Comment on egusphere-2022-1242

Abstract

<strong class="journal-contentHeaderColor">Abstract.</strong> The heterogeneous nucleation of ice is an important atmospheric process facilitated by a wide range of aerosols. Drop-freezing experiments are key for the determination of the ice nucleation activity of biotic and abiotic ice nucleators (INs). The results of these experiments are reported as the fraction of frozen droplets <em>f_ice</em> (<em>T</em>) as a function of decreasing temperature, and the corresponding cumulative freezing spectra <em>N</em>_<em>m</em> (<em>T</em>) computed using Vali&rsquo;s methodology. The differential freezing spectrum <em>n</em>_<em>m</em> (<em>T</em>) is in principle a direct measure of the underlying distribution of heterogeneous ice nucleation temperatures <em>P_u</em> (<em>T</em>) in the sample. However, <em>N_m</em> (<em>T</em>) can be noisy, resulting in a differential form <em>n_m</em> (<em>T</em>) that is challenging to interpret. Furthermore, there is no rigorous statistical analysis of how many droplets and dilutions are needed to obtain a well-converged <em>n_m</em> (<em>T</em>) that represents the underlying distribution <em>P_u</em> (<em>T</em>). Here, we present the &ldquo;Heterogeneous Underlying-based&rdquo; (HUB) method and associated Python codes that model (HUB-forward code) and interpret (HUB-backward code) the results of drop-freezing experiments. HUB-forward is the first available code that predicts <em>f_ice</em> (<em>T</em>) and <em>N_m</em> (<em>T</em>) from a proposed distribution <em>P_u</em> (<em>T</em>) of IN temperatures, allowing its users to test hypotheses regarding the role of subpopulations of nuclei in freezing spectra, and providing a guide for a more efficient collection of freezing data. HUB-backward uses a stochastic optimization method to compute <em>n_m</em> (<em>T</em>) from either <em>N_m </em>(<em>T</em>) or <em>f</em>_ice (<em>T</em>). The differential spectrum computed with HUB-backward is an analytical function that can be used to reveal and characterize the underlying number of IN subpopulations of complex biological samples (ice nucleating bacteria, fungi and pollen), and quantify the dependence of their subpopulations on environmental variables. By delivering a way to compute the differential spectrum from drop freezing data, and vice-versa, the HUB-forward and HUB-backward codes provide a hub between experiments and interpretative physical quantities that can be analysed with kinetic models and nucleation theory.