Home
Projects
Understanding polar aerosols and improving their representation in climate models

Understanding polar aerosols and improving their representation in climate models

Year: 2026
Primary Supervisor: Daniel Grosvenor <[email protected]>
Institution: University of Leeds (School of Earth and Environment)
Possibility of becoming a CASE project: Yes
Collaborative Project: No
Academic Supervisors: Ken Carslaw <[email protected]> (University of Leeds, School of Earth and Environment)
Research Themes: Atmospheric Physics, Climate
Project Partners: Met Office
Research Keywords: Aerosol, Aerosol Chemistry, Aerosol Model, Aerosols, AI, Antarctica, Artificial Intelligence, Atmosphere, Atmospheric Chemistry, Atmospheric Chemistry Modeling, Atmospheric measurements, Atmospheric Physics, Atmospheric Science, Big Data, Biogenic Volatile Organic Compound, Biogeochemistry, Climate, Climate Change, Climate Modeling, Climate Modelling, Climate Science, Cloud, Clouds, Data Science, Earth System Science, Field Measurements, Global Chemistry-Climate Model, Modelling, Observations, Ocean-air interactions, Remote Sensing, Satellite Data
Relevant Degree Courses: Any science or engineering based BSc or MSc, Applied Mathematics, Astronomy, Atmospheric Science, Chemical Engineering, Chemistry, Earth Science, Earth Sciences, Earth System Science, Geophysical Science, Geophysics, Meteorology, Natural Sciences, Physical Science, Physics, Remote Sensing

The influence of aerosol particles that act as seeds to form water droplets and ice within clouds represents the largest uncertainty in our predictions of future climate change. This is particularly true for the pre-industrial period that is used as a baseline for determining the influence of anthropogenic aerosols. In the pre-industrial period natural aerosol sources dominated and so there is a need to understand them in order to make accurate climate predictions. Remote regions, such as the high-latitude southern polar region, are dominated by natural aerosols and serve as a good proxy for the pre-industrial environment. This PhD aims to improve our understanding of natural polar aerosols, their effects on clouds and climate, and their representation in climate models.

Recent satellite studies and field campaigns suggest that the latest Earth System climate models (as will be used for the upcoming CMIP7 climate assessment) severely underestimate aerosol concentrations and cloud drop number concentrations (CDNC) at high latitudes. This PhD provides an opportunity to explore the reasons for this in the UK Earth System Model (UKESM) climate model and to test routes for fixing the bias. There is evidence for the formation of new aerosol particles from gases via several potential mechanisms at southern high latitudes, possibly linked to sea-ice. You will expand upon existing experimental new particle formation schemes in the UKESM to investigate whether they can be modified to match observations. The dataset of aerosol and CDNC field campaign observations from around Antarctica is growing and can be supplemented by novel satellite observations.

There is good evidence that phytoplankton in the oceans strongly influence aerosols and CDNC in remote regions via the release of sulphur-containing compounds. However, many of the details of the processes involved need to be clarified and their representation improved within climate models. Newly developed improvements in model sulphur chemistry and oceanic sulphur emission datasets can be tested within UKESM to assess their impact on the effects of phytoplankton on aerosols and clouds by comparing to observations.

As well as using observations to inform the UKESM model, the model can also be used to test observational and machine learning approaches through the use of the model as a test-bed. For example, in the model (unlike in reality) we can perform controlled experiments, for example, running with a process switched on and then with it switched off. Comparing the two answers allows us to determine what the model response to the process is. We can then simulate what satellite or field observations would observe if viewing our model output fields (with the process switched on in our example) and test whether observational approaches (combined with machine learning techniques) are likely to be able to correctly detect the influence of the process (which we know from our controlled experiment). If the process can be detected and characterised in the “model world” it gives us confidence that it is possible in the real world with real observations, allowing us to use those approaches to evaluate and improve model processes relating to high latitude aerosols.

Met Office partners will provide additional supervision, collaborations and access to the latest cutting edge UKESM earth system model and supercomputing facilities. There will also likely be an opportunity to work at the Met Office for at least three months allowing you to interact with the many world-class scientists there and to experience working life outside of academia.

This PhD will give you an exciting opportunity to help develop the next generation of climate models and to reduce uncertainty in future climate predictions. It will also enable you to develop highly desirable skills in machine learning, global climate modelling and the analysis of large datasets, field measurements and satellite observations.