We present results from a chemical data assimilation system using a global chemistry transport model (GCTM), the Community Atmosphere Model (CAM3) with chemistry, and NCAR's Data Assimilation Research Testbed (DART). The DART/CAM system has been developed as a flexible platform to provide improved estimates of atmospheric composition by integrating measurements at various scales with predictions from a three-dimensional atmospheric model. An advantage of the DART setup is the ease with which the GCTM was ported into the data assimilation system. Here, we focus on constraining the global CO distribution by assimilating meteorological observations of temperature and horizontal wind velocity as well as satellite measurements of CO from the Measurement of Pollution in The Troposphere (MOPITT) instrument. We conducted an Ensemble Kalman Filter (EnKF) assimilation of MOPITT CO retrievals using an ensemble of 20 members. Initial ensembles of CO were first generated based on perturbed dynamical states and perturbed emissions. We then assimilated observations into CAM3 at 6-hour cycles over a one-month test period. Results show that the current DART/CAM data assimilation system significantly reduces the relative bias and uncertainty in the modeled CO distribution. In addition, we show comparisons of the assimilated CO with available in-situ and aircraft measurements and highlight the potential of the assimilation system as a tool for chemical forecasting, model evaluation, and for studies to better understand the impact of global pollution on regional air quality.