Benchmarking Turbulence Models to Represent Cloud-Edge Mixing

TL;DR

This study evaluates various statistical turbulence models against direct numerical simulations to determine their effectiveness in representing cloud-edge mixing and microphysical processes.

Contribution

It compares multiple statistical approaches for small-scale turbulent mixing in clouds using DNS as a reference, highlighting their strengths and limitations.

Findings

All models successfully simulate thermodynamic evolution.

Not all models accurately capture microphysical changes.

Implications for subgrid-scale scheme development are discussed.

Abstract

Considering turbulence is crucial to understanding clouds. However, covering all scales involved in the turbulent mixing of clouds with their environment is computationally challenging, urging the development of simpler models to represent some of the processes involved. By using full direct numerical simulations as a reference, this study compares several statistical approaches for representing small-scale turbulent mixing. All models use a comparable Lagrangian representation of cloud microphysics, and simulate the same cases of cloud-edge mixing, covering different ambient humidities and turbulence intensities. It is demonstrated that all statistical models represent the evolution of thermodynamics successfully, but not all models capture the changes in cloud microphysics (cloud droplet number concentration, droplet mean radius, and spectral width). Implications of these results for using the presented models as subgrid-scale schemes are discussed.