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The influence of aerosol concentration on the cloud-droplet size distribution is investigated in a laboratory chamber that enables turbulent cloud formation through moist convection. The experiments allow steady-state microphysics to be achieved, with aerosol input balanced by cloud-droplet growth and fallout. As aerosol concentration is increased, the cloud-droplet mean diameter decreases, as expected, but the width of the size distribution also decreases sharply. The aerosol input allows for cloud generation in the limiting regimes of fast microphysics (τc < τt ) for high aerosol concentration, and slow microphysics (τc > τt ) for low aerosol concentration; here, τc is the phase-relaxation time and τt is the turbulence-correlation time. The increase in the width of the droplet size distribution for the low aerosol limit is consistent with larger variability of supersaturation due to the slow microphysical response. A stochastic differential equation for supersaturation predicts that the standard deviation of the squared droplet radius should increase linearly with a system time scale defined as τ s − 1 = τ c − 1 + τ t − 1 , and the measurements are in excellent agreement with this finding. The result underscores the importance of droplet size dispersion for aerosol indirect effects: increasing aerosol concentration changes the albedo and suppresses precipitation formation not only through reduction of the mean droplet diameter but also by narrowing of the droplet size distribution due to reduced supersaturation fluctuations. Supersaturation fluctuations in the low aerosol/slow microphysics limit are likely of leading importance for precipitation formation.

This study presents the first model intercomparison of aerosol‐cloud‐turbulence interactions in a controlled cloudy Rayleigh‐Bénard Convection chamber environment, utilizing the Pi Chamber at Michigan Technological University. We analyzed simulated cloud chamber‐averaged statistics of microphysics and thermodynamics in a warm‐phase, cloudy environment under steady‐state conditions at varying aerosol injection rates. Simulation results from seven distinct models (DNS, LES, and a 1D turbulence model) were compared. Our findings demonstrate that while all models qualitatively capture observed trends in droplet number concentration, mean radius, and droplet size distributions at both high and low aerosol injection rates, significant quantitative differences were observed. Notably, droplet number concentrations varied by over two orders of magnitude between models for the same injection rates, indicating sensitivities to the model treatments in droplet activation and removal and wall fluxes. Furthermore, inconsistencies in vertical relative humidity profiles and in achieving steady‐state liquid water content suggest the need for further investigation into the mechanisms driving these variations. Despite these discrepancies, the models generally reproduced consistent power‐law relationships between the microphysical variables. This model intercomparison underscores the importance of controlled cloud chamber experiments for validating and improving cloud microphysical parameterizations. Recommendations for future modeling studies are also highlighted, including constraining wall conditions and processes, investigating droplet/aerosol removal (including sidewall losses), and conducting simplified experiments to isolate specific processes contributing to model divergence and reduce model uncertainties. Plain Language Summary Understanding how tiny particles (aerosols) interact with clouds and turbulence is essential for improving weather forecasts and climate predictions, as these interactions play a crucial role in determining the properties and evolution of clouds. In this study, we compared different numerical cloud models that simulate these interactions within a controlled laboratory environment, the Pi Chamber at Michigan Technological University. We examined how these models simulated the formation and growth of cloud droplets when aerosols were injected at different rates into the chamber. Our findings show that while all models generally captured the expected trends in cloud droplet size and number concentrations, there were significant quantitative differences. These differences suggest that model results are sensitive to the different model treatments on how droplets are formed and removed, as well as how fluxes from the chamber walls are represented. Despite these differences, the models generally agreed on the overall relationships between aerosol amounts and cloud properties, matching laboratory observations. This study highlights the value of using cloud chamber experiments to test and improve these models. We suggest that to reduce model uncertainties, future research should focus on better defining the conditions at the chamber walls and investigating how particles are removed from the chamber. Key Points This study presents the first model intercomparison to study aerosol‐cloud‐turbulence interactions in a convection‐cloud chamber All models capture the observed microphysical response to varying aerosol injection rates, but large inter‐model discrepancies are present The study underscores the importance of laboratory experiments for validating and improving microphysical representation in cloud models

Changes in the ice nucleation properties of mineral dust particles due to soluble coatings are still not well understood. Here we show that the reactivity with soluble materials deposited on the surfaces of kaolinite particles is an important factor affecting the ice nucleation properties of the particles. Using kaolinite particles treated with levoglucosan or H2SO4(i.e., non‐reactive and reactive materials, respectively), we investigated the fraction of particles capable of nucleating ice at temperatures ranging from −34°C to −26°C. Below water saturation, both the levoglucosan and H2SO4 coatings similarly reduced the ice nucleating ability of kaolinite particles. Above water saturation, however, only the H2SO4coatings reduced the ice nucleating ability of the particles, particularly at warmer temperatures. We suggest that the absence or presence of surface chemical reactions plays an important role in determining the number concentrations of ice crystals formed from mineral dust ice nuclei under mixed‐phase cloud conditions. Key Points Surface chemistry is a key factor in discussing ice nucleation of kaolinite

In order to study the behaviour of cloud droplets at the cloud-clear interface, the “Solving The Entrainment Puzzle” (STEP) project examined a droplet stream in the Turbulent Leipzig Aerosol Cloud Interaction Simulator (LACIS-T). LACIS-T comprises two particle free air streams, that are turbulently mixed, and during the experiment one air stream was resembling in-cloud conditions, whereas the other air stream was set to out-of-cloud conditions. A droplet stream was injected by a droplet generator into the mixing plane of the two air streams. Droplet size distributions were observed with a phase Doppler anemometer at various levels in the measurement section of LACIS-T, corresponding to different residence times of the droplets in the turbulent environment. Additionally, observations were made using different flow speeds in the two air streams to create shear flows in the wind tunnel. The experiment was accompanied by computational fluid dynamics simulations to provide a full 3D representation of meteorological fields and turbulence parameters. This manuscript provides a description of the laboratory settings and instrumentation, the experimental design, the simulations, and a general overview of the data. We invite the scientific community for joint data analysis and numerical studies using the data which is freely available from the Eurochamp Data Centre, see Table 2 in the Data availability section for details.

The purpose of this meeting summary, therefore, is to outline the range of scientific questions, and the facility concepts that were explored.We began the workshop with a series of overview talks to highlight scientific questions that could be investigated in a large-scale aerosol-cloud-turbulence laboratory facility. Topics included warm and mixed-phase cloud microphysics, aerosol and cloud chemistry, atmospheric turbulence, radiative transfer, cloud/aerosol instrumentation, and remote sensing. There was ample time for questions and discussion following each overview presentation. We also broke into groups for several hours at various points to enable more active discussion among all participants.

The interactions between turbulence and cloud microphysical processes have been investigated primarily through numerical simulation and field measurements over the last 10 years. However, only in the laboratory we can be confident in our knowledge of initial and boundary conditions and are able to measure under statistically stationary and repeatable conditions. In the scope of this paper, we present a unique turbulent moist-air wind tunnel, called the Turbulent Leipzig Aerosol Cloud Interaction Simulator (LACIS-T) which has been developed at TROPOS in order to study cloud physical processes in general and interactions between turbulence and cloud microphysical processes in particular. The investigations take place under well-defined and reproducible turbulent and thermodynamic conditions covering the temperature range of warm, mixed-phase and cold clouds (25∘C>T>-40∘C). The continuous-flow design of the facility allows for the investigation of processes occurring on small temporal (up to a few seconds) and spatial scales (micrometer to meter scale) and with a Lagrangian perspective. The here-presented experimental studies using LACIS-T are accompanied and complemented by computational fluid dynamics (CFD) simulations which help us to design experiments as well as to interpret experimental results. In this paper, we will present the fundamental operating principle of LACIS-T, the numerical model, and results concerning the thermodynamic and flow conditions prevailing inside the wind tunnel, combining both characterization measurements and numerical simulations. Finally, the first results are depicted from deliquescence and hygroscopic growth as well as droplet activation and growth experiments. We observe clear indications of the effect of turbulence on the investigated microphysical processes.

Immersion freezing is the most relevant heterogeneous ice nucleation mechanism through which ice crystals are formed in mixed-phase clouds. In recent years, an increasing number of laboratory experiments utilizing a variety of instruments have examined immersion freezing activity of atmospherically relevant ice-nucleating particles. However, an intercomparison of these laboratory results is a difficult task because investigators have used different ice nucleation (IN) measurement methods to produce these results. A remaining challenge is to explore the sensitivity and accuracy of these techniques and to understand how the IN results are potentially influenced or biased by experimental parameters associated with these techniques. Within the framework of INUIT (Ice Nuclei Research Unit), we distributed an illite-rich sample (illite NX) as a representative surrogate for atmospheric mineral dust particles to investigators to perform immersion freezing experiments using different IN measurement methods and to obtain IN data as a function of particle concentration, temperature (T), cooling rate and nucleation time. A total of 17 measurement methods were involved in the data intercomparison. Experiments with seven instruments started with the test sample pre-suspended in water before cooling, while 10 other instruments employed water vapor condensation onto dry-dispersed particles followed by immersion freezing. The resulting comprehensive immersion freezing data set was evaluated using the ice nucleation active surface-site density, ns, to develop a representative ns(T) spectrum that spans a wide temperature range (−37 °C < T < −11 °C) and covers 9 orders of magnitude in ns. In general, the 17 immersion freezing measurement techniques deviate, within a range of about 8 °C in terms of temperature, by 3 orders of magnitude with respect to ns. In addition, we show evidence that the immersion freezing efficiency expressed in ns of illite NX particles is relatively independent of droplet size, particle mass in suspension, particle size and cooling rate during freezing. A strong temperature dependence and weak time and size dependence of the immersion freezing efficiency of illite-rich clay mineral particles enabled the ns parameterization solely as a function of temperature. We also characterized the ns(T) spectra and identified a section with a steep slope between −20 and −27 °C, where a large fraction of active sites of our test dust may trigger immersion freezing. This slope was followed by a region with a gentler slope at temperatures below −27 °C. While the agreement between different instruments was reasonable below ~ −27 °C, there seemed to be a different trend in the temperature-dependent ice nucleation activity from the suspension and dry-dispersed particle measurements for this mineral dust, in particular at higher temperatures. For instance, the ice nucleation activity expressed in ns was smaller for the average of the wet suspended samples and higher for the average of the dry-dispersed aerosol samples between about −27 and −18 °C. Only instruments making measurements with wet suspended samples were able to measure ice nucleation above −18 °C. A possible explanation for the deviation between −27 and −18 °C is discussed. Multiple exponential distribution fits in both linear and log space for both specific surface area-based ns(T) and geometric surface area-based ns(T) are provided. These new fits, constrained by using identical reference samples, will help to compare IN measurement methods that are not included in the present study and IN data from future IN instruments.

This study presents an analysis showing that the freezing probability of kaolinite particles from Fluka scales exponentially with particle surface area for different atmospherically relevant particle sizes. Immersion freezing experiments were performed at the Leipzig Aerosol Cloud Interaction Simulator (LACIS). Size-selected kaolinite particles with mobility diameters of 300, 700, and 1000 nm were analyzed with one particle per droplet. First, it is demonstrated that immersion freezing is independent of the droplet volume. Using the mobility analyzer technique for size selection involves the presence of multiply charged particles in the quasi-monodisperse aerosol, which are larger than singly charged particles. The fractions of these were determined using cloud droplet activation measurements. The development of a multiple charge correction method has proven to be essential for deriving ice fractions and other quantities for measurements in which the here-applied method of size selection is used. When accounting for multiply charged particles (electric charge itself does not matter), both a time-independent and a time-dependent description of the freezing process can reproduce the measurements over the range of examined particle sizes. Hence, either a temperature-dependent surface site density or a single contact angle distribution was sufficient to parameterize the freezing behavior. From a comparison with earlier studies using kaolinite samples from the same provider, it is concluded that the neglect of multiply charged particles and, to a lesser extent, the effect of time can cause a significant overestimation of the ice nucleation site density of one order of magnitude, which translates into a temperature bias of 5–6 K.

Ice surface properties can modify the scattering properties of atmospheric ice crystals and therefore affect the radiative properties of mixed-phase and cirrus clouds. The Ice Roughness Investigation System (IRIS) is a new laboratory setup designed to investigate the conditions under which roughness develops on single ice crystals, based on their size, morphology and growth conditions (relative humidity and temperature). Ice roughness is quantified through the analysis of speckle in 2-D light-scattering patterns. Characterization of the setup shows that a supersaturation of 20 % with respect to ice and a temperature at the sample position as low as −40 ∘C could be achieved within IRIS. Investigations of the influence of humidity show that higher supersaturations with respect to ice lead to enhanced roughness and irregularities of ice crystal surfaces. Moreover, relative humidity oscillations lead to gradual “ratcheting-up” of roughness and irregularities, as the crystals undergo repeated growth–sublimation cycles. This memory effect also appears to result in reduced growth rates in later cycles. Thus, growth history, as well as supersaturation and temperature, influences ice crystal growth and properties, and future atmospheric models may benefit from its inclusion in the cloud evolution process and allow more accurate representation of not just roughness but crystal size too, and possibly also electrification properties.

The Fast Infrared Hygrometer (FIRH), employing open-path tunable diode laser absorption spectroscopy at the wavelengths near the 1364.6896 nm line, was adapted to perform contactless humidity measurements at the Turbulent Leipzig Aerosol Cloud Interaction Simulator (LACIS-T), a unique turbulent moist-air wind tunnel. The configuration of the setup allows for scanning from outside the walls of the wind tunnel and at various positions without the need for repeated optics adjustments. We identified three factors which significantly influence the measurement – self-broadening of the absorption line, interference in the glass windows and parasitic absorption in the ambient air outside the wind tunnel – and developed correction methods which satisfactorily account for these effects. The comparison between FIRH and a reference hygrometer (dew-point mirror MBW 973) indicated a good agreement within the expected errors across the wide range of water vapour concentration 1.0–6.1×1017 cm−3 (equivalent to dew-point temperature of −5.4 to +21 ∘C at the temperature of 23 ∘C). High temporal resolution (∼2 kHz) allowed for studying turbulent fluctuations in the course of intensive mixing of two air streams which had the same mean velocity but differed in temperature and humidity, also including the settings for which the mixture can be supersaturated. The obtained results contribute to improved understanding and interpretation of cloud formation studies conducted in LACIS-T by complementing the previous characterizations of turbulent velocity and temperature fields inside the wind tunnel.