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Analysis of a bistable climate toy model with physics-based machine learning methods

Gelbrecht, M., Lucarini, V. ORCID: https://orcid.org/0000-0001-9392-1471, Boers, N. and Kurths, J. (2021) Analysis of a bistable climate toy model with physics-based machine learning methods. The European Physical Journal Special Topics. ISSN 1951-6355

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To link to this item DOI: 10.1140/epjs/s11734-021-00175-0

Abstract/Summary

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.

Item Type:Article
Refereed:Yes
Divisions:Interdisciplinary Research Centres (IDRCs) > Centre for the Mathematics of Planet Earth (CMPE)
Science > School of Mathematical, Physical and Computational Sciences > Department of Mathematics and Statistics
ID Code:98670
Publisher:Springer

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