PhD Studentship - Quantifying the Risk of Persistent Extreme Weather Events in a Changing Climate

Persistent extreme weather events such as heatwaves, cold snaps, or particularly rainy and/or stormy seasons have huge socio-economic impact; estimating the probabilities of their occurrence which can inform assessments of health or flood risks is therefore an important task. These probabilities are likely to change in a nonlinear way under climate change. This project will develop and explore novel data-driven techniques for quantifying the likelihood of persistent weather extremes. The approach is based on large-deviation theory, originally introduced in statistical physics to quantify probabilities of rare trajectories in dynamical systems. In particular, the methodology allows for extrapolation to events of so far unprecedented duration and severity. We will apply the methodology to data sets from the large-scale CMIP climate model simulations and the convection-permitting UK Climate Projections (UKCP18) as well as observational data sets such as the Central England Temperature time series and ERA5 reanalysis. The project offers scope for the student to shape its direction, whether by focusing on a particular type of extreme, investigating multivariate approaches designed for compound events, or linking persistent extremes with large-scale atmospheric circulation regimes. The student will receive sector-leading training encompassing technical research skills broadly in the areas of statistics, data science, machine learning and computing with applications in weather and climate science as well as generic skills such as science writing, presentation and networking. The research will be carried out in close collaboration with two project partners, the Met Office and the company JBA Risk Management, with interests in UK heatwaves as well as high cumulative precipitation events and their implications for flood risk. The collaborative partners will provide non-commercial and commercial, non-academic research environments; these will enhance the student's training and research experience through co-supervision directing the project towards relevant risk applications, provision of data sets, and opportunities for work placements. Funding For eligible students the studentship will cover home tuition fees plus an annual tax-free stipend.