Abstract

Abstract. 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 (M-PACE) 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.

Highlights

  • Clouds play a critical role in the surface energy budget of the Arctic, thereby affecting the Arctic sea ice and regional climate (Kay and Gettelman, 2009; Bennartz et al, 2013)

  • We show the important role of secondary ice production (SIP) in four types of clouds during Mixed-Phase Arctic Cloud Experiment (M-PACE), with the maximum enhancement in ice crystal number concentrations up to 4 orders of magnitude in moderately supercooled clouds

  • We focus on the Arctic mixed-phase clouds observed during the Department of Energy (DOE)’s Atmospheric Radiation Measurement (ARM) Mixed-Phase Arctic Cloud Experiment (M-PACE)

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Summary

Introduction

Clouds play a critical role in the surface energy budget of the Arctic, thereby affecting the Arctic sea ice and regional climate (Kay and Gettelman, 2009; Bennartz et al, 2013). Using a large-eddy simulation (LES) model and a Lagrangian parcel model, Sotiropoulou et al (2020) found that a combination of ice–ice collisional fragmentation and rime splintering provides a better agreement of the simulated ice crystal number concentrations (ICNCs) with observations in the summer Arctic stratocumulus. They found a low sensitivity of SIP to prescribed number concentrations of cloud condensation nuclei (CCN) and ice-nucleating particles (INPs).

Model description
Implementation of secondary ice production in CESM2
An emulated bin framework
Ice–ice fragmentation
Droplet shattering during rain freezing
Rime splintering
M-PACE case
Observation data
Model setup and description of model experiments
SIP impacts on different types of clouds during M-PACE
SIP impact on occurrence and phase partitioning of the mixed-phase clouds
Vertical distribution of ice crystal number concentration
PDF of ice crystal number concentration
Dependence of ice enhancement on cloud temperature
Full Text
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