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Inverse modelling of carbon sources and sinks requires an accurate qualityestimate of the modelling framework to obtain a realistic estimate of theinferred fluxes and their uncertainties. So-called "representation errors"result from our inability to correctly represent point observations withsimulated average values of model grid cells. They may add substantialuncertainty to the interpretation of atmospheric CO2 mixing ratio data.We simulated detailed variations in the CO2 mixing ratios with a highresolution (2 km) mesoscale model (RAMS) to estimate the representationerrors introduced at larger model grid sizes of 10–100 km. We found thatmeteorology is the main driver of representation errors in our study causingspatial and temporal variations in the error estimate. Within the nocturnalboundary layer, the representation errors are relatively large and mainlycaused by unresolved topography at lower model resolutions. During the day,convective structures, mesoscale circulations, and surface CO2 fluxvariability were found to be the main sources of representation errors.Interpreting observations near a mesoscale circulation as representative forair with the correct footprint relative to the front can reduce therepresentation error substantially. The remaining representation error is0.5–1.5 ppm at 20–100 km resolution

Modelling representation errors of atmospheric CO&lt;sub&gt;2&lt;/sub&gt; mixing ratios at a regional scale

Modelling representation errors of atmospheric CO<sub>2</sub> mixing ratios at a regional scale