Stochastic nucleation processes and substrate abundance explain time-dependent freezing in supercooled droplets

Area of Science:

• Atmospheric science and climate modeling
• Physical chemistry and materials science

Background:

• Atmospheric immersion freezing (IF) is a key process in cloud formation and climate regulation, involving ice nucleating particles (INPs) in supercooled water.
• Current implementation of IF in climate models is hindered by the complex variability of INPs in terms of scale, composition, and morphology.

Purpose of the Study:

• To develop a consistent framework for describing immersion freezing (IF) that addresses the challenges posed by INP variability.
• To provide a more robust method for incorporating IF data into climate and cloud models.

Main Methods:

• Developed a stochastic nucleation process model to describe immersion freezing (IF).
• The model explicitly accounts for uncertainties and variability in ice nucleating particle (INP) surface area.
• Validated the approach against existing experimental and field data, challenging phenomenological descriptions.

Main Results:

• Demonstrated that a stochastic nucleation process can consistently describe immersion freezing (IF) across various conditions.
• The model successfully incorporates time-dependent freezing and a wide range of INP surface areas.
• The findings align with fundamental nucleation theory.

Conclusions:

• The stochastic nucleation approach offers a more accurate and consistent description of immersion freezing (IF).
• This improved understanding and modeling capability will enhance the representation of ice formation in climate models.
• The principles are applicable to other supercooled liquid-substrate nucleation phenomena.