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  Uncertainties too large to predict tipping times of major Earth system components from historical data

Ben-Yami, M., Morr, A., Bathiany, S., Boers, N. (2024): Uncertainties too large to predict tipping times of major Earth system components from historical data. - Science Advances, 10, 31, eadl4841.
https://doi.org/10.1126/sciadv.adl4841

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https://zenodo.org/records/12569006 (Supplementary material)
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 Creators:
Ben-Yami, Maya1, Author              
Morr, Andreas1, Author              
Bathiany, Sebastian1, Author              
Boers, Niklas1, Author              
Affiliations:
1Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Abstract: One way to warn of forthcoming critical transitions in Earth system components is using observations to detect declining system stability. It has also been suggested to extrapolate such stability changes into the future and predict tipping times. Here, we argue that the involved uncertainties are too high to robustly predict tipping times. We raise concerns regarding (i) the modeling assumptions underlying any extrapolation of historical results into the future, (ii) the representativeness of individual Earth system component time series, and (iii) the impact of uncertainties and preprocessing of used observational datasets, with focus on nonstationary observational coverage and gap filling. We explore these uncertainties in general and specifically for the example of the Atlantic Meridional Overturning Circulation. We argue that even under the assumption that a given Earth system component has an approaching tipping point, the uncertainties are too large to reliably estimate tipping times by extrapolating historical information.

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Language(s): eng - English
 Dates: 2024-08-022024-08-02
 Publication Status: Finally published
 Pages: 11
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1126/sciadv.adl4841
PIKDOMAIN: RD4 - Complexity Science
Organisational keyword: RD4 - Complexity Science
Organisational keyword: FutureLab - Artificial Intelligence in the Anthropocene
Research topic keyword: Climate impacts
Research topic keyword: Nonlinear Dynamics
Research topic keyword: Oceans
Research topic keyword: Tipping Elements
Regional keyword: Global
Model / method: Nonlinear Data Analysis
MDB-ID: No MDB - stored outside PIK (see locators/paper)
OATYPE: Gold Open Access
 Degree: -

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Title: Science Advances
Source Genre: Journal, SCI, Scopus, p3, oa
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Pages: - Volume / Issue: 10 (31) Sequence Number: eadl4841 Start / End Page: - Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/161027
Publisher: American Association for the Advancement of Science (AAAS)