<p><strong>Abstract.</strong> Providing timely information on urban Greenhouse-Gas (GHG) emissions and their trends to stakeholders relies on reliable measurements of atmospheric concentrations and the understanding of how local emissions and atmospheric transport influence these observations.</p> <p>Portable Fourier Transform Infra-Red (FTIR) spectrometers were deployed at 5 stations in the Paris metropolitan area to provide column-averaged concentrations of CO<sub>2</sub> (XCO<sub>2</sub>) during a field campaign in spring of 2015. Here, we describe and analyze the variations of XCO<sub>2</sub> observed at different sites and how they changed over time. We find that observations upwind and downwind of the city centre differ significantly in their XCO<sub>2</sub> concentrations, while the overall variability of the daily cycle is similar, i.e., increasing during night-time with a strong decrease (typically 2&ndash;3<span class="thinspace"></span>ppm) during the afternoon.</p> <p>An atmospheric transport model framework (CHIMERE-CAMS) was used to simulate XCO<sub>2</sub> and predict the same behaviour seen in the observations, which supports key findings, e.g. that even in a densely populated region like Paris (over 12 Million people), biospheric uptake of CO<sub>2</sub> can be of major influence on daily XCO<sub>2</sub> variations. Despite a general offset between modelled and observed XCO<sub>2</sub>, the model correctly predicts the impact of the meteorological parameters (e.g. wind direction and speed) on the concentration gradients between different stations. Looking at the local gradients of XCO<sub>2</sub> for upwind and downwind station pairs, which is less sensitive to changes in XCO<sub>2</sub> regional background conditions, we find the model-data agreement significantly better. Our modelling framework indicates that the local XCO<sub>2</sub> gradient between the stations is dominated by the fossil fuel CO<sub>2</sub> signal of the Paris metropolitan area. This highlights the usefulness of XCO<sub>2</sub> observations to help optimise future urban GHG emission estimates.</p>