Constraining fossil fuel CO₂ emissions from urban area using OCO-2 observations of total column CO₂

Abstract. Expanding urban populations and the significant contribution of cities to global fossil-fuel CO₂ (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 CO₂ 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 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 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 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.