Can we use atmospheric CO₂ measurements to verify emission trends reported by cities? Lessons from a six-year atmospheric inversion over Paris

Abstract. Existing CO₂ emissions reported by city inventories usually lag real-time by a year or more and are prone to large uncertainties. This study responds to the growing need for timely and precise estimation of urban CO₂ emissions to support the present and future mitigation measures and policies. We focus on the Paris metropolitan area, the largest urban region in the European Union and the city with the densest atmospheric CO₂ observation network in Europe. We performed long-term atmospheric inversions to quantify the citywide CO₂ emissions, both fossil fuel and biogenic sources and sinks, over six years (2016–2021) using a Bayesian inverse modeling system. Our inversion framework benefits from a novel near-real-time hourly fossil fuel CO₂ emission inventory (Origins.earth) at 1 km spatial resolution. In addition to the mid-afternoon observations, we attempt to assimilate morning CO₂ concentrations based on the ability of the WRF-Chem transport model to simulate atmospheric boundary layer dynamics constrained by observed layer heights. Our results show a long-term decreasing trend by around 2 % per year in annual CO₂ emissions over the Paris region. The impact of COVID-19 pandemic led to a 13 %±1 % reduction in annual fossil fuel CO₂ emissions in 2020 with respect to 2019. Then, annual emissions increased by 5.2 % from 32.6±2.2 MtCO₂ in 2020 to 34.3±2.3 MtCO₂ in 2021. Based on a combination of up-to-date inventories, high-resolution atmospheric modeling, and high-precision observations, our current capacity could deliver near real-time CO₂ emission estimates at city scale in less than a month, and the results agree within 10 % with independent estimates from multiple city-scale inventories.