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

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 Zusammenfassung: 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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Sprache(n): eng - English
 Datum: 2025-06-122025-06-182025-09-15
 Publikationsstatus: Final veröffentlicht
 Seiten: 11
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: Expertenbegutachtung
 Identifikatoren: 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
 Art des Abschluß: -

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Titel: Agricultural and Forest Meteorology
Genre der Quelle: Zeitschrift, SCI, Scopus, p3
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Ort, Verlag, Ausgabe: -
Seiten: - Band / Heft: 372 Artikelnummer: 110693 Start- / Endseite: - Identifikator: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/journals15
Publisher: Elsevier