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  Mean exit times as global measure of resilience of tropical forest systems under climatic disturbances—Analytical and numerical results

Zheng, Y., Boers, N. (2023): Mean exit times as global measure of resilience of tropical forest systems under climatic disturbances—Analytical and numerical results. - Chaos, 33, 11, 113136.
https://doi.org/10.1063/5.0158109

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 Creators:
Zheng, Yayun1, Author
Boers, Niklas2, Author              
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Abstract: Both remotely sensed distribution of tree cover and models suggest three alternative stable vegetation states in the tropics: forest, savanna, and treeless states. Environmental fluctuation could cause critical transitions from the forest to the savanna state and quantifying the resilience of a given vegetation state is, therefore, crucial. While previous work has focused mostly on local stability concepts, we investigate here the mean exit time from a given basin of attraction, with partially absorbing and reflecting boundaries, as a global resilience measure. We provide detailed investigations using an established model for tropical tree cover with multistable precipitation regimes. We find that higher precipitation or weaker noise increases the mean exit time of the forest state and, thus, its resilience. Upon investigating the transition times from the forest state to other tree cover states, we find that in the bistable precipitation regime, the size of environmental fluctuations has a greater impact on the transition probabilities from the forest state compared to precipitation.

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Language(s): eng - English
 Dates: 2023-11-292023-11-29
 Publication Status: Finally published
 Pages: 7
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1063/5.0158109
MDB-ID: No data to archive
PIKDOMAIN: RD4 - Complexity Science
Organisational keyword: FutureLab - Artificial Intelligence in the Anthropocene
Model / method: Nonlinear Data Analysis
Model / method: Machine Learning
Research topic keyword: Tipping Elements
 Degree: -

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Title: Chaos
Source Genre: Journal, SCI, Scopus, p3
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Pages: - Volume / Issue: 33 (11) Sequence Number: 113136 Start / End Page: - Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/180808
Publisher: American Institute of Physics (AIP)