Carbon dioxide (CO2) is the leading contributor to global warming and climate change. The increases in fossil fuel emissions, deforestation, and changes of land use have resulted in increased CO2 levels in the atmosphere from 280 ppm in 1765 to 390 ppm in 2010. Carbon mitigation policies for managing the biosphere to increase net CO2 uptake are dependent upon accurate knowledge of the biosphere fluxes. However, Northern Hemisphere bottom-up and top-down biosphere flux estimates show significant discrepancies, especially in North America. In this study, we design an analysis framework that integrates observations with models with the goal of reducing some of the key uncertainties in estimating CO2 fluxes and concentrations in the Midwest, USA. In this research, the biosphere model, WRF-VPRM model (Ahmadov et al., 2007) is used to simulate CO2 biosphere fluxes and atmospheric CO2 concentrations in the Midwest, USA at high spatial resolution. Reducing uncertainties in the predictions is accomplished by improving the model transport configurations (i.e. the WRF planetary boundary layer (PBL) scheme, the number of vertical layers and the horizontal resolution), utilizing a more detailed land cover map, optimizing VPRM photosynthesis and respiratory parameters for major crops (i.e. corn and soybean) against flux towers, and integrating CO2 tall tower observations and model through a top-down data assimilation method to improve the VPRM model parameters and in turn improving the flux and concentration estimates. The WRF-VPRM model configuration with the YonSei University PBL scheme produced the most accurate CO2 concentration predictions at the WBI tower at all three tower levels with the maximum error reduction of 17.1%. Increasing the number of vertical layers improved the CO2 estimates during nighttime and early morning, especially at 30 m, where the error was reduced by a maximum of ~ 20%. The differences in the monthly average net fluxes over the State of Iowa between the high resolution WRF-VPRM model and coarse resolution Carbon Tracker were significant, 71%, 18%, and 62% in June, July, and August, respectively. The fluxes calculated by the VPRM model are primarily dependent on 4 model parameters, half saturation value of photosynthesis (PAR0), light use efficiency (ë), and respiration parameters (á and â). These parameters are specific to vegetation types, regions, and time period. The default settings do not distinguish between corn and soybean, which are major crops in the Midwest and have significant different photosynthesis rates. When corn and soybean are explicitly included in the model, the flux estimate changed by 31.3% at 12 pm and 24.5% at 12 am. Two different methods were used to optimize for the VPRM model parameters which are optimization against Ameriflux NEE and using a top-down variational method. The simulation using optimized parameters from the variational method reduced the error during the daytime from 11.6 ppm to 7.8 ppm. The average fluxes optimized using the variational method changed by 17% and 38.6% at 12 pm and 12 am, respectively. The more accurate VPRM parameters lead to the more accurate biosphere fluxes, which will ease the evaluation of benefits of different carbon mitigation policies.