Dr Jill Johnson

School of Mathematical and Physical Sciences

Lecturer in Statistics

Dr Jill Johnson
Dr Jill Johnson
Profile picture of Dr Jill Johnson
+44 114 222 3703

Full contact details

Dr Jill Johnson
School of Mathematical and Physical Sciences
Hicks Building
Hounsfield Road
S3 7RH

Dr Johnson studied at Newcastle University, graduating in 2010 with a PhD in Statistics. Her thesis was titled ‘Modelling Dependence in Extreme Environmental Events’. Following this, she worked in the civil service as a research statistician at the government’s Food and Environment Research Agency, looking at uncertainty quantification and risk analysis for multiple applications including food safety and land use change.

In December 2012, Dr Johnson returned to academia as a research associate in the aerosol research group at the Institute for Climate and Atmospheric Science, University of Leeds, where her work focussed on the quantification and constraint of key uncertainties in complex models of the atmosphere and climate.

In August 2021 she joined the School of Mathematics and Statistics as a Lecturer in Statistics.

Research interests

Uncertainty quantification for computer models; Surrogate modelling (emulation); Model-observation comparison (history matching).

My research interests are in the development and practical application of statistical methods to quantify, assess and then reduce uncertainty in large-scale complex models of real-world systems. To date, I have focussed on problems in environmental science and particularly in relation to the atmosphere and climate:  I have worked with a range of models on different scales including the simulation of an individual cloud, to the simulation of a cloud field, to the simulation of the global distribution of aerosols in the atmosphere. My approaches include expert elicitation to inform parameter choices, the use of surrogate statistical models (Gaussian process emulation) to enable dense sampling over a complex model’s input uncertainties, sensitivity analysis to understand the driving sources of uncertainty and ‘history matching’ to reduce uncertainty via comparison to observations.

In my research, I aim to address the statistical challenges of model-observation comparison to constrain the effects of high-dimensional model parameter uncertainty when using diverse observations that have sparse spatial and temporal coverage, and to explore model uncertainty when a model output is non-stationary.


Journal articles


Research group


Teaching activities