School of Earth and Environment

Jill Johnson Dr Jill Johnson

Research Fellow

Telephone number: +44(0) 113 34 34931
Email address:
Room: 11.121

Affiliation: Institute for Climate and Atmospheric Science


I am an applied statistician working as a research associate in the aerosol group at the University of Leeds. I studied at Newcastle University, graduating in 2010 with a PhD in Extreme Value Theory. Since then, I have worked as a research statistician for 3 years at the government’s Food and Environment Research Agency, looking at uncertainty quantification and risk analysis for applications including food safety and land-use change. I joined the aerosol group at Leeds in December 2012, and my work now focusses on the quantification of key uncertainties in complex aerosol and cloud models.


2010: PhD in Statistics, Newcastle University.

Thesis title: “Modelling Dependence in Extreme Environmental Events”

My work involved a spatial approach to modelling extreme values and extremal dependence for a complex data set of daily rainfall and mean wind speed observations over 20 years covering 25 sites across the UK.

2004: MMATHSTAT – Master of Mathematics and Statistics, Newcastle University.

My studies include: Bayesian Statistics, Linear Regression and GLMs, Experiment Designs, Probability Theory, Stochastic Processes, Multivariate Statistics, and Time Series Analysis.

Research Interests

My research interests lie in the application of statistical methods to explore complex environmental processes and systems, with a focus on modelling extreme events and uncertainty quantification in complex models.

The focus of my current research is to apply uncertainty modelling techniques to quantify the key uncertainties in the modelling of cloud microphysical processes and climate effects. This involves using statistical emulation and variance-based sensitivity analysis to explore and quantify the drivers of parametric uncertainty in a selection of response variables from a convective cloud microphysics model and a global aerosol model.

This work is funded as part of the NERC project consortium ACID-PRUF (Aerosol-Cloud Interactions – a Directed Programme to Reduce Uncertainty in Forcing).