Impacts of secondary ice production on Arctic mixed-phase clouds based on ARM observations and CAM6 single-column model simulations

• Published in: Atmospheric Chemistry and Physics
• Main Authors: Zhao, Xi, Liu, Xiaohong, Phillips, Vaughan T.J., Patade, Sachin
• Format: Article in Journal/Newspaper
• Language: English
• Published: Copernicus GmbH 2021
• Subjects: Meteorology and Atmospheric Sciences; Arctic; Rime
• Online Access: https://lup.lub.lu.se/record/813c09a4-644d-4562-a8e2-469303be9e83; https://doi.org/10.5194/acp-21-5685-2021

For decades, measured ice crystal number concentrations have been found to be orders of magnitude higher than measured ice-nucleating particle number concentrations in moderately cold clouds. This observed discrepancy reveals the existence of secondary ice production (SIP) in addition to the primary ice nucleation. However, the importance of SIP relative to primary ice nucleation remains highly unclear. Furthermore, most weather and climate models do not represent SIP processes well, leading to large biases in simulated cloud properties. This study demonstrates a first attempt to represent different SIP mechanisms (frozen raindrop shattering, ice ice collisional breakup, and rime splintering) in a global climate model (GCM). The model is run in the single column mode to facilitate comparisons with the Department of Energy (DOE) s Atmospheric Radiation Measurement (ARM) Mixed-Phase Arctic Cloud Experiment (MPACE) observations. We show the important role of SIP in four types of clouds during M-PACE (i.e., multilayer, single-layer stratus, transition, and frontal clouds), with the maximum enhancement in ice crystal number concentrations up to 4 orders of magnitude in moderately supercooled clouds. We reveal that SIP is the dominant source of ice crystals near the cloud base for the long-lived Arctic single-layer mixed-phase clouds. The model with SIP improves the occurrence and phase partitioning of the mixed-phase clouds, reverses the vertical distribution pattern of ice number concentrations, and provides a better agreement with observations. The findings of this study highlight the importance of considering SIP in GCMs.