PhD Studentship: Predicting 'unseen' stratospheric extremes: Integrating operational ensembles, AI, and health impact modelling

Key science questions and importance

Detailed observations of the stratosphere have existed only since the satellite era. What are the chances of unprecedented extremes, such as powerful sudden stratospheric warmings, not found in this record? How would these impact surface weather, and can predicting these events provide early warnings for public health? Understanding this is vital for climate resilience, as the stratosphere is a critical source of predictability on seasonal timescales.

Methods

The researcher will analyse a very large ensemble of operational seasonal prediction models from the EU’s Copernicus (C3S) database to estimate probabilities of unprecedented stratospheric extremes. They will run complementary ‘hindcast’ simulations using ACE2, a leading AI-based model used at Exeter and the Met Office. Finally, these outputs will be combined with epidemiological models to quantify public health impacts. The researcher will be encouraged to steer the project's direction by selecting specific geographic regions, targeted health impacts, or focusing more deeply on the AI simulation.

Training opportunities

The project will contribute towards a new initiative of the Stratospheric Network for the Assessment of Predictability (SNAP), within the World Climate Research Programme, providing international networking and communication experience. 'On-the-job' training will prioritise the computational skills needed to handle large-scale weather and climate data and AI models. The researcher can take taught modules in atmospheric fluid dynamics and will be encouraged to attend summer schools both climate modelling (e.g. NCAS Climate Modelling Summer School) and impacts (e.g. EEPE Environmental Epidemiology course).

Collaboration

The supervisory team bring complementary expertise in stratospheric dynamics (Exeter), seasonal prediction (Met Office), and impact modelling (Bristol). The researcher will be encouraged to spend extended periods visiting project partners at the relevant milestones. The team have an extensive record of collaboration, including in the co-supervision of PhD students and in several scientific publications.

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