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

2017 ◽  
Vol 17 (9) ◽  
pp. 5751-5774 ◽  
Author(s):  
Michael Buchwitz ◽  
Oliver Schneising ◽  
Maximilian Reuter ◽  
Jens Heymann ◽  
Sven Krautwurst ◽  
...  
Keyword(s):  
Central Valley ◽  
Methane Emissions ◽  
Data Driven ◽  
Emission Sources ◽  
Atmospheric Methane ◽  
Near Surface ◽  
Source Regions ◽  
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. Maps of time-averaged satellite-derived XCH4 show regionally elevated methane over several methane source regions. In order to obtain methane emissions of these source regions we use a simple and fast data-driven method to estimate annual methane emissions and corresponding 1σ uncertainties directly from maps of annually averaged satellite XCH4. From theoretical considerations we expect that our method tends to underestimate emissions. When applying our method to high-resolution atmospheric methane simulations, we typically find agreement within the uncertainty range of our method (often 100 %) but also find that our method tends to underestimate emissions by typically about 40 %. To what extent these findings are model dependent needs to be assessed. We apply our method to an ensemble of satellite 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 four source areas: Four Corners in the south-western USA, the southern part of Central Valley, California, 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 5–8 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 EDGAR.

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.

scholarly journals Variability and quasi-decadal changes in the methane budget over the period 2000–2012

2017 ◽  
Author(s):  
Marielle Saunois ◽  
Philippe Bousquet ◽  
Benjamin Poulter ◽  
Anna Peregon ◽  
Philippe Ciais ◽  
...  
Keyword(s):  
Biomass Burning ◽  
Methane Emissions ◽  
Data Driven ◽  
Atmospheric Methane ◽  
Top Down ◽  
Bottom Up ◽  
Ch4 Emissions ◽  
Ensemble Mean ◽  
The Mean ◽  
The Tropics

Abstract. Following the recent Global Carbon project (GCP) synthesis of the decadal methane (CH4) budget over 2000–2012 (Saunois et al., 2016), we analyse here the same dataset with a focus on quasi-decadal and inter-annual variability in CH4 emissions. The GCP dataset integrates results from top-down studies (exploiting atmospheric observations within an atmospheric inverse-modelling frameworks) and bottom-up models, inventories, and data-driven approaches (including process-based models for estimating land surface emissions and atmospheric chemistry, inventories of anthropogenic emissions, and data-driven extrapolations). The annual global methane emissions from top-down studies, which by construction match the observed methane growth rate within their uncertainties, all show an increase in total methane emissions over the period 2000–2012, but this increase is not linear over the 13 years. Despite differences between individual studies, the mean emission anomaly of the top-down ensemble shows no significant trend in total methane emissions over the period 2000–2006, during the plateau of atmospheric methane mole fractions, and also over the period 2008–2012, during the renewed atmospheric methane increase. However, the top-down ensemble mean produces an emission shift between 2006 and 2008, leading to 22 [16–32] Tg CH4 yr−1 higher methane emissions over the period 2008–2012 compared to 2002–2006. This emission increase mostly originated from the tropics with a smaller contribution from mid-latitudes and no significant change from boreal regions. The regional contributions remain uncertain in top-down studies. Tropical South America and South and East Asia seems to contribute the most to the emission increase in the tropics. However, these two regions have only limited atmospheric measurements and remain therefore poorly constrained. The sectorial partitioning of this emission increase between the periods 2002–2006 and 2008–2012 differs from one atmospheric inversion study to another. However, all top-down studies suggest smaller changes in fossil fuel emissions (from oil, gas, and coal industries) compared to the mean of the bottom-up inventories included in this study. This difference is partly driven by a smaller emission change in China from the top-down studies compared to the estimate in the EDGARv4.2 inventory, which should be revised to smaller values in a near future. Though the sectorial partitioning of six individual top-down studies out of eight are not consistent with the observed change in atmospheric 13CH4, the partitioning derived from the ensemble mean is consistent with this isotopic constraint. At the global scale, the top-down ensemble mean suggests that, the dominant contribution to the resumed atmospheric CH4 growth after 2006 comes from microbial sources (more from agriculture and waste sectors than from natural wetlands), with an uncertain but smaller contribution from fossil CH4 emissions. Besides, a decrease in biomass burning emissions (in agreement with the biomass burning emission databases) makes the balance of sources consistent with atmospheric 13CH4 observations. The methane loss (in particular through OH oxidation) has not been investigated in detail in this study, although it may play a significant role in the recent atmospheric methane changes.

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.

scholarly journals Using satellites to uncover large methane emissions from landfills

2021 ◽  
Author(s):  
Joannes Maasakkers ◽  
Daniel Varon ◽  
Aldís Elfarsdóttir ◽  
Jason McKeever ◽  
Dylan Jervis ◽  
...  
Keyword(s):  
High Resolution ◽  
Hot Spots ◽  
Buenos Aires ◽  
Methane Emissions ◽  
Emission Sources ◽  
Emission Inventories ◽  
Atmospheric Methane ◽  
Target Mode ◽  
Facility Level ◽  
Methane Concentrations

As atmospheric methane concentrations increase at record pace, it is critical to identify individual emission sources with high potential for mitigation. Landfills are responsible for large methane emissions that can be readily abated but have been sparsely observed. Here we leverage the synergy between satellite instruments with different spatiotemporal coverage and resolution to detect and quantify emissions from individual landfill facilities. We use the global surveying Tropospheric Monitoring Instrument (TROPOMI) to identify large emission hot spots, and then zoom in with high-resolution target-mode observations from the GHGSat instrument suite to identify the responsible facilities and characterize their emissions. Using this ‘tip and cue’ approach, we detect and analyze strongly emitting landfills (3-29 t hr−1) in Buenos Aires (Argentina), Delhi (India), Lahore (Pakistan), and Mumbai (India). We find that city-level emissions are 1.6-2.8 times larger than reported in commonly used emission inventories and that the landfills contribute 5-47% of those emissions. Our work demonstrates how complementary satellites enable global detection, identification, and monitoring of methane super-emitters at the facility-level.

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 Estimating global and North American methane emissions with high spatial resolution using GOSAT satellite data

2015 ◽  
Vol 15 (12) ◽  
pp. 7049-7069 ◽  
Author(s):  
A. J. Turner ◽  
D. J. Jacob ◽  
K. J. Wecht ◽  
J. D. Maasakkers ◽  
E. Lundgren ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
North American ◽  
Transport Model ◽  
Central Valley ◽  
Methane Emissions ◽  
Point Sources ◽  
Atmospheric Methane ◽  
Chemical Transport Model ◽  
Error Characterization ◽  
Methane Source

Abstract. We use 2009–2011 space-borne methane observations from the Greenhouse Gases Observing SATellite (GOSAT) to estimate global and North American methane emissions with 4° × 5° and up to 50 km × 50 km spatial resolution, respectively. GEOS-Chem and GOSAT data are first evaluated with atmospheric methane observations from surface and tower networks (NOAA/ESRL, TCCON) and aircraft (NOAA/ESRL, HIPPO), using the GEOS-Chem chemical transport model as a platform to facilitate comparison of GOSAT with in situ data. This identifies a high-latitude bias between the GOSAT data and GEOS-Chem that we correct via quadratic regression. Our global adjoint-based inversion yields a total methane source of 539 Tg a−1 with some important regional corrections to the EDGARv4.2 inventory used as a prior. Results serve as dynamic boundary conditions for an analytical inversion of North American methane emissions using radial basis functions to achieve high resolution of large sources and provide error characterization. We infer a US anthropogenic methane source of 40.2–42.7 Tg a−1, as compared to 24.9–27.0 Tg a−1 in the EDGAR and EPA bottom-up inventories, and 30.0–44.5 Tg a−1 in recent inverse studies. Our estimate is supported by independent surface and aircraft data and by previous inverse studies for California. We find that the emissions are highest in the southern–central US, the Central Valley of California, and Florida wetlands; large isolated point sources such as the US Four Corners also contribute. Using prior information on source locations, we attribute 29–44 % of US anthropogenic methane emissions to livestock, 22–31 % to oil/gas, 20 % to landfills/wastewater, and 11–15 % to coal. Wetlands contribute an additional 9.0–10.1 Tg a−1.

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 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.

scholarly journals A global gridded (0.1° × 0.1°) inventory of methane emissions from oil, gas, and coal exploitation based on national reports to the United Nations Framework Convention on Climate Change

2020 ◽  
Vol 12 (1) ◽  
pp. 563-575 ◽  
Author(s):  
Tia R. Scarpelli ◽  
Daniel J. Jacob ◽  
Joannes D. Maasakkers ◽  
Melissa P. Sulprizio ◽  
Jian-Xiong Sheng ◽  
...  
Keyword(s):  
Climate Change ◽  
United Nations ◽  
Oil And Gas ◽  
Methane Emissions ◽  
Point Sources ◽  
Atmospheric Methane ◽  
United Nations Framework Convention ◽  
Oil Refineries ◽  
The United Nations ◽  
Oil Gas

Abstract. Individual countries report national emissions of methane, a potent greenhouse gas, in accordance with the United Nations Framework Convention on Climate Change (UNFCCC). We present a global inventory of methane emissions from oil, gas, and coal exploitation that spatially allocates the national emissions reported to the UNFCCC (Scarpelli et al., 2019). Our inventory is at 0.1∘×0.1∘ resolution and resolves the subsectors of oil and gas exploitation, from upstream to downstream, and the different emission processes (leakage, venting, flaring). Global emissions for 2016 are 41.5 Tg a−1 for oil, 24.4 Tg a−1 for gas, and 31.3 Tg a−1 for coal. An array of databases is used to spatially allocate national emissions to infrastructure, including wells, pipelines, oil refineries, gas processing plants, gas compressor stations, gas storage facilities, and coal mines. Gridded error estimates are provided in normal and lognormal forms based on emission factor uncertainties from the IPCC. Our inventory shows large differences with the EDGAR v4.3.2 global gridded inventory both at the national scale and in finer-scale spatial allocation. It shows good agreement with the gridded version of the United Kingdom's National Atmospheric Emissions Inventory (NAEI). There are significant errors on the 0.1∘×0.1∘ grid associated with the location and magnitude of large point sources, but these are smoothed out when averaging the inventory over a coarser grid. Use of our inventory as prior estimate in inverse analyses of atmospheric methane observations allows investigation of individual subsector contributions and can serve policy needs by evaluating the national emissions totals reported to the UNFCCC. Gridded data sets can be accessed at https://doi.org/10.7910/DVN/HH4EUM (Scarpelli et al., 2019).

Sign in / Sign up

Export Citation Format

Share Document