# Summertime Aerosol Radiative Effects and Their Dependence on Temperature over the Southeastern USA
## 3 Results and Discussion
### Temperature Dependence of Summertime AOD over the Southeastern USA
AOD and LST are based on the L3 AATSR data while the tropospheric NO\({}_{2}\) column densities are based on the L3 OMI and the meridional wind speed on the ERA-Interim data. The error bars represent the uncertainty of the data points and \(n\) is the number of data points used in the calculation of each summer average. total fine mode organic aerosol. Furthermore, [PERSON] et al. (2019) and [PERSON] et al. (2020) showed that in the southeastern USA almost all isoprene-derived SOA is formed through the low-NO\({}_{\mathrm{x}}\) pathway and sulphate enhances the SOA yield by providing particle water and acidity. Under low-NO\({}_{\mathrm{x}}\) conditions SOA yields from isoprene are expected to be larger because peroxy radicals are not able to react with NO. Instead, they produce IEPOX through a HO\({}_{\mathrm{x}}\)-mediated mechanism ([PERSON] et al., 2016; [PERSON] et al., 2017; [PERSON] et al., 2020). The actual mechanism responsible for the anthropogenic effect on biogenic SOA formation cannot be discerned with the remote sensing datasets at our disposal, but the total effect of the anthropogenic emissions on AOD level can be estimated. In order to gain a better understanding of the effects of anthropogenic and biogenic emissions on the AOD, we investigated the time series shown in Figure 2 and Table S2 in more detail. As Figure 2 shows, the tropospheric NO\({}_{2}\) column densities decreased linearly between the summers 2005 and 2007. At the same time LST increased by 2 K, whereas AOD did not exhibit a clear trend. Moreover, the AOD values in the summers of 2007 and 2011 were larger than in the preceding summers while in these same summers the tropospheric NO\({}_{2}\) column densities were smaller than in the preceding summers. Therefore, it is not likely that the high AOD values in these summers are due to increased NO\({}_{2}\) concentrations. Instead, this increase in AOD in the summers of 2007 and 2011 with respect to that in the preceding summers could be caused by the increase in biogenic emissions in these years in response to enhanced temperatures. However, the comparison of the periods 2005-2007 and 2009-2011 indicates that the decreasing anthropogenic emissions have a larger effect on the AOD level than the increasing temperature. Both periods undergo similar increase in temperature but the AOD and tropospheric NO\({}_{2}\) levels do not exhibit a similar increase over that period. Thus, the data in Figure 2 suggest that there is a temperature-dependent component in the summertime AOD over the southeastern USA as [PERSON] et al. (2018) reported but its impact on the total AOD (anthropogenic + biogenic AOD) is considerably smaller than the impact of anthropogenic emissions and therefore, it is not clearly visible in our data due to the overwhelming effect of NO\({}_{2}\) reductions during the period used in the analysis. Therefore, Figure 1 appeared at first to contradict the findings of [PERSON] et al. (2018). To quantify the effects of temperature-induced biogenic emissions and anthropogenic emissions on the AOD a statistical analysis was undertaken using data averaged over individual \(1^{\circ}\times 1^{\circ}\) pixels. Even though the correlation coefficients between the domain-averaged parameters can be high, a statistically robust relation cannot be derived due to the small number of data points and the significant uncertainties in the observational values. For a robust estimate of the impact of different emission sources on AOD, we calculated the anomalies of all the parameters for individual \(1^{\circ}\times 1^{\circ}\) pixels within the studied domain (82 pixels in total) in a similar manner as we did for the whole region. We calculated pixel-wise anomalies to remove the spatial gradients that might complicate the analysis. When the pixel-wise anomalies of summertime tropospheric NO\({}_{2}\) column densities are compared to the corresponding AOD anomalies, a linear dependence can be seen, as the linear fit with 95% confidence intervals in Figure 3 shows.