scholarly journals Attribution of the accelerating increase in atmospheric methane during 2010–2018 by inverse analysis of GOSAT observations

2020 ◽  
Author(s):  
Yuzhong Zhang ◽  
Daniel J. Jacob ◽  
Xiao Lu ◽  
Joannes D. Maasakkers ◽  
Tia R. Scarpelli ◽  
...  
Keyword(s):  
Growth Rates ◽  
Inverse Analysis ◽  
Methane Emissions ◽  
Bayesian Optimization ◽  
Atmospheric Methane ◽  
Tropical Wetlands ◽  
Fire Emissions ◽  
Livestock Emissions ◽  
Oil Gas ◽  
Gosat Satellite

Abstract. We conduct a global inverse analysis of 2010–2018 GOSAT satellite observations to better understand the factors controlling atmospheric methane and its accelerating increase over the 2010–2018 period. The inversion optimizes 2010–2018 anthropogenic methane emissions and their trends on a 4º × 5º grid, monthly regional wetland emissions, and annual hemispheric concentrations of tropospheric OH (the main sink of methane) also for individual years. We use an analytical solution to the Bayesian optimization problem that provides closed-form estimates of error covariances and information content for the solution. Our inversion successfully reduces the errors against the independent methane observations from the TCCON network and reproduces the interannual variability of the methane growth rate inferred from NOAA background sites. We find that prior estimates of fuel-related emissions reported by individual countries to the United Nations are too high for China (coal) and Russia (oil/gas), and too low for Venezuela (oil/gas) and the U.S. (oil/gas). We show that the 2010–2018 increase in global methane emissions is mainly driven by tropical wetlands (Amazon and tropical Africa), boreal wetlands (Eurasia), and tropical livestock (South Asia, Africa, Brazil), with no significant trend in oil/gas emissions. While the rise in tropical livestock emissions is consistent with bottom-up estimates of rapidly growing cattle populations, the rise in wetland emissions needs to be better understood. The sustained acceleration of growth rates in 2016–2018 relative to 2010–2013 is mostly from wetlands, while the peak methane growth rates in 2014–2015 are also contributed by low OH concentrations (2014) and high fire emissions (2015). Our best estimate is that OH did not contribute significantly to the 2010–2018 methane trend other than the 2014 spike, though error correlation with global anthropogenic emissions limits confidence in this result.

Attribution of the accelerating increase in atmospheric methane during 2010–2018 by inverse analysis of GOSAT observations

2021 ◽  
Author(s):  
Yuzhong Zhang ◽  
Daneil J. Jacob ◽  
Xiao Lu ◽  
Joannes D. Maasakkers ◽  
Tia R. Scarpelli ◽  
...  
Keyword(s):  
Growth Rates ◽  
Inverse Analysis ◽  
Methane Emissions ◽  
Bayesian Optimization ◽  
Anthropogenic Emissions ◽  
Atmospheric Methane ◽  
Tropical Africa ◽  
Fire Emissions ◽  
Oil Gas ◽  
The U.S

<p>We conduct a global inverse analysis of 2010–2018 GOSAT satellite observations to better understand the factors controlling atmospheric methane and its accelerating increase over the 2010–2018 period. The inversion optimizes anthropogenic methane emissions and their 2010–2018 trends on a 4º×5º grid, monthly regional wetland emissions, and annual hemispheric concentrations of tropospheric OH (the main sink of methane). We use an analytical solution to the Bayesian optimization problem that provides closed-form estimates of error covariances and information content for the solution. We verify our inversion results with independent methane observations from the TCCON and NOAA networks. Our inversion successfully reproduces the interannual variability of the methane growth rate inferred from NOAA background sites. We find that prior estimates of fuel-related emissions reported by individual countries to the United Nations are too high for China (coal) and Russia (oil/gas), and too low for Venezuela (oil/gas) and the U.S. (oil/gas). We show large 2010–2018 increases in anthropogenic methane emissions over South Asia, tropical Africa, and Brazil, coincident with rapidly growing livestock populations in these regions. We do not find a significant trend in anthropogenic emissions over regions with large production or use of fossil methane, including the U.S., Russia, and Europe. Our results indicate that the peak methane growth rates in 2014–2015 are driven by low OH concentrations (2014) and high fire emissions (2015), while strong emissions from tropical (Amazon and tropical Africa) and boreal (Eurasia) wetlands combined with increasing anthropogenic emissions drive high growth rates in 2016–2018. Our best estimate is that OH did not contribute significantly to the 2010–2018 methane trend other than the 2014 spike, though error correlation with global anthropogenic emissions limits confidence in this result.</p>

scholarly journals Attribution of the accelerating increase in atmospheric methane during 2010–2018 by inverse analysis of GOSAT observations

2021 ◽  
Vol 21 (5) ◽  
pp. 3643-3666 ◽  
Author(s):  
Yuzhong Zhang ◽  
Daniel J. Jacob ◽  
Xiao Lu ◽  
Joannes D. Maasakkers ◽  
Tia R. Scarpelli ◽  
...  
Keyword(s):  
Growth Rates ◽  
Inverse Analysis ◽  
Oil And Gas ◽  
Methane Emissions ◽  
Bayesian Optimization ◽  
Anthropogenic Emissions ◽  
Atmospheric Methane ◽  
Tropical Africa ◽  
Fire Emissions ◽  
The Us

Abstract. We conduct a global inverse analysis of 2010–2018 GOSAT observations to better understand the factors controlling atmospheric methane and its accelerating increase over the 2010–2018 period. The inversion optimizes anthropogenic methane emissions and their 2010–2018 trends on a 4∘×5∘ grid, monthly regional wetland emissions, and annual hemispheric concentrations of tropospheric OH (the main sink of methane). We use an analytical solution to the Bayesian optimization problem that provides closed-form estimates of error covariances and information content for the solution. We verify our inversion results with independent methane observations from the TCCON and NOAA networks. Our inversion successfully reproduces the interannual variability of the methane growth rate inferred from NOAA background sites. We find that prior estimates of fuel-related emissions reported by individual countries to the United Nations are too high for China (coal) and Russia (oil and gas) and too low for Venezuela (oil and gas) and the US (oil and gas). We show large 2010–2018 increases in anthropogenic methane emissions over South Asia, tropical Africa, and Brazil, coincident with rapidly growing livestock populations in these regions. We do not find a significant trend in anthropogenic emissions over regions with high rates of production or use of fossil methane, including the US, Russia, and Europe. Our results indicate that the peak methane growth rates in 2014–2015 are driven by low OH concentrations (2014) and high fire emissions (2015), while strong emissions from tropical (Amazon and tropical Africa) and boreal (Eurasia) wetlands combined with increasing anthropogenic emissions drive high growth rates in 2016–2018. Our best estimate is that OH did not contribute significantly to the 2010–2018 methane trend other than the 2014 spike, though error correlation with global anthropogenic emissions limits confidence in this result.

scholarly journals Global distribution of methane emissions, emission trends, and OH concentrations and trends inferred from an inversion of GOSAT satellite data for 2010–2015

2019 ◽  
Author(s):  
Joannes D. Maasakkers ◽  
Daniel J. Jacob ◽  
Melissa P. Sulprizio ◽  
Tia R. Scarpelli ◽  
Hannah Nesser ◽  
...  
Keyword(s):  
Methane Emissions ◽  
Bayesian Optimization ◽  
Atmospheric Methane ◽  
Gas Emissions ◽  
Prior Estimate ◽  
Error Standard Deviation ◽  
Log Normal ◽  
Chinese Coal ◽  
Oil Gas ◽  
Gosat Satellite

Abstract. We use 2010–2015 observations of atmospheric methane columns from the GOSAT satellite instrument in a global inverse analysis to improve estimates of methane emissions and their trends over the period, as well as the global concentration of tropospheric OH (the hydroxyl radical, methane's main sink) and its trend. Our inversion solves the Bayesian optimization problem analytically including closed-form characterization of errors. This allows us to (1) quantify the information content from the inversion towards optimizing methane emissions and its trends, (2) diagnose error correlations between constraints on emissions and OH concentrations, and (3) generate a large ensemble of solutions testing different assumptions in the inversion. We show how the analytical approach can be used even when prior error standard deviation distributions are log-normal. Inversion results show large overestimates of Chinese coal emissions and Middle East oil/gas emissions in the EDGAR v4.3.2 inventory, but little error in the US where we use a new gridded version of the EPA national greenhouse gas inventory as prior estimate. Oil/gas emissions in the EDGAR v4.3.2 inventory show large differences with national totals reported to the United Nations Framework Convention on Climate Change (UNFCCC) and our inversion is generally more consistent with the UNFCCC data. The observed 2010–2015 growth in atmospheric methane is attributed mostly to an increase in emissions from India, China, and areas with large tropical wetlands. The contribution from OH trends is small in comparison. We find that the inversion provides strong independent constraints on global methane emissions (546 Tg a−1) and global mean OH concentrations (atmospheric methane lifetime against oxidation by tropospheric OH of 10.8 ± 0.4 years), indicating that satellite observations of atmospheric methane could provide a proxy for OH concentrations in the future.

scholarly journals Global distribution of methane emissions, emission trends, and OH concentrations and trends inferred from an inversion of GOSAT satellite data for 2010–2015

2019 ◽  
Vol 19 (11) ◽  
pp. 7859-7881 ◽  
Author(s):  
Joannes D. Maasakkers ◽  
Daniel J. Jacob ◽  
Melissa P. Sulprizio ◽  
Tia R. Scarpelli ◽  
Hannah Nesser ◽  
...  
Keyword(s):  
Oil And Gas ◽  
The United States ◽  
Methane Emissions ◽  
Bayesian Optimization ◽  
Atmospheric Methane ◽  
Gas Emissions ◽  
Prior Estimate ◽  
Error Standard Deviation ◽  
Chinese Coal ◽  
Gosat Satellite

Abstract. We use 2010–2015 observations of atmospheric methane columns from the GOSAT satellite instrument in a global inverse analysis to improve estimates of methane emissions and their trends over the period, as well as the global concentration of tropospheric OH (the hydroxyl radical, methane's main sink) and its trend. Our inversion solves the Bayesian optimization problem analytically including closed-form characterization of errors. This allows us to (1) quantify the information content from the inversion towards optimizing methane emissions and its trends, (2) diagnose error correlations between constraints on emissions and OH concentrations, and (3) generate a large ensemble of solutions testing different assumptions in the inversion. We show how the analytical approach can be used, even when prior error standard deviation distributions are lognormal. Inversion results show large overestimates of Chinese coal emissions and Middle East oil and gas emissions in the EDGAR v4.3.2 inventory but little error in the United States where we use a new gridded version of the EPA national greenhouse gas inventory as prior estimate. Oil and gas emissions in the EDGAR v4.3.2 inventory show large differences with national totals reported to the United Nations Framework Convention on Climate Change (UNFCCC), and our inversion is generally more consistent with the UNFCCC data. The observed 2010–2015 growth in atmospheric methane is attributed mostly to an increase in emissions from India, China, and areas with large tropical wetlands. The contribution from OH trends is small in comparison. We find that the inversion provides strong independent constraints on global methane emissions (546 Tg a−1) and global mean OH concentrations (atmospheric methane lifetime against oxidation by tropospheric OH of 10.8±0.4 years), indicating that satellite observations of atmospheric methane could provide a proxy for OH concentrations in the future.

scholarly journals Global distribution of methane emissions: a comparative inverse analysis of observations from the TROPOMI and GOSAT satellite instruments

2021 ◽  
Author(s):  
Zhen Qu ◽  
Daniel J. Jacob ◽  
Lu Shen ◽  
Xiao Lu ◽  
Yuzhong Zhang ◽  
...  
Keyword(s):  
Transport Model ◽  
Joint Inversion ◽  
Methane Emissions ◽  
Bayesian Optimization ◽  
Anthropogenic Source ◽  
Atmospheric Methane ◽  
Error Statistics ◽  
South Central ◽  
Prior Estimate ◽  
Gosat Satellite

Abstract. We evaluate the global atmospheric methane column retrievals from the new TROPOMI satellite instrument and apply them to a global inversion of methane sources for 2019 at 2° × 2.5° horizontal resolution. We compare the results to an inversion using the sparser but more mature GOSAT satellite retrievals, as well as a joint inversion using both TROPOMI and GOSAT. Validation of TROPOMI and GOSAT with TCCON ground-based measurements of methane columns, after correcting for retrieval differences in prior vertical profiles and averaging kernels using the GEOS-Chem chemical transport model, shows global biases of −2.7 ppbv for TROPOMI and −1.0 ppbv for GOSAT, and regional biases of 6.7 ppbv for TROPOMI and 2.9 ppbv for GOSAT. Intercomparison of TROPOMI and GOSAT shows larger regional discrepancies exceeding 20 ppbv, mostly over regions with low surface albedo in the shortwave infrared where the TROPOMI retrieval may be biased. Our inversion uses an analytical solution to the Bayesian optimization of methane sources, thus providing an explicit characterization of error statistics and information content together with the solution. TROPOMI has ~100 times more observations than GOSAT but error correlation on the 2° × 2.5° scale of the inversion and large spatial variations of the number of observations make it less useful than GOSAT for quantifying emissions at that resolution. Finer-scale regional inversions would take better advantage of the TROPOMI data density. The TROPOMI and GOSAT inversions show consistent downward adjustments of global oil/gas emissions relative to a prior estimate based on national inventory reports to the United Nations Framework Convention on Climate Change, but consistent increases in the south-central US and in Venezuela. Global emissions from livestock (the largest anthropogenic source) are adjusted upward by TROPOMI and GOSAT relative to the EDGAR v4.3.2 prior estimate. We find large artifacts in the TROPOMI inversion over Southeast China, where seasonal rice emissions are particularly high but in phase with extensive cloudiness, and where coal emissions may be misallocated. Future advances in the TROPOMI retrieval together with finer-scale inversions and improved accounting of error correlations should enable improved exploitation of TROPOMI observations to quantify and attribute methane emissions on the global scale.

scholarly journals 2010–2015 North American methane emissions, sectoral contributions, and trends: a high-resolution inversion of GOSAT satellite observations of atmospheric methane

2020 ◽  
Author(s):  
Joannes D. Maasakkers ◽  
Daniel J. Jacob ◽  
Melissa P. Sulprizio ◽  
Tia R. Scarpelli ◽  
Hannah Nesser ◽  
...  
Keyword(s):  
High Resolution ◽  
Analytical Solution ◽  
Oil Production ◽  
Methane Emissions ◽  
Gas Production ◽  
Satellite Observations ◽  
Anthropogenic Emissions ◽  
Atmospheric Methane ◽  
Oil Gas ◽  
Gosat Satellite

Abstract. We use 2010–2015 GOSAT satellite observations of atmospheric methane columns over North America in a high- resolution inversion of methane emissions, including contributions from different sectors and long-term trends. The inversion involves analytical solution to the Bayesian optimization problem for a Gaussian mixture model (GMM) of the emission field with up to 0.5° × 0.625° resolution in concentrated source regions. Analytical solution provides a closed-form characterization of the information content from the inversion and facilitates the construction of a large ensemble of solutions exploring the effect of different uncertainties and assumptions. Prior estimates for the inversion include a gridded version of the EPA Inventory of U.S. Greenhouse Gas Emissions and Sinks (GHGI) and the WetCHARTS model ensemble for wetlands. Our best estimate for mean 2010–2015 US anthropogenic emissions is 30.6 (range: 29.4–31.3) Tg a-1, slightly higher than the gridded EPA inventory (28.7 (26.4–36.2) Tg a-1). The main discrepancy is for the oil and gas production sectors where we find higher emissions than the GHGI by 35 % and 22 % respectively. The most recent version of the EPA GHGI revises downward its estimate of emissions from oil production and we find that these are a factor 2 lower than our estimate. Our best estimate of US wetland emissions is 10.2 (5.6–11.1) Tg a-1, on the low end of the prior WetCHARTS inventory uncertainty range (14.2 (3.3–32.4) Tg a-1) and calling for better understanding of these emissions. We find an increasing trend in US anthropogenic emissions over 2010–2015 of 0.4 % a-1, lower than previous GOSAT-based estimates but opposite to the decrease reported by the EPA GHGI. Most of this increase appears driven by unconventional oil/gas production in the eastern US. We also find that oil/gas production emissions in Mexico are higher than in the nationally reported inventory, though there is evidence for a 2010–2015 decrease in emissions from offshore oil production.

scholarly journals Updated Global Fuel Exploitation Inventory (GFEI) for methane emissions from the oil, gas, and coal sectors: evaluation with inversions of atmospheric methane observations

2021 ◽  
Author(s):  
Tia R. Scarpelli ◽  
Daniel J. Jacob ◽  
Shayna Grossman ◽  
Xiao Lu ◽  
Zhen Qu ◽  
...  
Keyword(s):  
Climate Change ◽  
Methane Emissions ◽  
Emission Inventories ◽  
Atmospheric Methane ◽  
Intergovernmental Panel ◽  
Gas Emissions ◽  
The Us ◽  
Prior Estimate ◽  
Future Evaluation ◽  
Oil Gas

Abstract. We present an updated version of the Global Fuel Exploitation Inventory (GFEI) for methane emissions and evaluate it with results from global inversions of atmospheric methane observations from satellite (GOSAT) and in situ platforms (GLOBALVIEWplus). GFEI allocates methane emissions from oil, gas, and coal sectors and subsectors to a 0.1° × 0.1° grid by using the national emissions reported by individual countries to the United Nations Framework Convention on Climate Change (UNFCCC) and mapping them to infrastructure locations. Our updated GFEI v2 gives annual emissions for 2010–2019 that incorporate the most recent UNFCCC national reports, new oil/gas well locations, and improved spatial distribution of emissions for Canada, Mexico, and China. Russia's oil/gas emissions decrease by 83 % in its latest UNFCCC report while Nigerian emissions increase sevenfold, reflecting changes in assumed emission factors. Global gas emissions in GFEI v2 show little net change from 2010 to 2019 while oil emissions decrease and coal emissions slightly increase. Global emissions in GFEI v2 are lower than the EDGAR v6 and IEA inventories for all sectors though there is considerable variability in the comparison for individual countries. GFEI v2 estimates higher emissions by country than the Climate TRACE inventory with notable exceptions in Russia, the US, and the Middle East. Inversion results using GFEI as a prior estimate confirm the lower Russian emissions in the latest UNFCCC report but Nigerian emissions are too high. Oil/gas emissions are generally underestimated by the national inventories for the highest emitting countries including the US, Venezuela, Uzbekistan, Canada, and Turkmenistan. Offshore emissions in GFEI tend to be overestimated. Our updated GFEI v2 provides a platform for future evaluation of national emission inventories reported to the UNFCCC using the newer generation of satellite instruments such as TROPOMI with improved coverage and spatial resolution. It responds to recent aspirations of the Intergovernmental Panel on Climate Change (IPCC) to integrate top-down and bottom-up information into the construction of national emission inventories.

Using a new divergence method to quantify methane emissions with TROPOMI on Sentinel-5p

2021 ◽  
Author(s):  
Mengyao Liu ◽  
Ronald Van der A ◽  
Michiel Van Weele ◽  
Henk Eskes ◽  
Xiao Lu ◽  
...  
Keyword(s):  
Large Scale ◽  
A Priori ◽  
The United States ◽  
Global Scale ◽  
Methane Emissions ◽  
Atmospheric Methane ◽  
Permian Basin ◽  
Wind Speeds ◽  
Gas Fields ◽  
Oil Gas

<p>The high-resolution Tropospheric Monitoring Instrument (TROPOMI) satellite observations of atmospheric methane offer a powerful tool to identify emission hot spots and quantify regional emissions. The divergence of horizontal fluxes of NO<sub>2</sub> has already been proven to be an efficient way to resolve and quantify high sources on a global scale. Since the lifetime of CH<sub>4</sub> is in the order of 10 years, the sinks can be ignored at the synoptic time scale which makes the divergence method even more applicable to CH<sub>4 </sub>than to short-lived NO<sub>2</sub>. <br>Because plumes of newly emitted CH<sub>4 </sub>disperse within the Planetary Boundary Layer (PBL), we first convert the satellite observed total column average (XCH<sub>4</sub>) to a regional enhancement of methane in the PBL (∆XCH<sub>4_PBL</sub>) by using the CAMS global methane background reanalysis fields above the PBL. These model fields represent the transport- and chemically-modulated large-scale distribution of methane. Secondly, the divergence of ∆XCH<sub>4_PBL</sub> is derived by the use of the wind speeds halfway within the PBL. Based on the divergence, methane emissions are estimated on a 0.25°× 0.25° grid. We tested our new method for Texas in the United States and quantified methane emissions from the well-known oil-gas fields in the Permian Basin, as well as from – less well quantitatively established – oil-gas fields located in southern coastal areas. <br>Compared to traditional inverse methods, our method is not restricted by an a priori emission inventory and so far unidentified local sources (i.e. emissions from livestock in feed yards) may be found. Due to its computational efficiency, the method might be applied in the near future globally on the current spatial resolution.</p>

scholarly journals High-resolution inversion of methane emissions in the Southeast US using SEAC<sup>4</sup>RS aircraft observations of atmospheric methane: anthropogenic and wetland sources

2017 ◽  
Author(s):  
Jian-Xiong Sheng ◽  
Daniel J. Jacob ◽  
Alexander J. Turner ◽  
Joannes D. Maasakkers ◽  
Melissa P. Sulprizio ◽  
...  
Keyword(s):  
Regional Scale ◽  
Methane Emissions ◽  
Gas Production ◽  
Anthropogenic Sources ◽  
Anthropogenic Source ◽  
Atmospheric Methane ◽  
Error Characterization ◽  
The Mean ◽  
Southeast Us ◽  
Oil Gas

Abstract. We use observations of boundary layer methane from the SEAC4RS aircraft campaign over the Southeast US in August–September 2013 to estimate methane emissions in that region through an inverse analysis with up to 0.25 ° x 0.3125 ° (25 x 25 km2) resolution and with full error characterization. The Southeast US accounts for about half of total US anthropogenic emissions according to the gridded EPA national inventory and also has extensive wetlands. Our inversion uses state-of-science emission inventories as prior estimates, including a gridded version of the anthropogenic EPA Greenhouse Gas Inventory and the mean of the WetCHARTs ensemble for wetlands. Inversion results are independently verified by comparison with surface (NOAA/ESRL) and column (TCCON) methane observations. Our posterior estimates for the Southeast US are 12.8 ± 0.9 Tg a−1 for anthropogenic sources (no significant change from the gridded EPA inventory) and 9.4 ± 0.8 Tg a−1 for wetlands (27 % decrease from the mean in the WetCHARTs ensemble). The largest source of error in the WetCHARTs wetlands ensemble is the landcover map specification of wetland areal extent. We find no regional bias in the anthropogenic EPA inventory, including for different source sectors, in contrast with previous inverse analyses that found the EPA inventory to be too low at national scales. These previous inversions relied on prior anthropogenic source patterns from the EDGAR v4.2 inventory that have considerable error, and also assumed low wetland emissions. Despite the regional-scale consistency, we find significant local errors in the EPA inventory for oil/gas production fields, suggesting that emission factors are more variable than assumed in the inventory.

scholarly journals Satellite-derived methane hotspot emission estimates using a fast data-driven method

2016 ◽  
Author(s):  
Michael Buchwitz ◽  
Oliver Schneising ◽  
Maximilian Reuter ◽  
Jens Heymann ◽  
Sven Krautwurst ◽  
...  
Keyword(s):  
Central Valley ◽  
Methane Emissions ◽  
Mitigation Strategies ◽  
Emission Sources ◽  
Fast Method ◽  
Atmospheric Methane ◽  
Near Surface ◽  
Uncertainty Estimates ◽  
Four Corners ◽  
Oil Gas

Abstract. Methane is an important atmospheric greenhouse gas and an adequate understanding of its emission sources is needed for climate change assessments, predictions and the development and verification of emission mitigation strategies. Satellite retrievals of near-surface-sensitive column-averaged dry-air mole fractions of atmospheric methane, i.e., XCH4, can be used to quantify methane emissions. Here we present a simple and fast method to estimate emissions of methane hotspots from satellite-derived XCH4 maps. We apply this method to an ensemble of XCH4 data products consisting of two products from SCIAMACHY/ENVISAT and two products from TANSO-FTS/GOSAT covering the time period 2003–2014. We obtain annual emissions of the source areas Four Corners in the southwestern USA, for the southern part of Central Valley, California, and for Azerbaijan and Turkmenistan. We find that our estimated emissions are in good agreement with independently derived estimates for Four Corners and Azerbaijan. For the Central Valley and Turkmenistan our estimated annual emissions are higher compared to the EDGAR v4.2 anthropogenic emission inventory. For Turkmenistan we find on average about 50 % higher emissions with our annual emission uncertainty estimates overlapping with the EDGAR emissions. For the region around Bakersfield in the Central Valley we find a factor of 6–9 higher emissions compared to EDGAR albeit with large uncertainty. Major methane emission sources in this region are oil/gas and livestock. Our findings corroborate recently published studies based on aircraft and satellite measurements and new bottom-up estimates reporting significantly underestimated methane emissions of oil/gas and/or livestock in this area in inventories.

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