About the Project
Background
Extratropical storms are a major driver of weather and climate variability across the mid-latitudes, bringing strong winds and heavy precipitation that can cause widespread disruption and economic losses. Understanding how these storms may change as the climate warms is therefore central to assessing future climate risk (e.g. Little et al 2023). Research combines observations, reanalyses and climate-model simulations with atmospheric dynamics, statistics and data science to understand storm…
formation, movement, intensity and impacts. However, projections remain uncertain because models represent atmospheric processes differently and natural climate variability can strongly affect regional changes. Improving our understanding and quantification of these uncertainties is vital for distinguishing robust climate signals from model and variability-driven differences, and for providing reliable information for adaptation and risk-informed decision making.
PhD project
Uncertainty in projections of the future behaviour of extratropical cyclones makes it difficult to assess how their associated risk may change. This project will aim to answer the question of How does uncertainty in climate-model projections of extratropical storms translate into uncertainty in the weather hazards and risks that matter to society? It will combine climate science with uncertainty quantification (including sensitivity analysis and emulation), statistical and machine learning approaches to improve understanding and prediction of extratropical storms. A key challenge is determining which uncertain inputs and processes have the greatest influence on projected outcomes in projections of storm frequency, intensity and location.
Sensitivity analysis will be used to quantify how uncertainty in model parameters contributes to uncertainty in storm-track characteristics, and the resulting wind and precipitation hazards. This will allow us to identify the most influential parameters and which uncertainties matter most for risk assessment and decision-making. A further challenge is computational: uncertainty and sensitivity analyses can require thousands of model simulations, but climate-models are expensive to run, so the aim will be to develop statistical emulators, such as Gaussian processes, to provide fast approximations of complex climate-model relationships to perform robust uncertainty quantification in a computationally feasible way.
The project will contribute to UNRISK by connecting climate-model uncertainty with hazards and risks relevant to decision making. With the guidance of the supervisors, Prof Jennifer Catto and Dr Hossein Mohammadi, the project will investigate where uncertainty is amplified or reduced as it propagates from atmospheric circulation to storm behaviour and surface hazards. Outcomes could identify priorities for climate-model development and provide better-calibrated information for climate-risk assessment.
Applicant Profile
We are seeking students with a quantitative background (e.g. Atmospheric Science, Physics, Maths, Statistics, Computer Science) and an interest in high impact weather who want to apply their skills to a problem of strong societal consequence.
Funding Notes
This project is part of the UNRISK CDT, which offers 15-18 fully-funded NERC studentships, covering full university tuition fees; a personal stipend at standard UKRI rates; £6000 individual research and training costs; £5000 (per student) of cohort-level training; and a ‘Flexible Fund’ for special projects.
International applicants will need to cover costs related to applying for a student visa and the international health surcharge (IHS)
Applications are open to UK and international applicants. The number of awards for international applicants is limited by UKRI rules.
More information is available on the UNRISK website: View Website
References
Little, A.S., Priestley, M.D.K. & Catto, J.L. (2023). Future increased risk from extratropical windstorms in northern Europe. Nat Commun 14, 4434. https://doi.org/10.1038/s41467-023-40102-6
https://experts.exeter.ac.uk/25960-jennifer-catto