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

Gelbrecht, M., Lucarini, V., Boers, N., Kurths, J. (2021): Analysis of a bistable climate toy model with physics-based machine learning methods. - European Physical Journal - Special Topics, 230, 13-15, 3121-3131.
https://doi.org/10.1140/epjs/s11734-021-00175-0

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???ViewItemFull_lblCiteItemAs???: https://publications.pik-potsdam.de/pubman/item/item_25715 ???ViewItemFull_lblCiteItemVersionAs???: https://publications.pik-potsdam.de/pubman/item/item_25715_4
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 ???ViewItemFull_lblCreators???:
Gelbrecht, Maximilian1, ???ENUM_CREATORROLE_AUTHOR???           
Lucarini, Valerio2, ???ENUM_CREATORROLE_AUTHOR???
Boers, Niklas1, ???ENUM_CREATORROLE_AUTHOR???                 
Kurths, Jürgen1, ???ENUM_CREATORROLE_AUTHOR???           
???ViewItemFull_lblAffiliations???:
1Potsdam Institute for Climate Impact Research, Potsdam, ou_persistent13              
2External Organizations, ou_persistent22              

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 ???ViewItemFull_lblAbstract???: 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.

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 ???ViewItemFull_lblDates???: 2021-06-112021-06-112021-10
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 ???ViewItemFull_lblIdentifiers???: ???ENUM_IDENTIFIERTYPE_DOI???: 10.1140/epjs/s11734-021-00175-0
???ENUM_IDENTIFIERTYPE_PIKDOMAIN???: RD4 - Complexity Science
???ENUM_IDENTIFIERTYPE_PIKDOMAIN???: RD4 - Complexity Science
???ENUM_IDENTIFIERTYPE_ORGANISATIONALK???: FutureLab - Artificial Intelligence in the Anthropocene
???ENUM_IDENTIFIERTYPE_RESEARCHTK???: Nonlinear Dynamics
???ENUM_IDENTIFIERTYPE_MODELMETHOD???: Machine Learning
???ENUM_IDENTIFIERTYPE_MDB_ID???: yes - 3239
???ENUM_IDENTIFIERTYPE_OATYPE???: Hybrid - DEAL Springer Nature
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???ViewItemFull_lblSourceTitle???: European Physical Journal - Special Topics
???ViewItemFull_lblSourceGenre???: ???ENUM_GENRE_JOURNAL???, SCI, Scopus, p3
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???ENUM_IDENTIFIERTYPE_PUBLISHER???: Springer