Sensitivity to the sources of uncertainties in the modeling of atmospheric CO 2 concentration within and in the vicinity of Paris

Abstract. The top-down atmospheric inversion method that couples atmospheric CO 2 observations with an atmospheric transport model has been used extensively to quantify CO 2 emissions from cities. However, the potential of the method is limited by several sources of misfits between the measured and modeled CO 2 that are of different origins than the targeted CO 2 emissions. This study investigates the critical sources of errors that can compromise the estimates of the city-scale emissions and identifies the signal of emissions that has to be filtered when doing inversions. A set of 1-year forward simulations is carried out using the WRF-Chem model at a horizontal resolution of 1 km focusing on the Paris area with different anthropogenic emission inventories, physical parameterizations, and CO 2 boundary conditions. The simulated CO 2 concentrations are compared with in situ observations from six continuous monitoring stations located within Paris and its vicinity. Results highlight large nighttime model–data misfits, especially in winter within the city, which are attributed to large uncertainties in the diurnal profile of anthropogenic emissions as well as to errors in the vertical mixing near the surface in the WRF-Chem model. The nighttime biogenic respiration to the CO 2 concentration is a significant source of modeling errors during the growing season outside the city. When winds are from continental Europe and the CO 2 concentration of incoming air masses is influenced by remote emissions and large-scale biogenic fluxes, differences in the simulated CO 2 induced by the two different boundary conditions (CAMS and CarbonTracker) can be of up to 5 ppm. Nevertheless, our results demonstrate the potential of our optimal CO 2 atmospheric modeling system to be utilized in atmospheric inversions of CO 2 emissions over the Paris metropolitan area. We evaluated the model performances in terms of wind, vertical mixing, and CO 2 model–data mismatches, and we developed a filtering algorithm for outliers due to local contamination and unfavorable meteorological conditions. Analysis of model–data misfit indicates that future inversions at the mesoscale should only use afternoon urban CO 2 measurements in winter and suburban measurements in summer. Finally, we determined that errors related to CO 2 boundary conditions can be overcome by including distant background observations to constrain the boundary inflow or by assimilating CO 2 gradients of upwind–downwind stations rather than by assimilating absolute CO 2 concentrations.