Spatially resolving methane emissions in California: constraints from the CalNex aircraft campaign and from present (GOSAT, TES) and future (TROPOMI, geostationary) satellite observations

Abstract

Abstract. We apply a continental-scale inverse modeling system for North America based on the GEOS-Chem model to optimize California methane emissions at 1/2° × 2/3° horizontal resolution using atmospheric observations from the CalNex aircraft campaign (May–June 2010) and from satellites. Inversion of the CalNex data yields a best estimate for total California methane emissions of 2.86 ± 0.21 Tg yr−1, compared with 1.92 Tg yr−1 in the EDGAR v4.2 emission inventory used as a priori and 1.51 Tg yr−1 in the California Air Resources Board (CARB) inventory used for state regulations of greenhouse gas emissions. These results are consistent with a previous Lagrangian inversion of the CalNex data. Our inversion provides 12 independent pieces of information to constrain the geographical distribution of emissions within California. Attribution to individual source types indicates dominant contributions to emissions from landfills/wastewater (1.1 Tg yr−1), livestock (0.87 Tg yr−1), and gas/oil (0.64 Tg yr−1). EDGAR v4.2 underestimates emissions from livestock while CARB underestimates emissions from landfills/wastewater and gas/oil. Current satellite observations from GOSAT can constrain methane emissions in the Los Angeles Basin but are too sparse to constrain emissions quantitatively elsewhere in California (they can still be qualitatively useful to diagnose inventory biases). Los Angeles Basin emissions derived from CalNex and GOSAT inversions are 0.42 ± 0.08 and 0.31 ± 0.08, respectively. An observation system simulation experiment (OSSE) shows that the future TROPOMI satellite instrument (2015 launch) will be able to constrain California methane emissions at a detail comparable to the CalNex aircraft campaign. Geostationary satellite observations offer even greater potential for constraining methane emissions in the future.

Detecting Urban Emissions Changes and Events With a Near‐Real‐Time‐Capable Inversion System

“…While this study does not attribute this feature, note that it is not explained by the timing of the 1-day periods used in the inversion, since the increase begins in periods which do not overlap the reported leak. Considering emissions magnitudes, when the full observing network is included, estimates using transport driven by WRF and NARR average 53 and 47 Mg/hr outside the Aliso Canyon leak period, respectively, in broad agreement with the 35-to 50-Mg/hr range of baseline emissions estimates in other studies (e.g., Peischl et al, 2013;Wecht et al, 2014;Wennberg et al, 2012;Wong et al, 2015). That our estimates fall at the upper end of that range is not surprising given that much of the previous work relied on observations taken in May-June 2010, not during the peak of the seasonal emissions cycle (see section 3.2).…”

“…Recent years have seen increased efforts to quantify greenhouse gas emissions at or below the scale of individual cities. In complement to process-based inventories (Gurney et al, 2012), aircraft campaigns (Mays et al, 2009;Wecht et al, 2014), and analysis of satellite data Ye et al, 2017) among other methods, a common approach has been to deploy a network of sensors within and around a city (Breon et al, 2014;McKain et al, 2015McKain et al, , 2012Pugliese, 2017;Richardson et al, 2016;Shusterman et al, 2016;Verhulst et al, 2017). The density and placement of sensors within a network, together with the local meteorology and the spatiotemporal pattern of emissions, determines the extent to which the network is reliably sensitive to emissions over the whole region of interest and within the relevant time scale.…”

Southern California Megacity CO<sub>2</sub>, CH<sub>4</sub>, and CO flux estimates using remote sensing and a Lagrangian model

“…The remaining flux was scaled by 42 % based on the population of the SoCAB, and 5 % of the Agriculture and Forestry emissions were added back in. Our estimate is lower than previous estimates of CH 4 fluxes using in situ (tower and aircraft) data (Hsu et al, 2010;Wennberg et al, 2012;Peischl et al, 2013;Wecht et al, 2014;Cui et al, 2015).…”

Atmospheric CH<sub>4</sub> and CO<sub>2</sub> enhancements and biomass burning emission ratios derived from satellite observations of the 2015 Indonesian fire plumes

“…XCH 4 data obtained using the Proxy approach described above have been used in many inversion studies (Fraser et al, 2013;Wecht et al, 2014;Fraser et al, 2014;Cressot et al, 2014;Alexe et al, 2015;Turner et al, 2015) to estimate both global and regional emissions of XCH 4 . Normally the main disadvantage of the Proxy XCH 4 retrieval is that it requires an accurate and unbiased XCO 2 model to convert the ratio back into XCH 4 (Schepers et al, 2012).…”