04 Oct 2022
 | 04 Oct 2022
Status: a revised version of this preprint is currently under review for the journal ACP.

The Chance of Freezing – Parameterizing temperature dependent freezing including randomness of INP concentrations

Hannah Carolin Frostenberg, André Welti, Mikael Luhr, Julien Savre, Erik S. Thomson, and Luisa Ickes

Abstract. Ice nucleating particle (INP) concentrations can spread over several orders of magnitude at any given temperature. However, this variability is rarely accounted for in heterogeneous ice nucleation parameterizations. We developed a scheme for immersion freezing where the INP concentration is drawn from a relative frequency distribution of cumulative INP concentrations. At each temperature, this distribution describing the INP concentrations is expressed as a log-normal frequency distribution. The new parameterization scheme does not require aerosol information from the driving model to represent the heterogeneity of INP concentrations. The scheme's performance is tested in a large-eddy simulation of an Arctic stratocumulus. We find that it leads to reasonable ice masses in the cloud. The scheme is sensitive to the median of the frequency distribution and highly sensitive to the standard deviation of the distribution. Generally, larger probability to draw high INP concentrations leads to substantially more ice in the simulated cloud.

Hannah Carolin Frostenberg et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on acp-2022-696', Anonymous Referee #1, 03 Nov 2022
  • RC2: 'Comment on acp-2022-696', Anonymous Referee #2, 20 Nov 2022
  • AC1: 'AC: Response to RC1 and RC2', Hannah Frostenberg, 31 Jan 2023

Hannah Carolin Frostenberg et al.

Hannah Carolin Frostenberg et al.


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Short summary
Observations show that ice nucleating particle concentrations (INPCs) have a large variety and follow log-normal distributions for a given temperature. We introduce a new immersion freezing parameterization that applies this log-normal behavior: INPCs are drawn randomly from a temperature-dependent log-normal distribution. We show that the ice content of the modeled Arctic stratocumulus cloud is highly sensitive to the probability to draw high INPCs.