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| Titel |
Detecting hotspots of atmosphere–vegetation interaction via slowing down – Part 1: A stochastic approach |
| VerfasserIn |
S. Bathiany, M. Claussen, K. Fraedrich |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
2190-4979
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| Digitales Dokument |
URL |
| Erschienen |
In: Earth System Dynamics ; 4, no. 1 ; Nr. 4, no. 1 (2013-02-26), S.63-78 |
| Datensatznummer |
250017771
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| Publikation (Nr.) |
 copernicus.org/esd-4-63-2013.pdf |
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| Zusammenfassung |
An analysis of so-called early warning signals (EWS) is proposed to identify
the spatial origin of a sudden transition that results from a loss in
stability of a current state. EWS, such as rising variance and
autocorrelation, can be indicators of an increased relaxation time (slowing
down). One particular problem of EWS-based predictions is the requirement of
sufficiently long time series. Spatial EWS have been suggested to alleviate
this problem by combining different observations from the same time. However,
the benefit of EWS has only been shown in idealised systems of predefined
spatial extent. In a more general context like a complex climate system
model, the critical subsystem that exhibits a loss in stability (hotspot) and
the critical mode of the transition may be unknown.
In this study we document this problem with a simple stochastic model of
atmosphere–vegetation interaction where EWS at individual grid cells are not
always detectable before a vegetation collapse as the local loss in stability
can be small. However, we suggest that EWS can be applied as a diagnostic
tool to find the hotspot of a sudden transition and to distinguish this
hotspot from regions experiencing an induced tipping. For this purpose we
present a scheme which identifies a hotspot as a certain combination of grid
cells which maximise an EWS. The method can provide information on the
causality of sudden transitions and may help to improve the knowledge on the
susceptibility of climate models and other systems. |
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