Review of “High-resolution inversion of methane emissions in the Southeast US using SEAC4RS aircraft observations of atmospheric methane: anthropogenic and wetland source” by Sheng et al.

2018 ◽  
Author(s):  
Anonymous
Keyword(s):  
High Resolution ◽  
Methane Emissions ◽  
Atmospheric Methane ◽  
Aircraft Observations ◽  
Southeast Us

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.

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 Comparative analysis of low-Earth orbit (TROPOMI) and geostationary (GeoCARB, GEO-CAPE) satellite instruments for constraining methane emissions on fine regional scales: application to the Southeast US

2018 ◽  
Vol 11 (12) ◽  
pp. 6379-6388 ◽  
Author(s):  
Jian-Xiong Sheng ◽  
Daniel J. Jacob ◽  
Joannes D. Maasakkers ◽  
Yuzhong Zhang ◽  
Melissa P. Sulprizio
Keyword(s):  
Cloud Cover ◽  
Transport Model ◽  
National Research Council ◽  
Methane Emissions ◽  
Low Earth Orbit ◽  
Total Carbon ◽  
Atmospheric Methane ◽  
Pixel Resolution ◽  
Error Correlation ◽  
Southeast Us

Abstract. We conduct Observing System Simulation Experiments (OSSEs) to compare the ability of future satellite measurements of atmospheric methane columns (TROPOMI, GeoCARB, GEO-CAPE) for constraining methane emissions down to the 25 km scale through inverse analyses. The OSSE uses the GEOS-Chem chemical transport model (0.25∘×0.3125∘ grid resolution) in a 1-week simulation for the Southeast US with 216 emission elements to be optimized through inversion of synthetic satellite observations. Clouds contaminate 73 %–91 % of the viewing scenes depending on pixel size. Comparison of GEOS-Chem to Total Carbon Column Observing Network (TCCON) surface-based methane column observations indicates a model transport error standard deviation of 12 ppb, larger than the instrument errors when aggregated on the 25 km model grid scale, and with a temporal error correlation of 6 h. We find that TROPOMI (7×7 km2 pixels, daily return time) can provide a coarse regional optimization of methane emissions, comparable to results from an aircraft campaign (SEAC4RS), and is highly sensitive to cloud cover. The geostationary instruments can do much better and are less sensitive to cloud cover, reflecting both their finer pixel resolution and more frequent observations. The information content from GeoCARB toward constraining methane emissions increases by 20 %–25 % for each doubling of the GeoCARB measurement frequency. Temporal error correlation in the transport model moderates but does not cancel the benefit of more frequent measurements for geostationary instruments. We find that GeoCARB observing twice a day would provide 70 % of the information from the nominal GEO-CAPE mission preformulated by NASA in response to the Decadal Survey of the US National Research Council.

scholarly journals Comparative analysis of low-Earth orbit (TROPOMI) and geostationary (GeoCARB, GEO-CAPE) satellite instruments for constraining methane emissions on fine regional scales: application to the Southeast US

2018 ◽  
Author(s):  
Jian-Xiong Sheng ◽  
Daniel J. Jacob ◽  
Joannes D. Maasakkers ◽  
Yuzhong Zhang ◽  
Melissa P. Sulprizio
Keyword(s):  
Cloud Cover ◽  
Transport Model ◽  
National Research Council ◽  
Methane Emissions ◽  
Low Earth Orbit ◽  
Atmospheric Methane ◽  
Chemical Transport Model ◽  
Pixel Resolution ◽  
Error Correlation ◽  
Southeast Us

Abstract. We conduct observing system simulation experiments (OSSEs) to compare the ability of future satellite measurements of atmospheric methane columns (TROPOMI, GeoCARB, GEO-CAPE) for constraining methane emissions down to the 25 km scale through inverse analyses. The OSSE uses the GEOS-Chem chemical transport model 0.25˚ × 0.3125˚ grid resolution) in a 1-week simulation for the Southeast US with 216 emission elements to be optimized through inversion of synthetic satellite observations. Clouds contaminate 73–91 % of the viewing scenes depending on pixel size. Comparison of GEOS-Chem to TCCON surface-based methane column observations indicates a model transport error standard deviation of 12 ppb, larger than the instrument errors when aggregated on the 25 km model grid scale, and with a temporal error correlation of 6 hours. We find that TROPOMI (7 × 7 km2 pixels, daily return time) can provide a coarse regional optimization of methane emissions, and is highly sensitive to cloud cover. The geostationary instruments can do much better and are less sensitive to cloud cover, reflecting both their finer pixel resolution and more frequent observations. The information content from GeoCARB toward constraining methane emissions increases by 20–25 % for each doubling of the GeoCARB measurement frequency. Temporal error correlation in the transport model moderates but does not cancel the benefit of more frequent measurements for geostationary instruments. We find that GeoCARB observing twice a day would provide 70 % of the information from the nominal GEO-CAPE mission considered by NASA in response to the Decadal Survey of the US National Research Council.

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

2021 ◽  
Vol 21 (6) ◽  
pp. 4339-4356
Author(s):  
Joannes D. Maasakkers ◽  
Daniel J. Jacob ◽  
Melissa P. Sulprizio ◽  
Tia R. Scarpelli ◽  
Hannah Nesser ◽  
...  
Keyword(s):  
High Resolution ◽  
Analytical Solution ◽  
Oil And Gas ◽  
Oil Production ◽  
Methane Emissions ◽  
Gas Production ◽  
Anthropogenic Emissions ◽  
Atmospheric Methane ◽  
Oil And Gas Production ◽  
Best Estimate

Abstract. We use 2010–2015 Greenhouse Gases Observing Satellite (GOSAT) observations of atmospheric methane columns over North America in a high-resolution inversion of methane emissions, including contributions from different sectors and their trends over the period. The inversion involves an 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. The 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 in the inverse analysis. Prior estimates for the inversion include a gridded version of the Environmental Protection Agency (EPA) Inventory of US 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 lower than our estimate by a factor of 2. 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), which calls 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 and gas production in the eastern US. We also find that oil and 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 Diel variability of methane emissions from lakes

2020 ◽  
Vol 117 (35) ◽  
pp. 21488-21494
Author(s):  
Anna K. Sieczko ◽  
Nguyen Thanh Duc ◽  
Jonathan Schenk ◽  
Gustav Pajala ◽  
David Rudberg ◽  
...  
Keyword(s):  
High Resolution ◽  
Correction Factor ◽  
Flux Measurement ◽  
Methane Emissions ◽  
Natural Source ◽  
Atmospheric Methane ◽  
Literature Information ◽  
Diel Variability ◽  
Measurement Approach

Lakes are considered the second largest natural source of atmospheric methane (CH4). However, current estimates are still uncertain and do not account for diel variability of CH4emissions. In this study, we performed high-resolution measurements of CH4flux from several lakes, using an automated and sensor-based flux measurement approach (in total 4,580 measurements), and demonstrated a clear and consistent diel lake CH4flux pattern during stratification and mixing periods. The maximum of CH4flux were always noted between 10:00 and 16:00, whereas lower CH4fluxes typically occurred during the nighttime (00:00–04:00). Regardless of the lake, CH4emissions were on an average 2.4 higher during the day compared to the nighttime. Fluxes were higher during daytime on nearly 80% of the days. Accordingly, estimates and extrapolations based on daytime measurements only most likely result in overestimated fluxes, and consideration of diel variability is critical to properly assess the total lake CH4flux, representing a key component of the global CH4budget. Hence, based on a combination of our data and additional literature information considering diel variability across latitudes, we discuss ways to derive a diel variability correction factor for previous measurements made during daytime only.

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 Sensitivity of wetland methane emissions to model assumptions: application and model testing against site observations

2012 ◽  
Vol 9 (7) ◽  
pp. 2793-2819 ◽  
Author(s):  
L. Meng ◽  
P. G. M. Hess ◽  
N. M. Mahowald ◽  
J. B. Yavitt ◽  
W. J. Riley ◽  
...  
Keyword(s):  
Soil Ph ◽  
High Latitude ◽  
Large Range ◽  
Rice Paddy ◽  
Temporal Variations ◽  
Methane Emissions ◽  
Biogeochemical Model ◽  
Atmospheric Methane ◽  
Rice Paddies ◽  
Community Land Model

Abstract. Methane emissions from natural wetlands and rice paddies constitute a large proportion of atmospheric methane, but the magnitude and year-to-year variation of these methane sources are still unpredictable. Here we describe and evaluate the integration of a methane biogeochemical model (CLM4Me; Riley et al., 2011) into the Community Land Model 4.0 (CLM4CN) in order to better explain spatial and temporal variations in methane emissions. We test new functions for soil pH and redox potential that impact microbial methane production in soils. We also constrain aerenchyma in plants in always-inundated areas in order to better represent wetland vegetation. Satellite inundated fraction is explicitly prescribed in the model, because there are large differences between simulated fractional inundation and satellite observations, and thus we do not use CLM4-simulated hydrology to predict inundated areas. A rice paddy module is also incorporated into the model, where the fraction of land used for rice production is explicitly prescribed. The model is evaluated at the site level with vegetation cover and water table prescribed from measurements. Explicit site level evaluations of simulated methane emissions are quite different than evaluating the grid-cell averaged emissions against available measurements. Using a baseline set of parameter values, our model-estimated average global wetland emissions for the period 1993–2004 were 256 Tg CH4 yr−1 (including the soil sink) and rice paddy emissions in the year 2000 were 42 Tg CH4 yr−1. Tropical wetlands contributed 201 Tg CH4 yr−1, or 78% of the global wetland flux. Northern latitude (>50 N) systems contributed 12 Tg CH4 yr−1. However, sensitivity studies show a large range (150–346 Tg CH4 yr−1) in predicted global methane emissions (excluding emissions from rice paddies). The large range is sensitive to (1) the amount of methane transported through aerenchyma, (2) soil pH (±100 Tg CH4 yr−1), and (3) redox inhibition (±45 Tg CH4 yr−1). Results are sensitive to biases in the CLMCN and to errors in the satellite inundation fraction. In particular, the high latitude methane emission estimate may be biased low due to both underestimates in the high-latitude inundated area captured by satellites and unrealistically low high-latitude productivity and soil carbon predicted by CLM4.

scholarly journals Observing atmospheric formaldehyde (HCHO) from space: validation and intercomparison of six retrievals from four satellites (OMI, GOME2A, GOME2B, OMPS) with SEAC<sup>4</sup>RS aircraft observations over the Southeast US

2016 ◽  
Author(s):  
Lei Zhu ◽  
Daniel J. Jacob ◽  
Patrick S. Kim ◽  
Jenny A. Fisher ◽  
Karen Yu ◽  
...  
Keyword(s):  
Transport Model ◽  
Research Groups ◽  
Chemical Transport Model ◽  
Shape Factors ◽  
Aircraft Observations ◽  
Volatile Organic ◽  
Satellite Retrievals ◽  
Mass Factor ◽  
The Mean ◽  
Southeast Us

Abstract. Formaldehyde (HCHO) column data from satellites are widely used as a proxy for emissions of volatile organic compounds (VOCs), but validation of the data has been extremely limited. Here we use highly accurate HCHO aircraft observations from the NASA SEAC4RS campaign over the Southeast US in August–September 2013 to validate and intercompare six operational and research retrievals of HCHO columns from four different satellite instruments (OMI, GOME2A, GOME2B and OMPS) and three different research groups. The GEOS-Chem chemical transport model provides a common intercomparison platform. We find that all retrievals capture the HCHO maximum over Arkansas and Louisiana, reflecting high emissions of biogenic isoprene, and are consistent in their spatial variability over the Southeast US (r = 0.4–0.8 on a 0.5° × 0.5° grid) as well as their day-to-day variability (r = 0.5–0.8). However, all satellite retrievals are biased low in the mean by 20–51 %, which would lead to corresponding bias in estimates of isoprene emissions from the satellite data. The smallest bias is for OMI-BIRA, which has the highest corrected slant columns and the lowest scattering weights in its air mass factor (AMF) calculation. Correcting the assumed HCHO vertical profiles (shape factors) used in the AMF calculation would further reduce the bias in the OMI-BIRA data. We conclude that current satellite HCHO data provide a reliable proxy for isoprene emission variability but with a low mean bias due both to the corrected slant columns and the scattering weights used in the retrievals.

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