Toy Models to Cloud Models: A Multifaceted Approach to understanding at atmospheric ice nucleation

The project will physically underpin the ice nucleation parametrizations used in weather and climate-relevant cloud models. Accurately predicting ice nucleation is critical for understanding and forecasting weather patterns and climate change, as it influences cloud formation, precipitation, and the Earth's radiation balance.

At present the parameterizations used to control primary ice formation in cloud models are either simple linear fits or empirical fits based on laboratory data. However, these fits are entirely decoupled from the underlying molecular physics of ice nucleation and are known to lack adequate skill in representing INPs in simulations of clouds.

In this project, we will address this gap using computational statistical models that capture the essential physics of ice nucleation (henceforth referred to as “toy” models). These models will be validated by both novel atomistic molecular dynamics simulations and by comparison to literature laboratory ice nucleation data and literature observations of ice nucleating particle (INP) concentrations in the atmosphere. These toy models, once validated both physically and observationally, will then be used to refine and improve there presentation of ice nucleation in the Met Office weather/climate model.

This is an entirely new approach that pushes the boundaries of the field by leveraging molecular-level insight to build a multi-scale methodology from atoms to clouds. By advancing the fundamental understanding of ice nucleation processes and improving the representation of these processes in cloud models, this project aims ultimately to significantly improve the realism of weather prediction and climate projections, there by contributing to more informed decision-making and climate policy development.