School of Earth and Environment

Leighton Regayre Dr Leighton Regayre

Research Fellow

Email address:
Room: 11.121

Affiliation: Institute for Climate and Atmospheric Science


I investigate the sources of uncertainty in aerosol radiative forcing in global climate models. I am researching methods to constrain the uncertainty using multiple measurements of the relationships between aerosols and clouds as part of the NERC funded A-CURE project.

You can find me on Twitter @LeightonRegayre


  • BSc (with honours in Statistics), 1997, The University of Queensland, Australia
  • Qualified Teacher Status (QTS), 2004, Bradford College, UK
  • Fast Track Teacher Status, 2009, National College for School Leadership, UK
  • MSc (with Distinction) in Atmosphere and Ocean Dynamics, 2012, The University of Leeds, UK
  • University of Leeds Teaching Award - Level 1 (with Merit), 2015, The University of Leeds, UK
  • PhD (recognised for Research Excellence) Atmospheric Science, Quantifying and interpreting the climatic effects of uncertainty in aerosol radiative forcing, 2016, The University of Leeds, UK


  • American Geophysical Union
  • Associate Fellow of the Higher Education Academy
  • Priestley International Centre for Climate

Research Interests

  • Aerosol-cloud interactions; Aerosol effective radiative forcing; Regional climte responses
  • Uncertainty analysis; Sensitivity analysis; Observational constraints

My PhD (2012-2015) research identified the causes of uncertainty in the aerosol-cloud interaction component of the aerosol effective radiative forcing over multiple forcing periods and multiple regions of climatic importance. My research highlights the power of using sophisticated statistical tools to perform sensitivity analysis experiments. By understanding and quantifying contributions to variance in aerosol radiative forcing at the global and regional scales, I have identified specific priorities for development in global climate models so as to reduce uncertainty in aerosol radiative forcing. The Global Model of Aerosol Processes (GLOMAP) is a major advance on previous global models and has been used to study a wide range of aerosol processes in the atmosphere, including new particle formation, marine aerosol, dust emission and transport, and cloud condensation nuclei. GLOMAP is also being used in the U.K. Hadley Centre Met Office's Earth System model (UK-ESM1) to study the interactions between aerosols, the oceans and the biosphere.

Teaching Interests

I have over a decade of teaching and leadership experience in secondary Mathematics education. At the University of Leeds I have tutored multiple Applied Mathematics and Statistics courses within the School of Earth and Environment and the School of Mathematics. I am particularly interested in creating experiences which elicit deep, long-term learning.

Support duties

ICAS internal seminar series organiser

Project details

Project title

Researcher (2017 - ), The Aerosol-Cloud Uncertainty REduction project (A-CURE)

Researcher (2015-2017), Securing Multidisciplinary UndeRstanding and Prediction of Hiatus and Surge events (SMURPHS)

Researcher (2015 - 2017), Copernicus Atmospheric Monitoring Service: Climate Forcings (CAMS 74)


Prof. Ken Carslaw, Professor of Atmospheric Science; University of Leeds; Royal Society Wolfson Merit Award holder

Prof. Phillip Stier, Professor of Atmospheric Physics; University of Oxford

Dr. Lindsay Lee, Leverhulme Research Fellow; University of Leeds

Dr. Anja Schmidt, Lecturer in Climate Modelling; University of Cambridge


Natural Environment Research Council (NERC)

Project outline

A-CURE tackles one of the most challenging and persistent problems in atmospheric science - to understand and quantify how changes in aerosol particles caused by human activities affect climate. The magnitude of the so-called aerosol radiative forcing is highly uncertain over the industrial period. A-CURE aims to reduce the uncertainty in aerosol radiative forcing through the most comprehensive ever synthesis of aerosol, cloud and atmospheric radiation measurements combined with innovative ways to analyse global model uncertainty. The overall approach will be to produce a large set of model simulations that spans the uncertainty range of the model input parameters. Advanced statistical methods will then be used to generate essentially millions of model simulations that enable the full uncertainty of the model to be explored. The spread of these simulations will then be narrowed by comparing the simulated aerosols and clouds against extensive atmospheric measurements.