Abstract
Abstract. Aerosols play a crucial role in the fog life cycle, as they determine the droplet number concentration and hence droplet size, which in turn controls both the fog's optical thickness and lifespan. Detailed aerosol-microphysics schemes which accurately represent droplet formation and growth are unsuitable for weather forecasting and climate models, as the computational power required to calculate droplet formation would dominate the treatment of the rest of the physics in the model. A simple method to account for droplet formation is the use of an aerosol activation scheme, which parameterises the droplet number concentration based on a change in supersaturation at a given time. Traditionally, aerosol activation parameterisation schemes were designed for convective clouds and assume that supersaturation is reached through adiabatic lifting, with many imposing a minimum vertical velocity (e.g. 0.1 m s−1) to account for the unresolved subgrid ascent. In radiation fog, the measured updraughts during initial formation are often insignificant, with radiative cooling being the dominant process leading to saturation. As a result, there is a risk that many aerosol activation schemes will overpredict the initial fog droplet number concentration, which in turn may result in the fog transitioning to an optically thick layer too rapidly. This paper presents a more physically based aerosol activation scheme that can account for a change in saturation due to non-adiabatic processes. Using an offline model, our results show that the equivalent cooling rate associated with the minimum updraught velocity threshold assumption can overpredict the droplet number by up to 70 % in comparison to a typical cooling rate found in fog formation. The new scheme has been implemented in the Met Office Natural Environment Research Council (NERC) Cloud (MONC) large eddy simulation (LES) model and tested using observations of a radiation fog case study based in Cardington, UK. The results in this work show that using a more physically based method of aerosol activation leads to the calculation of a more appropriate cloud droplet number. As a result, there is a slower transition to an optically thick (well-mixed) fog that is more in line with observations. The results shown in this paper demonstrate the importance of aerosol activation representation in fog modelling and the impact that the cloud droplet number has on processes linked to the formation and development of radiation fog. Unlike the previous parameterisation for aerosol activation, the revised scheme is suitable to simulate aerosol activation in both fog and convective cloud regimes.
Highlights
Fog can be defined as a cloud at ground level with a surface visibility of less than 1 km (WMO, 1966)
Our results show that the equivalent cooling rate associated with the minimum updraught velocity threshold assumption can overpredict the droplet number by up to 70 % in comparison to a typical cooling rate found in fog formation
A domain size of 132 × 132 m2 was chosen, as there is minimal impact on the fog’s turbulent kinetic energy (TKE) and liquid water when compared to simulations that were tested on a larger domain. Previous studies such as Maalick et al (2016) and Maronga and Bosveld (2017) have run large eddy simulation (LES) fog simulations at higher horizontal resolutions, we found that running our cases at 2 m allowed for us to address our objectives whilst compromising on both data storage and computational expense
Summary
Fog can be defined as a cloud at ground level with a surface visibility of less than 1 km (WMO, 1966). It can cause major disruption to road, aviation and marine transport, with associated economic losses that are comparable to those resulting from winter storms and hurricanes (Gultepe et al, 2007). Fog can have negative impacts on human health and the safety of certain activities. Thick fog on 5 September 2013 resulted in the Sheppey Crossing crash in southeast England, injuring 60 people (BBC, 2013). C. Poku et al.: Aerosol activation representation in fog derstanding the physics behind fog is crucial in improving fog forecasting and mitigating the impact of such events
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