| Abstract: | A regional surface carbon dioxide(CO_2) flux inversion system, the Tan-Tracker-Region, was developed by incorporating an assimilation scheme into the Community Multiscale Air Quality(CMAQ) regional chemical transport model to resolve fine-scale CO_2 variability over East Asia. The proper orthogonal decomposition-based ensemble four-dimensional variational data assimilation approach(POD-4 DVar) is the core algorithm for the joint assimilation framework, and simultaneous assimilations of CO_2 concentrations and surface CO_2 fluxes are applied to help reduce the uncertainty in initial CO_2 concentrations. A persistence dynamical model was developed to describe the evolution of the surface CO_2 fluxes and help avoid the "signal-to-noise" problem; thus, CO_2 fluxes could be estimated as a whole at the model grid scale, with better use of observation information. The performance of the regional inversion system was evaluated through a group of single-observation-based observing system simulation experiments(OSSEs). The results of the experiments suggest that a reliable performance of Tan-Tracker-Region is dependent on certain assimilation parameter choices, for example, an optimized window length of approximately 3 h, an ensemble size of approximately 100, and a covariance localization radius of approximately 320 km. This is probably due to the strong diurnal variation and spatial heterogeneity in the fine-scale CMAQ simulation, which could affect the performance of the regional inversion system. In addition, because all observations can be artificially obtained in OSSEs, the performance of Tan-Tracker-Region was further evaluated through different densities of the artificial observation network in different CO_2 flux situations. The results indicate that more observation sites would be useful to systematically improve the estimation of CO_2 concentration and flux in large areas over the model domain. The work presented here forms a foundation for future research in which a thorough estimation of CO_2 flux variability over East Asia could be performed with the regional inversion system. |
