scholarly journals Satellite observations of atmospheric methane and their value for quantifying methane emissions

2016 ◽  
Author(s):  
Daniel J. Jacob ◽  
Alexander J. Turner ◽  
Joannes D. Maasakkers ◽  
Jianxiong Sheng ◽  
Kang Sun ◽  
...  
Keyword(s):  
Methane Emissions ◽  
Point Sources ◽  
Satellite Observations ◽  
Anthropogenic Sources ◽  
Satellite Measurement ◽  
Emission Inventories ◽  
Atmospheric Methane ◽  
High Quality ◽  
Spatial Coverage ◽  
Wide Range

Abstract. Methane is a greenhouse gas emitted by a range of natural and anthropogenic sources. Atmospheric methane has been measured continuously from space since 2003, and new instruments are planned for launch in the near future that will greatly expand the capabilities of space-based observations. We review the value of current, future, and proposed satellite observations to better quantify and understand methane emissions through inverse analyses, down to the scale of point sources and in combination with suborbital (surface and aircraft) data. Current observations from GOSAT are of high quality but have sparse spatial coverage. They provide limited information to quantify methane emissions on a regional (100–1000 km) scale. TROPOMI to be launched in late 2016 is expected to quantify daily emissions on the regional scale and will also effectively detect large point sources. Future satellite instruments with much higher spatial resolution, such as the recently launched GHGSat with 50 × 50 m2 resolution over targeted viewing domains, have the potential to detect a wide range of methane point sources. Geostationary observation of methane, still in the proposal stage, will have unique capability for mapping source regions with high resolution while also detecting transient "super-emitter" point sources. Exploiting the rapidly expanding satellite measurement capabilities to quantify methane emissions requires a parallel effort to construct high-quality spatially and sectorally resolved emission inventories. Partnership between top-down inverse analyses of atmospheric data and bottom-up construction of emission inventories is crucial to better understand methane emission processes and from there to inform climate policy.

scholarly journals Satellite observations of atmospheric methane and their value for quantifying methane emissions

2016 ◽  
Vol 16 (22) ◽  
pp. 14371-14396 ◽  
Author(s):  
Daniel J. Jacob ◽  
Alexander J. Turner ◽  
Joannes D. Maasakkers ◽  
Jianxiong Sheng ◽  
Kang Sun ◽  
...  
Keyword(s):  
Regional Scale ◽  
Methane Emissions ◽  
Point Sources ◽  
Satellite Observations ◽  
Anthropogenic Sources ◽  
Emission Inventories ◽  
Atmospheric Methane ◽  
High Quality ◽  
Spatial Coverage ◽  
Wide Range

Abstract. Methane is a greenhouse gas emitted by a range of natural and anthropogenic sources. Atmospheric methane has been measured continuously from space since 2003, and new instruments are planned for launch in the near future that will greatly expand the capabilities of space-based observations. We review the value of current, future, and proposed satellite observations to better quantify and understand methane emissions through inverse analyses, from the global scale down to the scale of point sources and in combination with suborbital (surface and aircraft) data. Current global observations from Greenhouse Gases Observing Satellite (GOSAT) are of high quality but have sparse spatial coverage. They can quantify methane emissions on a regional scale (100–1000 km) through multiyear averaging. The Tropospheric Monitoring Instrument (TROPOMI), to be launched in 2017, is expected to quantify daily emissions on the regional scale and will also effectively detect large point sources. A different observing strategy by GHGSat (launched in June 2016) is to target limited viewing domains with very fine pixel resolution in order to detect a wide range of methane point sources. Geostationary observation of methane, still in the proposal stage, will have the unique capability of mapping source regions with high resolution, detecting transient "super-emitter" point sources and resolving diurnal variation of emissions from sources such as wetlands and manure. Exploiting these rapidly expanding satellite measurement capabilities to quantify methane emissions requires a parallel effort to construct high-quality spatially and sectorally resolved emission inventories. Partnership between top-down inverse analyses of atmospheric data and bottom-up construction of emission inventories is crucial to better understanding methane emission processes and subsequently informing climate policy.

scholarly journals Quantifying methane point sources from fine-scale (GHGSat) satellite observations of atmospheric methane plumes

2018 ◽  
Author(s):  
Daniel J. Varon ◽  
Daniel J. Jacob ◽  
Jason McKeever ◽  
Dylan Jervis ◽  
Berke O. A. Durak ◽  
...  
Keyword(s):  
Wind Speed ◽  
Methane Emissions ◽  
Point Sources ◽  
Small Scale ◽  
Atmospheric Methane ◽  
Flux Method ◽  
Pixel Resolution ◽  
Cross Sectional ◽  
Source Rate ◽  
The Cross

Abstract. Anthropogenic methane emissions originate from a large number of relatively small point sources. The planned GHGSat satellite fleet aims to quantify emissions from individual point sources by measuring methane column plumes over selected ~ 10 × 10 km2 domains with ≤ 50 × 50 m2 pixel resolution and 1–5 % measurement precision. Here we develop algorithms for retrieving point source rates from such measurements. We simulate a large ensemble of instantaneous methane column plumes at 50 × 50 m2 pixel resolution for a range of atmospheric conditions using the Weather Research and Forecasting model (WRF) in large eddy simulation (LES) mode and adding instrument noise. We show that standard methods to infer source rates by Gaussian plume inversion or source pixel mass balance are prone to large errors because the turbulence cannot be properly parameterized on the small scale of instantaneous methane plumes. The integrated mass enhancement (IME) method, which relates total plume mass to source rate, and the cross-sectional flux method, which infers source rate from fluxes across plume transects, are better adapted to the problem. We show that the IME method with local measurements of the 10-m wind speed can infer source rates with error of 0.07–0.17 t h−1 + 5–12 % depending on instrument precision (1–5 %). The cross-sectional flux method has slightly larger errors (0.07–0.26 t h−1 + 8–12 %) but a simpler physical basis. For comparison, point sources larger than 0.5 t h−1 contribute more than 75 % of methane emissions reported to the U.S. Greenhouse Gas Reporting Program. Additional error applies if local wind speed measurements are not available, and may dominate the overall error at low wind speeds. Low winds are beneficial for source detection but not for source quantification.

scholarly journals Monitoring global tropospheric OH concentrations using satellite observations of atmospheric methane

2018 ◽  
Vol 18 (21) ◽  
pp. 15959-15973 ◽  
Author(s):  
Yuzhong Zhang ◽  
Daniel J. Jacob ◽  
Joannes D. Maasakkers ◽  
Melissa P. Sulprizio ◽  
Jian-Xiong Sheng ◽  
...  
Keyword(s):  
Simulation Experiment ◽  
Short Wave ◽  
Methane Emissions ◽  
Satellite Observations ◽  
Atmospheric Methane ◽  
The Past ◽  
Methyl Chloroform ◽  
Systematic Component ◽  
Observing System Simulation Experiment ◽  
Better Than

Abstract. The hydroxyl radical (OH) is the main tropospheric oxidant and the main sink for atmospheric methane. The global abundance of OH has been monitored for the past decades using atmospheric methyl chloroform (CH3CCl3) as a proxy. This method is becoming ineffective as atmospheric CH3CCl3 concentrations decline. Here we propose that satellite observations of atmospheric methane in the short-wave infrared (SWIR) and thermal infrared (TIR) can provide an alternative method for monitoring global OH concentrations. The premise is that the atmospheric signature of the methane sink from oxidation by OH is distinct from that of methane emissions. We evaluate this method in an observing system simulation experiment (OSSE) framework using synthetic SWIR and TIR satellite observations representative of the TROPOMI and CrIS instruments, respectively. The synthetic observations are interpreted with a Bayesian inverse analysis, optimizing both gridded methane emissions and global OH concentrations. The optimization is done analytically to provide complete error accounting, including error correlations between posterior emissions and OH concentrations. The potential bias caused by prior errors in the 3-D seasonal OH distribution is examined using OH fields from 12 different models in the ACCMIP archive. We find that the satellite observations of methane have the potential to constrain the global tropospheric OH concentration with a precision better than 1 % and an accuracy of about 3 % for SWIR and 7 % for TIR. The inversion can successfully separate the effects of perturbations to methane emissions and to OH concentrations. Interhemispheric differences in OH concentrations can also be successfully retrieved. Error estimates may be overoptimistic because we assume in this OSSE that errors are strictly random and have no systematic component. The availability of TROPOMI and CrIS data will soon provide an opportunity to test the method with actual observations.

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.

Quantifying methane emissions from fossil fuel sources using a Bayesian inverse model and observations of ethane with an uncertain emissions ratio

2021 ◽  
Author(s):  
Alice Ramsden ◽  
Anita Ganesan ◽  
Luke Western ◽  
Alistair Manning ◽  
Matthew Rigby ◽  
...  
Keyword(s):  
Fossil Fuel ◽  
Transport Model ◽  
Atmospheric Transport ◽  
Synthetic Data ◽  
Methane Emissions ◽  
Inverse Model ◽  
Anthropogenic Sources ◽  
Atmospheric Methane ◽  
Livestock Farming ◽  
Emission Ratio

<p>Methane is an important greenhouse gas with a range of anthropogenic sources, including livestock farming and fossil fuel production. It is important that methane emissions can be correctly attributed to their source, to aid climate change policy and emissions mitigation efforts. For source attribution, many ‘top-down’ models of atmospheric methane use spatial maps of sources from emissions inventory data coupled with an atmospheric transport model. However, this can cause difficulties if sources are co-located or if there is uncertainty in the sources’ spatial distributions.</p><p>To help with this issue and reduce overall uncertainty in estimates of methane emissions, recent methods have used observations of a secondary trace gas and its correlation with methane to infer methane emissions from a target sector. Most previous work has assumed a fixed emissions ratio between the two gases, which often does not reflect the true range of possible emission ratios. In this work, measurements of atmospheric ethane and its emissions ratio relative to methane are used to infer emissions of methane from fossil fuel sources. Instead of assuming a fixed emission ratio, our method allows for uncertainty in the emission ratio to be statistically propagated through the inverse model and incorporated into the sectoral estimates of methane emissions. We further demonstrate the inaccuracies that can result in an assessment of fossil fuel methane emissions if this uncertainty is not considered.</p><p>We present this novel method for modelling sectoral methane emissions with examples from a synthetic data experiment and give results from a case study of UK methane emissions. Methane and ethane observations from a tall tower network across the UK were used with this model to produce monthly estimates of UK fossil fuel methane emissions with improved uncertainty characterisation.</p>

scholarly journals Four-dimensional variational data assimilation for inverse modelling of atmospheric methane emissions: method and comparison with synthesis inversion

2008 ◽  
Vol 8 (21) ◽  
pp. 6341-6353 ◽  
Author(s):  
J. F. Meirink ◽  
P. Bergamaschi ◽  
M. C. Krol
Keyword(s):  
Data Assimilation ◽  
Transport Model ◽  
Time Window ◽  
Methane Emissions ◽  
Satellite Observations ◽  
Inverse Modelling ◽  
Model Parameters ◽  
Full Potential ◽  
Atmospheric Methane ◽  
Assimilation System

Abstract. A four-dimensional variational (4D-Var) data assimilation system for inverse modelling of atmospheric methane emissions is presented. The system is based on the TM5 atmospheric transport model. It can be used for assimilating large volumes of measurements, in particular satellite observations and quasi-continuous in-situ observations, and at the same time it enables the optimization of a large number of model parameters, specifically grid-scale emission rates. Furthermore, the variational method allows to estimate uncertainties in posterior emissions. Here, the system is applied to optimize monthly methane emissions over a 1-year time window on the basis of surface observations from the NOAA-ESRL network. The results are rigorously compared with an analogous inversion by Bergamaschi et al. (2007), which was based on the traditional synthesis approach. The posterior emissions as well as their uncertainties obtained in both inversions show a high degree of consistency. At the same time we illustrate the advantage of 4D-Var in reducing aggregation errors by optimizing emissions at the grid scale of the transport model. The full potential of the assimilation system is exploited in Meirink et al. (2008), who use satellite observations of column-averaged methane mixing ratios to optimize emissions at high spatial resolution, taking advantage of the zooming capability of the TM5 model.

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 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).

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