Abstract
A persistent challenge for small-scale air quality modeling is the assessment of health impact and population exposure studies. Despite progress in computation and in the quality of model input (i.e., high-resolution information on land use and emission patterns), the uncertainty associated with input parameters cannot be eliminated. The aim of this paper is to study different sources of uncertainty that affect model results as the resolution increases. Mesoscale chemistry transport simulations at different resolutions are used and modeled 03 concentrations are compared with surface measurements. The case study consists of CHIMERE model simulations over the city of Paris. It is shown that the principal source of noise in model results is the resolution of the input emission fluxes. The O3 concentrations modeled with simulations forced by several horizontal resolutions of input emission data (from Δx = 48 km to Δx = 6 km) indicate that model results do not improve monotonously with resolution, but that after a certain point discrepancies become larger. Based on this result and as an alternative to the deterministic downscaling that resolves explicitly the finer scale (beyond the 1-km range), the authors propose a subgrid-scale approach that uses a statistical description of spatial scales finer than model resolution. As an example, the subgrid variability of modeled O3 concentration has been quantified, when modeled dry deposition processes occur over subgrid surfaces (land use fractions). The implementation of this modified calculation gives access to subgrid fluxes and subgrid surface concentrations instead of the mean values provided by the commonly used model calculation.
Corresponding author address: Myrto Valari, Laboratoire de Météorologie Dynamique, Ecole Polytechnique, 91128 Palaiseau, France. Email: myrto.valari@lmd.polytechnique.fr
