Simulation and Assimilation of CO2 Concentrations Based on the WRF-Chem Model

Accurate simulation and assimilation of CO2 concentrations are of great significance for global carbon cycle research, carbon emission monitoring, and climate policy formulation. In this study, we conducted simulation and assimilation of CO2 concentrations over central, eastern, and southern China from March to August 2020 using the WRF-Chem model (Weather Research and Forecasting model coupled with Chemistry) coupled with the Ensemble Adjustment Kalman Filter (EAKF) assimilation method. We designed four progressive experiments (CTRL, MET_DA, CO2_DA, and FULL_DA) to evaluate the impacts of assimilating meteorological observations and multi-satellite fused XCO2 concentrations on CO2 simulation performance. Compared to the CTRL simulation, the MET_DA experiment shows that the correlation coefficients (R) for meteorological elements, including wind speed, temperature, and relative humidity, improved by approximately 9.68%, 2.03%, and 16.05%, respectively. The CO2_DA experiment showed improved accuracy in CO2 concentration simulation. The validation against WDCGG (World Data Centre for Greenhouse Gases) and TCCON (Total Carbon Column Observing Network) observations demonstrated that R increased to 0.970 and 0.830, respectively, with corresponding RMSEs reduced to 2.598 ppm and 2.042 ppm. Building upon the improvements of CO2_DA, the FULL_DA experiment achieved greater accuracy, with R reaching 0.972 and 0.875, and RMSE reduced to 2.309 ppm and 1.693 ppm, respectively. In addition, the bias was lowered by 46.74% and 77.58%. The results show that assimilation of both meteorological and multi-source fused XCO2 datasets achieves optimal performance in enhancing the accuracy of CO2 concentration simulations. This study employs an hourly, multi-source fused CO2 dataset that features an increased number of observations and greater spatial coverage. By assimilating this dataset, we achieve more accurate simulations of CO2 concentrations, thereby providing reliable support for carbon monitoring and emission estimation.