the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
High-resolution U.S. methane emissions inferred from an inversion of 2019 TROPOMI satellite data: contributions from individual states, urban areas, and landfills
Daniel J. Jacob
Joannes D. Maasakkers
Alba Lorente
Zichong Chen
Lu Shen
Melissa P. Sulprizio
Margaux Winter
Shuang Ma
A. Anthony Bloom
John R. Worden
Robert N. Stavins
Cynthia A. Randles
Abstract. We quantify 2019 methane emissions in the contiguous U.S. (CONUS) at 0.25° × 0.3125° resolution by inverse analysis of atmospheric methane columns measured by the Tropospheric Monitoring Instrument (TROPOMI). A gridded version of the U.S. Environmental Protection Agency (EPA) Greenhouse Gas Emissions Inventory (GHGI) serves as the basis for the prior estimate for the inversion. We optimize emissions and quantify observing system information content for an eight-member inversion ensemble through analytical minimization of a Bayesian cost function. We achieve high resolution with a reduced-rank characterization of the observing system that optimally preserves information content. Our optimal (posterior) estimate of anthropogenic emissions in CONUS is 30.9 (30.0–31.8) Tg a-1, where the values in parentheses give the spread of the ensemble. This is a 13 % increase from the 2023 GHGI estimate for CONUS in 2019. We find livestock emissions of 10.4 (10.0–10.7) Tg a-1, oil and gas of 10.4 (10.1–10.7) Tg a-1, coal of 1.5 (1.2–1.9) Tg a-1, landfills of 6.9 (6.4–7.5) Tg a-1, wastewater of 0.6 (0.5–0.7), and other anthropogenic sources of 1.1 (1.0–1.2) Tg a-1. The largest increase relative to the GHGI occurs for landfills (51 %), with smaller increases for oil and gas (12 %) and livestock (11 %). These three sectors are responsible for 89 % of posterior anthropogenic emissions in CONUS. The largest decrease (28 %) is for coal. We exploit the high resolution of our inversion to quantify emissions from 73 individual landfills, where we find emissions are on median 77 % larger than the values reported to the EPA’s Greenhouse Gas Reporting Program (GHGRP), a key data source for the GHGI. We attribute this underestimate to overestimated recovery efficiencies at landfill gas facilities and to under-accounting of site-specific operational changes and leaks. We also quantify emissions for the 48 individual states in CONUS, which we compare to the GHGI’s new state-level inventories and to independent state-produced inventories. Our posterior emissions are on average 34 % larger than the 2022 GHGI in the largest 10 methane-producing states, with the biggest upward adjustments in states with large oil and gas emissions, including Texas, New Mexico, Louisiana, and Oklahoma. We also calculate emissions for 95 geographically diverse urban areas in CONUS. Emissions for these urban areas total 6.0 (5.4–6.7) Tg a-1 and are on average 39 (27–52) % larger than a gridded version of the 2023 GHGI, which we attribute to underestimated landfill and gas distribution emissions.
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Hannah Nesser et al.
Status: open (extended)
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RC1: 'Comment on egusphere-2023-946', Anonymous Referee #1, 21 Jul 2023
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Nesser et al., present detailed results for methane emissions in the contiguous U.S. as derived via inverse modelling using satellite observations of methane columns. They present new and very interesting results appropriate for EGUsphere and the paper is very well written. I strongly recommend publication in EGUsphere after the issues listed below have been carefully addressed by the authors.
Most of my comments relate to minor issues (see below) but there is one major aspect:
Whereas an ensemble of inversions has been used to obtain reliable results (which is very good) only one satellite data product has been used. Taking into account the high (also political) relevance of the results - the authors present very detailed emission results for various sectors and down to quite high spatial resolution (state, urban and even facility level) - I wonder if this is really appropriate. I have doubts in particular because an initial (i.e., quite old) satellite data product has been used which has known bias issues. Several of these bias issues are listed in the manuscript (line 209 following). It is written in the paper that because of the bias issues the authors could not use the data as they are but had to correct and filter them. For example, they had to remove data with too low methane values (e.g., data below 1700 ppb as these data were considered unrealistic). Bias corrections done by users are not uncommon but should be avoided if better data exist. On line 355 the authors write that they are aware of better data: „We also compare the TROPOMI v14 data used here to the most recent data (v19), which has improved bias corrections and performance compared to GOSAT in North America (Balasus et al., 2023). We find no correlation (R2 = 0.03) between our posterior scaling factors and the mean (v14 - v19) difference, suggesting that biases in the v14 data do not influence our posterior emissions.“. While this is good to know, this is by far not sufficient. The high spatial resolution methane emission information is contained in small scale methane gradients and even small differences of the methane data can have a large impact on the derived emissions. I therefore strongly recommend repeating the analysis using the latest (v19 or higher) product.
If resources permit, I recommend to also use an ensemble approach for the satellite data (even if only 2-3 products can be used) given the high relevance of the results and that these satellite data are the key input data for the inversion. This would give additional strong confidence that the quite large reported differences compared to the EPA inventory are a reliable finding and do not significantly depend on the peculiarities of a given satellite data product. In this context I would expect that the ensemble-based results will have larger (and likely more realistic) error bars than the (surprisingly small) error bars as shown in, e.g., Fig. 4. In this case I suggest to include also the TROPOMI WFMD v1.8 XCH4 data product described in Schneising et al., 2023. In that publication several comparisons with the operational TROPOMI product are shown (note that this operational RemoTeC algorithm product is very similar as the scientific RemoTeC product used here) indicating higher accuracy of the WFMD product due to much reduced bias (e.g., much reduced “striping errors” etc.). Such a satellite data ensemble approach would strengthen the manuscript even further, which is relevant here given the importance of the paper and the level of detail reported.
Minor aspects:
Line 61: Not sure if the “3% success rate” is valid for the real data used here as the cited (quite old) document (Hasekamp et al., 2019) covers algorithm related theoretical aspects not based on multi-year statistics.
Sect. 2: The time dependence of the emissions is prescribed and not optimized, or? If yes, then this needs to be explicitly mentioned somewhere.
Line 205: Statement “We use only high-quality retrievals …”. This is misleading. If data are flagged “good” than this does not necessarily mean they are “good”. Please rephrase to describe what actually has been done (We use only data with quality flag “good” (or equivalent)).
Eq. (6): If I understand correctly than emission delta_x is the methane emission with unit mass per time per area whereas all other emissions in the manuscript are reported as mass per time. If this is true than please add this information or add the division by area on the right hand side of the equation explicitly.
Line 338 following: “We isolate anthropogenic emissions by removing contributions from wetlands and other natural sources following Sect. 2.8.”. Perfect knowledge of wetland emissions compared to anthropogenic emissions is a very strong assumption given the distribution and magnitude of the wetland emissions as shown in Fig. 1. Is this considered somehow for the (surprisingly small) reported error bars (as shown in, e.g., Fig. 4)?
Line 364: Please check “27.6 (22.6 – 23.9)” as mean value outside range.
Line 444: Please check “2.8 (2.8 – 2.9)”.
Line 446: Please consider citing also Veefkind et al., 2023, studying also Permian emissions.
References
Schneising, O., Buchwitz, M., Hachmeister, J., Vanselow, S., Reuter, M., Buschmann, M., Bovensmann, H., and Burrows, J. P.: Advances in retrieving XCH4 and XCO from Sentinel-5 Precursor: improvements in the scientific TROPOMI/WFMD algorithm, Atmos. Meas. Tech., 16, 669–694, https://doi.org/10.5194/amt-16-669-2023, 2023.
Veefkind, J. P., Serrano-Calvo, R., de Gouw, J., Dix, B., Schneising, O., Buchwitz, M., Barré, J., van der A, R. J., Liu, M., Levelt, P. F., Widespread frequent methane emissions from the oil and gas industry in the Permian basin, Journal of Geophysical Research: Atmospheres, 128, e2022JD037479, pp. 13, https://doi.org/10.1029/2022JD037479, 2023.
Citation: https://doi.org/10.5194/egusphere-2023-946-RC1
Hannah Nesser et al.
Hannah Nesser et al.
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