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Satellite retrievals of total column CO2 (XCO2) such as those from NASAs OCO-2 yield unprecedented global spatial coverage of CO2. However, to estimate regional fluxes these need to be precise, unbiased, and extremely stable over time. Previous work has shown that information about surface flux processes is significantly diluted over the full atmospheric column. However, fewer studies have investigated the information content in these data relative to in-situ measurements that are rigorously calibrated to the WMO scale. Here, we use NOAAs CarbonTracker Lagrange (CT-L) inverse model to probe the utility and limitations of OCO-2 data to constrain regional scale Net Ecosystem Exchange of CO2 (NEE) over North America. CT-L uses surface sensitivity footprints from a Lagrangian particle dispersion model (STILT) driven by high-resolution meteorological simulations (WRF). WRF-STILT is run at 10 km and 30 km spatial resolution over temperate NA and boreal NA, respectively, and surface sensitivities are computed over all retrievals at 2s interval for OCO-2 soundings. This spatial and temporal resolution is significantly higher than most global Eulerian models, which use 10s averaged OCO-2 data and model spatial resolutions often one degree or larger. Using a suite of inversions, we seek to understand at what spatial and temporal resolutions OCO-2 can provide robust estimates of NEE and quantify the magnitude of flux uncertainty reduction. Additionally, we test the impact of sub-ppm scale biases in OCO-2 XCO2 on retrieved fluxes. Results are contrasted with inversions with in-situ CO2 mole fraction measurements from NOAA and Environment Canada. Withheld vertical profile data from NOAAs aircraft and AirCore networks are used to evaluate bias in optimized flux estimates. While North America provides an ideal test-bed due to the relatively dense network of well-calibrated measurements, these methods will be applied to constrain NEE over the Amazon in the coming years
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