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

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:

Advert information