Urban Area Observing System (UAOS) simulation experiment using DQ-1 total column concentration observations

Abstract. Satellite observations of the total column dry-air carbon dioxide (XCO2) have been proven to support the monitoring and constraining of fossil fuel CO2 (ffCO2) emissions at the urban scale. We utilized the XCO2 retrieval data from China's first laser carbon satellite dedicated to comprehensive atmospheric environmental monitoring, DQ-1, in conjunction with a high-resolution transport model and a Bayesian inversion system, to establish a system for quantifying and detecting CO2 emissions in urban areas. Additionally, we quantified the impact of uncertainties from satellite measurements, transport models, and biospheric fluxes on emission inversions. To address uncertainties from the transport model, we introduced random wind direction and speed errors to the ffCO2 plumes and conducted 104 simulations to obtain the error distribution. In our pseudo-data experiments, the inventory overestimated fossil fuel emissions for Beijing and Riyadh, while underestimating emissions for Cairo. Specifically, we simulated Beijing and leveraged DQ-1's active remote sensing capabilities, utilizing its rapid day-night revisit ability. We assessed the impact of daily biospheric fluxes on ffXCO2 enhancements and further analyzed the diurnal variations of biospheric flux impacts on local XCO2 enhancements using three-hourly average NEE data. The results of a case study indicate that a significant proportion of local XCO2 enhancements are notably influenced by biospheric CO2 variations, potentially leading to substantial biases in ffCO2 emission estimates. Moreover, considering biospheric flux variations separately under day and night conditions can improve simulation accuracy by 20 %–70 %. With appropriate representations of uncertainty components and a sufficient number of satellite tracks, our constructed system can be used to quantify and constrain urban ffCO2 emissions effectively.