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  Amazon deforestation intensifies atmospheric aridity through locally dominant biophysical mechanisms

Cheng, M., You, Q., Zuo, Z., Li, M., Chen, D., Boers, N. (2025 online): Amazon deforestation intensifies atmospheric aridity through locally dominant biophysical mechanisms. - Agricultural and Forest Meteorology, 372, 110693.
https://doi.org/10.1016/j.agrformet.2025.110693

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 Creators:
Cheng, Mengqi1, Author
You, Qinglong1, Author
Zuo, Zhiyan1, Author
Li, Mingcai1, Author
Chen, Deliang1, Author
Boers, Niklas2, Author                 
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Abstract: The Amazon basin has experienced severe deforestation in recent decades; however, the impact of this deforestation on vapor pressure deficit (VPD) remains unclear. VPD is a key variable used to characterize atmospheric aridity. Here, we analyze idealized deforestation experiments with coupled Earth system models, exploring the bidirectional relationship between deforestation and increased VPD. In simulations, Amazon deforestation causes a substantial increase in VPD, and initiated a positive feedback mechanism. We find that 40 % of the deforestation-induced increase in VPD in the Amazon basin is contributed by increasing atmospheric vapor demand, while 60 % is contributed by decreasing atmospheric vapor supply. Specifically, increased local radiative forcing due to changes in shortwave transmissivity and aerodynamic resistance results in local warming, which plays a dominant role in the increase in atmospheric vapor demand. Meanwhile, reduced evapotranspiration caused by deforestation dominates the decrease in atmospheric vapor supply. Our results show a possible existence of a positive feedback mechanism between deforestation and VPD increase in the Amazon, and suggest that large-scale Amazon deforestation may trigger uncontrollable increases in atmospheric aridity.

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Language(s): eng - English
 Dates: 2025-06-122025-06-18
 Publication Status: Published online
 Pages: 11
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1016/j.agrformet.2025.110693
MDB-ID: No data to archive
PIKDOMAIN: RD4 - Complexity Science
Organisational keyword: RD4 - Complexity Science
Working Group: Artificial Intelligence
Research topic keyword: Tipping Elements
Regional keyword: South America
 Degree: -

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Title: Agricultural and Forest Meteorology
Source Genre: Journal, SCI, Scopus, p3
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Pages: - Volume / Issue: 372 Sequence Number: 110693 Start / End Page: - Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/journals15
Publisher: Elsevier