Many real-world systems can be modeled in terms of systems of differential equations. Oftentimes, they have inherently nonlinear dynamics which can induce sudden, large and irreversible changes when an external forcing parameter is varied. We analyze these bifurcations in the realm of uncertainty [1]. In this talk, we will consider parameter uncertainty. This arises naturally when model parameters need to be estimated from measurement data or cannot even be measured directly but need to be inferred from other observable quantities.
We will see that parameter uncertainty can crucially alter the bifurcation landscape. I will illustrate this by an example from climate science, the Atlantic Meridional Overturning Circulation, where we model tipping points (TPs) in terms of bifurcations [2]. The system is of particular importance for the North Atlantic heat transport. We highlight the uncertain locations of TPs along the arising probabilistic bifurcation curves. Thereby, we contribute to an uncertainty quantification of high impact, low likelihood climate outcomes.
This talk covers joint work with Christian Kuehn (Technical University of Munich, Germany), Peter
Ashwin (University of Exeter, UK), Richard Wood (Met Office, UK), and Jonathan Baker (Met
Office, UK).
[1] C. Kuehn and K. Lux. Uncertainty Quantification of Bifurcations in Random Ordinary Differ-
ential Equations. SIAM J. Appl. Dyn. Syst., 20(4):2295-2334, 2021.
[2] K. Lux, P. Ashwin, R. Wood, and C. Kuehn. Assessing the impact of parametric uncertainty
on tipping points of the atlantic meridional overturning circulation. Environmental Research
Letters, 17(7):075002, 2022.