Aerosols, Clouds, and Climate Projections: Tracing Uncertainty Across Scales

Background

A central challenge in climate science is turning advances in process-level understanding into models that can more reliably simulate future climate. Despite major progress in model complexity and an ever-growing number of observational datasets, model uncertainty – characterized by the spread of future climate projections, hasn’t narrowed for several decades. This persistent uncertainty stems primarily from uncertainty related to the cooling influence of aerosol radiative forcing, which has historically masked part of the greenhouse gas driven warming. Steady advances in aerosol physics and our understanding of how aerosols interact with clouds and radiation have not yet been converted into reductions in model uncertainty.

PhD Opportunity

Regional models can represent aerosol and cloud processes in far greater detail than global climate models, which trade complexity for spatial coverage. In this PhD, you will investigate how uncertainties in aerosols and clouds propagate through regional- to global-scale climate models and how these uncertainties affect projections of future climate.

Our team at Leeds, in collaboration with the UK Met Office, have developed a framework for creating and analysing large ensembles of model simulations that account for a wide range of model uncertainties – known as perturbed parameter ensembles (PPEs). Combined with machine-learning type approaches, PPEs allow millions of model variants to be tested against observations. You will apply these techniques across a hierarchy of UK climate models with varying levels of process complexity – from highly detailed regional simulations to global models – to identify how key uncertainties arise and propagate across levels.

A particular focus will be constraining aerosol-cloud-radiation interactions using new aircraft campaign data (curated by co-supervisor at the National Centre for Atmospheric Research; NCAR). Comparisons with these observations may reveal cases where existing model parametrizations are too simplistic, motivating the design of novel “regime-aware” approaches that account for differences in aerosol and cloud types.

Alongside the scientific challenge, this PhD offers a rich training environment providing hands-on experience in climate modelling, machine learning-type techniques, and the analysis of large, complex datasets. Co-supervision with NCAR includes the possibility of a research placement in Boulder, Colorado, with access to world-leading expertise. This combination of advanced scientific training, technical skill development and international collaboration in cutting-edge research area, makes the project a unique opportunity to make a real impact on climate science.

Applicant Profile

Our experience shows that motivated STEM student with a genuine interest in climate modelling can rapidly develop the skills needed to excel in this field of research. Although the approach draws on advanced methods in data science and machine learning, students typically find that with guidance and curiosity they quickly acquire the necessary expertise to make important insights and make meaningful contributions to understanding how aerosols and clouds shape our future climate. If you’re eager to push the boundaries of knowledge, whilst acquiring highly transferable skills, reach out to our group members to find out more.

Other information

https://environment.leeds.ac.uk/see/staff/1496/dr-leighton-regayre
https://environment.leeds.ac.uk/see/staff/1196/professor-ken-carslaw-frs
https://www.arm.gov/news/features/post/72476