## 1 Introduction
Significant and varying CO\({}_{2}\) enhancement from Marseille has been recorded in measurements taken in and around Marseille as well as further north at the Obervatoire Haute-Provence (OHP) dating back to 2016 [19; 40; 41] Additionally, an intensive campaign with the acronym ESCOMPTE was undertaken in the early 2000s to map out some of the atmospheric dynamics in the Marseille region, particularly wind dynamics and pollutant transport, for the purpose of gaining a deeper understanding of the atmospheric behaviors in this complex terrain for use in future regional emission transport studies [42; 43; 44]. While ESCOMPTE focused some of its efforts on pollutant transport, there has not yet been a CO\({}_{2}\)-specific study in the region, which is an additional motivation behind the present study. Furthermore, ESCOMPTE focused only on the summer season, whereas here we include seasonal comparisons. Forward-model comparisons have proven to be sufficiently accurate for regional constraining of CO\({}_{2}\) emissions, and often serve as a crucial first step towards the implementation of inverse atmospheric methods [22]. In this study, we establish a CO\({}_{2}\) modeling framework in the complex urban-coastal SUD-PACA region of France and assess its effectiveness in replicating contemporaneous CO\({}_{2}\) mixing ratio measurements. The Weather Research and Forecasting Model with Chemistry (WRF-CHEM) atmospheric transport model is adapted to include the most complete and accurate available emissions estimates for all CO\({}_{2}\) components in the region. We analyze and discuss the capacity of the model to reproduce the observations at different spatial and temporal scales, including testing the effectiveness of the Urban Heat Island (UHI) module. We couple the Lagrangian Particle Dispersion Model (LPDM) as an adjoint to assess the capacity of the utilized measurement We configured the WRF system to run simulations spanning three one-month periods: July 2016, which we call \"Summertime 2016\", 27 January-27 February 2017, which we call \"Wintertime 2017\", and 22 April-22 May 2017, which we call \"Springtime 2017\". This was to allow for seasonal comparisons, as typically more CO\({}_{2}\) uptake is expected in the summer after the plants have bloomed. Springtime 2017 was included because most of the CO\({}_{2}\) uptake in the region was found to occur during this period rather than during the summer. Following [49], each month of simulations was created in segments of 5 d with an additional 12 h overlapping time window to allow the meteorology to spin up. In addition, an extra segment of 5.5 d was run before the period of interest to spin up the CO\({}_{2}\) mixing ratios in the region from the boundary conditions, i.e., to allow enough time for the domain to be filled with ambient background values. The model was adapted to include multiple CO\({}_{2}\) components which could run simultaneously and be combined post-simulation for direct comparison against other models and in situ measurements taken at the sampling sites. For this study, the relevant components included the Open-source Data Inventory for Anthropogenic CO\({}_{2}\) (ODIAC2017) product for anthropogenic emissions [53; 54], the European Centre for Medium-Range Weather Forecasts (ECMWF)-based C-TESSEL model estimates for biogenic emissions (gross primary production (GPP), net ecosystem emissions (NEE), respiration (including both autotrophic and heterotrophic) [55; 56], Fire INventory from NCAR (FINN), consisting of the version 1.5 model estimates for fire emissions [57], a manually-created ocean flux product, and Carbon Tracker Europe for boundary conditions [58; 59]. Each of these components are explained in further detail in the following subsection.