DNS of a mixed‐phase cloud with a Lagrangian microphysics scheme, and temperature and supersaturation fluctuations

Mixed-phase clouds are widespread throughout the troposphere across all seasons, extending from polar to tropical regions (Korolev & Milbrandt, 2022). These clouds play a critical role in the Earth’s climate system; however, their representation in numerical weather prediction and global climate models remains highly uncertain (e.g., McCoy et al. 2016), largely due to their inherently complex physical nature. Clouds constitute dispersed multiphase flows in which supercooled liquid droplets and ice crystals coexist and interact in and with a turbulent environment over a wide range of spatial and temporal scales (Bodenschatz, et al. 2010). As a consequence of this intrinsic complexity, key uncertainties persist concerning mixed-phase clouds’ microphysical behavior despite extensive observational and laboratory studies. In particular, Lagrangian investigations that resolve the coupled evolution of supercooled droplets and ice crystals remain scarce, especially in turbulent cloud-top regions.

 

To address these limitations, we employ a Direct Numerical Simulation (DNS) approach to quantify the impact of turbulent temperature and saturation fluctuations on the glaciation of mixed-phase clouds. Within this framework, we investigate the condensational growth of supercooled liquid droplets, heterogeneous droplet freezing, and the subsequent diffusional growth of ice crystals. The simulations are performed using the Eulerian–Lagrangian turbulence solver Tlab (https://github.com/turbulencia/tlab), extended with the TINIA module, which has been developed as an add-on to Tlab to represent ice nucleation and growth processes. We will present first results concerning the influence of turbulence-induced thermodynamic fluctuations on droplet growth, freezing, and ice crystal evolution in mixed-phase clouds.

Korolev & Milbrandt (2022), Geophysical Research Letters, 49(18), e2022GL099578,

https://doi.org/10.1029/2022GL099578

McCoy, et al. (2016), J. Adv. Model. Earth Syst., 8, 650–668,

https://doi.org/10.1002/2015MS000589

Bodenschatz, et al. (2010), Science, 327.5968 : 970-971,

https://www.science.org/doi/10.1126/science.1185138

 

Acknowledgement:
We gratefully acknowledge the funding by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - Project Number STR 453/14-1 (Project name: TINIA).