Cloud forecast models rely on crude, empirically-fitted equations to decide when ice forms in clouds—and those equations are wrong. Ice-nucleating particles (INPs)—tiny specks of dust, pollen, or pollutants—determine whether a cloud droplet freezes, which in turn dictates cloud lifetime, brightness, and rainfall. Current models treat this process with simple linear fits that ignore the molecular physics of water freezing at a surface. This project builds a “toy model” that connects atomistic molecular dynamics simulations of supercooled water on materials like polyvinyl alcohol (PVA) to the probability of finding an ice-nucleating site of a given size. The model will be validated against real-world measurements of INP concentrations in the atmosphere. If successful, the resulting parameterisation will be dropped directly into existing weather and climate models, replacing guesswork with physics. Better ice nucleation predictions mean more reliable forecasts of rainfall, storm tracks, and cloud feedbacks—factors that underpin flood warnings, drought planning, and long-term climate projections. This is fundamental science: it does not produce a new gadget or drug, but it fixes a hidden flaw in the machinery that generates every weather forecast and climate scenario we rely on.
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This project aims to enhance weather and climate predictions by addressing a critical gap in our understanding of cloud formation, specifically the process of ice nucleation. Ice Nucleating Particles (INPs) significantly influence cloud properties and, consequently, weather and climate systems. However, the current parameterizations controlling primary ice formation in cloud models are simplistic and detached from the molecular physics of ice nucleation, leading to less reliable predictions. This project proposes a novel, multidisciplinary approach to physically underpin ice nucleation parameterizations used in weather and climate-relevant cloud models. Currently, these models rely on simple linear fits or empirical fits based on laboratory data, which do not accurately reflect the complex nature of ice formation in clouds. This has profound implications for the structure, composition, and functioning of clouds in our atmosphere, affecting everything from weather patterns to global climate regulation. To address this, we will develop a simple 'toy model' correlated with atomistic molecular dynamics simulations and corroborated by observational data of INP concentrations in the atmosphere. Such a model will bridge the gap between large-scale atmospheric models and the molecular-level details of ice formation. By leveraging molecular simulations of supercooled water at the interface with prototypical ice nucleating materials such as polyvinyl alcohol (PVA), we will derive insights into the probability of finding an ice nucleating site of a given size on specific surfaces. This data will then be used to predict the freezing rate corresponding to ice formation in clouds, which is a pivotal factor in cloud development and behaviour. The ultimate goal is to implement a validated model, grounded in both physical theory and empirical observation, into existing cloud models. This will significantly enhance their predictive capabilities by providing a more accurate description of ice nucleation processes. By doing so, the project aims to remedy the current deficits in cloud modeling, thereby improving our ability to predict weather and climate outcomes. The expected benefits of this research are far-reaching. Improved cloud models will lead to better weather forecasting, aiding in disaster preparedness and resource management. In terms of climate science, a deeper understanding of cloud physics is crucial for accurate simulations of future climate scenarios, which are essential for policy-making and environmental management. Furthermore, this project will contribute to the broader scientific community by providing a framework for connecting molecular-level phenomena with large-scale atmospheric processes. In conclusion, this project stands to significantly advance our understanding and modeling of cloud formation, particularly the role of ice nucleation, by integrating molecular physics with atmospheric science. The outcome will be more reliable weather and climate predictions, benefiting not just the scientific community but society at large by informing policy and enhancing our ability to respond to environmental changes. This research represents a critical step forward in our quest to understand and predict the complex interplay between atmospheric processes and climate change.
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