Sensitivity to the sources of uncertainties in the modeling of atmospheric CO 2 concentration within and in the vicinity of Paris
2021
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.
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