Urban CO2 inversions for Paris using ICOS Cities observations and GRAMM/GRAL

Cities contribute substantially to anthropogenic greenhouse gas emissions and are increasingly implementing mitigation policies.  Robust, sector-resolved emission estimates are needed to assess the effectiveness of these policies. We analyse the capabilities of an inversion framework with a 10m resolution transport model to provide sector-specific emission estimates for the city of Paris.

We use the atmospheric transport model GRAMM/GRAL to compute hourly steady-state wind fields and concentration maps covering central Paris with a horizontal resolution of 10 m. The high resolution makes it possible to simulate street channelling and building effects in densely populated urban areas.

We use two different prior inventories of anthropogenic CO₂ fluxes – TNO GHGco_v4 and Origins.earth – and the Vegetation Photosynthesis and Respiration Model (VPRM) for the biogenic fluxes at high spatial and temporal resolution. We then evaluate the spatial and temporal patterns of the simulated concentrations with in-situ measurements from the ICOS Cities project’s network of mid- and high-cost instruments in Paris and discuss shortcomings and uncertainties induced by the model.

Finally, we conduct a Bayesian inversion for an optimized emission estimate based on the available CO₂ data for 2023 and 2024. We assess the robustness of the inversion by testing the sensitivity of posterior fluxes on key methodological choices and input data sets, and we discuss the implications of our findings for the city of Paris.