# Summertime Aerosol Radiative Effects and Their Dependence on Temperature over the Southeastern USA
## 2 Methodology
### Aerosol-Climate Model

Table 2: Reaction coefficients for VOC oxidation in the form \(k=k_{0}\exp(E^{\prime}_{a}/T)\), with \(k_{0}\) in m\({}^{3}\) mol\({}^{-1}\) s\({}^{-1}\) and \(E^{\prime}_{a}\equiv E_{a}/R\) in K, \(\text{k}=\text{k}_{0}\exp(\text{E}^{\prime}_{a}/T)\). where \(R\) is the gas constant, \(E_{a}\) is the activation enthalpy, \(T\) is temperature, and \(k_{0}\) the reference reaction coefficient at 298 K.  
classes based on their saturation vapour pressure, C*: VBS0 (C* = 0 \(\mathrm{\SIUnitSymbolMicro g}\) cm\({}^{-3}\); non-volatiles), VBS1 (C* = 1 \(\mathrm{\SIUnitSymbolMicro g}\) cm\({}^{-3}\); low-volatiles), and VBS10 (C* = 10 \(\mathrm{\SIUnitSymbolMicro g}\) cm\({}^{-3}\); semi-volatiles). The second category, which represents species forming SOA in the aqueous phase, contains isoprene epoxydiols (IEPOX) and glyoxals (GLYX). Gas phase IEPOX and GLYX are removed via further oxidation, and the applied reaction coefficients are also listed in Table 2.  
The partitioning of SOA forming compounds is assumed to be a non-equilibrium process and it is calculated by solving the condensation equations for all size classes using the Analytical Predictor of Condensation method [61; 62]. Aqueous production of SOA by IEPOX and GLYX is modelled via their reactive partitioning into aerosol water [63], with partitioning coefficients obtained from [PERSON] et al. [63] for GLYX and [PERSON] et al. [64] for IEPOX. Partitioning of organic compounds into cloud water was not considered in this study. The simulations were done using T63 horizontal resolution (roughly 1.9\({}^{\circ}\)\(\times\) 1.9\({}^{\circ}\)) and 31 pressure levels that reached up to 10 hPa. The model's large scale circulation (divergence, vorticity and surface pressure) in our simulations was nudged towards the ECMWF reanalysis data (ERA-Interim; [65]) to ensure compatibility between the model and the observed atmospheric conditions. The simulation period was 2002-2010, with a three-month spin-up. It has to be noted that, due to its coarse resolution, the climate model is not expected to fully replicate the observations, but as it includes a state-of-the-art description of biogenic SOA it is a suitable tool to investigate the magnitude of the effect of biogenic aerosol sources on the aerosol load over the southeastern USA. The intention of the model analysis was not to reproduce all the details of the observations. Instead, it was used as a tool to estimate the biogenic contribution to AOD using sensitivity simulations. This enabled us to evaluate whether the observed relationship between temperature and AOD could be explained by biogenic aerosols. In order to do this, two model simulations were undertaken: a control simulation where all the schemes described above were in use (CONTROL) and a sensitivity simulation that did not include biogenic SOA precursors (noBIOSOA).The significance of biogenic emissions was then estimated by comparing the sensitivity simulation to the CONTROL run. To ensure that the ECHAM-HAMMOZ model could reproduce the main AOD and LST characteristics over the studied region, the modelling results were compared with AATSR observations. The monthly AOD and LST averages from the CONTROL simulation were in a reasonable agreement with the values from the AATSR retrievals for the years 2003-2010. The correlation coefficients for the AOD and LST values were 0.77 and 0.94, respectively. The simulated AOD values overestimated the lowest AOD values but underestimated the largest ones. (See Figures S4 and S5 for more details).