Southern California megacity CO<sub>2</sub>, CH<sub>4</sub>, and CO flux estimates using ground- and space-based remote sensing and a Lagrangian model

Abstract

Abstract. We estimate the overall CO2, CH4, and CO flux from the South Coast Air Basin using an inversion that couples Total Carbon Column Observing Network (TCCON) and Orbiting Carbon Observatory-2 (OCO-2) observations, with the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model and the Open-source Data Inventory for Anthropogenic CO2 (ODIAC). Using TCCON data we estimate the direct net CO2 flux from the SoCAB to be 104 ± 26 Tg CO2 yr−1 for the study period of July 2013–August 2016. We obtain a slightly higher estimate of 120 ± 30 Tg CO2 yr−1 using OCO-2 data. These CO2 emission estimates are on the low end of previous work. Our net CH4 (360 ± 90 Gg CH4 yr−1) flux estimate is in agreement with central values from previous top-down studies going back to 2010 (342–440 Gg CH4 yr−1). CO emissions are estimated at 487 ± 122 Gg CO yr−1, much lower than previous top-down estimates (1440 Gg CO yr−1). Given the decreasing emissions of CO, this finding is not unexpected. We perform sensitivity tests to estimate how much errors in the prior, errors in the covariance, different inversion schemes, or a coarser dynamical model influence the emission estimates. Overall, the uncertainty is estimated to be 25 %, with the largest contribution from the dynamical model. Lessons learned here may help in future inversions of satellite data over urban areas.

Top‐Down Constraints on Methane Point Source Emissions From Animal Agriculture and Waste Based on New Airborne Measurements in the U.S. Upper Midwest

“…Such spatial and temporal errors in bottom‐up methane emission estimates will also affect the prior estimates used for inverse modeling, potentially leading to miscategorization of sources. A possible step to reduce such biases is through more detailed spatial allocation of methane emissions from CAFOs (Hedelius et al, ). Recent studies (Varon et al, , ) have explored the possibility of using satellite data to quantify methane point sources, which, if anticipated performance targets are met, would offer the dual advantages of sustained temporal sampling and regional (or broader) spatial coverage.…”

Bayesian inverse estimation of urban CO2 emissions: Results from a synthetic data simulation over Salt Lake City, UT

“…In slight contrast to these frameworks, Bayesian-style approaches incorporate uncertainties of observations and fluxes as constraining inputs, and have been successfully applied over urban ground-based measurement networks to optimize existing bottom-up estimates of CO 2 fluxes over Cape Town (Nickless et al, 2018), Indianapolis (Lauvaux et al, 2016;Oda et al, 2017;Gurney et al, 2017;Turnbull et al, 2019), Paris (Bréon et al, 2015, and Davos (Lauvaux et al, 2013). Studies have also used aircraft campaign data as the atmospheric measurements for urban inverse analyses over Los Angeles (Gourdji et al, 2018;Cui et al, 2015;Brioude et al, 2013) and Houston (Brioude et al, 2011(Brioude et al, , 2012, while satellite and ground-based data were used to analyze carbon fluxes over California (Fisher et al, 2017, Hedelius et al, 2018.…”

Prediction of Satellite-Based Column CO2 Concentration by Combining Emission Inventory and LULC Information

“…The assessment of satellite-derived XCO 2 measurements and the CO 2 emission estimates for each other has been found in different applications, for example, to determine the impact of regional fossil fuel emissions on global XCO 2 fields [42], estimate the fire CO 2 emission by using XCO 2 [43], determine CO 2 emissions from megacities by using XCO 2 through inverse modeling approach [44] or from the individual middle to large-sized coal power plants through plume model simulations [45], and observe the anthropogenic CO 2 emission by deriving CO 2 anomalies through deseasonalizing and detrending XCO 2 measurements [46]. This article is one of the initial attempts for modeling the emission inventories as auxiliary variables to generate a mapping surface of spaceborne XCO 2 measurements of OCO-2.…”