Spatiotemporal distributions of column-weighted CO₂ concentration above and below the clouds in China

Accurate monitoring of near-surface CO₂ concentrations is essential for quantifying anthropogenic carbon emissions and developing effective mitigation strategies. Current passive satellite remote sensing is limited by its susceptibility to aerosol and cloud interference and its limitation to providing the only column-averaged dry-air mole fraction of carbon dioxide (XCO₂). These limitations hinder the precise capture of near-surface carbon sources and sinks. To address this issue, this study leverages the vertical profiling capabilities of spaceborne active light detection and ranging (LIDAR) to investigate CO₂ concentrations both above and below clouds. Utilizing observations from the Aerosol and Carbon Detection LIDAR (ACDL) onboard the Atmospheric Environment Monitoring Satellite (DQ-1) in 2023, we developed a sub-cloud CO₂ signal separation model based on the integrated path differential absorption (IPDA) principle. This approach enables effective separation of CO₂ signals above and below clouds over China. Subsequently, by integrating data collected at Waliguan Atmospheric Baseline Station and nighttime light imagery, we systematically analyzed the spatiotemporal distributions, seasonal variations, and diurnal differences of XCO₂ above and below clouds across China for the year 2023. The analysis indicates that: (1) XCO₂ above clouds is minimally affected by surface perturbations and exhibits stable temporal variations, reliably reflecting the long-term background atmospheric concentration; (2) XCO₂ below clouds illustrates a distinct spatial gradient, with higher concentrations in the east and lower concentrations in the west, as well as seasonal oscillations characterized by summer minima and winter maxima. The monthly average concentration peaks in January (~426.1 ppm^①) and reaches a minimum in August (~419.2 ppm). Additionally, a pronounced diurnal difference is observed, with nighttime concentrations exceeding daytime levels by an average of 0.7 ppm; (3) XCO₂ below clouds exhibits a significant positive bias compared to total-column concentrations, with an average enhancement of 9.1 ppm. Furthermore, areas of high concentration show high spatial consistency with major urban agglomerations (e.g., Beijing-Tianjin-Hebei and the Yangtze River Delta) and regions with high nighttime light intensity. Spaceborne active LIDAR offers a unique capability for vertical profiling, enabling effective separation of background signals from the middle and upper atmosphere. The retrieved below-cloud XCO₂ more precisely captures near-surface anthropogenic carbon emissions and the dynamics of regional carbon sources and sinks, providing a more robust scientific basis for regional carbon budget assessments.