NET4TIP: Network Approaches to Climate Tipping Mechanisms

Tipping elements in the climate system—such as ocean circulation, the Amazon rainforest, or ice sheets—can trigger abrupt shifts in temperature and precipitation patterns when the carbon cycle is sufficiently perturbed. We will use complex networks to represent interactions within the climate system and to study its behaviour during tipping phases. Simulation outputs will guide the application of network approaches to satellite data. We expect this project to open new perspectives for understanding tipping behaviour in the climate system, leveraging the crucial interplay between topological network structures and dynamics, which we also plan to investigate using AI-based pattern recognition algorithms.

In this project, the PhD student will:

• Run the MIT General Circulation Model (MITgcm) together with additional models for continental ice and vegetation components under increasing atmospheric CO₂ concentrations.
• Perform climate simulations exhibiting tipping behaviour of one or multiple tipping elements on multi-millennial time scales.
• Test different types of climate-network constructions and indicators, using available packages such as pyUnicorn and XGI, in order to identify the approach best suited as an Early Warning Signal (EWS) for a given tipping element.
• Develop operational EWS workflows by applying the network approach to satellite data.

In the Group of Applied Physics and the Institute for environmental sciences, the Nonlinearity and climate group of the University of Geneva investigates various nonlinear systems and their analogies. It especially focuses on nonlinear optics, hydrodynamics, climate, and modeling of natural systems.

We are looking for a highly motivated, independent person who is keen to develop new techniques in nonlinear physics, computer science, and statistics at the interface of climate physics, Earth sciences, and environmental change. We offer outstanding working conditions in Geneva. The student will be co-supervised by Dr Maura Brunetti and Dr Gregory Giuliani (both in Geneva) and Prof. Cristina Masoller (in Barcelona).

Applicants are required to have completed (by the start date of the PhD) a Master’s degree—or equivalent—in a relevant field such as physics, climate physics, geophysics or environmental science. Familiarity with climate modelling is an advantage. Programming skills (in MATLAB or Python) are considered a strong asset. Applicants should also have a very good level of written and spoken English, with knowledge of French considered a plus.