## 1 Introduction
#### 2.2.2 Model CO\({}_{2}\) Flux Components  
For the anthropogenic component, ODIAC estimates from the 2017 version were used, hereinafter referred to as ODIAC2017. ODIAC is a satellite-based product that  
Figure 1: Extent of the 9 km, 3 km, and 1 km domains, shown in green, yellow, and red, respectively, along with labels in blue for measurement sites where CO\({}_{2}\) is recorded. The Cap Corse station (ERSA), is used here as the observation background station, is beyond the boundaries of the 3 km and 1 km domains. uses nighttime light data to estimate population, which is then used as a proxy for CO\({}_{2}\) emissions [53; 54]. Although it lacks the sector-specific nature of more traditional bottom-up inventory assessment, it provides a global product, allowing for estimates in regions where trustworthy bottom-up products may not yet be available. In comparisons, it has been shown to match well with other inventory flux estimates [50; 60]. For our study, the ODIAC2017 estimates were taken at 1 km resolution and then interpolated onto the 9 km, 3 km, and 1 km simulation domains, as shown in Figure 1. Figure 2 shows three example hourly flux maps for the innermost domain for 3 July 2016 at 14:00 UTC, 3 February 2017 at 14:00 UTC, and 3 May 2017 at 14:00 UTC. The emissions are plotted on a log-10 scale to allow the spatial variations to be seen, otherwise the very strong point sources would wash out the colorscale. The ODIAC2017 files are provided as monthly totals for the years 2015 and 2016 [61]. For this analysis, we used the files from 2016 for each of our July 2016, Wintertime 2017, and Springtime 2017 periods of interest, as 2017 files were not yet available at the time of this analysis. In order to convert these emissions files to the hourly resolution needed to match our WRF model, the emissions were first divided equally among all the hours of the month. We then imposed a diurnal cycle on the model based on the one used by  
Figure 2: Flux maps for several of the tracers included in the model. From top to bottom: ODIAC2017 anthropogenic emissions, ECMWF NEE, and derived sea flux (using ECMWF SST and 10-m winds). The left column is for 3 July 2016 at 14:00 UTC, the middle column is for 3 February 2017 at 14:00 UTC, and the right column is for 3 May 2017 at 14:00 UTC, chosen as arbitrary demonstrative examples from each seasonal period. Units are all in mol/km\({}^{2}\)/h, while ODIAC2017 is plotted on a log-10 scale because of the large spatial emission variations. where \(J\) indicates the total number of pixels in the domain, \(j\) specifies an individual pixel, \(hr\) is the subscript denoting a particular hour, \(X_{j\_{\_}{hr}}\) is the AtmoSud estimated flux at a given pixel at this hour, and \(\overline{X_{\_}{j\_}{day}}\) is the mean AtmoSud estimated flux at this pixel for the whole day. The sum of emissions for all hours in a day is then preserved at the same value irrespective of whether or not this scaling factor is used. Pixels with emissions greater than \(4\times 10^{5}\) mol/km\({}^{2}\)/h are assumed to contain a plant with emissions that are not expected to follow a normal diurnal cycle, and as such are excluded from this scaling factor treatment. For the biogenic CO\({}_{2}\) flux estimates, we use the ECMWF land-surface model known as C-TESSEL [55], which offers global coverage of natural land CO\({}_{2}\) fluxes that are especially useful in atmospheric-model-based CO\({}_{2}\) emissions analyses (e.g., [18; 20; 62]). C-TESSEL provides estimates of GPP, NEE, and Respiration (including both autotrophic and heterotrophic) at a resolution of approximately 0.09\({}^{\circ}\) latitude and longitude. These are then interpolated onto our relevant WRF grid using linear triangulation, as was done with the other CO\({}_{2}\) component estimates. Figure 2 includes example flux maps of NEE on the innermost 1 km domain for 3 July 2016 (middle row, left column), 3 February 2017 (middle row, middle column), and 3 May 2017 (middle row, right column), all at 14:00 UTC.