have added the year of China’s UNFCCC report to the text (2014).\nLine 341: The discussion makes some sweeping statements about satellite inverse estimates, like “satellite\nobservations are too sparse” but clarification is needed as to whether some of these limitations are related\nto the existing satellite observation capabilities and whether they could be overcome with better revisit\nrates, better diurnal sampling, higher spatial resolution, lower biases or some other potential future\nimprovement to observing characteristics.\nWe expanded the discussion to include comments about potential for different observing strategies to\nimprove satellite observation density. Lines 359-371 now read\nOur inversions of TROPOMI satellite observations provide information to guide improvement in the\nbottom-up estimates used as prior estimates. National agencies can use these results to identify sectors\nwhere their emission estimates are too low or too high and track progress towards NDCs. In turn,\nimproved bottom-up inventories can improve the inversions because the prior estimates play an essential\nrole in regularizing the solution; the satellite observations are too sparse otherwise, especially at high\nlatitudes and in regions with excessive cloudiness. In this work, the anomalously low UNFCCC reports\nfor rice emissions in Bangladesh and coal emissions in Indonesia challenge the ability of the inversion to\noptimize these emissions. We were able to achieve a correction in Bangladesh where satellite\nobservations are dense and an alternative bottom-up rice emission inventory was available, but we were\nnot able to correct Indonesia where observations are sparse. New satellite observation capabilities have\nthe potential to improve inverse emissions estimates; for example, employing higher spatial resolution or\nintelligent pointing shows promise for providing increased observation density57,58.\nLine 351: Are the authors referring to “temporal variability”? This is an area where CO2 modelling is\nahead of methane since scaling for the representation of seasonal, diurnal and even weekly cycles (e.g.\nNassar et al. 2013, https://doi.org/10.1029/2012JD018196) for anthropogenic CO2 emissions in the\nforward model has become commonplace.\nYes, we have modified the sentence and citation and lines 376-377 now read:\nIncorporating spatial and temporal information with inventory reporting would assist the inversions and\nimprove the sectoral attribution of inversion results58.\nLine 361: “up to 25 km” is not specific enough. In an abstract or introduction, a statement like this is\nacceptable, but in the methods section, more exactness is required. What is the full range? Is the range\nonly the result of the change in grid box area with latitude (i.e. is the size fixed in degrees?) or is the\nresolution actually variable?\nWe modified the methods to be more descriptive. The original line referenced in this comment is in the\ndiscussion section, and we did not change it as we believe that detail is not needed in the summary\nparagraph. We note that Fig. S12 shows the size distribution of a state vector. Lines 410-411 are modified\nand now read:\nWe use the Integrated Methane Inversion (IMI) version 2.026 for Bayesian optimization of methane\nemissions worldwide at up to 25-km grid resolution using TROPOMI satellite observations and prior\nanthropogenic emission estimates from UNFCCC reports. Details on the state vector are given in the\nstate vector construction subsection.\nLines 508-523 are also modified and now read:\nThe inversions optimize individual elements of the state vector 𝑥. For each regional inversion domain, the\nstate vector is composed of emissions from individual and clustered 25-km (0.25° × 0.3125°) GEOS-\nChem grid cells over land and offshore, and boundary conditions in the cardinal directions (north, south,\nwest, east) along the edges of the domains. The maximum resolution (smallest size) of state vector\nelements is the 0.25° × 0.3125° resolution of the GEOS-Chem forward model, referred to as the native\nresolution. To reduce the computational cost of the inversion and maximize information content based on\nprior emissions and TROPOMI observation density, lower-information grid cells are aggregated from the\nnative resolution into clusters using k-means26. The resolution at which emissions are optimized in the\ninversion therefore varies, with higher resolution where there are large prior emissions and observation\ndensity as determined by averaging kernel sensititivities. Fig. S12 shows the complete size distribution\nand emissions content of the state vector for an example region (Europe and North Africa). The\ndistribution has a mode of 25-km and and an emission-weighted mean of 93-km (corresponding to a\ncluster of 14 0.25° × 0.3125° grid cells). In the k-means clustering, we impose that clusters do not cross\nnational boundaries in order to avoid ambiguity in attributing emissions to individual countries.\nLine 366: Should provide an uncertainty on global anthropogenic emission estimate.\nUncertainty on the global estimate has been added.\nTable S1: Caption for Table S1 could not be found. What is the column labelled ‘A’? Eswatini is\nerroneously last in the table, possibly related to a capitalization issue (e.g. appears as “eSwatini”). Should\nconsider suggestion related to Figure S11 regarding year of UNFCCC reporting. Recommend simplifying\n“UNFCCC/prior” to just “Prior”.\nCaption has been added in the TableS1.xlsx file. Column “A” is the reduced averaging kernel for the\ncountry and is now described in the caption. We fixed eSwatini so it is now in alphabetical order based on\n“e”. There seems to be disagreement about whether the country’s name should be stylized eSwatini or\nEswatini, and we here choose eSwatini as it aligns with official pronouncements. We keep the term\n“UNFCCC” in the row names since the totals for some sectors are the UNFCCC reports and this is an\nimportant aspect of the paper. We added the UNFCCC categories to columns which come from\nUNFCCC, so it is more clear which columns are UNFCCC.\nLine 419: “Sentinel-5 Precursor” rather than “Sentinel-5p”\nThe manuscript has been modified to include this fix.\nLine 420: strictly speaking 1:30 pm is the “equator crossing time” but the local overpass time for other\nlatitudes will differ by a finite number of minutes.\n“local overpass time” has been changed to “equatorial crossing time.”\nLine 421: There is more than one TROPOMI XCH4 retrieval in use by the community. While the\noperational retrieval is “full-physics”, this clarification should be stated, although it is not necessary to\nexplain the alternative retrieval methods (e.g. WFMD), which are not used.\nWe clarified that we refer to the SRON operational retrieval and lines 447-452 now read:\nThe TROPOMI XCH operational product from the Space Research Organisation Netherlands (SRON)\n4\nretrieves in the 2.3 µm band using a full-physics retrieval with 5.5×7 km2 nadir pixel resolution and daily\nglobal coverage11. Retrievals are limited to clear-sky scenes. We use the blended TROPOMI+GOSAT\nXCH product25 that corrects artifacts in the operational TROPOMI product version 02.04 – 02.06 using\n4\nthe more precise but sparser GOSAT satellite observations.\nLine 422: Is there a version number for the blended TROPOMI+GOSAT data product or simply for the\nindividual input data products that can be specified? Is the XCH4 data bias-corrected? Are data quality\nflags used for filtering or any other filters?\nWe have added this information and lines 450-455 now read:\nWe use the blended TROPOMI+GOSAT XCH product25 that corrects artifacts in the operational\nobservations. We exclude coastal pixels, inland water pixels with poor goodness-of-fit in the operational\nproduct (SWIR chi-square value > 20000), and glint observations over the oceans which are subject to\nresidual artifacts. The blended TROPOMI+GOSAT product includes only observations for which the\noperational product has a quality assurance value of 1.\nLine 422-423: What about glint over land and smaller bodies of water (e.g. lakes)?\nThis is now addressed in the previous response, these pixels are mostly filtered out.\nLine 460-461: It is not optimal wording to refer to x*ai and xai as the prior emission after adjustment and\nthe unadjusted prior. It is recommended to remove the term ‘prior’ in the explanation for this stage, then\nlater say that the adjusted emissions are used as a prior.\nWe made the change and lines 493-496 now read:\nFor each gridcell 𝑖 in a given country, 𝑥∗ is emission after adjustment by GHGSsat, 𝑥 is the\n!,# !,#\nunadjusted emission from GFEI summing up to UNFCCC national totals28, and 𝐺 is the gridded GHGSat\n#\nemission rate. This adjustment is applied separately for oil-gas and coal emissions. The adjusted\nemissions are then used as prior estimates in the inversion.\nLine 468-470: The statement “Yellow closed circles show locations of plumes observed by EMIT,\nPRISMA, Sentinel-2, Landsat, EnMAP, GOES, or TROPOMI used to enforce 25-km native resolution\nstate vector elements” is unclear. What is meant by “enforcing state vector elements”? In general, the\npoint source inventory component could use more details in the explanation of its methodology.\nWe have modified the caption and text to be more descriptive. The caption for Fig. 6 now reads:\nYellow closed circles show locations of plumes observed by EMIT, PRISMA, Sentinel-2, Landsat,\nEnMAP, GOES, and TROPOMI, identifying grid cells to be included in the state vector at the native 25-\nkm native resolution.\nWe expanded the state vector construction section to include a separate paragraph for the text describing\nhow point source locations are used to define the state vector. Lines 495-524 now read:\nThe inversions optimize individual elements of the state vector 𝑥. For each regional inversion domain, the\nstate vector is composed of emissions from individual and clustered 25-km (0.25° × 0.3125°) GEOS-\nChem grid cells over land and offshore, and boundary conditions in the cardinal directions (north, south,\nwest, east) along the edges of the domains. The maximum resolution (smallest size) of state vector\nelements is the 0.25° × 0.3125° resolution of the GEOS-Chem forward model, referred to as the native\nresolution. To reduce the computational cost of the inversion and maximize information content based on\nprior emissions and TROPOMI observation density, lower-information grid cells are aggregated from the\nnative resolution into clusters using k-means26. The resolution at which emissions are optimized in the\ninversion therefore varies, with higher resolution where there are large prior emissions and observation\ndensity as determined by averaging kernel sensititivities. Fig. S12 shows the complete size distribution\nand emissions content of the state vector for an example region (Europe and North Africa). The\ndistribution has a mode of 25-km and and an emission-weighted mean of 93-km (corresponding to a\ncluster of 14 0.25° × 0.3125° grid cells). . In the k-means clustering, we impose that clusters do not cross\nnational boundaries in order to avoid ambiguity in attributing emissions to individual countries.\nBefore clustering the state vector elements, we identify grid cells that are candidates for remaining at\nnative resolution using locations of 8717 methane plumes observed by point source imaging satellite\ninstruments. These include (1) EMIT, PRISMA, Sentinel-2, Landsat, EnMAP, and GOES as reported by\nthe International Methane Emissions Observatory (IMEO) data platform (https://methanedata.unep.org);\n(2)TROPOMI used for point source detection72; and (3) EMIT reported by Carbon Mapper\n(https://data.carbonmapper.org). Locations of plumes are shown in Fig. 6. Candidate grid cells are all\ngrid cells that contain one or more plumes. Once candidate grid cells are identified, we include at native\nresolution only those grid cells containing plumes with emission rates above a given threshold after\naveraging together emission rates of all plumes in the grid cell. For each domain, we test multiple\nthresholds by building state vectors using thresholds of 0 kg h-1 (no threshold; all point source locations\nused), 2500 kg h-1, and 10000 kg h-1, and comparing the estimated information content of each. The final\nstate vector is constructed by choosing the plume rate emission threshold and k-means clustering criteria\nthat yields the highest information content26. Grid cells chosen to be at native resolution on the basis of\nplume locations contain 11% of the global anthropogenic emissions in the posterior estimate. Grid cells\nin which the prior was adjusted by GHGSat emissions contain 11% of oil-gas and coal emissions.\nReviewed by Ray Nassar\nReviewer #1 (Remarks on code availability):\nI can confirm that the code was made available in an appropriate manner, but I have not downloaded the\nfiles or assessed the code.\nNewly added references:\n53. FAOSTAT. Crops and livestock products. (2025).\n54. Nikolaisen, M. et al. Methane emissions from rice paddies globally: A quantitative statistical\nreview of controlling variables and modelling of emission factors. Journal of Cleaner Production 409,\n137245 (2023).\n57. Frankenberg, C. et al. Data Drought in the Humid Tropics: How to Overcome the Cloud Barrier\nin Greenhouse Gas Remote Sensing. Geophysical Research Letters 51, e2024GL108791 (2024).\n58. Nassar, R. et al. Intelligent pointing increases the fraction of cloud-free CO2 and CH4\nobservations from space. Front. Remote Sens. 4, 1233803 (2023).\n60. Nassar, R. et al. Improving the temporal and spatial distribution of CO emissions from global\n2\nfossil fuel emission data sets. JGR Atmospheres 118, 917–933 (2013).\n76. Yu, K. et al. Errors and improvements in the use of archived meteorological data for chemical\ntransport modeling: an analysis using GEOS-Chem v11-01 driven by GEOS-5 meteorology. Geosci.\nModel Dev. 11, 305–319 (2018).\nAuthor responses to comments from Reviewer #2\nResponses in blue. Line numbers refer to the track changes version of the text.\nReviewer #2 (Remarks to the Author):\nQuantifying national methane inventories is critical for setting and tracking mitigation targets under the\nParis Agreement and the Global Methane Pledge. This study combines TROPOMI satellite data with\nUNFCCC prior inventories and GHGSat point-sources information within a consistent inversion\nframework to produce 25-km resolution, top-down estimates for 161 countries. The disaggregation by\ncountry and sector is particularly valuable for identifying high emitters and under-reporting, enabling\ntargeted mitigation and strengthening NDC/MRV quantification and accountability.\nThank you for the helpful comments. We have carefully considered and responded to each comment. We\nhave expanded the discussion and methods in response to deficiencies noted by the reviewer. Line\nnumbers in our responses refer to the “tracked changes” version of the revised manuscript.\nI have only several specific comments as follows:\n1. In the sentence “We find global anthropogenic emissions to be 15% higher than UNFCCC reporting\n(32% for oil-gas), with national emissions more than 50% higher for a quarter of the countries.” Please\nspecify the baseline for the “>50%” comparison (UNFCCC reports, your prior, or another reference)\nThe baseline for this comparison is the UNFCCC reports. We made the change and the abstract now\nreads:\nWe find global anthropogenic emissions to be 15% higher than UNFCCC reporting (32% for oil-gas),\nwith national emissions more than 50% higher than reporting for a quarter of the countries.\n2. Line 130, The text mentions minor anthropogenic sources totaling 26 Tg. Please list which categories\nare included and the reference year.\nWe have added the specific categories included as minor anthropogenic sources. Because we do not use\nUNFCCC reports for these values, there are general values and not year-specific. The posterior emissions\nthat we report in that sentence are for 2023. Lines 130-137 now read:\nPosterior anthropogenic emissions are 375 (299 – 392) Tg a-1, 15% larger than prior emissions (326 Tg\na-1). They include 313 (247 – 328) Tg a-1 from UNFCCC-reporting countries for livestock, waste, oil-gas,\ncoal, and rice, 13 (10 – 13) Tg a-1 from countries not reporting to the UNFCCC (most importantly 9 (8 –\n9) Tg a-1 from Pakistan), 24 (19 – 25) Tg a-1 from hydroelectric reservoirs, and 26 (21 – 28) Tg a-1 from\nminor anthropogenic sources not consistently included in UNFCCC reports including combustion for\npower generation, combustion for manufacturing, aviation, shipping, rail, road transportation, energy for\nbuildings, chemical production, and iron and steel production.\n3. L141 Delete the duplicated “are”\nTypo is now fixed.\n4. L145 Please add the unit of Tg\nThe numbers on this line refer to multiplicative factors – for example, Malawi’s posterior emissions are\n3.4 times larger than its prior emissions.\n5. India’s total rice area has been ~48 Mha in recent years (with ~30 Mha irrigated, comparable to China),\nyet the inferred rice emissions are much lower than China’s. However, its emission is much lower than\nChina? Might be, this is resulted from the underestimate of the priors and the limited satellite\nobservations in this region? This should be discussed. Please discuss potential causes.\nWe added a discussion about lower rice emissions in India relative to China on lines 315-319:\nAlthough India’s total rice production is similar to that of China, and its harvested area is over 60%\nlarger (48 Mha versus 29 Mha)53, its emissions of 4.9 (4.4 – 4.9) Tg a-1 are 51% lower. Substantially\nlower emissions factors in India (0.95 kg CH ha-1 d-1 versus 2.41 kg CH ha-1 d-1 for China) due to a\n4 4\nheavier reliance on rainfed lowland harvesting in India54 and a shorter rice growing season50 both\ncontribute to lower rice emissions from India than China.\n6. Please describe how overlap was handled (masks, de-duplication rules, sectoral prior fractions at 0.25°)\nand provide diagnostics of sectoral identifiability.\nWe added a description for how overlaps are handled on lines 604-607:\nIn some cases, inversion domains overlap. Fig. S13 and Fig. 1a show the inversion domains. To avoid\nerrors due to overlap between different regional inversions, each country’s emissions are optimized by a\nsingle inversion. For locations where inversion domains overlap, posterior emissions for the country are\ntaken from the primary inversion domain for that country, as shown in Fig. S13.\nTo support this, we added a new Fig. S13 to show how posterior emissions are assigned to each country,\navoiding overlaps. Fig. S13 and its caption are:\nF\nC\nE\nA\nG\nD\nH\nB\nFig. S13 | Map of inversion domains and target countries. Emissions in each country are optimized by a\nsingle inversion. The map shows which countries are optimized by which inversion domains. For\nlocations where inversion domains overlap, posterior emissions from the primary domain only are used.\nFor example, areas of Egypt are contained in inversion domains C and E. Egypt’s inversion domain is\nEurope and North Africa (C), so the inversion results from that domain only are used in the posterior\nemissions.\nThe reviewer mentions “sectoral prior fractions at 0.25°.” We have modified the text to describe our\nsectoral allocation more clearly and lines 590-592 now read:\nWe use prior sectoral fractions on the 0.25°×0.3125° grid for posterior sectoral allocation, meaning we\neffectively assume that the relative emission correction within a grid cell applies equally to all sectors in\nthat grid cell22,74, but at 25-km resolution the resulting error is minimized because a single sector is more\nlikely to dominate.\nThe best diagnostics for sectoral identifiability are the posterior error correlations, which we include in\nFigs. S3-S10 for all regions and for all sectors. This shows the correlation coefficient (R) among all\nsectors in the inversion result. Large correlations (near 1 or -1) indicates that the inversion cannot\ncalculate independent estimates of emissions for those sectors, whereas correlations near zero indicate\nindependent estimates. Our work finds smaller posterior error correlation compared to previous work that\nreports these correlations, for example Chen et al. 2022 and Chen et al. 2023. We also draw the\nreviewer’s attention to lines 110-115 where we discuss these posterior error correlations (not new text):\nThe inversions can resolve emissions from all individual countries except small countries with weak\nemissions, as shown by analysis of posterior error correlation matrices (Figs. S3-S10). They can also\ndistinguish between major emission sectors (Figs. S3-S10) except for landfills and wastewater treatment,\nwhich we combine as waste.\nReferences:\nChen, Z. et al. Methane emissions from China: a high-resolution inversion of TROPOMI satellite\nobservations. Atmos. Chem. Phys. 22, 10809–10826 (2022).\nChen, Z. et al. Satellite quantification of methane emissions and oil–gas methane intensities from\nindividual countries in the Middle East and North Africa: implications for climate action. Atmos. Chem.\nPhys. 23, 5945–5967 (2023).\n7. Since the paper emphasizes country- and sector-resolved posteriors, please add a short discussion on\npractical implementation: how national agencies could use these results to prioritize mitigation (e.g.,\nLDAR for oil/gas, landfill gas capture, AWD in rice), link to MRV/NDC processes, and reduce abatement\ncosts.\nWe expanded the Discussion section to suggest potential uses of the results by national agencies.\nApplications to specific mitigation practices, an important topic with practical implications as the\nreviewer notes, are reviewed in Nisbet et al. 2020 (cited in the Introduction). A major value of our inverse\nestimates is for improving the prior inventories, which begins by identifying sectors which differ from\nexpectation, an application we now note in Lines 359-365 in response to the reviewer:\nOur inversions of TROPOMI satellite observations provide information to guide improvement in the\nbottom-up estimates used as prior estimates. National agencies can use these results to identify sectors\nwhere their emission estimates are too low or too high and track progress towards NDCs. In turn,\nimproved bottom-up inventories can improve the inversions because the prior estimates play an essential\nrole in regularizing the solution; the satellite observations are too sparse otherwise, especially at high\nlatitudes and in regions with excessive cloudiness.\nReferences:\nNisbet, E. G. et al. Methane Mitigation: Methods to Reduce Emissions, on the Path to the Paris\nAgreement. Reviews of Geophysics 58, e2019RG000675 (2020).\n8. L440; The study mixes priors from UNFCCC portal values, BUR/NC reports, EDGAR (v8), GFEI\n(v3), GRPI, and national products (GHGI-V2-EE; Scarpelli et al.) with differing definitions and vintages,\nthen downscales national totals to 0.25° using proxy datasets—both choices can induce sectoral\nmisallocation and potential double counting. Please provide a supplementary table listing, by country and\nsector, the prior data source and year and document how overlaps were detected and de-duplicated.\nWe have added a column for the year of the UNFCCC report to Table S1. All other requested information\nis provided in subsection “UNFCCC reports and prior estimates” in the Methods section, which the\nreviewer notes, and in Table S1. We note that there is no double-counting of emissions. Country totals\ncome from UNFCCC portal values, or from BUR/NC reports for South America and Sub-Saharan Africa.\nHowever, these reports do not provide information about the spatial distribution of emissions. Therefore,\nwe need additional information to distribute the total emissions within each country, and we use the\nbottom up inventories for this purpose. We keep only the relative spatial distribution from the bottom-up\ninventories; the emissions magnitudes come from the UNFCCC reports. In cases where multiple bottom-\nup inventories provide information about the same sector, the text on lines 474-481 describes which\nbottom-up inventory is chosen. If there was double counting in the country’s UNFCCC report, then that\nwould also be in our prior, but it is beyond the scope of our analysis to identify those cases. By definition\nthere are no duplications in the posterior estimate. Posterior sectoral allocation is done by relative\nfractions from the prior on the grid cell level. These relative fractions sum to one for each grid cell, so the\ntotal emissions from the inversion are conserved.\n9. Overall, your estimates align well with previous assessments (e.g., the 2023 global methane budget)\nand are informative for national/sectoral mitigation. Could you also reflect the sensitivity of your\nnational/sectoral estimates to climate-year variability? We note differing wetland CH4 trends between\n2019 and 2024 across studies—for example, your “Attributing 2019–2024 methane growth using\nTROPOMI satellite observations” suggests declining wetland emissions after 2019,\nwhereas other studies (your refs 20 and 31) report increases. Please provided discussion on seasonal-sensitivity.\nAlthough our analysis of a single year of observations from TROPOMI is not suited for commentary on\nyear-to-year trends in wetlands emissions, we agree with the reviewer about the importance of seasonality\nin wetland emissions for interpreting inverse results. We have included a discussion about this on lines\n379-385 in the Discussion section, expanded in response to the reviewer’s comment:\nImproved characterization of the spatial and seasonal distribution of wetland emissions, along with their\ninterannual variability, is critical for attribution of anthropogenic emissions in inversions. Current\nwetland emission inventories have large uncertainties61,62 and there is extensive spatial overlap of\nwetland and anthropogenic emissions, for example for oil-gas in Canada and Nigeria, for livestock in\nSouth America and Africa, and for rice in Bangladesh and Southeast Asia. Seasonal knowledge of\nwetland emissions would provide a lever for separating them from anthropogenic emissions in the\ninversion. New observation products such as the CYGNSS inundation data could be useful for this\npurpose63.\nOverall, the findings front this study had strong and policy-relevant contribution. Addressing the points\nabove would improve clarity on baselines and strengthen sectoral/national attribution.\nNewly added references:\n53. FAOSTAT. Crops and livestock products. (2025).\n54. Nikolaisen, M. et al. Methane emissions from rice paddies globally: A quantitative statistical\nreview of controlling variables and modelling of emission factors. Journal of Cleaner Production 409,\n137245 (2023).\n57. Frankenberg, C. et al. Data Drought in the Humid Tropics: How to Overcome the Cloud Barrier\nin Greenhouse Gas Remote Sensing. Geophysical Research Letters 51, e2024GL108791 (2024).\n58. Nassar, R. et al. Intelligent pointing increases the fraction of cloud-free CO2 and CH4\nobservations from space. Front. Remote Sens. 4, 1233803 (2023).\n60. Nassar, R. et al. Improving the temporal and spatial distribution of CO emissions from global\n2\nfossil fuel emission data sets. JGR Atmospheres 118, 917–933 (2013).\n76. Yu, K. et al. Errors and improvements in the use of archived meteorological data for chemical\ntransport modeling: an analysis using GEOS-Chem v11-01 driven by GEOS-5 meteorology. Geosci.\nModel Dev. 11, 305–319 (2018)."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The manuscript “Worldwide inference of national methane emissions by inversion of satellite observations with UNFCCC\nprior estimates” has been improved significantly based on the revisions to address comments from myself and the other\nreviewer. I appreciate the extent to which my comments and suggestions were addressed, spanning improvements to\nfigures, more complete reporting of uncertainties and textual revisions.\nIn my opinion, only a few very minor issues remain.\nLine 399: Wording here is a bit awkward: “per protein”. I suggest “per unit mass of protein” or “per kg of protein”.\nLine 505: repeated word “and and”.\nLine 616: It might be useful (but not essential) to include the global bias and RMSE values as the final row in Table S2.\nLine 619: Should change “<1.1” to “≤1.1” (based on number of decimal places shown in Table S2).\nWith the authors improved methodological description and a second reading, I now have a better understanding of how the\nGHGSat and other plume mapping data are used in the study. My understanding is that GHGSat emission plumes estimates\nare included in the prior, but that the other plume mapping missions (EMIT, PRISMA, EnMAP, …) are only used to construct\nthe state vector, therefore it is primarily the location of the plume that is most relevant and the posterior emission estimates\nare not directly dependent on emission estimates from these other plume mappers (they are not part of the prior and their\nobservations are not ingested into the inversion). It advisable to add one statement in the “State Vector Construction” section\nto clarify this for the reader.\nRay Nassar", "reviewer_2": "The revised manuscript has addressed all of my comments. I am satisfied with the revision and recommend it for publication\nin Nature Communications.\nHanqin Tian"}, "author_response": ""}]

Decision letter