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Global high-resolution observations of atmospheric composition from satellites can greatly improve our understanding of surface emissions through inverse analyses. Variational inverse methods can optimize surface emissions at any resolution but do not readily quantify the error and information content of the posterior solution. The information content of satellite data may be much lower than its coverage would suggest because of failed retrievals, instrument noise, and error correlations that propagate through the inversion. Analytical solution of the inverse problem provides closed-form characterization of posterior error statistics and information content but requires the construction of the Jacobian matrix that relates emissions to atmospheric concentrations. Building the Jacobian matrix is computationally expensive at high resolution because it involves perturbing each emission element, typically individual grid cells, in the atmospheric transport model used as the forward model for the inversion. We propose and analyze two methods, reduced dimension and reduced rank, to construct the Jacobian matrix at greatly decreased computational cost while retaining information content. Both methods are two-step iterative procedures that begin from an initial native-resolution estimate of the Jacobian matrix constructed at no computational cost by assuming that atmospheric concentrations are most sensitive to local emissions. The reduced-dimension method uses this estimate to construct a Jacobian matrix on a multiscale grid that maintains a high resolution in areas with high information content and aggregates grid cells elsewhere. The reduced-rank method constructs the Jacobian matrix at native resolution by perturbing the leading patterns of information content given by the initial estimate. We demonstrate both methods in an analytical Bayesian inversion of Greenhouse Gases Observing Satellite (GOSAT) methane data with augmented information content over North America in July 2009. We show that both methods reproduce the results of the native-resolution inversion while achieving a factor of 4 improvement in computational performance. The reduced-dimension method produces an exact solution at a lower spatial resolution, while the reduced-rank method solves the inversion at native resolution in areas of high information content and defaults to the prior estimate elsewhere.

Under the Paris Agreement, countries report their anthropogenic greenhouse gas emissions in national inventories, which are used to track progress towards mitigation goals, but they must be independently verified. Atmospheric observations of CO2 , interpreted using inverse methods, can potentially provide that verification. Conventional CO2 inverse methods infer natural CO2 fluxes by subtracting a priori estimates of fuel combustion from the a posteriori net CO2 fluxes, assuming that a priori knowledge for combustion emissions is better than for natural fluxes. We describe an inverse method that uses measurements of CO2 and carbon monoxide (CO), a trace gas that is co-emitted with CO2 during combustion, to report self-consistent combustion emissions and natural fluxes of CO2. We use an ensemble Kalman filter and the GEOS-Chem atmospheric transport model to explore how satellite observations of CO and CO2 collected by the TROPOspheric Monitoring Instrument (TROPOMI) and Orbiting Carbon Observatory-2 (OCO-2), respectively, can improve understanding of combustion emissions and natural CO2 fluxes across the UK and mainland Europe in 2018–2021. We assess the value of using satellite observations of CO2 , with and without CO, above what is already available from the in situ network. Using CO2 satellite observations leads to small corrections to a priori emissions that are inconsistent with in situ observations, due partly to the insensitivity of the atmospheric CO2 column to CO2 emission changes. When we introduce satellite CO observations, we find better agreement with our in situ inversion and a better model fit to atmospheric CO2 observations. Our regional mean a posteriori combustion CO2 emission ranges from 4.6–5.0 Gta-1 (1.5 %–2.4 % relative standard deviation), with all inversions reporting an overestimate for Germany's wintertime emissions. Our national a posteriori CO2 combustion emissions are highly dependent on the assumed relationship between CO2 and CO uncertainties, as expected. Generally, we find better results when we use grid-scale-based a priori CO2:CO uncertainty estimates rather than a fixed relationship between the two species. [ABSTRACT FROM AUTHOR]

Under the Paris Agreement, countries report their anthropogenic greenhouse gas emissions in national inventories, used to track progress towards mitigation goals, but they must be independently verified. Atmospheric observations of CO2, interpreted using inverse methods, can potentially provide that verification. Conventional CO2 inverse methods infer natural CO2 fluxes by subtracting a priori estimates of fuel combustion from the a posteriori net CO2 fluxes, assuming that a priori knowledge for combustion emissions is better than for natural fluxes. We describe an inverse method that uses measurements of CO2 and carbon monoxide (CO), a trace gas that is co-emitted with CO2 during combustion, to report self-consistent combustion emissions and natural fluxes of CO2. We use an ensemble Kalman filter and the GEOS-Chem atmospheric transport model to explore how satellite observations of CO and CO2 collected by TROPOMI and OCO-2, respectively, can improve understanding of combustion emissions and natural CO2 fluxes across the UK and mainland Europe, 2018-2021. We assess the value of using satellite observations of CO2, with and without CO, above what is already available from the in situ network. Using CO2 satellite observations lead to small corrections to a priori emissions that are inconsistent with in situ observations, due partly to the insensitivity of the atmospheric CO2 column to CO2 emission changes. When we introduce satellite CO observations, we find better agreement with our in situ inversion and a better model fit to atmospheric CO2 observations. Our regional mean a posteriori combustion CO2 emission ranges 4.6-5.0 Gt a-1 (1.5-2.4 % relative standard deviation), with all inversions reporting an overestimate for Germany's wintertime emissions. Our national a posteriori CO2 combustion emissions are highly dependent on the assumed relationship between CO2 and CO uncertainties, as expected. Generally, we find better results when we use grid-scale based a priori CO2:CO uncertainty estimates rather than a fixed relationship between the two species. [ABSTRACT FROM AUTHOR]

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° x 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. [ABSTRACT FROM AUTHOR]

We quantify methane emissions and their 2010-2017 trends by sector in the contiguous United States (CONUS), Canada, and Mexico by inverse analysis of in situ (GLOBALVIEWplus CH4 ObsPack) and satellite (GOSAT) atmospheric methane observations. The inversion uses as prior estimate the national anthropogenic emission inventories for the three countries reported by the US Environmental Protection Agency (EPA), Environment and Climate Change Canada (ECCC), and the Instituto Nacional de Ecologia y Cambio Climatico (INECC) in Mexico to the United Nations Framework Convention on Climate Change (UNFCCC), and thus serves as an evaluation of these inventories in terms of their magnitudes and trends. Emissions are optimized with a Gaussian mixture model (GMM) at 0.5°Ã—0.625° resolution and for individual years. Optimization is done analytically using log-normal error forms. This yields closed-form statistics of error estimates and information content on the posterior (optimized) estimates, allows better representation of the high tail of the emission distribution, and enables construction of a large ensemble of inverse solutions using different observations and assumptions. We find that GOSAT and in situ observations are largely consistent and complementary in the optimization of methane emissions for North America. Mean 2010-2017 anthropogenic emissions from our base GOSAT + in situ inversion, with ranges from the inversion ensemble, are 36.9 (32.5-37.8) Tg a-1 for CONUS, 5.3 (3.6-5.7) Tg a-1 for Canada, and 6.0 (4.7-6.1) Tg a-1 for Mexico. These are higher than the most recent reported national inventories of 26.0 Tg a-1 for the US (EPA), 4.0 Tg a-1 for Canada (ECCC), and 5.0 Tg a-1 for Mexico (INECC). The correction in all three countries is largely driven by a factor of 2 underestimate in emissions from the oil sector with major contributions from the south-central US, western Canada, and southeast Mexico. Total CONUS anthropogenic emissions in our inversion peak in 2014, in contrast to the EPA report of a steady decreasing trend over 2010-2017. This reflects combined effects of increases in emissions from the oil and landfill sectors, decrease from the gas, and flat emissions from the livestock and coal sectors. We find decreasing trends in Canadian and Mexican anthropogenic methane emissions over the 2010-2017 period, mainly driven by oil and gas emissions. Our best estimates of mean 2010-2017 wetland emissions are 8.4 (6.4-10.6) Tg a-1 for CONUS, 9.9 (7.8-12.0) Tg a-1 for Canada, and 0.6 (0.4-0.6) Tg a-1 for Mexico. Wetland emissions in CONUS show an increasing trend of 2.6 (1.7-3.8) % a-1 over 2010-2017 correlated with precipitation. [ABSTRACT FROM AUTHOR]

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° x 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° x 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. [ABSTRACT FROM AUTHOR]

Global high-resolution observations of atmospheric composition from satellites can greatly improve our understanding of surface emissions through inverse analyses. Variational inverse methods can optimize surface emissions at any resolution but do not readily quantify the error and information content of the posterior solution. In fact, the information content of satellite data may be orders of magnitude lower than its coverage suggests because of failed retrievals, instrument noise, and error correlations that propagate through the inversion. Analytical solution to the inverse problem provides closed-form characterization of posterior error statistics and information content but requires the construction of the Jacobian matrix that relates emissions to atmospheric concentrations. Building the Jacobian matrix is computationally expensive at high resolution because it involves perturbing each emission element, typically individual grid cells, in the atmospheric transport model used as forward model for the inversion. We propose and analyze two methods, reduced-dimension and reduced-rank, to construct the Jacobian matrix at greatly decreased computational cost while retaining information content. Both methods begin from an initial native-resolution estimate of the Jacobian matrix constructed at no computational cost by assuming that atmospheric concentrations are most sensitive to local emissions. The reduced-dimension method uses this estimate to construct a Jacobian matrix on a multiscale grid that maintains high resolution in areas with high information content and aggregates grid cells elsewhere. The reduced-rank method constructs the Jacobian matrix at native resolution by perturbing the leading patterns of information content given by the initial estimate. We demonstrate both methods in an analytical Bayesian inversion of GOSAT methane satellite data with augmented information content over North America in July 2009. We show that both methods reproduce the results of the native-resolution inversion while achieving a factor of 4 improvement in computational performance. The reduced-dimension method produces an exact solution at lower spatial resolution while the reduced-rank method solves the inversion at native resolution in areas of high information content and defaults to the prior estimate elsewhere. [ABSTRACT FROM AUTHOR]

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 4° χ 5° 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. [ABSTRACT FROM AUTHOR]

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° x 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. [ABSTRACT FROM AUTHOR]

We use satellite (GOSAT) and in situ (GLOBALVIEWplus CH4 ObsPack) observations of atmospheric methane in a joint global inversion of methane sources, sinks, and trends for the 2010-2017 period. The inversion is done by analytical solution to the Bayesian optimization problem, yielding closed-form estimates of information content to assess the consistency and complementarity (or redundancy) of the satellite and in situ datasets. We find that GOSAT and in situ observations are to a large extent complementary, with GOSAT providing a stronger overall constraint on the global methane distributions, but in situ observations being more important for northern mid-latitudes and for relaxing global error correlations between methane emissions and the main methane sink (oxidation by OH radicals). The GOSAT observations achieve 212 independent pieces of information (DOFS) for quantifying mean 2010-2017 anthropogenic emissions on 1009 global model grid elements, and a DOFS of 122 for 2010-2017 emission trends. Adding the in situ data increases the DOFS by about 20-30%, to 262 and 161 respectively for mean emissions and trends. Our joint inversion finds that oil/gas emissions in the US and Canada are underestimated relative to the values reported by these countries to the United Nations Framework Convention on Climate Change (UNFCCC) and used here as prior estimates, while coal emissions in China are overestimated. Wetland emissions in North America are much lower than in the mean WetCHARTs inventory used as prior estimate. Oil/gas emissions in the US increase over the 2010-2017 period but decrease in Canada and Europe. Our joint GOSAT+in situ inversion yields a global methane emission of 551 Tg a-1 averaged over 2010-2017 and a methane lifetime of 11.2 years against oxidation by tropospheric OH (86% of the methane sink). [ABSTRACT FROM AUTHOR]

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 10.7910/DVN/HH4EUM (Scarpelli et al., 2019). [ABSTRACT FROM AUTHOR]