Microphysical processes involving the vapour phase dominate in simulated low-level Arctic clouds

• Published in: Atmospheric Chemistry and Physics
• Main Authors: Kiszler, Theresa, Ori, Davide, Schemann, Vera
• Format: Text
• Language: English
• Published: 2024
• Subjects: Arctic; polar night
• Online Access: https://doi.org/10.5194/acp-24-10039-2024; https://acp.copernicus.org/articles/24/10039/2024/

Current general circulation models struggle to capture the phase-partitioning of clouds accurately, both overestimating and underestimating the supercooled liquid substantially. This impacts the radiative properties of clouds. Therefore, it is of interest to understand which processes determine the phase-partitioning. In this study, microphysical-process rates are analysed to study what role each phase-changing process plays in low-level Arctic clouds. Several months of cloud-resolving ICON simulations using a two-moment cloud microphysics scheme are evaluated. The microphysical-process rates are extracted using a diagnostic tool introduced here, which runs only the microphysical parameterization using previously simulated days. It was found that the processes impacting ice are more efficient during polar night than polar day. For the mixed-phase clouds (MPCs), it became clear that phase changes involving the vapour phase dominated in contrast to processes between liquid and ice. Computing the rate of the Wegener–Bergeron–Findeisen process further indicated that the MPCs frequently (42 % of the time) seemed to be glaciating. Additionally, the dependence of each process on the temperature, vertical wind, and saturation was evaluated. This showed that, in particular, the temperature influences the occurrence and interactions of different processes. This study helps to better understand how microphysical processes act in different regimes. It additionally shows which processes play an important role in contributing to the phase-partitioning in Arctic low-level mixed-phase clouds. Therefore, these processes could potentially be better targeted for improvements in the ICON model that aim to more accurately represent the phase-partitioning of Arctic low-level mixed-phase clouds.