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Satellite retrievals for column CO2 with better spatial and temporalsampling are expected to improve the current surface flux estimates ofCO2 via inverse techniques. However, the spatial scale mismatch betweenremotely sensed CO2 and current generation inverse models can inducerepresentation errors, which can cause systematic biases in flux estimates.This study is focused on estimating these representation errors associatedwith utilization of satellite measurements in global models with ahorizontal resolution of about 1 degree or less. For this we used simulatedCO2 from the high resolution modeling framework WRF-VPRM, which linksCO2 fluxes from a diagnostic biosphere model to a weather forecastingmodel at 10×10 km2 horizontal resolution. Sub-grid variability ofcolumn averaged CO2, i.e. the variability not resolved by globalmodels, reached up to 1.2 ppm with a median value of 0.4 ppm. Statisticalanalysis of the simulation results indicate that orography plays animportant role. Using sub-grid variability of orography and CO2 fluxesas well as resolved mixing ratio of CO2, a linear model can beformulated that could explain about 50% of the spatial patterns in thesystematic (bias or correlated error) component of representation error incolumn and near-surface CO2 during day- and night-times. These findingsgive hints for a parameterization of representation error which would allowfor the representation error to taken into account in inverse models or dataassimilation systems

High resolution modeling of CO&lt;sub&gt;2&lt;/sub&gt; over Europe: implications for representation errors of satellite retrievals

High resolution modeling of CO<sub>2</sub> over Europe: implications for representation errors of satellite retrievals