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Recent advances in atmospheric transport model inversions could significantlyreduce uncertainties in land carbon uptake through the assimilation of CO2concentration measurements at weekly and shorter timescales. The potentialof these measurements for reducing biases in estimated land carbon sinksdepends on the strength of covariation between surface fluxes and atmospherictransport at these timescales and how well transport models represent thiscovariation. Daily to seasonal covariation of surface fluxes and atmospherictransport was estimated in observations at the US Southern Great PlainsAtmospheric Radiation Measurement Climate Research Facility, and compared toan atmospheric transport model inversion (CarbonTracker). Covariation oftransport and surface fluxes was stronger in CarbonTracker than inobservations on synoptic (daily to weekly) timescales, with a wet year(2007) having significant covariation compared to a dry year (2006).Differences between observed and CarbonTracker synoptic covariation resultedin a 0.3 ppm CO2 enhancement in boundary layer concentrations during thegrowing season, and a corresponding enhancement in carbon uptake by 13% ofthe seasonal cycle amplitude in 2007, as estimated by an offline simplifiedtransport model. This synoptic rectification of surface flux variability wasof similar magnitude to the interannual variability in carbon sinks alone,and indicates that interannual variability in the inversions can be affectedby biases in simulated synoptic rectifier effects. The most significantcovariation of surface fluxes and transport had periodicities of 10 days andgreater, suggesting that surface flux inversions would benefit from improvedsimulations of the effects of soil moisture on boundary layer heights andsurface CO2 fluxes. Soil moisture remote sensing could be used along withCO2 concentration measurements to further constrain atmospheric transportmodel inversions

Biases in regional carbon budgets from covariation of surface fluxes and weather in transport model inversions