Predicting Ice Nucleation Particle properties in a Boreal Environment using machine learning

Abstract. Mixed-phase clouds, which are dominant in mid- and high-latitude regions, strongly influence Earth’s radiative balance and precipitation processes. Their formation depends critically on the presence of ice-nucleating particles (INPs), which are rare relative to cloud condensation nuclei. The HyICE-2018 measurement campaign took place at the SMEAR II station in the high-latitude boreal forest of Hyytiälä, Finland, between February and June 2018. Two continuous-flow diffusion chambers Portable Ice Nucleation Chamber I and II (PINC and PINCii) with high-frequency sampling were deployed to measure INP concentrations. We applied machine-learning techniques to explore predictors of INP variability using more than 500 high-resolution atmospheric, aerosol, and ecosystem variables measured continuously at Station for Measuring Ecosystem-Atmosphere Relations (SMEAR) II. We identify distinct differences between winter and spring/summer measurements. The winter measurements conducted with PINC appear to be nearly independent of any monitored variable. In contrast, the spring/summer measurements conducted with PINCii appear to be more closely linked to and responsive to ambient aerosol properties. Furthermore, we find that classical parameterizations based on particle concentration overestimate observed INP concentrations in the boreal environment. However, similar empirical fits based on local proxies, such as a marker of biogenic aerosol or nitrate, yield improved agreement during spring and summer, while no improvement occurs during winter. These results underscore the need for site-specific parameterizations to capture INP variability in the complex boreal environments.