Constraining fossil fuel CO<sub>2</sub> emissions from urban area using OCO-2 observations of total column CO<sub>2</sub>

Abstract

Abstract. Expanding urban populations and the significant contribution of cities to global fossil-fuel CO 2 (CO 2ff ) emissions emphasize the necessity of achieving independent and accurate quantifications of the emissions from urban area. In this paper, we assess the utility of total column dry air CO 2 mole fraction (X CO2 ) data retrieved from NASA's Orbiting Carbon Observatory 2 (OCO-2) observations to quantify CO 2ff emissions from cities. Observing System Simulation Experiments (OSSEs) are implemented by forward modeling of meteorological fields and column X CO2 . The impact of transport model 15 errors on the inverse emissions estimates is examined for two -plume cities‖ (Riyadh, Cairo) and a -basin city‖ (Los Angeles metropolitan region, LA). The pseudo data experiments indicate convergence of emission uncertainties related to transport model errors with increasing amount of observations. The 1-σ uncertainty of emission estimates is constrained to approximately 15%/5% with about 10 pseudo tracks for plume city/basin city. The systematic wind speed biases in simulated wind fields for LA lead to overestimations in total CO 2ff emission, which require data assimilation to improve high-resolution 20 atmospheric transport. The contribution of biogenic fluxes gradients in urban and rural area of Pearl River Delta metropolitan region in China are examined by simulations with biospheric fluxes imposed by the Net Ecosystem Exchange (NEE) from multiple terrestrial biosphere models, which show about 24±21% (1σ) and 19±15% (1σ) contributions to the total X CO2 enhancements for the two cases examined. The representations of transport model errors for the emission optimization are examined for Riyadh, Cairo and LA in real cases. The determination of background X CO2 is discussed for LA by using constant 25 and simulated background with biospheric fluxes included, demonstrating the need of careful consideration of the variations in background X CO2 for identifying concentration enhancements due to fossil fuel emissions.

Building the COllaborative Carbon Column Observing Network (COCCON): long-term stability and ensemble performance of the EM27/SUN Fourier transform spectrometer

“…These limitations mostly inhibit a straightforward estimation of the emission strength of localized sources of CO 2 and CH 4 like cities, landfills, swamps or fracking and mining areas from satellite observations. Recently OCO-2 data were used for estimating the source strength of power plants (Nassar et al, 2017) and urban emissions (Ye et al, 2017). However, this can only be done for power plants and urban areas that lie directly under the OCO-2 overpass locations.…”

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

“…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). 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).…”

“…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.…”

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.…”