Parameterization and Explicit Modeling of Cloud Microphysics: Approaches, Challenges, and Future Directions

Abstract

Cloud microphysical processes occur at the smallest end of scales among cloud-related processes and thus must be parameterized not only in large-scale global circulation models (GCMs) but also in various higher-resolution limited-area models such as cloud-resolving models (CRMs) and large-eddy simulation (LES) models. Instead of giving a comprehensive review of existing microphysical parameterizations that have been developed over the years, this study concentrates purposely on several topics that we believe are understudied but hold great potential for further advancing bulk microphysics parameterizations: multi-moment bulk microphysics parameterizations and the role of the spectral shape of hydrometeor size distributions; discrete vs “continuous” representation of hydrometeor types; turbulence-microphysics interactions including turbulent entrainment-mixing processes and stochastic condensation; theoretical foundations for the mathematical expressions used to describe hydrometeor size distributions and hydrometeor morphology; and approaches for developing bulk microphysics parameterizations. Also presented are the spectral bin scheme and particle-based scheme (especially, super-droplet method) for representing explicit microphysics. Their advantages and disadvantages are elucidated for constructing cloud models with detailed microphysics that are essential to developing processes understanding and bulk microphysics parameterizations. Particle-resolved direct numerical simulation (DNS) models are described as an emerging technique to investigate turbulence-microphysics interactions at the most fundamental level by tracking individual particles and resolving the smallest turbulent eddies in turbulent clouds. Outstanding challenges and future research directions are explored as well.

Locally narrow droplet size distributions are ubiquitous in stratocumulus clouds

“…For example, the results have implications for how to address the implicit dependence of distribution width on distribution moments, such as effective radius or liquid water content, i.e., the dispersion effect ( 40 ). Our findings can also guide the development of new approaches that are already underway, such as three-moment bulk representation of clouds in GCMs with distribution shape considered as a third prognostic variable ( 41 ). Specifically, the observations quantify the variability in distribution shape and the associated length scales related to size distribution changes.…”

Raindrop Deformation in Turbulence

“…In numerical models, raindrop coalescence and breakup remain the most uncertain and theoretically challenging liquid microphysical processes to quantify (Morrison et al., 2020). In the pursuit of advancing physically based parameterizations of raindrop collision‐coalescence and breakup, it is essential to account for the influence of turbulence on raindrop microphysics (Liu et al., 2023; Morrison et al., 2020). In addition, raindrops are assumed to fall at terminal speeds without a broad distribution of fall speeds affected by turbulence, as observed in this study, even in state‐of‐the‐art Lagrangian particle‐based schemes (Shima et al., 2020).…”

Why (Still) Studying Turbulence in Fluids and Plasmas?

“…Turbulence in the atmosphere also influences cloud formation and dissipation and accurately representing clouds in climate models is vital for predicting future climate changes. However, there are various challenges in representing clouds in climate models, including the complex interactions between turbulence and cloud microphysics (Liu et al, 2023). Turbulence affects cloud formation and dissipation, and the interactions between turbulence and cloud particles can be highly non-linear and difficult to model accurately (Shaw, 2003).…”