https://doi.org/10.1140/epjs/s11734-021-00175-0
Regular Article
Analysis of a bistable climate toy model with physics-based machine learning methods
1
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
Received:
11
November
2020
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
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