The implications of unresolved cloud phase heterogeneities on precipitation formation

Mixed-phase clouds, consisting of both liquid and ice phases, are crucial for precipitation formation over continents. The presence of the ice phase acts as a catalyst for forming precipitable particles through depositional growth, aggregation, and riming. The efficiency of these growth processes is strongly governed by the spatial distribution of the liquid and ice phases and the interfaces between them. A homogeneous mixture of liquid and ice particles maximizes the growth of the ice particles, and with that expedites precipitation formation. In contrast, a strong separation into a liquid and ice clusters may limit the growth by reducing phase interactions. Observations indicate that these heterogeneous clusters exist down to a spatial extent of 100 m [1] potentially creating a limiting factor for the efficiency of a mixed-phase cloud to precipitate.

Here, we show that these cloud phase heterogeneities even exist down to the meter-scale based on in-situ observations. A total of 19 glaciogenic seeding experiments conducted in supercooled low-stratus clouds in Switzerland within the CLOUDLAB project [2], were sampled with an in-house developed holographic imager, capable of distinguishing cloud droplets from ice crystals. Applying thresholds to separate liquid, mixed, and ice clusters, we demonstrate the highly variable nature of mixed-phase cloud phase structures. We furthermore contextualize these findings with high-resolution model simulations with the weather model ICON [3] at 50 m and 250 m. These simulations highlight the importance of resolving cloud phase heterogeneities for efficiently forming precipitation. By combining novel cloud in situ observations with high-resolution modeling, this study emphasizes the need to capture the heterogeneity in mixed-phase clouds and its importance for numerical weather models.

 

 

[1] A. Korolev and J. Milbrandt, “How are mixed-phase clouds mixed?,” Geophysical Research Letters, 49, e2022GL099578, DOI: 10.1029/2022GL099578

[2] J. Henneberger, F. Ramelli, R. Spirig, N. Omanovic, A. J. Miller, C. Fuchs, H. Zhang, J. Bühl, M. Hervo, Z. A. Kanji, K. Ohneiser, M. Radenz, M. Rösch, P. Seifert, and U. Lohmann, “Seeding of Supercooled Low Stratus Clouds with a UAV to Study Microphysical Ice Processes: An Introduction to the CLOUDLAB Project,” Bulletin of the American Meteorological Society, vol. 104, no. 11, E1962–E1979, 2023, ISSN: 0003-0007, 1520-0477. DOI: 10.1175/BAMS-D-22-0178.1

[3] G. Zängl, D. Reinert, P. Ripodas, and M. Baldauf, “The ICON (ICOsahedral Non-hydrostatic) modelling framework of DWD and MPI-M: Description of the non-hydrostatic dynamical core,” Quarterly Journal of the Royal Meteorological Society, vol. 141, no. 687, pp. 563–579, 2015, ISSN: 1477-870X. DOI: 10.1002/qj.2378