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

Autor(es)

Gelbrecht, Maximilian ^{[1, 2]} ; Lucarini, Valerio ^{[3, 4]} ; Boers, Niklas ^{[2, 5, 6, 7]} ; Kurths, Juergen ^{[1, 2, 8]}

Afiliação do(s) autor(es)

^[1] Humboldt Univ, Dept Phys, Berlin - Germany

^[2] Potsdam Inst Climate Impact Res, Potsdam - Germany

^[3] Univ Reading, Dept Math & Stat, Reading, Berks - England

^[4] Univ Reading, Ctr Math Planet Earth, Reading, Berks - England

^[5] Univ Exeter, Global Syst Inst, Exeter, Devon - England

^[6] Univ Exeter, Dept Math, Exeter, Devon - England

^[7] Free Univ Berlin, Dept Math & Comp Sci, Berlin - Germany

^[8] Lobachevsky State Univ Nizhny Novgorod, Nizhny, Novgorod - Russia

• Tipo de documento: Artigo Científico
• Fonte: European Physical Journal-Special Topics; v. 230, n. 14-15, p. 3121-3131, OCT 2021.
• Citações Web of Science: 2

Resumo

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. (AU)

• Processo FAPESP: 15/50122-0 - Fenômenos dinâmicos em redes complexas: fundamentos e aplicações
• Beneficiário: Elbert Einstein Nehrer Macau
• Modalidade de apoio: Auxílio à Pesquisa - Temático