PNNL

Abstract

Glaciation in mixed-phase clouds predominately occurs through immersion freezing mode where ice nucleating particles (INP) immersed within a supercooled droplet induces nucleation of ice. Currently, model representations of this process are a large source of uncertainty in simulating cloud radiative properties, and to constrain these estimates, continuous flow diffusion chamber (CFDC) style INP devices are commonly used to assess the immersion freezing efficiencies of INPs. In this study, a new approach to operate such a ice chamber was explored. Referred to as nonstandard style of operation, the ice chamber performance was evaluated using four INP species: K-feldspar, illite-NX, Argentinian soil dust, and ambient desert dust that had shown ice nucleation over a wide span of supercooled temperatures. The individual INPs from each species were activated to droplets in the growth section of the chamber maintained at ~-20 °C and water relative humidity (RHw) ~113%, and these droplets were supercooled at a steady cooling rate (0.5 °Cmin-1) within the evaporation section until all the droplets froze. The high RHw conditions to maximize the droplet activation was achieved with a longer and independently temperature-controlled evaporation section. This new mode of operation provided immersion freezing spectra of INPs as a function of supercooled temperatures. The ice nucleation efficiency measured with the surface active site density (ns) metric was higher by about one order of magnitude compared to the literature results, however, they were comparable with the literature data determined via immersion freezing method where all individual particles are activated to cloud droplets. Further, a better agreement between the surface area normalized freezing rate (R/A) and the heterogeneous nucleation rate (Jhet) was observed only after distribution in the form of a linear fit of temperature-dependence contact angle was used. These results allow us to gain additional insights into immersion freezing properties of INPs, and such dataset would help to reconcile different formulations of describing the ice nucleating ability in cloud models to improve the prediction of ice formation in clouds.