Imperial College London


Faculty of Natural SciencesDepartment of Physics

Reader in Atmospheric Physics



a.voulgarakis Website




Huxley 709BHuxley BuildingSouth Kensington Campus





Associate Director (& Founding Director) - Leverhulme Centre for Wildfires, Environment, and Society

Google Scholar Profile


  • Atmospheric composition and climate
  • Wildfires, air pollution and climate change
  • Short-lived climate pollutants
  • Emission metrics for climate policy
  • Tropospheric oxidizing capacity
  • Earth observation and Earth system modelling


  • PhD in Atmospheric Chemistry, University of Cambridge, UK (2008)
  • MSc in Environmental Engineering, Technical University of Crete, Greece (2004)
  • BA in Physics, Aristotle University of Thessaloniki, Greece (2002)


  • NASA Goddard Institute for Space Studies & Columbia University Center for Climate Systems Research; Postdoctoral Research Scientist (2009-10) & Associate Research Scientist (2011-12)
  • University of Cambridge, Centre for Atmospheric Science; Research Associate (2008)



        Hantson S, Kelley DI, Arneth A, et al., Quantitative assessment of fire and vegetation properties in simulations with fire-enabled vegetation models from the Fire Model Intercomparison Project, Geoscientific Model Development, ISSN:1991-959X

        Wild O, Voulgarakis A, O'Connor F, et al., 2020, Global sensitivity analysis of chemistry-climate model budgets of tropospheric ozone and OH: exploring model diversity, Atmospheric Chemistry and Physics, Vol:20, ISSN:1680-7316, Pages:4047-4058

        Richardson TB, Forster PM, Smith CJ, et al., 2019, Efficacy of climate forcings in PDRMIP models, Journal of Geophysical Research: Atmospheres, Vol:124, ISSN:2169-897X, Pages:12824-12844

        Scannell C, Booth BBB, Dunstone NJ, et al., 2019, The influence of remote aerosol forcing from industrialized economies on the future evolution of East and West African rainfall, Journal of Climate, Vol:32, ISSN:0894-8755, Pages:8335-8354


        Nowack P, Ong QYE, Braesicke P, et al., 2019, Machine learning parameterizations for ozone: climate model transferability, 9th International Workshop on Climate Informatics, UCAR,, Pages:263-268

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