Projects that started in 2025 (UNRISK cohort 1)
Physics-informed machine learning for decision making related to future extreme weather events
- Charles Benoist
- Mathematics and Statistics, University of Exeter
- Supervisors: Stefan Siegert and Jemma Shipton
- Partners: KAUST
- Email: mb1499 ‘at’ exeter.ac.uk
This project develops physics-informed machine learning (ML) methods to improve predictions of extreme weather events under climate change. Probabilistic ML models can estimate the risk of short-lived, localised events like severe precipitation, but ensuring physical realism remains a challenge. This research will explore how to embed physical constraints into ML models—through architecture design, loss functions, or augmented training data—and evaluate their impact on predictive skill. It will also advance techniques for generating spatiotemporal probabilistic forecasts and robust evaluation metrics. The goal is to deliver accurate, physically consistent predictions to support climate risk assessment and resilience planning.
Novel statistical AI approaches for modelling and evaluating extreme windstorm risk
- Serin Mary Binoy
- Mathematics and Statistics, University of Exeter
- Supervisors: David Stephenson, Matthew Priestley and Theo Economou
- Partners: Willis Towers Watson
- Email: sb1635 ‘at’ exeter.ac.uk
My project investigates how advanced statistical AI methods can improve our understanding and modelling of extreme windstorm risk. Extra-tropical cyclones cause significant societal and financial impacts, and current catastrophe models used by insurers rely on simplifying assumptions. This project will apply modern techniques such as Bayesian hierarchical spatial models to analyse windstorm footprint data across Europe, Japan, and beyond. By characterising extreme wind speeds and their relationship to climate variability and change, the research will support more accurate risk assessments and help insurers develop better protection strategies. Collaboration with Willis Towers Watson and the Met Office ensures real-world relevance.
Advanced uncertainty quantification for decision support: Balancing risks and preferences for policy decisions
- Rose Crocker
- LEEP, University of Exeter
- Supervisors: Amy Binner and Danny Williamson
- Email: rc961 ‘at’ exeter.ac.uk
My project aims to develop advanced tools for decision-making under uncertainty, focusing on land-use strategies for achieving goals like the UK’s Net Zero target. Policymakers face complex choices influenced by climate, economic, and regulatory uncertainties. This project combines environmental and economic modelling with cutting-edge uncertainty quantification and preference elicitation techniques. By creating transparent, user-friendly tools, it will allow decision-makers to explore alternative strategies, assess risks, and incorporate their priorities. Methods such as emulation and model calibration will enable rapid, evidence-based evaluation of options, supporting adaptive, robust policies for sustainable land management and long-term societal and environmental outcomes.
Quantifying the role of land cover in ‘slowing the flow’ for flood risk reduction in a changing climate
- Jake Senior
- School of Geography, University of Leeds
- Supervisors: Steph Bond and Joe Holden
- Partners: National Trust, United Utilities
- Email: fnrg0632 ‘at’ leeds.ac.uk
My project investigates how land cover management can reduce flood risk through Nature-Based Solutions (NBS) in a changing climate. While NBS such as “slowing the flow” are widely promoted, their effectiveness under future climate uncertainty remains unclear. This research will combine field data, hydrological modelling, and data science to quantify how vegetation and surface roughness influence overland flow and flood risk. It will also explore how climate-driven changes in storms and vegetation affect model predictions and uncertainty. The findings will inform practical strategies for flood risk reduction and support evidence-based policy for sustainable catchment management.
Predicting biodiversity loss in mountain rivers due to glacier retreat
- Arthur Lamoliere
- School of Geography, University of Leeds
- Supervisors: Lee Brown and Jonathan Carrivick
- Email: pjrw0504 ‘at’ leeds.ac.uk
My project aims to predict biodiversity loss in glacier-fed mountain rivers as glaciers retreat under climate change. While physical processes in these rivers are well understood, major uncertainties remain about species responses and extinction risks, limiting effective conservation. This project will reduce these uncertainties by improving species distribution models (SDMs) through better datasets, advanced modelling, and integration of climate and hydrological projections. Work will include analysing data gaps, collecting new biological data in regions such as the Alps or Himalayas, and evaluating uncertainty from multiple modelling approaches. The goal is to inform conservation strategies for vulnerable mountain ecosystems.
Reducing uncertainty in the effect of clouds on climate change
- Soffie Wisniewska
- School of Earth and Environment, University of Leeds
- Supervisors: Ken Carslaw and Paul Field
- Partners: Met Office
- Email: fqgk0327 ‘at’ leeds.ac.uk
My project focuses on reducing one of the largest uncertainties in climate projections: the role of clouds. Cloud feedbacks and aerosol–cloud interactions strongly influence global warming, yet current climate models struggle to represent these processes accurately. This research will combine high-resolution weather-scale simulations, satellite and aircraft observations, and advanced data science techniques to identify the physical processes driving uncertainty. By using machine learning emulators trained on large ensembles of model simulations, the project will explore ways to constrain cloud behaviour, improve model realism, and deliver more reliable long-term climate predictions.
Using new high-resolution ensembles to quantify uncertainty in projections of African climate processes
- Ella Thomas
- School of Earth and Environment, University of Leeds
- Supervisors: John Marsham and Ben Maybee
- Partners: Met Office
- Email: ee23ert ‘at’ leeds.ac.uk
My project explores the connections between large-scale drivers and convective precipitation in West Africa. It uses a new ensemble of convection-permitting simulations run over continental Africa for two 10-year periods (current and future climate). Rainfall in West Africa is dominated by convection, which acts on a small spatial scale but produces large amounts of energy, feeding back into large-scale circulations. Convective-scale simulations are necessary to resolve these processes and develop better physical understanding of the mechanisms controlling them. This understanding will be used to reduce uncertainty in user-relevant metrics such as monsoon onset and future projections of rainfall extremes.
Protecting UK infrastructure from landslides triggered by climate extremes
- Dylan Dearnaley
- School of Earth and Environment, University of Leeds
- Supervisors: Cathryn Birch, Fleur Loveridge, Susanne Lorenz
- Partners: Met Office, rail industry
- Email: ee16dd ‘at’ leeds.ac.uk
My research addresses the growing risk of landslides to UK rail infrastructure under climate extremes. Landslides, often triggered by intense rainfall, already cause frequent disruptions and are projected to increase with climate change. The project will quantify the rainfall characteristics that lead to slope failures, assess how these may change in the future, and develop methods to combine and communicate uncertainties in rare event prediction. Working with rail industry partners and the Met Office, the research will create tools and best practices for risk management, early warning, and long-term adaptation strategies to protect critical transport networks.
The influence of physical process representations on regional and global-scale climate model output
- Celine Tchaghlassian
- School of Earth and Environment, University of Leeds
- Supervisors: Leighton Regayre and Ken Carslaw
- Partners: CICERO, Norway
- Email: cn21c2t ‘at’ leeds.ac.uk
My project examines how representations of physical processes in climate models influence global climate projection uncertainty. Aerosol–cloud interactions remain one of the largest sources of forcing uncertainty, masking the true extent of greenhouse gas warming. Using large ensembles of simulations with the UK Earth System Model (UKESM2) alongside multi-model perturbed parameter ensembles, the research will identify how cloud microphysics, aerosol and atmospheric dynamics contribute to uncertainty. Novel methods will disentangle structural from parametric causes of aerosol–cloud forcing uncertainty, providing insights into causes of model divergence, and guiding model developments that target improvements in climate prediction.
Leveraging machine learning algorithms for improved Arctic sea-ice prediction using the Met Office suite of models
- Qien Cai
- Earth Sciences, University College London
- Supervisors: Michel Tsamados
- Partners: Met Office
- Email: qien.cai.25 ‘at’ ucl.ac.uk
My project focuses on improving sea-ice forecasting by combining satellite observations, machine learning, and physical models. Satellite altimetry has transformed our ability to estimate sea-ice thickness and ocean circulation, and recent advances now allow thickness mapping even in summer months. This project will build on these breakthroughs by developing deep learning algorithms trained on long ocean–sea ice reanalysis datasets and high-resolution Arctic models. The aim is to enhance short-range and seasonal forecasts of sea-ice concentration and thickness, and compare machine learning predictions with traditional dynamical models, supporting better climate prediction and operational decision-making in polar regions.
Reducing uncertainty in climate risk perception to enhance resilience: A data science approach using the World Risk Poll
- Beth Dunstan
- Risk and Disaster Reduction, University College London
- Supervisors: Mohammad Shamsudduha and Carina Fearnley
- Partners: Lloyd’s Register Foundation
- Email: beth.dunstan.25 ‘at’ ucl.ac.uk
My project investigates how perceptions of climate risk evolve globally and how they align with actual climate hazards. Using the Lloyd’s Register Foundation World Risk Poll (2019–2023), it will analyse spatiotemporal trends in climate risk perception before and after the COVID-19 pandemic, focusing on differences across regions and socio-economic contexts. Advanced data science and visualisation methods will uncover drivers of perception gaps and their implications for resilience. By comparing perceptions with real hazard exposure, the research will inform strategies for effective risk communication and adaptation, with a particular emphasis on vulnerable nations in the Global South.
End-to-end machine learning quantification of hydrological uncertainties: from climate to flood risk management
- Valentin Brekke
- Statistical Science, University College London
- Supervisors: Serge Guillas and Erica Thompson
- Partners: RIKEN Center for Computational Science
- Email: valentin.brekke.25 ‘at’ ucl.ac.uk
My project aims to use machine learning to improve how uncertainties are quantified across the entire chain from climate projections to flood risk. Recent advances in deep learning allow the creation of fast, accurate emulators for complex hydrological processes, reducing computational costs while maintaining fidelity. Working with high-resolution models developed at RIKEN-CCS in Japan, this project will integrate climate, hydrology, and decision-making under uncertainty. The goal is to develop efficient ML techniques that propagate uncertainties through the modelling chain and support practical decisions for flood mitigation, water resource management, and infrastructure planning in a changing climate.
Inclusive storylines for sustainable governance of urban adaptation to uncertainties in future heat extremes
- Xumeng (Andy) Deng
- STEaPP, University College London
- Supervisors: Bipashyee Ghosh and Erica Thompson
- Email: xumeng.deng.25 ‘at’ ucl.ac.uk
My project explores how inclusive climate storylines can support urban adaptation to extreme heat under uncertainty. While heat extremes are a certain impact of climate change, uncertainties remain in their severity and in social and technological responses. Working with detailed case studies, the project will analyse climate projections alongside urban planning data to understand how heat events affect infrastructure systems and governance decisions. It will then develop evidence-based, plural storylines to engage diverse stakeholders and guide robust, inclusive policies. By linking physical climate science with governance challenges, the project aims to improve anticipatory planning for resilient urban futures.
Combining models and uncertainties to support flood risk assessment and mitigation strategies
- Conor Lamb
- STEaPP, University College London
- Supervisors: Erica Thompson and Arthur Peterson
- Partners: FloodRe
- Email: conor.lamb.25 ‘at’ ucl.ac.uk
My project aims to explore, and improve, how models support decision making within flood risk, public policy and (re)insurance. The project partners with Flood Re, a UK government scheme aiming to ensure all UK residential properties can obtain affordable flood insurance. The scheme uses multiple levers, across public policy and (re)insurance, to fulfil its purpose. My project will use a variety of qualitative and quantitative methods to explore how models support decision making across Flood Re, in the context of uncertainty, and make actionable suggestions about how this process may be improved.
Projects that start in 2026 (UNRISK cohort 2)
Harnessing chaos to estimate climate hazards
- Elise O’Sullivan-Simms
- Mathematics and Statistics, University of Exeter
- Supervisors: Adam Scaife, James Screen, Amanda Maycock
- Partners: Met Office
- Email: eo407 ‘at’ exeter.ac.uk
This PhD project explores how chaotic climate simulations and AI-generated weather data can improve understanding of extreme climate hazards such as heatwaves, floods, and storms. Using large ensembles of model simulations and the UNSEEN method, it aims to estimate the limits of plausible extreme events, assess future risks, identify the atmospheric mechanisms behind unprecedented extremes, and evaluate whether AI models can realistically generate such events. The research will support decision-makers and contingency planners by reducing uncertainty about climate hazards, with opportunities to study global extremes in collaboration with the Universities of Exeter and Leeds.
Climate Change and Uncertainty in Catastrophe Modelling: Aligning UK Flood Risk Perceptions with Reality
- Mohammad Shoeb Ansari
- School of Geography, University of Leeds
- Supervisors: Joseph Holden, Erica Thompson
- Partners: Flood Re
- Email: wdmz0593 ‘at’ leeds.ac.uk
This PhD examines how climate change affects UK flood risk and insured losses, challenging the assumption that increased rainfall directly leads to higher insurance claims. Working with Flood Re, the research will investigate uncertainties in catastrophe models related to climate projections, property exposure, and insurer risk perceptions. Using mathematical modelling and stakeholder engagement, it will assess how factors such as flood mitigation measures and market sentiment influence loss estimates. The project aims to improve understanding of climate-driven flood risk, support more accurate insurance modelling, and help maintain affordable flood insurance for UK households.
Expert judgement and communication of uncertainty in the production of climate information
- Meredith Hess
- School of Business, University of Leeds
- Supervisors: Andrea Taylor
- Partners: JBA
- Email: cgfk0932 ‘at’ leeds.ac.uk
This PhD project examines how expert judgement shapes the characterisation and communication of uncertainty in climate information services used for decision-making. Working with JBA, it will investigate how uncertainties from climate data, models, scenarios, and variability are identified, prioritised, and communicated throughout the climate-service process. Using methods such as expert elicitation, interviews, surveys, and cognitive process tracing, the research will explore how decision makers interpret and use uncertainty information. The project aims to improve climate risk communication through approaches such as storylines and probabilistic methods, supporting more informed decisions in sectors including flood risk management, infrastructure, and finance.
Quantifying uncertainty and outlining plausible futures for compound heat-health risks
- Poppy Webb
- School of Geography, University of Leeds
- Supervisors: Yuchen Li, Cathryn Birch, Zihao An
- Partners: TBC
- Email: krcx0181 ‘at’ leeds.ac.uk
This PhD project examines how compound heat events such as prolonged heatwaves, warm nights, humidity, and indoor/outdoor temperature interactions affect human health in the UK under climate change. Using UK Biobank health data and climate projections, it will assess current and future risks from heat-related mortality and illness, including cardiovascular and respiratory conditions. The research will focus on quantifying uncertainties arising from climate models, demographic changes, and adaptation measures, and will develop plausible future risk scenarios rather than single forecasts. Findings will support public health planning and climate-risk assessments, including work by the UK Health Security Agency.
Uncertainty and Predictability of Rainfall for Agricultural Planning in Kenya
- Mathilda Huntingford
- School of Earth, Environment & Sustainability, University of Leeds
- Supervisors: Caroline Wainwright, Andy Challinor, Chetan Deva
- Partners: CGIAR
- Email: lcbz0443 ‘at’ leeds.ac.uk
This PhD project investigates rainfall predictability and uncertainty in Kenya to support agricultural planning in a changing climate. It will examine rainfall drivers across timescales from weeks to decades, develop agriculturally relevant rainfall metrics, and assess uncertainty in future climate projections. By combining climate science, risk assessment, and potentially machine learning, the research will improve understanding of changing agricultural risks and support the development of probabilistic agricultural advisories. Working with Kenyan stakeholders, the project aims to make climate information more useful for decision-making, helping farmers and planners adapt to rainfall variability and long-term climate change.
Freezing Points: Ice Nucleation Experiments, Climate Intervention, and the Politics of the Sky
- Alicia Alli
- School of Earth, Environment & Sustainability, University of Leeds
- Supervisors: Thomas Whale, Olaf Corry
- Partners: TBC
- Email: sckr0164 ‘at’ leeds.ac.uk
This interdisciplinary PhD investigates cirrus cloud thinning (CCT) as a potential climate intervention by combining atmospheric science with analysis of climate politics and governance. The research will use laboratory experiments on ice-nucleating particles to improve understanding of cirrus cloud formation and evaluate the feasibility of CCT. It will also examine how scientific evidence and uncertainty are translated into policy, legal frameworks, and geopolitical debates, while reflecting on the researcher’s role in shaping knowledge. Through collaboration between climate science and political science, the project aims to inform more responsible decision-making on geoengineering and climate intervention strategies.
Reducing uncertainty in glacial lake outburst flood (GLOF) risk and decision making
- Nancy Howe
- School of Geography, University of Leeds
- Supervisors: Scott Watson, Jonathan Carrivick, John Elliott
- Partners: JBA Trust
- Email: fdwj0562 ‘at’ leeds.ac.uk
This PhD project aims to reduce uncertainty in glacial lake outburst flood (GLOF) risk under climate change, focusing on vulnerable high-mountain regions in the Global South, particularly High-Mountain Asia. Using remote sensing, climate projections, flood modelling, and machine learning, it will assess lake instability, improve hazard and topographic models, and quantify how uncertainties affect flood risk estimates. The research will develop decision-support tools and evaluate risk reduction strategies, including engineering measures, land-use planning, risk communication, and early warning systems. Collaboration with organisations such as JBA, UNDP, and the Nepal Development Research Institute will support real-world applications.
Space-time extreme value models for heatwaves under different climatic conditions
- Haya Sharif Mohamed Anis Kamel
- Statistical Science, University College London
- Supervisors: Emma Simpson, Ben Youngman, Paul Northrop
- Partners: Met Office
- Email: haya.kamel.26 ‘at’ ucl.ac.uk
This project aims to improve understanding and prediction of UK heatwaves, which are expected to worsen with climate change. It will develop advanced statistical models using extreme value theory and conditional extremes methods to simulate heatwaves across space and time, while accounting for different atmospheric circulation regimes. By combining statistical modelling, machine learning, and climate projections, the research will reduce uncertainty in future heatwave risks, support adaptation planning, and generate large sets of realistic heatwave scenarios. The project involves collaboration between UCL, the University of Exeter, and the Met Office.
Climate change adaptation targets and indicators under uncertainty
- Prerana Balu
- School of Earth, Environment & Sustainability, University of Leeds
- Supervisors: Suraje Dessai, Amanda Maycock, David Williams
- Partners: OEP, Ofgem
- Email: pbks0401 ‘at’ leeds.ac.uk
This PhD project aims to develop climate adaptation targets and indicators that remain effective despite uncertainty in future climate and socio-economic conditions. Focusing on environment and energy case studies with the Office for Environmental Protection and Ofgem, the research will combine climate science and decision science to design measurable, evidence-based adaptation goals. Through expert engagement, public consultation, and risk assessment methods, it will link adaptation targets to practical indicators and evaluate how well adaptation strategies perform under different future scenarios. The project seeks to strengthen climate resilience planning and improve the measurement of adaptation progress.
Advanced Machine Learning Techniques for Quantifying Uncertainty in Climate Impact Assessment on Biological Systems
- Oreoluwa Solanke
- School of Maths, University of Leeds
- Supervisors: Luisa Cutillo, David Westhead
- Partners: TBC
- Email: rsnq0315 ‘at’ leeds.ac.uk
This PhD project uses advanced machine learning, statistical modelling, and gene expression data to quantify how climate change affects biological systems, including plants, animals, and marine ecosystems. By analysing large biological datasets, the research will develop probabilistic models, such as Bayesian frameworks and Gaussian graphical models, to identify how climate stressors alter gene and protein networks and to assess associated uncertainties. The project aims to improve understanding of biological resilience and vulnerability to climate change, while creating decision-support tools for risk assessment and adaptation planning. Students will gain expertise in bioinformatics, machine learning, network analysis, and climate impact assessment.
