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.

Highlights

  • Clouds are important for the global climate due to their ability to reflect solar radiation and trap outgoing longwave infrared radiation but there are still several unknowns and uncertainties related to their dynamics including 15 aerosol-cloud interactions (e.g., Wood, 2012; Seinfeld et al, 2016; Schneider et al, 2017; Rosenfeld et al, 2019)

  • 335 In this study we developed three cloud base updraft velocity parameterisations that can be used in global atmospheric models

  • The parameterisations represent the predictions of the large-eddy simulation model UCLALES-SALSA (Tonttila et al, 2017) for a wide range of marine boundary layer clouds described by the global climate model ECHAM

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Summary

Introduction

Clouds are important for the global climate due to their ability to reflect solar radiation (shortwave radiation) and trap outgoing longwave infrared radiation but there are still several unknowns and uncertainties related to their dynamics including 15 aerosol-cloud interactions (e.g., Wood, 2012; Seinfeld et al, 2016; Schneider et al, 2017; Rosenfeld et al, 2019). Cloud formation requires supersaturation with respect to water vapour and aerosol particles that can act as cloud condensation nuclei (CCN). Several processes can lead to the formation of supersaturation within an air parcel, the most important one is the adiabatic cooling caused by updrafts. Malavelle et al (2014) state that updraft velocities strongly control the activation of aerosol particles. Together with aerosol properties and concentrations, the strength of updraft determines the number 20 of droplets formed. Cloud droplet number concentration (CDNC) directly impacts both the precipitation formation and the radiative properties of clouds

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