Studying Tipping Points and Noise-Induced Transitions in the Climate System with rare event simulation techniques

Supervisors

• Dr. Francesco Ragone fr120@leicester.ac.uk
• Professor Valerio Lucarini v.lucarini@leicester.ac.uk

Project

The Earth is a complex system featuring variability on a vast range of spatial and temporal scales. One of its features is metastability, the existence of multiple competing states for given boundary conditions, and of processes that allow for transitions between such states. These transitions are often associated with the so-called tipping points, whose understanding is one of the major challenges of contemporary research in the area of the climate crisis. Tipping phenomena occur as a result of the presence of parametric modulations in the system or of the presence of noise. Examples of tipping elements of the climate system include the North-Atlantic SubPolar Gyre (SPG), the Atlantic Meridional Overturning Circulation (AMOC) or the Amazon rainforest.

Studying noise induced transitions with complex numerical models is however computationally challenging. Very long simulations and/or very large ensembles are typically necessary to sample even a few transitions. Often this makes it impossible to study metastability in comprehensive numerical models, unless unrealistic, ad hoc forcing are artificially imposed on the system. This problem can be tackled using rare event algorithms, numerical tools designed to reduce the computational effort required to sample rare events in numerical models. These methods typically take the form of genetic algorithms, where a set of suppression and cloning rules are applied to the members of an ensemble simulation, in order to oversample trajectories leading to the events of interest.

Rare event algorithms have been applied in the past few years to a wide range of problems in climate science, including heatwaves, sea ice reduction, tropical cyclones, and noise induced transitions associated with the collapse of the AMOC. The improved sampling allows to reduce statistical uncertainties, study the dynamical properties of events impossible to obtain with direct sampling, and identify possible preferential transition paths between metastable states. The goal of the project is to develop and apply rare event simulation techniques to study noise-induced transitions of selected tipping elements of the climate system, with a particular focus on the SPG and AMOC collapse, in order to improve our understanding of their dynamics and predictability.

We will study transitions both in stationary conditions (purely noise-induced) and in non-stationary conditions, when the system approaches a tipping point under a parametric modulation. The student will test existing different methods in numerical climate models of intermediate complexity, and develop new approaches based on integrating rare event algorithms with data driven techniques. We are looking for candidates with a Master degree in mathematics, physics, engineering, data science or atmospheric/oceanographic sciences. Candidates should have strong mathematical and statistical skills, as well as an interest in working with numerical models and innovative computational methods. The student will be part of a research program involving collaboration with UK and international institutions via the AdvanTip ARIA project.

References

• F. Ragone, J. Wouters, F. Bouchet, Computation of extreme heat waves in climate models using a large deviation algorithm, Proc. Natl. Acad. Sci. U.S.A., 115(1), 24-29 (2018)
• R. Webber, D. A. Plotkin, M.E. O’Neill, D.S. Abbot, J. Weare. Practical rare event sampling for extreme mesoscale weather. Chaos 29(5) (2019)
• F. Ragone, F. Bouchet, Computation of extremes values of time averaged observables in climate models with large deviation techniques, J. Stat. Phys., 179, 1637-1665 (2020)
• F. Ragone, F. Bouchet, Rare event algorithm study of extreme warm summers and heatwaves over Europe, Geophysical Research Letters, 48, e2020GL091197 (2021)
• V.M. Galfi, V. Lucarini, F. Ragone, J. Wouters, Applications of Large Deviation Theory in climate science and geophysical fluid dynamics, Riv. Nuovo Cimento 44, 291–363 (2021)
• V. Lucarini, M. Chekroun, Theoretical tools for understanding the climate crisis from Hasselmann’s programme and beyond, Nature Reviews Physics 5, 744–765 (2023)
• M. Cini, G. Zappa, F. Ragone, S. Corti. Simulating AMOC tipping driven by internal climate variability with a rare event algorithm. npj Clim Atmos Sci 7(31) (2024)
• J. Lohmann, V. Lucarini, Melancholia States of the Atlantic Meridional Overturning Circulation, Phys. Rev. Fluids 9, 123801 (2024)
• J. Sauer, J. Demaeyer, G. Zappa, F. Massonnet, F. Ragone. Extremes of summer Arctic sea ice reduction investigated with a rare event algorithm. Clim Dyn 62, 5219–5237 (2024)
• M. Cini, G. Zappa, F. Ragone, S. Corti. Noise-shaped hysteresis cycles of the AMOC under increasing CO2 forcing. Chaos 35(2) (2025)
• J. Sauer, F. Massonnet, G. Zappa, F. Ragone. Ensemble design for seasonal climate predictions: studying extreme Arctic sea ice lows with a rare event algorithm, Earth Syst. Dynam., 16, 683–702 (2025)