Exploring ice nucleation particle concentrations in a boreal environment: limits of machine-learning-assisted variable screening

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 (CFDCs), PINC and PINCii (Portable Ice Nucleation Chambers I and II), were deployed with high-frequency sampling to measure INP concentrations. We applied machine-learning techniques to explore predictors of INP concentrations using more than 500 high-resolution atmospheric, aerosol, and ecosystem variables measured continuously at the Station for Measuring Ecosystem–Atmosphere Relations (SMEAR) II, of which 84 were retained after quality screening for the analysis. 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 parameterisations based on aerosol 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. A core cautionary finding of this study is that, even with more than 500 co-located, high-resolution variables at one of the world's most heavily instrumented atmospheric stations, strong, deterministic links between INP concentrations and monitored parameters remain elusive. These results underscore the need for site-specific parameterisations to capture INP variability in the complex boreal environments; local biogenic and chemical proxies, such as fluorescent particle concentrations and nitrate aerosol mass, emerge as the most promising predictors for the spring and summer period.