Using laboratory data to inform superdroplet representations of surface-active organics

Biogenic secondary organic aerosol are omnipresent in Earth’s atmosphere, with sources from boreal and tropical forests as well as oceans. Laboratory and field work has shown the tendency for organic compounds to preferentially partition to the surface and depress the surface tension of these aerosol. Incorporating this level of detail into traditional bulk cloud microphysics schemes is challenging. Instead, we use PySDM, a pythonic particle-based microphysics model using the super-droplet method, to investigate the role of surface-active organic species in cloud droplet activation and subsequent cloud evolution. We calibrate three thermodynamic frameworks for surface partitioning of organics based on measurements of cloud droplet activation in a laboratory flow-tube experiment. We assess the implications of surface partitioning by organic species on the resulting macrophysical cloud properties in an adiabatic parcel modeling framework. Surface partitioning is found to influence climate-relevant quantities, including cloud droplet number concentration, liquid water path, and cloud albedo. However, the magnitude of this effect varies between the different thermodynamic model assumptions and depends on the meteorological regime; surface partitioning is most influential in regimes of slow updraft velocities.