Parameterising cloud base updraft velocity of marine stratocumuli

Abstract

Abstract. The number of cloud droplets formed at the cloud base depends both on the properties of aerosol particles and the updraft velocity of an air parcel at the cloud base. As the spatial scale of updrafts is too small to be resolved in global atmospheric models, the updraft velocity is commonly parameterised based on the available turbulent kinetic energy. Here we present alternative methods through parameterising updraft velocity based on high-resolution large eddy simulation (LES) runs in the case of marine stratocumulus clouds. First we use our simulations to assess the accuracy of a simple linear parametrisation where the updraft velocity depends only on cloud top radiative cooling. In addition, we present two different machine learning methods (Gaussian process emulation and random forest) that account for different boundary layer conditions and cloud properties. We conclude that both machine learning parameterisations reproduce the LES-based updraft velocities at about the same accuracy, while the simple approach employing radiative cooling only produce on average lower coefficient of determination and higher root mean square error values. Finally, we apply these machine learning methods to find the key parameters affecting cloud base updraft velocities.

Response to reviewers of acp-2022-265

“…While the importance of sea spray emission temperature dependence has been pointed out by several sources (Grythe et al, 2014;Liu et al, 2021;Mårtensson et al, 2003), its magnitude and even direction differs between different parameterizations.…”

“…Grythe et al (2014) compared a number of sea spray emission schemes and showed that the emitted mass fluxes can differ by more than an order of magnitude with strong disagreements in emitted size distributions and the wind speed and sea surface temperature (SST) dependence of the emission. Observations and model-measurement comparisons support the growth of sea salt mass flux with increasing SST (Jaeglé et al, 2011;Liu et al, 2021;Sofiev et al, 2011) and point out the importance of including it in model parameterizations. However, the case is not clear for the particle number flux that mostly consists of small particles.…”

“…The 2017) is replaced by the double-moment scheme by Seifert and Beheng (2001). In UCLALES-SALSA, these schemes essentially move the cloud droplets that grow to drizzlesize from the dry-size based cloud bins to the wet-size based rain bins, allowing accurate modelling of precipitation formation via condensation and coalescence processes (Ahola et al, 2021). The parameterization is a computationally efficient alternative for explicitly simulating the initial rain formation step based on collisions between cloud droplets (Tonttila et al, 2021).…”