Abstract
Abstract. In order to understand the impact of ice formation in clouds, a quantitative understanding of ice nucleation is required, along with an accurate and efficient representation for use in cloud resolving models. Ice nucleation by atmospherically relevant particle types is complicated by interparticle variability in nucleating ability, as well as a stochastic, time-dependent, nature inherent to nucleation. Here we present a new and computationally efficient Framework for Reconciling Observable Stochastic Time-dependence (FROST) in immersion mode ice nucleation. This framework is underpinned by the finding that the temperature dependence of the nucleation-rate coefficient controls the residence-time and cooling-rate dependence of freezing. It is shown that this framework can be used to reconcile experimental data obtained on different timescales with different experimental systems, and it also provides a simple way of representing the complexities of ice nucleation in cloud resolving models. The routine testing and reporting of time-dependent behaviour in future experimental studies is recommended, along with the practice of presenting normalised data sets following the methods outlined here.
Highlights
Clouds are known to exert a significant radiative impact on Earth’s energy budget with lower altitude clouds making the largest net contribution due to their dominating albedo effect and global spatial extent (Hartmann et al, 1992)
In the past the question has been whether time dependence is important, but this question should be rephrased to whether a particular ice nucleating particle (INP) species has a strong time dependence or not, and at what point this stops having an impact on ice nucleation rates; i.e. is there a limiting value of λ beyond which the singular freezing model is adequate?
Used classifications of dust/metallic, black carbon and organic aerosols in a similar method for modelling a population of INP species; and Barahona (2012) introduced the ice nucleation spectrum framework, capable of relating different aerosol properties to ice nucleation in the deposition mode, with the potential to extend to immersion freezing. Whilst these models are capable of describing separate species it may be more realistic to represent a series of dominant components so that the time dependence and interparticle variability can be accurately described for a complex, evolving INP population
Summary
Clouds are known to exert a significant radiative impact on Earth’s energy budget with lower altitude clouds making the largest net contribution due to their dominating albedo effect and global spatial extent (Hartmann et al, 1992). Bacteria species belonging to the Pseudomonas genera catalyse freezing at temperatures above 265 K and exhibit a steep function of freezing rate (Wolber et al, 1986; Mortazavi et al, 2008), whereas mineral dust has been found to catalyse freezing at lower temperatures and exhibit a weaker gradient (Niedermeier et al, 2011) Along with this variability in nucleating ability, the importance of the stochastic, time-dependent nature of ice nucleation is reported to vary between INP species. In this study we use a multiple-component stochastic model to establish the key relationships between the nucleation-rate coefficient of an INP and its observable time-dependent behaviour, which are captured in a simple framework. For a description of the terms used throughout the text see Appendix A
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