Can a more accurate representation of aerosol activation improve simulations of fog?

Abstract:

Aerosols play a crucial role for fog, as they determine the fog droplet number concentration, which in turn controls both the fog's optical thickness and life span. However, detailed aerosol-microphysics schemes which accurately represent droplet formation are unsuitable for weather forecasting models, as the computational power required to calculate droplet formation would dominate the treatment of the rest of the physics in the model. A more appropriate method to account for droplet formation is to use an aerosol activation scheme, which parameterises the droplet number concentration based on a change in supersaturation at a given time. Traditionally, aerosol activation schemes assume that supersaturation is reached through adiabatic lifting, with many schemes imposing a minimum vertical velocity e.g. 0.1 m/s. These schemes are not be suitable for fog modelling. In radiation fog, initial formation is primary driven by radiative cooling, with the measured updrafts during this time being around 0 m/s. Consequently, using traditional aerosol activation schemes in fog modelling will initially provide inaccurate cooling rates, resulting in the fog transitioning to an optically thick layer too fast.

This work addresses the suitability of most aerosol activation schemes used in fog modelling, by introducing a more physically based scheme that can account for atmospheric saturation due to non-adiabatic processes. Using an offline model, our results show that the minimum updraft velocity threshold assumption can overpredict the droplet number by up to 70% in comparison to a cooling rate found in fog formation. In addition, simply using an adiabatic cooling rate can underpredict the same population by up to 20%. This scheme has been implemented in the Met Office NERC Cloud (MONC) LES model, and tested using the observations of a radiation fog case study based in Cardington, UK. Our results show that by using a more physically based method of aerosol activation leads to a more appropriate calculated cloud droplet number. This allowed for a slower transition to a well-mixed fog, which was more in-line with observations.

Through this work, we demonstrate the importance of aerosol activation representation in fog modelling, and its impact on processes linked to the formation and development of radiation fog.