Here we present the development of a new regional-scale ensemble-based data assimilation system for estimating surface CO2 fluxes over North America, with a special focus on investigating the error covariance structure and evolution and contribution of different error sources to the posterior CO2 flux uncertainties. The system is based on the Ensemble Kalman Filter (EnKF) component of The Pennsylvania State University Unified Data Assimilation system and uses the Weather Research and Forecasting model coupled with chemical transport (WRF-Chem) as its dynamical model. Observations of both CO2 concentrations and atmospheric dynamical and thermodynamic variables will be assimilated simultaneously. We will present recent results from observing system simulation experiments that explore the impact of atmospheric transport error and CO2 tower observing network on the estimated CO2 fluxes and their posterior uncertainties. The focus will be on biogenic fluxes in the eastern United States during the summer of 2016, when the first ACT-America flight campaign took place. Finally, we will discuss some specific techniques for this particular data assimilation application, including covariance inflation and localization, and the optimum design of observing network and assimilation techniques for carbon estimation and monitoring.