Constraining Fossil Fuel CO₂ Emissions From Urban Area Using OCO‐2 Observations of Total Column CO₂

Abstract

Satellite observations of the total column dry-air CO 2 (X CO2 ) are expected to support the quantification and monitoring of fossil fuel CO 2 (ffCO 2 ) emissions from urban areas. We evaluate the utility of the Orbiting Carbon Observatory 2 (OCO-2) X CO2 retrievals to optimize whole-city emissions, using a Bayesian inversion system and high-resolution transport modeling. The uncertainties of constrained emissions related to transport model, satellite measurements, and local biospheric fluxes are quantified. For the first two uncertainty sources, we examine cities of different landscapes: "plume city" located in relatively flat terrain, represented by Riyadh and Cairo; and "basin city" located in basin terrain, represented by Los Angeles (LA). The retrieved scaling factors of emissions and their uncertainties show prominent variabilities from track to track, due to the varying meteorological conditions and relative locations of the tracks transecting plumes. To explore the performance of multiple tracks in retrieving emissions, pseudo data experiments are carried out. The estimated least numbers of tracks required to constrain the total emissions for Riyadh (<10% uncertainty), Cairo (<10%), and LA (<5%) are 8, 5, and 7, respectively. Additionally, to evaluate the impact of biospheric fluxes on derivation of the ffX CO2 enhancements, we conduct simulations for Pearl River Delta metropolitan area. Significant fractions of local X CO2 enhancements associated with local biospheric X CO2 variations are shown, which potentially lead to biased estimates of ffCO 2 emissions. We demonstrate that satellite measurements can be used to improve urban ffCO 2 emissions with a sufficient amount of measurements and appropriate representations of the uncertainty components.

PMIF v1.0: assessing the potential of satellite observations to constrain CO&lt;sub&gt;2&lt;/sub&gt; emissions from large cities and point sources over the globe using synthetic data

“…Broquet et al (2018) showed that systematic error could hamper the ability of the inversion system to reduce the errors in the emissions estimates. Thirdly, we neglect the impact of uncertainties in diffuse fossil fuel CO 2 emissions (outside clumps) and non-fossil CO 2 fluxes (within and outside clumps), the latter including net ecosystem exchange (NEE) from the terrestrial biosphere, the CO 2 emitted by the burning of biofuel, the respiration 5826 Y. Wang et al: Potential of satellite observations to constrain CO 2 emissions from humans and animals (Ciais et al, 2020), and the net CO 2 fluxes between the atmosphere and ocean. For example, the signals from terrestrial NEE can be strong during the growing season, and the signals from ocean CO 2 fluxes may have a critical impact on the overall XCO 2 patterns in proximity to coastlines.…”

“…For example, the signals from terrestrial NEE can be strong during the growing season, and the signals from ocean CO 2 fluxes may have a critical impact on the overall XCO 2 patterns in proximity to coastlines. In principle, the signals of diffuse fossil fuel CO 2 emissions and non-fossil CO 2 fluxes outside the clumps can be potentially filtered by removing the local background XCO 2 field to extract plumes generated only by emissions from clumps (Kuhlmann et al, 2019;Reuter et al, 2019;Ye et al, 2020;Zheng et al, 2020). The nonfossil CO 2 fluxes within clumps vary from clump to clump and could contribute a non-negligible fraction of the total CO 2 fluxes in many clumps (Bréon et al, 2015;Ciais et al, 2020;Wu et al, 2018).…”

“…However, the design of GOSAT and OCO-2 observations with sparse sampling was mainly focused on the monitoring of CO 2 natural fluxes. Recent studies show a limited amount of clear detections of transects of XCO 2 plumes from cities or plants in OCO-2 observations (Zheng et al, 2020) so that GOSAT and OCO-2 data keep on being hardly used to estimate CO 2 city emissions. The potential for reducing uncertainties in CO 2 emissions at the scale of point sources (Bovensmann et al, 2010), cities (Broquet et al, 2018;Pillai et al, 2016) and agglomerations of several cities (O'Brien et al, 2016) should dramatically change with the planned satellite missions with imaging capabilities.…”

Terrestrial CO2 Fluxes, Concentrations, Sources and Budget in Northeast China: Observational and Modeling Studies

“…The subsequent study indicated that the WRF‐VPRM was able to capture the atmospheric CO 2 temporal and spatial distributions and performed better than other global models in simulating diurnal variability of atmospheric CO 2 concentration field (Ahmadov et al, 2009). In the past decade, the application and evaluation of WRF‐VPRM mainly focused on North America (Feng et al, 2016; Hu et al, 2019b, 2020; Park et al, 2018; Ye et al, 2017) and Europe (Pillai et al, 2011). The model performances remain unknown in Asia including China due to the lack of observations, as well as model uncertainties due to uncertainties of VPRM parameters in the region (Dayalu et al, 2018; Hilton et al, 2013; Liu et al, 2015; Zhang et al, 2017a).…”

“…Diao et al (2015) used WRF‐VPRM to investigate the spatiotemporal characteristics of NEE and surface CO 2 concentrations over the Yangtze River Delta region in China for 5 days, from 28 July to 2 August 2010. Ye et al (2017) assessed the biotic contribution to the XCO 2 enhancements during two short episodes (12–15 January and 1–4 August 2015) over the Pearl River Delta metropolitan region in China using the WRF‐VPRM. However, these short‐term (a few days) simulations could not reveal long‐term variations of CO 2 fluxes/concentrations.…”