About the Project
Background
The influence of aerosol particles (e.g., emitted by transport and energy generation) on Earth's energy balance is a major uncertainty in climate science. Aerosols mask warming from greenhouse gases and so aerosol uncertainty limits our understanding of the influence of greenhouses gases in the past, which affects how precisely we can simulate the future. The uncertainty also has implications for geoengineering schemes that seek to modify the climate through the deliberate release of aerosols.
Shipping is a major source of aerosols, but in 2020 legislation limited aerosol emissions from shipping. A record temperature rise followed in 2023 raising the question of whether the shipping regulations were partly responsible and bringing the aerosol impacts from shipping into the spotlight. The effect of shipping aerosols on clouds is considered the largest contributor to shipping-climate interactions and aerosol-cloud interactions are one of the largest sources of climate uncertainty.
PhD project
This project will reduce uncertainty in shipping aerosol climate effects using data science/machine learning (ML), modelling and observations with the student playing a leading role in steering the research questions and methods. Uncertainties in the representation of shipping aerosol in models and its interaction with clouds could be explored using a Perturbed Parameter Ensemble (PPE) ML method to characterize the effect of varying model parameter settings and functions/relationships. This tool has been used extensively at the U. Leeds. It quantifies the feasible range of aerosol impacts (i.e., model uncertainty) and the importance of the different input parameters and functions, allowing the targeting of specific processes for improvement. Observations (satellite and field campaign data) will play a vital role in both improving model process relationships and ruling out unrealistic model variants (constraining the PPEs) and therefore reducing the model uncertainty. Process relationships between clouds and aerosols have been characterized at U. Leeds using other ML methods applied to satellite observations to isolate the influence of aerosol within noisy data; these relationships could be used to constrain the PPEs. This approach could be expanded and applied to shipping; e.g., by incorporating shipping emissions from real ship data and determining its influence upon clouds.
Modelling tools bridging scales from tens of metres to the ~100km resolution of global climate models are available to investigate challenges such as determining the importance of multiple local individual ship tracks vs the wider-scale effect of aerosols from ships once it is diluted and spread over a larger area further downwind.
As well as reducing uncertainty in shipping-aerosol interactions and hence in the recent accelerated warming, the project will reduce uncertainty in aerosol-cloud interacations in general, which will feed through to reduced uncertainty in climate projections.
Applicant Profile
Students with a background in physics, maths or computer/data science with coding skills who want to apply their skills to atmospheric or climate science are particularly encouraged.
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
- https://www.nature.com/articles/s43247-024-01442-3
- https://environment.leeds.ac.uk/homepage/176/atmospheric-chemistry-and-aerosols
- https://www.youtube.com/watch?v=hg6tSM4BoHg
- https://egusphere.copernicus.org/preprints/2026/egusphere-2026-2654/
- https://acp.copernicus.org/articles/24/13681/2024/
Project supervisors
Dr. Daniel Grosvenor
Career overview
Dr Daniel Grosvenor completed his MPhys in Physics with Astrophysics at UMIST. He then pursued a PhD at the University of Manchester, supervised by Prof. Tom Choularton, focusing on ''Tropical Deep Convection: The Effect on the Water Content of the Upper Troposphere and Lower Stratosphere and the Response to Aerosol.'' During his time at Manchester, Dr Grosvenor also engaged in research involving aircraft observations and modelling of Antarctic clouds and flow over the Antarctic Peninsula. Following his PhD, he relocated to Seattle, USA, to work at the University of Washington under Prof. Rob Wood, where he developed enhanced satellite microphysical observational datasets and applied them to various scientific inquiries, including the assessment of low clouds in climate models. Dr Grosvenor joined the University of Leeds in 2013, where he employs the UM regional model with the CASIM microphysics scheme alongside satellite data to explore aerosol-cloud interactions.
Research interests
Dr Grosvenor''s research focuses on clouds and cloud-aerosol interactions, regional and global modelling, and atmospheric science. He investigates aerosol-cloud-dynamics interactions, particularly how aerosols influence the formation of Pockets of Open Cells in stratocumulus clouds, which are significant for climate models. His work includes simulating various aerosol-cloud feedback regimes using large-domain simulations with the Met Office Unified Model (UM) and the CASIM microphysics scheme. Dr Grosvenor has developed improved satellite microphysical observational datasets, particularly for high latitude clouds, and has worked on quantifying the impacts of retrieval artefacts in satellite data. He is also involved in testing climate models using observations to assess the representation of low clouds and cloud-aerosol interactions in models like UKCA and UKESM.
Professor Ken Carslaw FRS
Career overview
Professor Ken Carslaw is a prominent figure in the field of climate and atmospheric science, currently serving at the University of Leeds. He completed his BSc in Physics and MSc in Atmospheric Science at the University of East Anglia, where he also earned his PhD, focusing on thermodynamic models to demonstrate the existence of liquid polar stratospheric cloud particles. In 1994, he joined the Aerosol Microphysics group at the Max Planck Institute for Chemistry in Mainz, where he published influential papers on polar stratospheric clouds. Since 1999, he has led a significant research group at Leeds, concentrating on aerosol effects on climate. His leadership roles included Director of Research in the School of Earth and Environment from 2005 to 2008 and Director of the Institute for Climate and Atmospheric Science from 2014 to 2017, during which he established the Centre for Environmental Modelling and Computation. He co-founded the European Geosciences Union journal Atmospheric Chemistry and Physics in 2001, which has been a pioneer in open access publishing and transparent peer review in geosciences, and currently serves as Co-Chief Editor. Professor Carslaw has received numerous accolades for his research, including election as a Fellow of the American Geophysical Union in 2019 and a Fellow of the Royal Society in 2024. His research interests encompass a wide range of topics related to atmospheric aerosols, their formation, and their impact on climate, contributing significantly to the understanding of climate change and air quality.
Research interests
Professor Ken Carslaw''s research aims to enhance the understanding of atmospheric aerosol particles and their effects on climate. His publications encompass a wide range of topics, including natural aerosols, Arctic aerosols, aerosol formation, dust and biogeochemistry, ice-nucleating particles, radiative forcing, volcanic impacts on climate and health, palaeo-aerosols, air quality, uncertainty quantification, geoengineering, stratospheric aerosols, polar stratospheric clouds, and the ozone hole. His early research contributed to the discovery of liquid polar stratospheric clouds and the role of large cloud particles in Arctic denitrification. He was instrumental in developing a global model of aerosol microphysics (GLOMAP), which is now integrated into the UK Earth System Model. His group''s findings established that new particle formation accounts for approximately half of the climate-relevant aerosol particles in the atmosphere. In the CERN CLOUD experiment, his research led to the first global model of new particle formation based entirely on laboratory measurements, a milestone previously achieved for gas-phase chemistry. Professor Carslaw''s group has a strong interest in natural aerosols, demonstrating that they significantly contribute to the uncertainty surrounding aerosol effects on climate. They also developed a global model of ice-nucleating particles based on laboratory measurements of their physical properties, which provided insights into large biases in radiation in climate models. His research increasingly focuses on reducing model uncertainty through the application of novel statistical techniques and extensive observational data. Currently, his research group is engaged in various projects, including global modelling of ice-nucleating particles, studying the influence of these particles on high-latitude mixed-phase clouds, and investigating the effects of marine biogenic activity on high-latitude clouds and climate. Other topics of interest include aerosol modulation of Arctic clouds and climate, new particle formation, and trends in aerosols and clouds over China and the North Pacific. The group is also working on statistical methods to quantify and reduce uncertainty in aerosol forcing.
Dr Leighton Regayre
Career overview
Dr Leighton Regayre holds a joint position funded by the UK Met Office (70%) and CEMAC at the University of Leeds (30%). He is responsible for developing aerosol evaluation tools and methods to enhance understanding of aerosols in Met Office models. Dr Regayre has broad experience in various institutions, including roles as an applied statistician in agricultural science, teaching secondary school mathematics, and leading mathematics education teams. He has participated in and led several significant research projects, including Aerosol-MFR (2023 – Present), the FORCeS project (2021 – 2023), CSSP-China (2018 - 2021), the Aerosol-Cloud Uncertainty REduction project (2017 - 2021), SMURPHS (2016 - 2017), and the Copernicus Atmospheric Monitoring Service: Climate Forcings (2016 - 2017). His PhD research, titled ‘Quantifying and interpreting the climatic effects of uncertainty in aerosol radiative forcing’, was recognised for ‘Research Excellence’ by the University of Leeds in 2016. Dr Regayre has over a decade of teaching and leadership experience in secondary mathematics education and has tutored multiple Applied Mathematics and Statistics courses at the University of Leeds. He has lectured on modules such as ‘Data Analysis and Visualisation’ and ‘Climate Change: Science and Impacts’. He currently supervises multiple PhD and Masters students. His qualifications include a PhD in aerosol radiative forcing uncertainty, an MSc in Atmosphere and Ocean Dynamics from the University of Leeds, a BSc (Hons) in Statistics from the University of Queensland, and Qualified Teacher Status from Bradford College. He is an Associate Fellow of the Higher Education Academy and a member of the American Geophysical Union and the Priestley Centre for Climate Futures.
Research interests
Dr Regayre''s research focuses on aerosol and cloud interactions, model uncertainty, and the implications of aerosols on climate change. He is involved in developing aerosol evaluation tools and methods to enhance the understanding of aerosols in Met Office models. His work includes processing observational datasets to make them model-ready, contributing to multi-model experiments within the aerosol community, and identifying model structural errors using perturbed parameter ensembles. Dr Regayre''s research goals align with projects such as Aerosol-MFR, where he is developing statistical and machine-learning methods to constrain aerosol forcing uncertainty. He has also worked on the ACURE project, which aimed to tackle aerosol forcing uncertainty through a comprehensive synthesis of measurements and innovative analysis methods. His PhD research identified causes of uncertainty in aerosol-cloud interactions and their climatic effects, revealing important regional and seasonal differences in uncertainty sources. He is interested in finding measurement combinations that provide strong constraints on aerosol forcing uncertainty, which is crucial for improving climate model predictions.