# 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
The error bars represent the uncertainty of the observations (one standard deviation). The linear fit shown with the red dashed line is based on the individual data points (\(AOD_{anom}=2.56e^{-16}\big{(}\pm 2.27\)e\({}^{-17}\))\(NO_{2,trop,anom}+0.005\)(\(\pm\) 0.006), 574 observations, \(r=0.43\), with 95% confidence intervals given in the parenthesis). The red curtain represents the 95% confidence interval for the linear fit. The colour of the dots indicates the density of the overlapping data points: the darker the colour, the more overlapping points there are. from the total AOD and found that the correlation coefficients between these fractions and V10 were even higher: 0.70 and 0.45 for sea salt and dust, respectively. These results indicate that the dependence of AOD on SW10 is most likely caused by the transport of sea salt and dust aerosol to the region. The dust aerosols are likely transported long-range from the Sahara [82]. Although the observations indicate that the temperature-dependent AOD component is likely caused by biogenic emissions, it is not possible to ascertain the cause based solely on the observations. Therefore, we used climate model simulations as described in Section 2.2, to test whether the effect of biogenic emissions on the AOD would provide a plausible explanation for the observed temperature dependence of AOD. As a first step, we calculated the contribution of different aerosol species to the summertime AOD in the southeastern USA to see which aerosol sources are the most dominant ones. Based on our CONTROL simulation the contributions from water, SO\({}_{4}\), biogenic SOA, OC, sea salt, mineral dust, and BC to AOD are 54%, 27%, 11%, 3%, 2%, 1%, and 1%, respectively. Water is the most abundant aerosol component but as it is usually present with most aerosol types in a concentration depending on hygroscopicity and ambient relative humidity, it does not provide us information on the sources of aerosols. However, the prevalence of the other components is in line with our observational results: anthropogenic emissions (SO\({}_{4}\)) are the dominant drivers of AOD but biogenic emissions (biogenic SOA and part of OC) have also a strong impact on AOD. Biomass burning emissions (BC and OC), and marine (sea salt) and dust sources have minor contributions to the seasonal AOD level. Next, we estimated the contribution of biogenic aerosol by subtracting the average summertime AOD values (summers 2002-2010) of the noBIOSOA simulation from the corresponding values of the CONTROL run. As Figure 4 shows, modelled AOD of biogenic SOA has a clear temperature dependence (\((4\pm 1)\times 10^{-3}\) K\({}^{-1}\)), which is within the uncertainty range of the temperature dependence of AOD obtained from the satellite observations ((7 \(\pm\) 6) \(\times\) 10\({}^{-3}\) K\({}^{-1}\)). Since BVOC emissions and the following SOA formation are temperature-dependent in the model, this may seem like a trivial result. However, the magnitude of the impact these emissions have on AOD is not predefined and it depends on atmospheric chemistry (see for example [9] for more details), state and composition. Therefore, the simulations can be used to evaluate the observational results. As the simulated and observation-based temperature dependence of AOD are in the same range, this agreement indicates that biogenic emissions could be the main cause for the temperature dependence of AOD in this region. Figure 4: Biogenic AOD (based on the difference between the CONTROL and the noBIOSOA simulations) vs. LST anomaly for the summers (JJA) 2002–2010. Pentagons represent averages over the whole domain (\(r\) = 0.65) while the dots represent 1.9\({}^{\circ}\)\(\times\) 1.9\({}^{\circ}\) pixels within the domain.