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  Early-warning signals for Dansgaard-Oeschger events in a high-resolution ice core record

Boers, N. (2018): Early-warning signals for Dansgaard-Oeschger events in a high-resolution ice core record. - Nature Communications, 9, 2556.
https://doi.org/10.1038/s41467-018-04881-7

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Boers, Niklas1, Autor              
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1Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Zusammenfassung: The Dansgaard–Oeschger (DO) events, as observed in oxygen isotope ratios from the North Greenland Ice Core Project (NGRIP) record, are an outstanding example of past abrupt climate transitions. Their physical cause remains debated, and previous research indicated that they are not preceded by classical early-warning signals (EWS). Subsequent research hypothesized that the DO events are caused by bifurcations of physical mechanisms operating at decadal timescales, and proposed to search for EWS in the high-frequency fluctuation levels. Here, a time series with 5-year resolution is obtained from the raw NGRIP record, and significant numbers of EWS in terms of variance and autocorrelation increases are revealed in the decadal-scale variability. Wavelet analysis indicates that the EWS are most pronounced in the 10–50-year periodicity band, confirming the above hypothesis. The DO events are hence neither directly noise-induced nor purely externally forced, which provides valuable constraints regarding potential physical causes.

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 Datum: 2018
 Publikationsstatus: Final veröffentlicht
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 Identifikatoren: DOI: 10.1038/s41467-018-04881-7
PIKDOMAIN: Transdisciplinary Concepts & Methods - Research Domain IV
eDoc: 8403
Research topic keyword: Tipping Elements
Research topic keyword: Paleoclimate
Model / method: Machine Learning
Model / method: Nonlinear Data Analysis
Regional keyword: Arctic & Antarctica
Organisational keyword: RD4 - Complexity Science
Organisational keyword: FutureLab - Artificial Intelligence in the Anthropocene
Working Group: Development of advanced time series analysis techniques
Working Group: Network- and machine-learning-based prediction of extreme events
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Titel: Nature Communications
Genre der Quelle: Zeitschrift, SCI, Scopus, p3, oa
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Seiten: - Band / Heft: 9 Artikelnummer: 2556 Start- / Endseite: - Identifikator: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/journals354