Jonathan Owen

I am a Postdoctoral Research Associate working on the NERC funded project “Aerosol-MFR: Towards Maximum Feasible Reduction in Aerosol Forcing Uncertainty”, working with Dr Jill Johnson and Prof Jeremy Oakley. In this project I am developing and applying Bayesian statistical and uncertainty quantification methodology to reduce aerosol radiative forcing uncertainty within the Met Office’s UK Earth System Model (UKESM1). This entails: emulation of numerous high- dimensional model outputs; uncertainty quantification linking model outputs to the real world including identifying sources of structural model discrepancy; and comparison with observation data to determine optimal combinations to address parametric uncertainty and thus reduce model uncertainty.

I obtained my PhD in Mathematical Statistics at Durham University supervised by Prof Ian Vernon and Prof Michael Goldstein working on Bayesian uncertainty analysis and decision support for complex computer models of physical systems with application to production optimisation of subsurface energy resources. Subsequently I undertook a postdoctoral research associate position at Durham University supervised by Prof Ian Vernon performing Bayes linear emulation and history matching of the JUNE model, a stochastic agent-based model for the transmission of infectious diseases, for the Covid-19 pandemic in England and a large refugee camp in collaboration with the UN.

Prior to my current position I was a Postdoctoral Research Fellow on the UKRI Future Leaders Fellowship project “SMB-Gen: Constraining projections of ice sheet instabilities and future sea level rise”, with Dr Lauren Gregoire and Prof Daniel Williamson, University of Exeter. In this project I developed Bayesian statistical emulation methodology for surface mass balance within coupled climate and ice sheet computer models to aid the assessment of ice-sheet instabilities and the resulting sea level rise. Furthermore, I used statistical methods to link past, present, and future coupled climate and ice-sheet simulations in order to constrain future predictions of ice-sheet evolution.