PhD Studentship: High resolution climate prediction for an equitable and economically sound transition to Net Zero. 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

Achieving Net Zero requires more than emission cuts; it demands direct carbon sequestration to offset sectors like agriculture and aviation. Land-use changes, such as tree planting, offer a solution, but with most UK land privately owned, policymakers must create incentives that drive the right land uses for negative emissions while balancing food and energy security, economic growth, and biodiversity restoration. Modeling these impacts involves a wide range of systems, each relying on high-resolution climate data. While CMIP6 projections range from 250 km to 10 km, our needs are at 50–100 m, as even 1 km is too coarse. Downscaling is part of the solution, but moving from the few emissions scenarios explored by CMIP to the deeper, probabilistic uncertainty quantification required for economic analysis remains a major open challenge.

High-resolution climate downscaling to the field level is essential for model-based climate mitigation and adaptation support, as it accurately simulates climate impacts on systems like trees, agriculture, biodiversity, and water quality. Translating mid-scale data (e.g., 10 km) to the field scale requires mechanistic models that capture interactions between regional climate and local topography, including lapse rate, coastal effects, and cold air drainage. Downscaling large-scale simulations to mid-scale without a regional model or supercomputer is feasible through spatio-temporal statistical methods or machine learning, though preserving mid-scale structures (e.g., banded rainfall) while capturing uncertainty remains complex.

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