Methane emissions from China: a high-resolution inversion of TROPOMI satellite observations

Abstract

Abstract. We quantify methane emissions in China and the contributions from different sectors by inverse analysis of 2019 TROPOMI satellite observations of atmospheric methane. The inversion uses as prior estimate the national sector-resolved anthropogenic emission inventory reported by the Chinese government to the United Nations Framework Convention on Climate Change (UNFCCC) and thus serves as a direct evaluation of that inventory. Emissions are optimized with a Gaussian mixture model (GMM) at up to 0.25° × 0.3125° resolution. The optimization is done analytically assuming lognormally distributed errors on prior emissions. Errors and information content on the optimal estimates are obtained directly from the analytical solution and also through a 36-member inversion ensemble. Our optimal estimate for total anthropogenic emissions in China is 65.0 (57.7–68.4) Tg a-1, where parentheses indicate uncertainty range. Contributions from individual sectors include 16.6 (15.6–17.6) Tg a-1 for coal, 2.3 (1.8–2.5) for oil, 0.29 (0.23–0.32) for gas, 17.8 (15.1–21.0) for livestock, 9.3 (8.2–9.9) for waste, 11.9 (10.7–12.7) for rice paddies, and 6.7 (5.8–7.1) for other sources. Our estimate is 21 % higher than the Chinese inventory reported to the UNFCCC (53.6 Tg a-1), reflecting upward corrections to emissions from oil (+147 %), gas (+61 %), livestock (+37 %), waste (+41 %), and rice paddies (+34 %), but downward correction for coal (-15 %). It is also higher than previous inverse studies (43–62 Tg a-1) that used the much sparser GOSAT satellite observations and were conducted at coarser resolution. We are in particular better able to separate coal and rice emissions. Our higher livestock emissions are attributed largely to northern China where GOSAT has little sensitivity. Our higher waste emissions reflect at least in part a rapid growth in wastewater treatment in China. Underestimate of oil emissions in the UNFCCC report appears to reflect unaccounted super-emitting facilities. Gas emissions in China are mostly from distribution, in part because of low emission factors from production and in part because 42 % of the gas is imported. Our estimate of emissions per unit of domestic gas production indicates a low life-cycle loss rate of 1.7 (1.3–1.9) %, which would imply net climate benefits from the current coal-to-gas energy transition in China. However, this small loss rate is somewhat misleading considering China’s high gas imports, including from Turkmenistan where emission per unit of gas production is very high.

Integrated Methane Inversion (IMI 1.0): a user-friendly, cloud-based facility for inferring high-resolution methane emissions from TROPOMI satellite observations

“…It represents the number of independent pieces of information on the state vector that the observations can quantify. The diagonal entries of A are referred to as averaging kernel sensitivities, and they give an estimate of how much the posterior solution for a given state vector element is informed by the observations as opposed to the prior estimates (Cui et al, 2014;Brasseur and Jacob, 2017). An emission element with averaging kernel sensitivity 0 is not quantified by the observations at all, and the inversion results for that grid cell return the prior value.…”

“…It enables no-cost error analysis by producing an ensemble of solutions to explore the sensitivity to inversion parameters. The algorithm is fully documented in the literature Maasakkers et al, 2019Zhang et al, 2021;Lu et al, 2022), including applications to TROPOMI data (Zhang et al, 2020;Qu et al, 2021;Shen et al, 2021Shen et al, , 2022Z. Chen et al, 2022).…”

“…This initial version of the IMI (version 1.0) has some limitations in functionality and does not include some of the newer capabilities recently developed within the analytical inversion framework. Priority developments for future IMI versions include (1) extension of pre-cut GEOS windows to continental domains outside of North America, Europe, and Asia; (2) the option to use lognormal rather than normal error pdf's for prior emissions to resolve the long tail of the emission distribution (Maasakkers et al, 2019;Z. Chen et al, 2022); (3) the option to use non-uniform prior and observational error covariance matrices, including off-diagonal terms; (4) upgrade of the global GEOS-Chem simulation used to generate boundary conditions from 4 • × 5 • to 2 • × 2.5 • resolution; (5) more optimal selection of state vector elements with a Gaussian mixture model ; (6) use of Kalman filter techniques for continuous emission monitoring with user-specified update frequency (Varon et al, 2022b); (7) incorporation of data from future globally surveying satellite instruments including GeoCarb (Moore et al, 2018), CO2M (Sierk et al, 2019), MethaneSAT (Wofsy and Hamburg, 2019), and GOSAT-GW (Kasahara et al, 2020); and (8) application to inversions for CO and CO 2 emissions.…”

“…The retrieval success rate averages only 3 % because of clouds and dark and heterogeneous surfaces (Hasekamp et al, 2019), but the data density is still at least 2 orders of magnitude higher than for GOSAT (Qu et al, 2021). TROPOMI data have been used in regional inversions at up to 25 km resolution (Zhang et al, 2020;Shen et al, 2021Shen et al, , 2022Z. Chen et al, 2022).…”

East Asian methane emissions inferred from high-resolution inversions of GOSAT and TROPOMI observations: a comparative and evaluative analysis

“…A number of studies have applied GOSAT data in inversions on a range of scales (Cressot et al, 2014;Feng et al, 2022;Lu et al, 2021;Maasakkers et al, 2019;Monteil et al, 2013;Pandey et al, 2016;Zhang et al, 2021). TROPOMI data have also been applied in several regional inversion studies (Chen et al, 2022;McNorton et al, 2022;Shen et al, 2021;Shen et al, 2022;Zhang et al, 2020) often with the focus on resolving fine-scale emission hotspots. performed global inversions of GOSAT and TROPOMI observations at 2° × 2.5° resolution in a comparative analysis, and they showed that methane emissions inferred from the two inversions are generally consistent on the global scale but with significant regional discrepancies including over China.…”