Sensitivities of simulated mixed-phase Arctic multilayer clouds to primary and secondary ice processes

Artikel i vetenskaplig tidskrift, 2025

Multilayered clouds are frequent in the Arctic, but their detailed analysis is underrepresented. Here, we simulate two cases observed during the 2019/2020 MOSAiC expedition using the ICosahedral Non-hydrostatic (ICON) model to explore the most accurate representation of these multilayer clouds. With a limited-area setup, we investigate how cloud layers respond to perturbations in cloud droplet activation, primary ice, and secondary ice production (SIP). Using the measured aerosol concentration, we constrain our model through a new immersion freezing parameterisation. We find that multilayered clouds are challenging to simulate in remote areas with sparsely assimilated thermodynamics and that large-scale biases in the global forcing carry over to high-resolution simulations. In terms of cloud microphysics, high-temperature ice-nucleating particles (INPs) are necessary to capture the cloud phase of warm mixed-phase clouds. However, constraining the model to the observed INPs is insufficient; a factor of 106 is required to reach observed ice mass concentrations, which is also achieved by including SIP. Breakup upon ice-ice collisions is explosive and can increase the cloud ice number concentration by a factor of 106. Furthermore, the seeder-feeder mechanism significantly boosts snowfall by a factor of 103. An accurate representation of these microphysical processes is crucial to simulate multilayer clouds.