Jonathan Owen

Jonathan Owen is a Research Associate in the School of Mathematical and Physical Sciences at the University of Sheffield. His current work focuses on the NERC-funded project 'Aerosol-MFR,' where he develops Bayesian statistical methods to reduce uncertainties in the Met Office's UK Earth System Model (UKESM1). He also collaborates with UN teams on epidemiological modeling and has prior experience with ice sheet and climate simulations through the UKRI Future Leaders Fellowship project.

Jonathan holds a PhD in Mathematical Statistics from Durham University, supervised by Prof Ian Vernon and Prof Michael Goldstein, where he researched Bayesian uncertainty analysis for energy production optimization. He later worked on the JUNE model at Durham, applying Bayes linear emulation to study infectious disease transmission during the Covid-19 pandemic in England and refugee camps.

His research interests span Bayesian emulation, Gaussian processes, uncertainty quantification, and model-observation comparison techniques. He specializes in applying these methods to environmental science challenges, including climatology, glaciology, and atmospheric sciences, with supplementary expertise in epidemiology and subsurface energy resource modeling.

His publications highlight methodological innovations in Bayesian emulation for complex systems, such as handling partial discontinuities and hierarchical ensemble modeling. He emphasizes linking past, present, and future climate-ice sheet simulations to improve predictive accuracy for sea level rise and glacial dynamics.

Notably, Jonathan collaborates with interdisciplinary teams across institutions like the University of Exeter and the Met Office, contributing to projects funded by NERC and UKRI. His work bridges statistical theory with real-world applications, particularly in decision support for environmental and energy systems.