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Abstract. This study delves into the small-scale temperature structure inside the turbulent convection Π Chamber under three temperature differences (10, 15, and 20 K) at Rayleigh number Ra∼109 and Prandtl number Pr≈0.7. We performed high-frequency measurements (2 kHz) with the UltraFast Thermometer (UFT) at selected points along the vertical axis. The miniaturized design of the sensor with a resistive platinum-coated tungsten wire, 2.5 µm thick and 3 mm long, mounted on a miniature wire probe, allowed for vertically undisturbed temperature profiling through the chamber's depth spanning from 8 cm above the bottom to 5 cm below the top. The collected data, consisting of 19 and 3 min time series, were used to investigate the variability of the temperature field within the chamber, aiming to better address scientific questions related to its primary objective: understanding small-scale aerosol–cloud interactions. The analyses reveal substantial variability in both variance and skewness of temperature distributions near the top and bottom plates and in the bulk (central) region, which were linked to local thermal plume dynamics. We also identified three spectral regimes termed “inertial range” (slopes of ∼-7/5), “transition range” (slopes of ∼-3), and “dissipative range”, characterized by slopes of ∼-7. Furthermore, the analysis showed a power law relationship between the periodicity of large-scale circulation (LSC) and the temperature difference. Notably, the experimental results are in good agreement with direct numerical simulation (DNS) conducted under similar thermodynamic conditions, illustrating a comparative analysis of this nature. 

Thermal convection in a closed chamber is driven by a warm bottom, a cold top, and side walls at various temperatures. Although wall fluxes are the source of convection energy, accurately modeling these fluxes (i.e., the wall model) is challenging. In large-eddy simulations (LESs), many wall models are traditionally derived from the canonical boundary layer, which may be unsuitable for thermal convection bounded by both horizontal and vertical walls. This study conducts a model intercomparison of dry convection in a cubic-meter chamber using three direct numerical simulations (DNSs) and four LESs with different wall models. The LESs employ traditional wall models, a new wall model employing physics-aware neural networks, and a refined grid near the walls. The experiment involves four cases with varying sidewall temperatures. Our results show that LESs capture the main flow features and the trends of mean fluxes. The physics-aware neural networks and refined wall grids can improve the temporally averaged local fluxes when the large-scale circulation has a preferred direction. Even without the local improvement of wall fluxes, the LES flow quantities (temperature and velocities) can still largely match those in DNSs, provided the mean flux largely matches the DNSs. Additionally, DNSs reveal that a variation in corner treatments has minimal impacts on the flow quantities away from corners. Finally, LESs underestimate the mean fluxes of the entire wall due to their inability to resolve corner regions, but their mean flux away from the corner can better match DNS. 

Abstract Cloud formation in the Pi Convection–Cloud Chamber is achieved via ionization in humid conditions, without the injection of aerosol particles to serve as cloud condensation nuclei (CCN). Abundant ions, turbulence, and supersaturated water vapor combine to produce new particles, which grow to become CCN sized and eventually are activated to form clouds. Coupling between the new particle formation and cloud droplets causes predator-prey type oscillations in aerosol and droplet concentrations under turbulent conditions. Leading terms are identified in the budgets for Aitken and accumulation mode aerosols and for cloud droplets. The cloud coupling is proposed to be a result of cloud-induced runaway CCN production through aerosol scavenging. The experiments suggest potential applications to marine cloud brightening, in which ions rather than sea-salt aerosols are generated. 

Abstract Variability of ice microphysical properties like crystal size and density in cirrus clouds is important for climate through its impact on radiative forcing, but challenging to represent in models. For the first time, recent laboratory experiments of particle growth (tied to crystal morphology via deposition density) are combined with a state‐of‐the‐art Lagrangian particle‐based microphysics model in large‐eddy simulations to examine sources of microphysical variability in cirrus. Simulated particle size distributions compare well against balloon‐borne observations. Overall, microphysical variability is dominated by variability in the particles' thermodynamic histories. However, diversity in crystal morphology notably increases spatial variability of mean particle size and density, especially at mid‐levels in the cloud. Little correlation between instantaneous crystal properties and supersaturation occurs even though the modeled particle morphology is directly tied to supersaturation based on laboratory measurements. Thus, the individual thermodynamic paths of each particle, not the instantaneous conditions, control the evolution of particle properties. 

Abstract Turbulent fluctuations of scalar and velocity fields are critical for cloud microphysical processes, e.g., droplet activation and size distribution evolution, and can therefore influence cloud radiative forcing and precipitation formation. Lagrangian and Eulerian water vapor, temperature, and supersaturation statistics are investigated in direct-numerical simulations (DNS) of turbulent Rayleigh–Bénard convection in the Pi Convection Cloud Chamber to provide a foundation for parameterizing subgrid-scale fluctuations in atmospheric models. A subgrid model for water vapor and temperature variances and covariance and supersaturation variance is proposed, valid for both clear and cloudy conditions. Evaluation of phase change contributions through an a priori test using DNS data shows good performance of the model. Supersaturation is a nonlinear function of temperature and water vapor, and relative external fluxes of water vapor and heat (e.g., during entrainment-mixing and phase change) influence turbulent supersaturation fluctuations. Although supersaturation has autocorrelation and structure functions similar to the independent scalars (temperature and water vapor), the autocorrelation timescale of supersaturation differs. Relative scalar fluxes in DNS without cloud make supersaturation PDFs less skewed than the adiabatic case, where they are highly negatively skewed. However, droplet condensation changes the PDF shape response: it becomes positively skewed for the adiabatic case and negatively skewed when the sidewall relative fluxes are large. Condensation also increases correlations between water vapor and temperature in the presence of relative scalar fluxes but decreases correlations for the adiabatic case. These changes in correlation suppress supersaturation variability for the non-adiabatic cases and increase it for the adiabatic case. Implications of this work for subgrid microphysics modeling using a Lagrangian stochastic scheme are also discussed. 

Moist Rayleigh–Bénard convection with water saturated boundaries is explored using a One-Dimensional Turbulence model. The system involves both temperature $$T$$ and water vapour pressure $$e_{v}$$ as driving scalars. The emphasis of the work is on a supersaturation $$s$$ , a nonlinear combination of $$T$$ and $$e_{v}$$ that is crucial to cloud formation. Its mean as well as fluctuation statistics determine cloud droplet growth and therefore precipitation formation and cloud optical properties. To explore the role of relative scalar diffusivities for temperature ( $$D_{t}$$ ) and water vapour ( $$D_{v}$$ ), three different regimes are considered: $$D_{v}>D_{t}$$ , $$D_{v}\approx D_{t}$$ and $$D_{v}