Exploring uncertainty due to clouds in modelled future climate change systematically. UNRISK NERC Centre for Doctoral Training PhD studentship 2025/26 Entry. Ref: 5449

Supervisors

Primary Supervisor

Prof Hugo Lambert (F.H.Lambert@exeter.ac.uk)

Institution

University of Exeter (Mathematics and Statistics)

Academic Supervisors

James Salter (J.M.Salter@exeter.ac.uk) (University of Exeter, Mathematics and Statistics)

Project Partners

Met Office

Understanding Uncertainty to Reduce Climate Risks (UNRISK) is a Centre for Doctoral Training – Recruiting now!

UNRISK is a Centre for Doctoral Training with fully funded PhD research opportunities at the University of Leeds, University College London, and the University of Exeter collaborating with over 40 external partners. UNRISK will train students with the multidisciplinary knowledge and skills across climate science, data science and decision science to tackle the pressing challenge of reducing the risks associated with rapid climate change. UNRISK will fund 40 PhD students in cohorts of 12-15 per year over three years, providing them with a stipend, university fees and residential training for 3 years and 9 months. Find out more at https://unrisk-cdt.ac.uk/ and browse the projects at https://unrisk-cdt.ac.uk/projects/.

Project Information

Changes in clouds are one of the biggest uncertainties affecting predictions of future climate change. Because their size is typically much smaller than the gridlength of numerical climate models, clouds must be “parametrised”, meaning that they are represented approximately using information from larger-scale conditions.

It is difficult to quantify the difference between climate models, which typically have different model parametrisations written in terms of different functions. Some progress has been made through “perturbed physics ensembles”, which take one model structure and perturb uncertain model parameters through their ranges of possible values. However, climate models are expensive to run, meaning that only a few parameter combinations can be tried. Features of model behaviour for unexplored parameter combinations must instead be estimated via statistical emulation. Parameter values that are unrealistic given available data are then identified via “history matching”.

We have developed Continuous Structural Parametrisation (CSP), which is a way of approximating structurally different model parametrisations as functions of the same variables, effectively writing them as members of a perturbed physics ensemble. CSP also allows us to represent observations or high-resolution process models within the same structure, allowing us to benchmark our parametrisations.

Clouds are the most uncertain process affecting the interaction between radiation and climate, playing a key role in the degree to which the planet will warm in future. This project will start by writing down cloud parametrisations and high-resolution model realisations of cloud using CSP. The research can then take a number of directions:

1) A Gaussian process emulator or neural network can be applied in conjunction with CSP to provide a more complete description of a parametrised process while still retaining human-readability. Our preliminary work indicates that this is helpful for cloud, but more statistical modelling work is needed to constrain low cloud, which is notoriously

difficult to understand.

2) Emulation can fill in the CSP parameter space, giving us a prediction of key cloud responses between the parametrisations that we have. History matching can then be used to find which parts of our parameter space are not inconsistent with high-resolution models and observations, and highlight new areas of parameter space that model parametrisations should explore, allowing new climate models to better explore future climate impacts.

3) The CSP emulator can be placed directly within the Met Office Unified Model climate model to explore the potential impacts of uncertainty in cloud parametrisation on future surface climate.

The project is linked to the UK Met Office with collaborator Mark Webb providing expertise in clouds and climate modelling. Co-supervisor James Salter is a statistician who will provide expertise in emulation and history matching.

This project will suit students with a strong background in mathematics or statistics or another numerate degree such as Physics or Meteorology, and an interest in Earth’s atmosphere, climate and climate change. Experience in coding in python or another high-level language would be an advantage, but is not essential.

• Continuous Structural Parametrisation: https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2020MS002085
• History matching: https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2020MS002217
• Uncertainty in climate change due to errors in modelling of cloud: https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2021JD035198

Funding

UNRISK will offer around 40 fully funded PhD positions (around 12-15 per year over three cohorts). Funding covers university fees, a maintenance grant, as well as research and training costs. Further up to date information about studentship funding is available from UK Research and Innovation and will be posted here when available for 2025/26 entry. 

Applications are open to UK and international applicants, although the number of awards for international applicants is limited by UKRI rules.

Some additional places are also available for students who have their own funding, such as scholarships, and whose research is closely aligned with UNRISK.  

Entry requirements

Academic Entry Requirements

You should hold a first or upper-second class Bachelor’s degree or a taught Master’s degree in an appropriate subject from a UK university. Non-UK qualifications of an equivalent standard are also accepted.

Residency

The UNRISK CDT studentships are available to UK and International applicants. Following Brexit, the UKRI now classifies EU students as international unless they have rights under the EU Settlement Scheme. The GW4 partners have agreed to cover the difference in costs between home and international tuition fees. This means that international candidates will not be expected to cover this cost and will be fully funded but need to be aware that they will be required to cover the cost of their student visa, healthcare surcharge and other costs of moving to the UK to do a PhD. All studentships will be competitively awarded and there is a limit to the number of International students that we can accept into our programme (up to 30% cap across our partners per annum).
 

English Language Requirements

If English is not your first language you will need to meet the English language requirements of the university that will host your PhD by the start of the programme. Please refer to the relevant university website for further information.  This will be at least 6.5 in IELTS or an acceptable equivalent. Please refer to the relevant university for further information. Please refer to the English Language requirements web page for further information.

How to apply

You must apply for funding via the University of Leeds website further details can be found here Application Process – Understanding Uncertainty to Reduce Climate Risks

Please note that only those applications submitted directly via the University of Leeds application system will be assessed for funding. Applying directly to your chosen programme of study at the University of Exeter will not constitute an application for funding.

Data Protection

If you are applying for a place on a collaborative programme of doctoral training provided by University of Leeds and other universities, research organisations and/or partners please be aware that your personal data will be used and disclosed for the purposes set out below.

Your personal data will always be processed in accordance with the General Data Protection Regulations of 2018. University of Leeds (“University”) will remain a data controller for the personal data it holds, and other universities, research organisations and/or partners (“HEIs”) may also become data controllers for the relevant personal data they receive as a result of their participation in the collaborative programme of doctoral training (“Programme”).

Further Information

For an overview of the UNRISK NERC CDT programme please see the website https://unrisk-cdt.ac.uk/

Summary