Abstract
Abstract. 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.
Highlights
The potential role certain ice nucleating biological particles may play in cloud physics, meteorology, and global climate has been an active area of research for decades (Ariya et al, 2009; DeMott and Prenni, 2010; Franc and Demott, 1998; Möhler et al, 2007; Morris et al, 2004; Schnell and Vali, 1976)
4 Conclusions A new heterogeneous ice nucleation (HIN) mixing model was formulated to better understand and predict how cloud droplet systems containing more than one component of ice nucleating particles (INPs) behave
The new model successfully predicted the freezing spectra of droplets containing Snomax bacterial particles as well as a mixture of Snomax and illite NX mineral particles, a proxy for bio-dust particle mixtures in the atmosphere
Summary
The potential role certain ice nucleating biological particles may play in cloud physics, meteorology, and global climate has been an active area of research for decades (Ariya et al, 2009; DeMott and Prenni, 2010; Franc and Demott, 1998; Möhler et al, 2007; Morris et al, 2004; Schnell and Vali, 1976). (1) or (2) with g in place of g, and the fraction of droplets frozen, F , of a large ensemble of these droplets N is equal to the freezing probability of each droplet, Pf. On the other hand, when a particle possesses a surface area below that type’s critical surface area, a random sampling of contact angles to generate a discrete distribution of HIN activity g∗ is required for each particle surface in the particle population. On the other hand, when a particle possesses a surface area below that type’s critical surface area, a random sampling of contact angles to generate a discrete distribution of HIN activity g∗ is required for each particle surface in the particle population In this case the frozen fraction of a large ensemble of droplets, F , is the arithmetic mean of the individual droplet freezing probabilities and can be evaluated using the following: F. where Pf,i is the freezing probability of droplet i and can be evaluated using Eqs. Gbg is a normal distribution multiplied by a pre-factor making it a function of three independent parameters: the pre-factor (C), the mode (μ), and the standard deviation (σ )
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