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Organised by MFC CDT and iMPT
Dates:Ìý2nd – 5th June 2026
Mode of Delivery:ÌýOnline via Zoom, withÌý hybrid session on 5th June. Further details will be shared with registered participants by Wednesday, 28 May.

*Please note that registration is now closed as we have received the maximum number of expressions of interest.

Overview

This Summer School, jointly organised by the EPSRC MFC CDT and iMPT, aims to bring together experts from different fields to explore the methodological and practical aspects of uncertainty quantification in climate data, models, and simulations.

Topics covered will include, but are not limited to:

• Uncertainty quantification methodology
• Data assimilation
• Statistical modelling of extreme events
• Characterising and modelling uncertainties in observational data
• Integrating machine learning tools into climate simulations

Acknowledgements
We would like to thank the organisers of the workshop on Uncertainty Quantification for Climate Science (November 2025) for scientific advice and support.

Speakers and Topics

Tuesday, 2 June –Ìý Wednesday, 3 June 2026

,ÌýÌýUniversité Grenoble Alpes
Title: An introduction to data assimilation

,ÌýÌýÉcole Polytechnique
Title: An introduction to uncertainty quantification

Thursday, 4 June 2026

, Oregon State University
Title: TBA

Friday, 5th June 2026

, UCL
Title- TBA

ÌìÃÀ´«Ã½
Title-ÌýTransforming covariates to enhance spatio-temporal predictions in climate applications
In multivariate spatio-temporal statistics our starting point is often a linear model like multiple linear regression or vector autoregression. However, sometimes the cross-sectional interactions between variables are somewhat more subtle, and exist only in the tails of the distribution, or in some other nonlinear sense. In this talk we provide three practical case studies where transforming covariates provides improved models and predictions. The first looks at the nonlinear effects of extreme precipitation on deforestation in Nepal, using novel approaches from functional data analysis. The second looks at the nonlinear effects of ocean currents on the abundance of Antarctic krill, using novel approaches from spectral analysis. Finally, the third introduces a new methodology for performing causal discovery in a nonlinear multivariate time series setting, using novel approaches from extreme value analysis.

Schedule: Please note that the timing indicated is UK time.