Background

Marine heatwaves are prolonged periods of unusually warm ocean temperatures that can disrupt marine ecosystems, fisheries and coastal communities. They are becoming increasingly important as ocean warming changes the frequency, intensity and duration of extreme temperatures, with effects that vary between regions, seasons and depths. Researchers combine observations, climate models, statistical methods and machine learning to understand how marine heatwaves develop, predict their future behaviour and assess their consequences. Recent advances have improved monitoring and climate projections, but major uncertainties remain because deep and seafloor temperatures are less well observed than surface conditions, and links between physical extremes and ecological impacts are not fully understood. Addressing these gaps is vital for reliable climate-risk assessments, marine conservation, sustainable fisheries and effective adaptation planning.

PhD project

Marine heatwaves are usually identified using sea-surface temperatures, yet marine organisms experience conditions throughout the water column, including at the seafloor. This project will investigate how extreme sea temperatures vary across space and depth in the northwest European Shelf seas, and how their probability and intensity may change under future climate scenarios. The central challenge is to describe rare, spatially varying temperatures while accounting for uncertainty in climate projections and relationships between extremes and their physical drivers.

The project will use extreme value theory and generalised additive models to represent smooth, three-dimensional changes in extreme temperatures across latitude, longitude and depth. Using high-resolution marine climate projections, it will produce region-wide estimates of extreme temperatures. The framework will also incorporate atmospheric and oceanic drivers, combining statistical modelling, machine learning and physical understanding to identify the processes associated with marine heatwaves and assess how they may change under different Shared Socioeconomic Pathways. It will be designed to transfer to other shallow seas and coastal regions.

The framework will balance flexibility, physical consistency and interpretability. It must distinguish climate signals from natural variability and quantify uncertainty in estimates of extremes. Outputs will include risk maps and scenario-based estimates identifying locations and habitats most exposed to future marine heatwaves. These results could support fisheries risk assessments, conservation priorities and marine management.

The project will link climate hazards, data science and decision-making under uncertainty. It will provide training in extreme-event modelling, machine learning, uncertainty quantification, model evaluation and risk communication. The PhD student will work closely with the Met Office, including regular visits throughout the project.

Applicant Profile

Students with a strong background in mathematics or statistics, who want to apply these skills to environmental data, and have at least some experience in programming.

Other Information

https://byoungman.github.io/
https://doi.org/10.5194/sp-4-osr8-7-2024
https://doi.org/10.5194/os-20-835-2024