Reducing uncertainty in land surface model projections. UNRISK NERC Centre for Doctoral Training PhD studentship 2025/26 Entry. Ref: 5448

Supervisors

Primary Supervisor

James Salter (J.M.Salter@exeter.ac.uk)

Institution

University of Exeter (Mathematics and Statistics)

Academic Supervisors

Doug McNeall (Met Office , Hadley Centre)

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

It is uncertain how much CO2 will be taken up by the land under climate change, with a wide range of possible futures captured across CMIP6 (Coupled Model Intercomparison Project) models. Perturbing parameters within the UK Land Surface Model, JULES, suggests this uncertainty may be even wider, and it is unclear whether the land surface will overall be a carbon source or sink under different futures. Better constraints of the uncertainty of this part of climate models is key for reducing uncertainty in climate projections.

When running simulations with JULES, there are many drivers of output uncertainty. Model simulations can be computationally expensive, and statistical or machine learning emulators are commonly used as an approximation for the true model, allowing the high-dimensional input and output spaces to be more fully explored, and enabling calibration of inputs to be more feasible. This project will build on past work emulating JULES, in particular for projections of land carbon uptake under different climate change scenarios.

The overarching aims of this project are to reduce uncertainty in simulations of JULES under future emissions scenarios, to better understand whether the carbon sink is negative/positive, and the implications this has for future climate. As an efficient way of exploring these aims, the project will train ‘emulators’ for exploring the model output, for use in calibration to observational data, and for future projections. The project will combine emulation and calibration, but within each of these areas there are many potential avenues:

– Emulation: across large number of unknown input parameters, across boundary conditions, across future emissions scenarios, or across all these unknowns at once. Training emulators in large input spaces, and across multiple related time series outputs is non-trivial and may require novel methodology, building on existing statistical and machine learning approaches. Training emulators to coupled versions of the model is challenging due to expense, but could be achievable via multi-level emulation techniques.

– Calibration: history matching is a powerful technique to identify ‘good’ versions of model inputs, but which outputs we emulate and calibrate to, and how we define structural error (differences between model-world and reality that can’t be removed by tuning) will affect future projections, and work is required to properly account for this. Within this, the project could gain an understanding in how sensitive future projections are to inputs and the assumptions we make during calibration.

Regardless of the route taken, this will feed into better understanding and constraint of uncertainties relating to future projections of the carbon cycle, whilst advances in methodology will be more widely applicable to other environmental problems.

Academic profile: Students with a strong background in mathematics/statistics or another quantitative subject, with an interest in climate or environmental problems.

• https://gmd.copernicus.org/articles/17/1059/2024/gmd-17-1059-2024.html

 
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