Search

The new single-pixel TROPESS (TRopospheric Ozone and its Precursors from Earth System Sounding) profile retrievals of carbon monoxide (CO) from the Cross-track Infrared Sounder (CrIS) are evaluated using vertical profiles of in situ observations from the National Oceanic and Atmospheric Administration (NOAA) Global Monitoring Laboratory (GML) aircraft program and from the Atmospheric Tomography Mission (ATom) campaigns. The TROPESS optimal estimation retrievals are produced using the MUSES (MUlti-SpEctra, MUlti-SpEcies, MUlti-Sensors) algorithm, which has heritage from retrieval algorithms developed for the EOS/Aura Tropospheric Emission Spectrometer (TES). TROPESS products provide retrieval diagnostics and error covariance matrices that propagate instrument noise as well as the uncertainties from sequential retrievals of parameters such as temperature and water vapor that are required to estimate the carbon monoxide profiles. The validation approach used here evaluates biases in column and profile values as well as the validity of the retrieval error estimates using the mean and variance of the compared satellite and aircraft observations. CrIS–NOAA GML comparisons had biases of 0.6 % for partial column average volume mixing ratios (VMRs) and (2.3, 0.9, −4.5) % for VMRs at (750, 511, 287) hPa vertical levels, respectively, with standard deviations from 9 % to 14 %. CrIS–ATom comparisons had biases of −0.04 % for partial column and (2.2, 0.5, −3.0) % for (750, 511, 287) hPa vertical levels, respectively, with standard deviations from 6 % to 10 %. The reported observational errors for TROPESS/CrIS CO profiles have the expected behavior with respect to the vertical pattern in standard deviation of the comparisons. These comparison results give us confidence in the use of TROPESS/CrIS CO profiles and error characterization for continuing the multi-decadal record of satellite CO observations.

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 in its latest UNFCCC report (4.1 Tg a−1 for 2019) decrease by 83 % compared to its previous report while Nigeria's latest reported oil–gas emissions (3.1 Tg a−1 for 2016) increase 7-fold compared to its previous report, 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 from the oil, gas, and coal sectors in GFEI v2 (26, 22, and 33 Tg a−1, respectively in 2019) are lower than the EDGAR v6 inventory (32, 44, and 37 Tg a−1 in 2018) and lower than the IEA inventory for oil and gas (38 and 43 Tg a−1 in 2019), though there is considerable variability between inventories 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 where TRACE is up to an order of magnitude higher than GFEI v2. Inversion results using GFEI as a prior estimate confirm the lower Russian emissions in the latest UNFCCC report but find that Nigeria's reported UNFCCC 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 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. This increased observational data density will be especially beneficial in regions where current inversion systems have limited sensitivity including Russia. Our work 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.

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 a 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 Ecología y Cambio Climático (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∘×0.625∘ resolution and for individual years. Optimization is done analytically using lognormal error forms. This yields closed-form statistics of error covariances 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 southeastern 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 offsetting effects of increasing emissions from the oil and landfill sectors, decreasing emissions from the gas sector, 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 to +3.8)% a−1 over 2010–2017 correlated with precipitation.

We review the capability of current and scheduled satellite observations of atmospheric methane in the shortwave infrared (SWIR) to quantify methane emissions from the global scale down to point sources. We cover retrieval methods, precision and accuracy requirements, inverse and mass balance methods for inferring emissions, source detection thresholds, and observing system completeness. We classify satellite instruments as area flux mappers and point source imagers, with complementary attributes. Area flux mappers are high-precision ( %) instruments with 0.1–10 km pixel size designed to quantify total methane emissions on regional to global scales. Point source imagers are fine-pixel ( m) instruments designed to quantify individual point sources by imaging of the plumes. Current area flux mappers include GOSAT (2009–present), which provides a high-quality record for interpretation of long-term methane trends, and TROPOMI (2018–present), which provides global continuous daily mapping to quantify emissions on regional scales. These instruments already provide a powerful resource to quantify national methane emissions in support of the Paris Agreement. Current point source imagers include the GHGSat constellation and several hyperspectral and multispectral land imaging sensors (PRISMA, Sentinel-2, Landsat-8/9, WorldView-3), with detection thresholds in the 100–10 000 kg h−1 range that enable monitoring of large point sources. Future area flux mappers, including MethaneSAT, GOSAT-GW, Sentinel-5, GeoCarb, and CO2M, will increase the capability to quantify emissions at high resolution, and the MERLIN lidar will improve observation of the Arctic. The averaging times required by area flux mappers to quantify regional emissions depend on pixel size, retrieval precision, observation density, fraction of successful retrievals, and return times in a way that varies with the spatial resolution desired. A similar interplay applies to point source imagers between detection threshold, spatial coverage, and return time, defining an observing system completeness. Expanding constellations of point source imagers including GHGSat and Carbon Mapper over the coming years will greatly improve observing system completeness for point sources through dense spatial coverage and frequent return times.

We use optimal estimation (OE) to quantify methane fluxes based on total column CH4 data from the Greenhouse Gases Observing Satellite (GOSAT) and the GEOS-Chem global chemistry transport model. We then project these fluxes to emissions by sector at 1∘ resolution and then to each country using a new Bayesian algorithm that accounts for prior and posterior uncertainties in the methane emissions. These estimates are intended as a pilot dataset for the global stock take in support of the Paris Agreement. However, differences between the emissions reported here and widely used bottom-up inventories should be used as a starting point for further research because of potential systematic errors of these satellite-based emissions estimates. We find that agricultural and waste emissions are ∼ 263 ± 24 Tg CH4 yr−1, anthropogenic fossil emissions are 82 ± 12 Tg CH4 yr−1, and natural wetland/aquatic emissions are 180 ± 10 Tg CH4 yr−1. These estimates are consistent with previous inversions based on GOSAT data and the GEOS-Chem model. In addition, anthropogenic fossil estimates are consistent with those reported to the United Nations Framework Convention on Climate Change (80.4 Tg CH4 yr−1 for 2019). Alternative priors can be easily tested with our new Bayesian approach (also known as prior swapping) to determine their impact on posterior emissions estimates. We use this approach by swapping to priors that include much larger aquatic emissions and fossil emissions (based on isotopic evidence) and find little impact on our posterior fluxes. This indicates that these alternative inventories are inconsistent with our remote sensing estimates and also that the posteriors reported here are due to the observing and flux inversion system and not uncertainties in the prior inventories. We find that total emissions for approximately 57 countries can be resolved with this observing system based on the degrees-of-freedom for signal metric (DOFS > 1.0) that can be calculated with our Bayesian flux estimation approach. Below a DOFS of 0.5, estimates for country total emissions are more weighted to our choice of prior inventories. The top five emitting countries (Brazil, China, India, Russia, USA) emit about half of the global anthropogenic budget, similar to our choice of prior emissions but with the posterior emissions shifted towards the agricultural sector and less towards fossil emissions, consistent with our global posterior results. Our results suggest remote-sensing-based estimates of methane emissions can be substantially different (although within uncertainty) than bottom-up inventories, isotopic evidence, or estimates based on sparse in situ data, indicating a need for further studies reconciling these different approaches for quantifying the methane budget. Higher-resolution fluxes calculated from upcoming satellite or aircraft data such as the Tropospheric Monitoring Instrument (TROPOMI) and those in formulation such as the Copernicus CO2M, MethaneSat, or Carbon Mapper can be incorporated into our Bayesian estimation framework for the purpose of reducing uncertainty and improving the spatial resolution and sectoral attribution of subsequent methane emissions estimates.