Satellite-derived methane hotspot emission estimates using a fast data-driven method

Abstract

Abstract. Methane is an important atmospheric greenhouse gas and an adequate understanding of its emission sources is needed for climate change assessments, predictions, and the development and verification of emission mitigation strategies. Satellite retrievals of near-surface-sensitive columnaveraged dry-air mole fractions of atmospheric methane, i.e. XCH 4 , can be used to quantify methane emissions. Maps of time-averaged satellite-derived XCH 4 show regionally elevated methane over several methane source regions. In order to obtain methane emissions of these source regions we use a simple and fast data-driven method to estimate annual methane emissions and corresponding 1σ uncertainties directly from maps of annually averaged satellite XCH 4 . From theoretical considerations we expect that our method tends to underestimate emissions. When applying our method to high-resolution atmospheric methane simulations, we typically find agreement within the uncertainty range of our method (often 100 %) but also find that our method tends to underestimate emissions by typically about 40 %. To what extent these findings are model dependent needs to be assessed. We apply our method to an ensemble of satellite XCH 4 data products consisting of two products from SCIAMACHY/ENVISAT and two products from TANSO-FTS/GOSAT covering the time period 2003-2014. We obtain annual emissions of four source areas: Four Corners in the south-western USA, the southern part of Central Valley, California, Azerbaijan, and Turkmenistan. We find that our estimated emissions are in good agreement with independently derived estimates for Four Corners and Azerbaijan. For the Central Valley and Turkmenistan our estimated annual emissions are higher compared to the EDGAR v4.2 anthropogenic emission inventory. For Turkmenistan we find on average about 50 % higher emissions with our annual emission uncertainty estimates overlapping with the EDGAR emissions. For the region around Bakersfield in the Central Valley we find a factor of 5-8 higher emissions compared to EDGAR, albeit with large uncertainty. Major methane emission sources in this region are oil/gas and livestock. Our findings corroborate recently published studies based on aircraft and satellite measurements and new bottom-up estimates reporting significantly underestimated methane emissions of oil/gas and/or livestock in this area in EDGAR.

Assessment of Anthropogenic Methane Emissions over Large Regions Based on GOSAT Observations and High Resolution Transport Modeling

“…Mainly, pockets of high observed anomalies are seen over Asia, Europe, and South and North Americas, which match with the anthropogenic source regions in these regions, as also shown in more simple analysis by Buchwitz et al [28]. Since the observed XCH 4 anomalies are noisy, a direct comparison with the simulated XCH 4 abundance is difficult.…”

“…Considering the sparsity of the ground-based observation networks and the necessity for wide spatial and temporal coverage, satellite observations such as from GOSAT can be an additional or alternative tool for estimation and monitoring of anthropogenic greenhouse gas emissions (e.g., [25][26][27][28][29]) by emission hotspots such as megacities and power plants and other intensive sources such as biomass burning [21,30]. Therefore, there is an emerging interest in the use of space-based observation of greenhouse gases for estimation and verification of their emissions.…”

MERLIN: A French-German Space Lidar Mission Dedicated to Atmospheric Methane

“…Instruments based on thermal infrared (TIR) spectroscopy can retrieve XCH 4 at high latitudes, but are much more sensitive to mid-troposphere concentrations than to concentrations in the boundary layer where CH 4 emissions occur. Recently, CH 4 data from SCIAMACHY and GOSAT was also used to detect and quantify local emission hotspots from oil/gas production and coal mining (e.g., [44,45]). Although it offers very low sensitivity in the boundary layer, data from Infrared Atmospheric Sounding Interferometer (IASI) flying on the European MetOp satellite series since 2006 [46] also provides statistically consistent methane emissions [42].…”

Assessing the capability of different satellite observing configurations to resolve the distribution of methane emissions at kilometer scales

“…SCIAMACHY and GOSAT demonstrated the capability for high-precision (< 1 %) measurements of methane from space (Buchwitz et al, 2015), but SCIAMACHY had coarse pixels (30 × 60 km 2 in nadir) and GOSAT has sparse coverage (10 km diameter pixels separated by 250 km). Inverse analyses have used observations from these satellitebased instruments to estimate methane emissions at ∼ 100-1000 km spatial resolution (e.g., Bergamaschi et al, 2009Bergamaschi et al, , 2013Fraser et al, 2013;Monteil et al, 2013;Wecht et al, 2014a;Cressot et al, 2014;Kort et al, 2014;Turner et al, 2016a;Alexe et al, 2015;Tan et al, 2016;Buchwitz et al, 2017;Sheng et al, 2018a, b). But such coarse resolution makes it difficult to resolve individual source types because of spatial overlap (Maasakkers et al, 2016).…”