Enhancing CO2 Flux Inversion Accuracy in a Coastal Megacity: Impacts of Land Cover and Topography in a Regional Carbon Assimilation System

Human activities have significantly increased carbon dioxide (CO 2 ) emissions, exacerbating global warming. Therefore, accurately estimating anthropogenic emissions is crucial for developing effective emission reduction policies. This study focuses on evaluate the impact of land cover (LC) and topography (DEM) on inverted CO 2 fluxes using a new developed regional Carbon Assimilation System (RCAS). Our primary aim is to assess the extent to which LC and DEM influence CO 2 flux inversion in Shenzhen, a rapidly urbanizing coastal city. To achieve this, we analyzed the forward simulations of meteorological variables (wind speed/direction, relative humidity, temperature, and pressure) alongside CO 2 concentration simulations. Following this, we evaluated the posterior CO 2 concentration estimates with the CO 2 flux from the inversion system. The findings demonstrate that new LC and DEM data led to a mean bias reduction from -1.08 ppm to -0.88 ppm, representing a 0.2 ppm improvement, and a slight decrease in standard deviation values from 5.59 ppm to 5.57 ppm in the posterior CO 2 concentration simulation. These results underscore the critical importance of accurate LC and DEM data in enhancing CO 2 flux inversion accuracy, providing valuable insights for improving regional-scale carbon monitoring systems.