PhD Studentship: Improving Turbulent Mixing in Weather and Climate Simulations - NERC GW4+ DTP PhD Studentship for September 2025 Entry Ref 5406

About the Partnership

This project is one of a number that are in competition for funding from the NERC Great Western Four+ Doctoral Training Partnership (GW4+ DTP).  The GW4+ DTP consists of the Great Western Four alliance of the University of Bath, University of Bristol, Cardiff University and the University of Exeter plus five Research Organisation partners: British Antarctic Survey, British Geological Survey, Centre for Ecology and Hydrology,  the Natural History Museum and Plymouth Marine Laboratory. The partnership aims to provide a broad training in earth and environmental sciences, designed to train tomorrow’s leaders in earth and environmental science. For further details about the programme please see nercgw4plus.ac.uk

Project details

For information relating to the research project please contact the lead Supervisor via r.j.beare@exeter.ac.uk

Project Aims and Methods

The stratified atmospheric boundary layer is a key component in high-impact weather and climate predictions. Forecast applications include polar surface temperatures, the timing of thunderstorms and night-time low temperatures. There is a need to improve the way the stratified boundary layer is represented (parametrized) in these simulations and interrogate the models with high-quality observations. In this project, we will investigate extending the current parametrization to beyond the first order typically currently used to second order and using state-of-the art observations for the British Antarctic Survey. This work is also timely as it connects to the NERC ParaChute project which aims to improve the boundary layer parametrization for high-resolution numerical weather prediction. 

In this project, the student will develop skills in:

• The structure of weather and climate models, 
• Interrogating weather and climate models with observations from the British Antarctic Survey, 
• Computational modelling of the atmosphere using python and FORTRAN, 
• Identifying turbulence patterns using machine learning. 

This will equip them for a career in atmospheric science. 

Training

The DTP offers funding to undertake specialist training relating to the student’s specialist area of research.

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