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PhD Studentship: NERC GW4+ DTP PhD Studentship for 2024 Entry - Emergent Constraints on Future Climate Extremes

University of Exeter - Department of Mathematics

Qualification Type: PhD
Location: Exeter
Funding for: UK Students, EU Students, International Students
Funding amount: £18,622 p.a. for 2023-24
Hours: Full Time
Placed On: 3rd November 2023
Closes: 9th January 2024
Reference: 4961

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.

Project Background

The general circulation models (GCMs) used to make projections of future climate change are vitally important to inform climate mitigation and adaptation strategies but are also invaluable tools for testing hypotheses about the functioning of the Earth System. Climate modelling centres around the world have devoted increasing effort to improving GCMs since the first IPCC report in 1990. As a result, they now provide a more complete representation of the myriad of interactions and feedbacks that determine how the climate will change in response to human and natural forcing factors. Unfortunately, the range of possible futures they project has not significantly reduced despite this. It is critically important to reduce projection uncertainty so as to provide better information to impact national and global climate policy action (Cox et al., 2018, Nijsse et al., 2020). 

Project Aims and Methods

A promising method for reducing this uncertainty, the emergent constraint approach, combines empirical relationships found in model ensembles with observations to constrain an unknown sensitivity. The basic idea is to identify an element of the observable climate (𝑋) that varies significantly across the model ensemble, and which exhibits a statistically significant relationship, 𝑓, with variations in some important variable (𝑌) describing the simulated future climate. 

There is uncertainty in the mean temperature and precipitation we will experience in the future, however there is even more uncertainty in deviations from the mean. Large deviations are climate extremes such as heat waves and floods. Although these are rare events, this project will use the emergent constraint technique to get a better handle on these future extremes – will heat waves become less common or more frequent leading to more fires? 

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