We present a proposal to reduce uncertainty in the carbon cycle processes that create the so- called missing sink of atmospheric CO2. Our overall objective is to improve characterization of CO2 source/sink processes globally with improved formulations for atmospheric transport, terrestrial uptake and release, biomass and fossil fuel burning, and observational data analysis. The motivation for this study follows from the perspective that progress in determining CO2 sources and sinks beyond the current state of the art will rely on utilization of more extensive and intensive CO2 and related observations including those from satellite remote sensing.

Our proposed approach is to perform several interrelated tasks to advance models and data analysis methods that are required to realize the benefits of existing new and planned future observations: 1) Continue development of the parameterized chemistry and transport model using analyzed meteorological fields from the Goddard Global Modeling and Assimilation Office, with comparison to real time data in both forward and inverse modes. 2) Couple an advanced biosphere model, constrained by remote sensing data, with the global transport model to produce distributions of CO2 fluxes and concentrations that are consistent with actual meteorological variability. 3) Employ improved remote sensing estimates for biomass burning emission fluxes in the transport model and data comparisons to better characterize interannual variability in the atmospheric CO2 budget and to better constrain the land use change source. 4) Evaluate the impact of temporally resolved fossil fuel emission distributions on atmospheric CO2 gradients and variability. 5) Test the impact of existing and planned remote sensing data sources (e.g., AIRS, MODIS, OCO) on inference of CO2 sources and sinks, and use the model to help establish measurement requirements for future remote sensing instruments.

The anticipated results are improved, data-constrained models that resolve transport and emission distributions at global to synoptic scales, methods to use the information contained in simulations and data at these scales, and refined inferences of CO2 sources and sinks and their dependence on environmental conditions. These results are of potentially high value to NASA carbon cycle science in designing remote sensing approaches to determine global distributions and fluxes of carbon, to prepare for the use of OCO and other satellite data, and to develop modeling tools that contribute to a multi-disciplinary carbon data assimilation system for analysis and prediction of carbon cycle changes and carbon/climate interactions.