On the representation of immersion and condensation freezing in cloud models using different nucleation schemes

Abstract

Abstract. Ice nucleation in clouds is often observed at temperatures > 235 K, pointing to heterogeneous freezing as a predominant mechanism. Many models deterministically predict the number concentration of ice particles as a function of temperature and/or supersaturation. Several laboratory experiments, at constant temperature and/or supersaturation, report heterogeneous freezing as a stochastic, timedependent process that follows classical nucleation theory; this might appear to contradict deterministic models that predict singular freezing behavior.We explore the extent to which the choice of nucleation scheme (deterministic/stochastic, single/multiple contact angles θ) affects the prediction of the fraction of frozen ice nuclei (IN) and cloud evolution for a predetermined maximum IN concentration. A box model with constant temperature and supersaturation is used to mimic published laboratory experiments of immersion freezing of monodisperse (800 nm) kaolinite particles (∼ 243 K), and the fitness of different nucleation schemes. Sensitivity studies show that agreement of all five schemes is restricted to the narrow parameter range (time, temperature, IN diameter) in the original laboratory studies, and that model results diverge for a wider range of conditions.The schemes are implemented in an adiabatic parcel model that includes feedbacks of the formation and growth of drops and ice particles on supersaturation during ascent. Model results for the monodisperse IN population (800 nm) show that these feedbacks limit ice nucleation events, often leading to smaller differences in number concentration of ice particles and ice water content (IWC) between stochastic and deterministic approaches than expected from the box model studies. However, because the different parameterizations of θ distributions and time-dependencies are highly sensitive to IN size, simulations using polydisperse IN result in great differences in predicted ice number concentrations and IWC between the different schemes. The differences in IWC are mostly due to the different temperatures of the onset of freezing in the nucleation schemes that affect the temporal evolution of the ice number concentration. The growth rates of ice particles are not affected by the choice of the nucleation scheme, which leads to very similar particle sizes. Finally, since the choice of nucleation scheme determines the temperature range over which ice nucleation occurs, at habit-prone temperatures (∼ 253 K), there is the potential for variability in the initial inherent growth ratio of ice particles, which can cause amplification or reduction in differences in predicted IWC.

Refreeze experiments with water droplets containing different types of ice nuclei interpreted by classical nucleation theory

“…For most natural samples, the sample heterogeneity indeed leads to a large spread of nucleation efficiencies of sites and the temperature dependence is likely to exceed the time dependence . This was confirmed by a sensitivity study per-formed by Ervens and Feingold (2012) and is in agreement with Welti et al (2012), who found the time dependence to be of minor importance for immersion freezing experiments with kaolinite particles. Therefore, a singular or deterministic approach to describe ambient ice-nucleating particles in models may be appropriate and justified.…”

Effect of particle surface area on ice active site densities retrieved from droplet freezing spectra

“…Therefore, a cloud parcel model would be unable to accurately predict the freezing onset or the temperature range over which freezing occurs using a single n s curve obtained from high concentration data. This has important consequences for the accurate simulation of the microphysical evolution of the cloud system under study, such as the initiation of the WegenerBergeron-Findeisen and the consequent glaciation and pre-cipitation rates (Ervens et al, 2011;Ervens and Feingold, 2012). Figure 11 shows the range of n s values for illite NX mineral compiled from 17 measurement methods used by different research groups, the details of which are described by Hiranuma et al (2015a).…”

“…Ervens and Feingold (2012) tested different nucleation schemes in an adiabatic parcel model and found that critical cloud features, such as the initiation of the WBF process, liquid water content, and ice water content, all diverged for the different ice nucleation parameterizations. This strongly affected cloud evolution and lifetime.…”

Cloud droplet size distribution broadening during diffusional growth: ripening amplified by deactivation and reactivation

“…The original version of the model was designed to study cirrus clouds by Heymsfield and Sabin (1989), and then warm clouds (Feingold and Heymsfield, 1992;Feingold et al, 1998). In recent years, this model has been modified and applied to investigate various microphysical problems (e.g., Feingold and Kreidenweis, 2000;Xue and Feingold, 2004;Ervens and Feingold, 2012;Yang et al, 2012Yang et al, , 2016Li et al, 2013). In the current version of the parcel model, air pressure (p), parcel height (h), air temperature (T ), water vapor mixing ratio (q v ) and radii of haze and cloud droplets (r i ) are prognostic variables, which are calculated using the variable-coefficient ordinary differential equation solver (VODE) (Brown et al, 1989).…”