Atmospheric Carbon Dioxide Measurement from Aircraft and Comparison with OCO-2 and Carbon Tracker Model Data

Abstract. Accurate monitoring of the atmospheric carbon dioxide (CO2) and its distribution is of great significance for studying the carbon cycle and predicting the future climate change. Compared to the ground observational sites, the airborne observations cover a wider area, and simultaneously observe a variety of surface types, which help in effectively monitoring the distribution of CO2 sources and sinks. In this work, an airborne experiment was carried out in March 2019 over Shanhaiguan area, China (39–41N,119–121E). An Integrated Path Differential Absorption (IPDA) Light Detection and Ranging (LIDAR) system and a commercial instrument, the Ultraportable Greenhouse Gas Analyzer (UGGA), were used installed on an aircraft to observe the CO2 distribution over various surface types. The Pulse Integration Method (PIM) algorithm was used to calculate the Differential Absorption Optical Depth (DAOD) from the LIDAR data. The CO2 column-averaged dry-air mixing ratio (XCO2) was calculated over different types of surfaces including mountain, ocean and urban areas. The concentrations of the XCO2 calculated from LIDAR measurements over ocean, mountain, and urban areas were 421.11, 427.67, and 430 ppm, respectively. Moreover, through the detailed analysis of the data obtained from the UGGA, the influence of pollution levels on the CO2 concentration was also studied. During the whole flight campaign, March 18 was heavily polluted with an Air Quality Index (AQI) of 175 and PM2.5 of 131. The Aerosol Optical Depth (AOD) reported by a sun photometer installed at the Funning ground station was 1.28. Compared to the other days, the CO2 concentration measured by UGGA at different heights was the largest on March 18 with an average value of 422.59 ppm, that was about 10 ppm higher than the measurements recorded on March 16. Moreover, the vertical profiles of Orbiting Carbon observatory-2 (OCO-2) OCO-2 and CarbonTracker were also compared with the aircraft measurements. All the datasets showed a similar variation trend with some differences in their CO2 concentrations, which proved the existence of a good agreement among them.