Dr Lindsay Lee

Research Fellow

Telephone number: +44(0) 113 34 36473
Email address: L.A.Lee@leeds.ac.uk
Room: 10.127

Biography

I joined the department in January 2010 to work on the NERC funded AEROS project. The project involves carrying out a sensitivity analysis of the global aerosol model GLOMAP (developed here at Leeds) in order to quantify the effects of uncertainty in the model parameters. The aim of the project is to identify the real gain from increasing model complexity before parameter uncertainty dominates. The work is providing us with a complete understanding of GLOMAP accounting for its parameter uncertainties.

I am a statistician and my research interests involve using statistical methods to better understand Earth science models and their uncertainties so that research can be focussed on improving model predictions. Currently my work focusses on sensitivity analysis but my interests extend to experimental design, calibration, uncertainty analysis and dimension reduction.

My current work uses Gaussian process emulation for analysis of complex global models and variance-based sensitivity analysis to quantify the relative importance of uncertain model parameters. I use R to carry out statistical analysis.

Qualifications

2010: PhD in Probability and Statistics, University of Sheffield

Title: Climate variability and its effect on the UK carbon distribution. Used Gaussian process emulation and dynamic linear modelling to quantify the sensitivities of carbon fluxes to variability in the climate using the Sheffield dynamic global vegetation model (SDGVM).

2006: MSc in Statistics, University of Sheffield

Modules included linear modelling, time series analysis, multivariate data analysis, computational inference, Bayesian inference. Dissertation used time series analysis to count layers in ice core data.

2005: BSc in Mathematics, University of Sheffield

Research Interests

My research interests are in quantifying and understanding the effect of uncertainty in complex global models. I have so far working with a global vegetation model and a global aerosol model but the techniques can be used in many different Earth system models. I apply well-established statistical methods to models whose uncertainty is not yet well-understood. I currently look at parametric uncertainty which I believe is the first step to really understand model uncertainty. I am also interested in how studying parametric uncertainty can help to identify structural uncertainties and understand model diversities.