## 3 Results and Discussion
### Impact of the Urban Heat Island Effect  
The Urban Heat Island (UHI) effect describes the additional heat that becomes trapped in urban areas compared to adjacent rural areas as a result of human activities [86]. These activities include anthropogenic heat emissions, topographical changes (especially to land cover, where permeable moist ground is replaced by impermeable dry pavement and concrete [87]), buildings and their surroundings [88], lowered wind speeds, and generally diminished evaporative surfaces [89; 90; 91]. In cities with a population of over one million, the mean yearly air temperature can be 1-3 degrees Celsius higher than in adjacent rural areas [92], and the effect tends to scale with city size [89]. While this effect is more pronounced at night, it is still present during the day, and the warmer air promotes turbulent mixing of pollutants [93]. An analysis was performed to test the differences in model performance compared to observations when the UHI effect is not simulated. As stated in Section 2.2.1, the WRF-Chem configuration for this investigation uses the Noah LSM scheme land surface model, the MYNN 2.5 level TKE boundary layer model, and the built-in urban canopy model that was intended for use with the Noah LSM. Additional runs were performed with the UHI module turned off. In order to achieve this, the Noah LSM was replaced by a simple thermal diffusion scheme that only uses five layers of soil temperature for its calculations, and no urban canopy model was assigned. Simulations were run without the UHI module for each of the Summertime 2016, Wintertime 2017, and Springtime 2017 periods. Figure 5 shows a comparison of the time series of the simulated boundary layer heights with and without the UHI effect for each of the three stations (OHP, CAV, and SME) recording CO\({}_{2}\) measurements. These time series include only the daytime hours of 12:00-18:00 UTC. Overall, Figure 5 shows that there is an enhanced Planetary Boundary Layer (PBL) when the UHI is included in the model, as is to be expected. This difference is the greatest in Summertime 2016, the warmest season. The differences in simulated total CO\({}_{2}\) will be touched upon in the succeeding section. The figure also seems to indicate that the boundary layer heights track well with each other. In order to better quantify the boundary layer height difference with and without the UHI module, we calculated the percentage difference of the mean values across the three analysis periods for each of the three sites, with the results presented in Table 3. \begin{table}
\begin{tabular}{l l c c c} \hline  & & **Summertime 2016** & **Wintertime 2017** & **Springtime 2017** \\ \hline ME & ws & 1.0 ms\({}^{-1}\) & 1.4 ms\({}^{-1}\) & 0.7 ms\({}^{-1}\) \\  & wd & \(-\)10.78\({}^{\circ}\) & 8.1\({}^{\circ}\) & 6.6\({}^{\circ}\) \\ MAE & ws & 2.4 ms\({}^{-1}\) & 2.3 ms\({}^{-1}\) & 1.8 ms\({}^{-1}\) \\  & wd & 58.36\({}^{\circ}\) & 29.3\({}^{\circ}\) & 28.5\({}^{\circ}\) \\ \hline \end{tabular}
\end{table} 
Table 2: The mean error (ME) and mean absolute error (MAE) for wind speed (ws) and wind direction (wd) across all 12 meteorological stations in Figure 11 for the analyzed hours of the Summertime 2016, Wintertime 2017, and Springtime 2017 periods. Table 3 shows that the simulations with and without the UHI module agree rather well in the wintertime and springtime periods, which is potentially a result of the PBL having less variability compared to the summertime. Additionally, they agree best at the urban station, CAV. The disagreement at SME, which is at the ocean-land border, may result from difficulties with accurately capturing the PBL along this boundary. The site/period combination with the highest amount of disagreement is the SME station during Summertime 2016.