About the Project
Background
Planners in climate-sensitive organisations are starting to use information about future climate that match their planning horizons, e.g. the next 3-6 months; 1-2 years or 25 years. This climate information has included long-term climate projections/scenarios (e.g., for costal management or infrastructure planning), seasonal forecasts (e.g., for agricultural planning) and very recently interannual /decadal predictions (e.g., water management). Different types of climate information have…
different levels of maturity and uncertainty depending on geographical and temporal characteristics of the climate variable of interest. Cutting edge scientific advancements are starting to show promise in the development of seamless regional climate predictions that span the next season(s), year(s) and decade(s). It is unclear and unknown how seamless regional climate predictions could be used and useful to stakeholders wanting to adapt to climate variability and change.
PhD project
Seamless climate prediction aims to provide a continuous chain of climate information from the next few months through to decades or centuries ahead, using consistent modelling, observations and uncertainty information. The key idea is that different timescales aren’t treated as completely separate products. Instead, information and physical processes are carried forward from one timescale to the next, while the balance between predictable and uncertain components changes.
This project will utilise seamless regional climate predictions for the UK (or Europe) and test their usefulness in decision-making across temporal scales (planning for the months to decades). Adaptation and resilience decision contexts will be explored through at least two case studies that exemplify high and low uncertainty/confidence, e.g., in long-term projections there is low uncertainty that heatwaves will increase at the European scale; however, there is high uncertainty about how rainfall will change in the summer in northern England. The project will identify decision-making contexts (e.g., in health and/or water management) that span multiple planning horizons to explore the possibility of utilising seamless regional climate predictions to inform planning and decision-making, working with in collaboration with stakeholders if appropriate. The project will include climate science research on uncertainty characterisation of seamless regional climate predictions using qualitative and/or quantitative methods. It will also use decision sciences to demonstrate the feasibility of incorporating this new type of information into planning and decision-making. The project will use climate science and decision sciences to improve our understanding of this important research gap which will benefit the UK’s Government development of UK Climate Information and Europe’s Copernicus Climate Change Service.
Applicant Profile
Students with a strong quantitative background who want to apply these skills to climate science and decision science.
Funding Notes
This project is part of the UNRISK CDT, which offers 15-18 fully-funded NERC studentships, covering full university tuition fees; a personal stipend at standard UKRI rates; £6000 individual research and training costs; £5000 (per student) of cohort-level training; and a ‘Flexible Fund’ for special projects.
International applicants will need to cover costs related to applying for a student visa and the international health surcharge (IHS).
Applications are open to UK and international applicants. The number of awards for international applicants is limited by UKRI rules.
More information is available on the UNRISK website: View Website
References
European ASPECT project: https://www.aspect-project.eu/
Sarojini, B. B., M. A. Abid, P. Cos, C. Delgado-Torres, S. Dessai, F. Doblas-Reyes, M. G. Donat, F. Garry, D. Krieger, J. A. Lowe, C. McSweeney, D. Sexton, V. Torralba and A. Weisheimer (2026). “”Combining Observations, Forecasts and Projections into Seamless Climate Information: Recent Advances and Insights in User Applications.”” Bulletin of the American Meteorological Society: BAMS–D–26–0079.0071. https://doi.org/10.1175/BAMS-D-26-0079.1
Delgado-Torres, C., M. G. Donat, N. Pérez-Zanón, V. Torralba, R. Bilbao, P. A. Bretonnière, M. Samsó-Cabré, A. Soret and F. J. Doblas-Reyes (2026). “”Seamless climate information from months to multiple years: constraining decadal predictions with seasonal predictions and past observations, and their comparison to multi-annual predictions.”” Earth Syst. Dynam. 17(1): 41–56. https://esd.copernicus.org/articles/17/41/2026/