A Model Intercomparison Study of Mixed-Phase Clouds in a Laboratory Chamber

TL;DR

This study addresses the challenge of representing mixed-phase cloud microphysics under turbulent transport by performing a ten-configuration intercomparison of Pi Chamber simulations across DNS, LES, and bulk/bin/Lagrangian microphysics. It calibrates each model to a liquid-phase steady state with a mean droplet radius of $r_{\mathrm{mean}}=7.75\,\mu\mathrm{m}$ and $N=25\,\mathrm{cm}^{-3}$, then injects ice at rates from $0.5$ to $15\,\mathrm{cm}^{-3}\,\mathrm{min}^{-1}$ to examine glaciation. All models capture the qualitative glaciation trends—liquid water depletion and ice growth—but show quantitative differences due to wall forcing, turbulence treatment, and particle-removal schemes; most LES do not reach full glaciation, whereas core-only or coarser-grid setups tend toward it. Supersaturation fluctuations, particularly near the bottom wall, sustain liquid droplets even under negative mean supersaturation, highlighting the importance of near-wall processes. The work demonstrates the value of laboratory experiments for validating and guiding improvements in turbulent, microphysical representations in atmospheric models and outlines pathways to improve predictions of cloud radiative and precipitation processes.

Abstract

Mixed-phase clouds, composed of supercooled liquid droplets and ice crystals, play a critical role in weather and climate systems. Their complex microphysical interactions and coupling with turbulence at microscales govern the cloud properties at macroscales, yet remain challenging to observe and quantify under atmospheric conditions. This model intercomparison study utilizes ten model configurations to simulate mixed-phase cloud evolution in the Michigan Technological University's Pi Chamber. The models span a range of frameworks, including box models, direct numerical simulation, and large-eddy simulation models, and incorporate both bin and Lagrangian microphysics. Each model was tuned to reproduce the observed liquid-phase steady state prior to ice injection. Ice particles were then introduced into the domain at various rates to examine cloud glaciation behavior. By the intercomparison design, all models successfully reproduced the observed mean droplet radius and number concentration during the liquid-phase stage. Increasing ice particle injection rates led to consistent qualitative trends across models: depletion of liquid water, reduced total water content, and a shift in particle size distributions toward larger radii. However, quantitative differences arose due to variations in model treatment in dynamics and microphysics, including subgrid-scale turbulence parameterizations, wall forcing, and particle removal parameterizations. Most models that simulate the full chamber retained liquid droplets near the lower boundary, where supersaturation forcing is strongest and droplets are replenished before mixing into the core region. These surviving liquids droplets were absent in simulations assuming a well-mixed domain, excluding the near-wall region, or using coarse grid spacing.

Figures (9)

• Figure 1: The mean droplet radius and number concentration at steady-state during the liquid-phase stage by each model compared to the Pi Chamber observation. The observational constraints on droplet radius ($7.75\ \mu m$) and number concentration ($25\ cm^{-3}$) are indicated by red dashed lines.
• Figure 2: The total particle size distribution (including liquid and ice) at two injection rates of ice (for models) or INP (for observation).
• Figure 3: The relationships between (a) mean particle radius and number concentration, (b) total water content and number concentration, and (c) ice radius and ice number concentration (simulated results only). For total water content calculation, ice is assumed to have the same density as liquid water. Black curves with labeled values indicate observed INP injection rates (unit: cm$^{-3}$ min$^{-1}$), and arrows along each curve indicate the direction of increasing ice (model) or INP (observation) injection rate.
• Figure 4: Ice number concentration at steady-state versus the ice injection rate given by each model.
• Figure 5: Ice mass fraction (a), Liquid water content (b), and ice water content (c) versus ice number concentrations.