Analysis of a bistable climate toy model with physics-based machine learning methods
Potsdam Institute for Climate Impact Research, Potsdam, Germany
2 Department of Physics, Humboldt-Universität zu Berlin, Berlin, Germany
3 Department of Mathematics and Statistics, University of Reading, Reading, UK
4 Centre for the Mathematics of Planet Earth, University of Reading, Reading, UK
5 Department of Mathematics and Computer Science, Freie Universität Berlin, Berlin, Germany
6 Department of Mathematics and Global Systems Institute, University of Exeter, Exeter, UK
7 Lobachevsky State University of Nizhny Novgorod, Nizhny, Novgorod, Russia
Accepted: 7 May 2021
Published online: 11 June 2021
We propose a comprehensive framework able to address both the predictability of the first and of the second kind for high-dimensional chaotic models. For this purpose, we analyse the properties of a newly introduced multistable climate toy model constructed by coupling the Lorenz ’96 model with a zero-dimensional energy balance model. First, the attractors of the system are identified with Monte Carlo Basin Bifurcation Analysis. Additionally, we are able to detect the Melancholia state separating the two attractors. Then, Neural Ordinary Differential Equations are applied to predict the future state of the system in both of the identified attractors.
© The Author(s) 2021
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.