Event announcement as pdf-Download (https://tu-dresden.de/mn/phys ik/ressourcen/dateien/physikalisches-kolloquium/2026-07-14-Phys_Kolloq-Spu ler-SoSe2026.pdf).
Abstract: Climate attribution science can now link individual extreme events\, such as the June 2026 Eur opean heatwave\, to anthropogenic greenhouse-gas emissions. However\, proj ecting how the climate of any particular region will change over the next few years to decades remains challenging. This is because in any given yea r\, the severity of observed extremes reflects both the emerging forced re sponse of the climate system to anthropogenic emissions\, as well as the i nfluence of internal modes of variability such as the El Niño Southern Os cillation or variability in the eddy-driven jets. Both influences are high ly uncertain\, and existing literature shows that internal variability can dominate observed trends in the coming decade. However\, building robust knowledge of internal modes of variability and their relationship with the emerging climate change signal is challenged by their misrepresentation i n many physics-based global climate models\, while data-driven approaches struggle to robustly extrapolate observed relationships to a changing clim ate and rely on low-dimensional representations of modes of variability th at can obscure relevant dynamical processes. This talk will present causal representation learning\, an emerging field of AI research\, as an approa ch to learn physically interpretable representations of modes of variabili ty\, and show examples of how this approach can be used to understand mode s of internal variability as well as the influence of climate change on re gional extremes.
Short bio: Fiona completed her P hD in climate physics at the University of Reading\, UK\, and the Alan Tur ing Institute in London\, UK\, and holds an MSc in Mathematical Physics (U niversity of Edinburgh) and an MSc in Environmental Change and Management (University of Oxford). Her PhD research focused on developing causal repr esentation learning approaches to study climate dynamics\, in particular on disentangling drivers of regional extremes across timescales. Next to h er PhD\, Fiona co-developed and maintains the open-source software package ibicus for the comparison and evaluation of statistical bias adjustment o f climate models. Prior to starting her PhD\, she worked for two years at a not-for-profit organization on climate finance and was part of the inter national Mercator Fellowship program\, working on finance for climate resi lience and loss and damage.