Robbin Bastiaansen

Robbin Bastiaansen is assistant professor at the Mathematical Institute and the Institute for Marine and Atmospheric Research Utrecht (IMAU) at the departments of Mathematics and Physics at Utrecht University. Robbin received a MSc degree in Applied Mathematics from Leiden University. He received his PhD degree in Applied Mathematics in 2019 from Leiden University for research on the role of pattern formation in desertification for which he was awarded the C.J. Kok Jury Award. Since then he has been active in consortia researching climate change and tipping points. His research interests include dynamical systems, climate and ecosystem dynamics, pattern formation, asymptotic analysis and tipping points.

About the talk: Climate tipping points and spatial pattern formation

In the current Anthropocene, it has become clear that human activity can have large effects on many different ecosystems, climate subsystems and on the global climate of our Earth. For instance, greenhouse gas emissions force the global climate, leading to the current global warming. On smaller scales, these global human-induced climatic changes can have devastating effects and cause, for example, melting of glaciers, desertification and extinction of species. Hence, in order to prepare for what is yet to come, it is therefore vital to understand how these complex nonlinear systems work and respond to external forcing, and how they might interact.

In this talk, I will illustrate how techniques and insights from dynamical system theory help in answering these questions. In particular, I will highlight the possibility of climate surprises in the form of so-called tipping points, relating them to bifurcations in dynamical systems. In the first part, I will start with the commonly used theory for bifurcations in ordinary differential equations. However, in the second part I will highlight how the response of spatially patterned systems (modelled with partial differential equations) is more intricate and does not necessarily follow the typical tipping point narrative.