ALBERT

Description: Some biological particles, such as Snomax, are very active ice nucleating particles, inducing heterogeneous freezing in supercooled water at temperatures above −15 and up to −2 °C. Despite their exceptional freezing abilities, large uncertainties remain regarding the atmospheric abundance of biological ice nucleating particles, and their contribution to atmospheric ice nucleation. It has been suggested that small biological ice nucleating macromolecules or fragments can be carried on the surfaces of dust and other atmospheric particles. This could combine the atmospheric abundance of dust particles with the ice nucleating strength of biological material to create strongly enhanced and abundant ice nucleating surfaces in the atmosphere, with significant implications for the budget and distribution of atmospheric ice nucleating particles, and their consequent effects on cloud microphysics and mixed-phase clouds. The new critical surface area g framework that was developed by Beydoun et al. (2016) is extended to produce a heterogeneous ice nucleation mixing model that can predict the freezing behavior of multicomponent particle surfaces immersed in droplets. The model successfully predicts the immersion freezing properties of droplets containing Snomax bacterial particles across a mass concentration range of 7 orders of magnitude, by treating Snomax as comprised of two distinct distributions of heterogeneous ice nucleating activity. Furthermore, the model successfully predicts the immersion freezing behavior of a low-concentration mixture of Snomax and illite mineral particles, a proxy for the biological material–dust (bio-dust) mixtures observed in atmospheric aerosols. It is shown that even at very low Snomax concentrations in the mixture, droplet freezing at higher temperatures is still determined solely by the second less active and more abundant distribution of heterogeneous ice nucleating activity of Snomax, while freezing at lower temperatures is determined solely by the heterogeneous ice nucleating activity of pure illite. This demonstrates that in this proxy system, biological ice nucleating particles do not compromise their ice nucleating activity upon mixing with dust and no new range of intermediary freezing temperatures associated with the mixture of ice nucleating particles of differing activities is produced. The study is the first to directly examine the freezing behavior of a mixture of Snomax and illite and presents the first multicomponent ice nucleation model experimentally evaluated using a wide range of ice nucleating particle concentration mixtures in droplets.

Description: We present the laboratory results of immersion freezing efficiencies of cellulose particles at supercooled temperature (T) conditions. Three types of chemically homogeneous cellulose samples are used as surrogates that represent supermicron and submicron ice-nucleating plant structural polymers. These samples include microcrystalline cellulose (MCC), fibrous cellulose (FC) and nanocrystalline cellulose (NCC). Our immersion freezing dataset includes data from various ice nucleation measurement techniques available at 17 different institutions, including nine dry dispersion and 11 aqueous suspension techniques. With a total of 20 methods, we performed systematic accuracy and precision analysis of measurements from all 20 measurement techniques by evaluating T-binned (1 ∘C) data over a wide T range (−36 ∘C 

Description: Heterogeneous ice nucleation remains one of the outstanding problems in cloud physics and atmospheric science. Experimental challenges in properly simulating particle induced freezing processes under atmospherically relevant conditions have largely contributed to the absence of a consistent and comprehensive parameterization of immersion freezing properties. Here we formulate an ice active surface site based stochastic model of heterogeneous freezing with the unique feature of invoking a continuum assumption on the ice nucleating activity (contact angle) of an aerosol particle's surface, that requires no assumptions about the size or number of active sites. The result is a particle specific property g that defines a distribution of local ice nucleation rates. Upon integration this yields a full freezing probability function for an ice-nucleating particle. Current cold plate droplet freezing measurements provide a great resource for studying the freezing properties of many atmospheric aerosol systems. A method based on statistical significance to determine an ice nucleating species' specific critical surface area is presented that can resolve the two-dimensional nature of the ice nucleation ability of aerosol particles: internal variability in active site strengths and freezing rates along an individual particle's surface, as well as external variability between two particles of the same type in an aerosol population. By applying this method to experimental data we demonstrate its ability to comprehensively interpret immersion freezing temperature spectra of droplets containing variable particle concentrations. It is shown that general active site density functions such as the popular ns parameterization cannot be reliably extrapolated below this critical area threshold to describe freezing curves for lower particle surface area concentrations. Freezing curves obtained below this threshold translate to higher ns values, while the ns values are essentially the same from curves obtained above the critical area threshold. However, we can successfully predict the lower concentration freezing curves, which are more atmospherically relevant, through a process of random sampling from the statistically significant global distribution obtained from high particle concentration data. Our analysis further revealed that one individual atmospheric illite mineral particle will not contain the entire range of ice active site activity for that system (its internal variability). Comprehensive parameterizations that can predict the temporal evolution of the frozen fraction of cloud droplets in larger atmospheric models are also derived from this new framework.

Description: Some biological particles, such as Snomax, are very active ice nucleating particles, inducing heterogeneous freezing in supercooled water at temperatures above −15° C and up to −2° C. Despite their exceptional freezing abilities, large uncertainties remain regarding the atmospheric abundance of biological ice nucleating particles, and their contribution to atmospheric ice nucleation. It has been suggested that small biological ice nucleating macromolecules or fragments can be carried on the surfaces of dust and other atmospheric particles. This could combine the atmospheric abundance of dust particles with the ice nucleating strength of biological material to create strongly enhanced and abundant ice nucleating surfaces in the atmosphere, with significant implications for the budget and distribution of atmospheric ice nucleating particles, and their consequent effects on cloud microphysics and mixed-phase clouds. The new critical surface area “g” framework that was developed by Beydoun et al. (2016), is extended to produce a heterogeneous ice nucleation mixing model that can predict the freezing behavior of multi-component particle surfaces immersed in droplets. The model successfully predicts the immersion freezing properties of droplets containing Snomax bacterial particles across a mass concentration range of seven orders of magnitude, by treating Snomax as comprised of two distinct distributions of heterogeneous ice nucleating activity. Furthermore, the model successfully predicts the immersion freezing behavior of a low concentration mixture of Snomax and illite mineral particles, a proxy for the biological-dust mixtures observed in atmospheric aerosols. It is shown that even at very low Snomax concentrations in the mixture, droplet freezing at higher temperatures is still determined solely by the second less active and more abundant distribution of heterogeneous ice nucleating activity of Snomax, while freezing at lower temperatures is determined solely by the heterogeneous ice nucleating activity of pure illite. This demonstrates that in this proxy system, biological ice nucleating particles do not compromise their ice nucleating activity upon mixing with dust and no new range of intermediary freezing temperatures associated with the mixture of ice nucleating particles of differing activities is produced. The study is the first to directly examine the freezing behavior of a mixture of Snomax and illite and presents the first multi-component ice nucleation model experimentally evaluated using a wide range of ice nucleating particle concentration mixtures in droplets.

Description: We present the laboratory results of immersion freezing efficiencies of cellulose particles at supercooled temperature (T) conditions. Three types of chemically homogeneous cellulose samples are used as surrogates that represent supermicron and submicron ice nucleating plant structural polymers. These samples include micro-crystalline cellulose (MCC), fibrous cellulose (FC) and nano-crystalline cellulose (NCC). Our experimental data show that particles resembling the MCC lab particle occur also in the atmosphere. Our immersion freezing dataset includes data from various ice nucleation measurement techniques available at seventeen different institutions, including nine dry dispersion and eleven aqueous suspension techniques. With a total of twenty methods, we performed systematic accuracy and precision analysis of measurements from all twenty measurement techniques by evaluating T-binned (1 °C) data over a wide T range (−36 °C 

Description: Heterogeneous ice nucleation remains one of the outstanding problems in cloud physics and atmospheric science. Experimental challenges in properly simulating particle-induced freezing processes under atmospherically relevant conditions have largely contributed to the absence of a well-established parameterization of immersion freezing properties. Here, we formulate an ice active, surface-site-based stochastic model of heterogeneous freezing with the unique feature of invoking a continuum assumption on the ice nucleating activity (contact angle) of an aerosol particle's surface that requires no assumptions about the size or number of active sites. The result is a particle-specific property g that defines a distribution of local ice nucleation rates. Upon integration, this yields a full freezing probability function for an ice nucleating particle. Current cold plate droplet freezing measurements provide a valuable and inexpensive resource for studying the freezing properties of many atmospheric aerosol systems. We apply our g framework to explain the observed dependence of the freezing temperature of droplets in a cold plate on the concentration of the particle species investigated. Normalizing to the total particle mass or surface area present to derive the commonly used ice nuclei active surface (INAS) density (ns) often cannot account for the effects of particle concentration, yet concentration is typically varied to span a wider measurable freezing temperature range. A method based on determining what is denoted an ice nucleating species' specific critical surface area is presented and explains the concentration dependence as a result of increasing the variability in ice nucleating active sites between droplets. By applying this method to experimental droplet freezing data from four different systems, we demonstrate its ability to interpret immersion freezing temperature spectra of droplets containing variable particle concentrations. It is shown that general active site density functions, such as the popular ns parameterization, cannot be reliably extrapolated below this critical surface area threshold to describe freezing curves for lower particle surface area concentrations. Freezing curves obtained below this threshold translate to higher ns values, while the ns values are essentially the same from curves obtained above the critical area threshold; ns should remain the same for a system as concentration is varied. However, we can successfully predict the lower concentration freezing curves, which are more atmospherically relevant, through a process of random sampling from g distributions obtained from high particle concentration data. Our analysis is applied to cold plate freezing measurements of droplets containing variable concentrations of particles from NX illite minerals, MCC cellulose, and commercial Snomax bacterial particles. Parameterizations that can predict the temporal evolution of the frozen fraction of cloud droplets in larger atmospheric models are also derived from this new framework.