# 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
It is worth noting that the linear relation derived based on the \(1^{\circ}\times 1^{\circ}\) pixels compares well with the domain anomalies, which indicates that the relationship based on the individual pixels is also representative for the whole region. This relationship between AOD and NO\({}_{2}\) explains most of the variation in AOD but not all of it. To investigate whether a dependence of the AOD on LST or other parameters could be quantified despite the large changes in anthropogenic emissions we used Orthogonal Distance Regression (ODR) to separate the different factors affecting the total AOD level. We checked all available quantities (e.g., tropospheric NO\({}_{2}\) column densities, total column water vapour, soil moisture, fire radiative power, and meteorological parameters) to see whether AOD exhibits a statistically reliable relationship with any of them. The reliability of the linear relationships was based on the 95% confidence intervals of the linear fits. For example, fire radiative power, which we used as a proxy for biomass burning emissions, did not exhibit any kind of relationship with AOD, which is a clear indication that biomass burning emissions do not explain the variation in AOD level. This was to be expected as the contribution of biomass burning emissions to the aerosol load in this region is at a minimum during summers ([PERSON] et al., 2016). The regression analysis showed that in addition to the tropospheric NO\({}_{2}\) column density anomalies only LST anomalies and southerly wind speeds (SW10) exhibited linear relationships with AOD. We confirmed with regression analysis that these quantities were not correlated with each other and used them as independent parameters in the analysis. To quantify the relation between AOD and these parameters, we calculated a multivariate fit to the data and obtained the following equation (with 95% confidence intervals given in parenthesis):  
\[AOD_{anom}=a\ NO_{2,trop,anom}+b\ LST_{anom}+c\ SWV10_{anom}+d, \tag{1}\]  
where  
\(a=2.68\)e\({}^{-16}\pm 2.87\)e\({}^{-17}\) cm\({}^{2}\) molec\({}^{-1}\),  
\(b=0.007\pm 0.006\) K\({}^{-1}\),  
\(c=0.032\pm 0.019\) s m\({}^{-1}\), and  
\(d=-0.003\pm 0.006\). The dependence of AOD on the tropospheric NO\({}_{2}\) column density anomalies and LST anomalies was expected as they are proxies for anthropogenic and biogenic emissions, respectively, which are known to influence AOD levels [18; 73]. However, the dependence of AOD on SW10 was not detected from our earlier analysis with the domain averages (Figure 1) as it was likely hidden by the spatial averaging. Nevertheless, a wind speed dependence on aerosol concentrations is to be expected as it can be caused both by transport of aerosols generated elsewhere to the studied area as well as by transport of aerosols away and thus reducing the aerosol load with higher wind speeds. To investigate this effect of wind speed on the observed AOD we used our CONTROL simulation to see which quantities were correlated with meridional wind speeds (V10). For the analysis, we calculated summer averages for all the grid cells in the southeastern USA for the summers 2002-2010. Based on this dataset, none of the meteorological variables (relative humidity, vertically integrated water vapour, and tropopause height) exhibited a linear relationship with V10. Then we looked at the AODs of different aerosol species and found that only the AODs of sea salt and dust aerosols were positively correlated with V10. The correlation coefficients for sea salt and dust AOD were 0.58 and 0.33, respectively. To evaluate the significance of these species regarding the total AOD, we calculated their fraction  
Figure 3: Summertime (JJA) anomalies of AOD vs. tropospheric NO\({}_{2}\) column densities over the southeastern USA for the years 2005–2011. Blue pentagons represent averages over the whole domain (\(r=0.92\)) while the dots represent \(1^{\circ}\times 1^{\circ}\) degree pixels within the domain. AOD is from L3 AATSR and tropospheric NO\({}_{2}\) from L3 OMI.