Trial Lecture and Thesis Defense - Alessandro Cotronei

Master of Science Alessandro Cotronei will hold the Trial Lecture and Thesis Defense for the PhD degree in Science on Friday, October 9, 2026.

Trial Lecture

The Trial Lecture will take place 10:15 - 11:15.

The title of the Trial Lecture is:

« Complex networks in climate dynamics »

Thesis Defense

The Thesis Defense will take place 12:15 - 15:00.

The title of the thesis is:

« Stability and Feedbacks in Conceptual Climate Models - From Early-Warning Signals to Machine Learning »

Abstract

Anthropogenic climate change is altering the thermodynamic properties of our planet, with Earth system components responding in significant yet uncertain ways. Some changes take place gradually, but others involve tipping elements. These are components that, once a critical threshold (a tipping point) is crossed, may shift dramatically and irreversibly. Tipping points represent a potential existential risk to humanity, and the difficulty in predicting them undermines the development of timely and effective adaptation strategies. Detecting tipping points is highly challenging, as every available methodology comes with its own specific limitations. For example, approaches based on simplified mathematical results may overestimate tipping point signals, whereas methodologies derived from complex models often overemphasize stability and can neglect the possibility of abrupt change. As a result, the future trajectory of Earth system components with respect to tipping points remains highly uncertain.

The objective of this thesis is to identify the limitations of simple tipping-point detection methods and to improve conceptual models based on dynamical systems modeling, physical principles, and observational data, with a focus on Arctic regions. This is accomplished through three chapters, which group five studies and three conceptual ideas applied to three distinct tipping elements. Each topic is accompanied by relevant theoretical background and an introduction to the tipping element.

The first two studies assess the limitations of early-warning signal methods that, in theory, can anticipate tipping points. We propose extensions that account for additional factors (i.e., different modeling assumptions and effects of forcing scenarios) underexplored in the literature, applying them to the Atlantic Meridional Overturning Circulation.
The third study investigates the role of feedbacks within Greenland ice sheet tipping, focusing on the formation of supraglacial lakes that can accelerate melting. We develop a simplified model to quantify this feedback and assess its potential contribution to the processes associated with tipping behavior.

The fourth and fifth studies introduce a machine learning methodology to predict tipping behavior in simplified spatial phenomena. Motivated by results on cellular automata, the approach is then applied to Arctic sea ice, where we investigate the conditions under which bistable behavior (associated with tipping) can emerge.

We find that the identification of potential tipping signals is highly sensitive to relatively simple changes in model assumptions, including alternative formulations and underlying causal mechanisms, leading to increased uncertainty regarding future scenarios. This should be interpreted as a limitation of the current methods rather than as improved predictive capability, highlighting that available natural records are often too short and not sufficiently reliable to robustly constrain tipping behavior. We also demonstrate that, in the case of the Greenland ice sheet, supraglacial lakes contribute substantially to driving climate feedbacks and accelerate ice melting in idealized scenarios connected to future warming projections. Finally, we show that a machine learning modeling approach can reproduce bistable behavior in Arctic sea ice under certain conditions. Arctic sea ice bistability, which is associated with tipping behavior, is not observed in the available observational record but emerges in idealized climate scenarios within our conceptual model. Together, these results show that understanding and predicting tipping points is inherently difficult: feedbacks and interactions increase system complexity, and future scenarios remain hard to predict with available approaches. These findings suggest that improving predictive skill requires methods that explicitly account for structural uncertainty and system-specific feedback dynamics, rather than relying solely on generic indicators.

Supervisory Committee

• Professor Martin Rypdal, Department of Mathematics and Statistics, UiT  (Main Supervisor)
• Professor Rune Graversen, Department of Physics and Technology, UiT
• Professor Per Kristen Jakobsen, Department of Mathematics and Statistics, UiT
• Associate Professor Nikolas Olson Aksamit, Department of Mathematics and Statistics, UiT
• Ulrike Feudel, Carl von Ossietzky University of Oldenburg, Germany 

• Claudio Gallicchio, University of Pisa, Italy 

Evaluation Committee

• 1st Opponent: Professor Peter Ashwin, Department of Mathematics and Statistics, University of Exeter, United Kingdom
• 2nd Opponent: Professor Lorenzo Livi, Open Institute of Technology (OPIT), Msida, Malta
• Internal member and leader of the committee: Professor Sigrunn Sørbye, ​Department of Mathematics and Statistics​, UiT, Norway

Streaming

The Trial Lecture and Thesis Defense will be streamed via Panopto:

Trial Lecture (10:15 - 11:15)
Watch the Trial Lecture

Thesis Defense (12:15 - 15:00)
Watch the Thesis Defense

Thesis

The thesis is available in Munin:

View the thesis in Munin