Self-amplified Amazon forest loss due to vegetation-atmosphere feedbacks

Article English OPEN
Zemp, Delphine Clara ; Schleussner, Carl Friedrich ; Barbosa, Henrique M J ; Hirota, Marina ; Montade, Vincent ; Sampaio, Gilvan ; Staal, Arie ; Wang-Erlandsson, L. ; Rammig, Anja (2017)
  • Publisher: Nature Publishing Group
  • Journal: volume 8 (eissn: 2041-1723)
  • Related identifiers: pmc: PMC5355804, doi: 10.1038/ncomms14681
  • Subject: Amazon forest, loss; vegetation-atmosphere feedbacks | Article

Reduced rainfall increases the risk of forest dieback, while in return forest loss might intensify regional droughts. The consequences of this vegetation–atmosphere feedback for the stability of the Amazon forest are still unclear. Here we show that the risk of self-amplified Amazon forest loss increases nonlinearly with dry-season intensification. We apply a novel complexnetwork approach, in which Amazon forest patches are linked by observation-based atmospheric water fluxes. Our results suggest that the risk of self-amplified forest loss is reduced with increasing heterogeneity in the response of forest patches to reduced rainfall. Under dry-season Amazonian rainfall reductions, comparable to Last Glacial Maximum conditions, additional forest loss due to self-amplified effects occurs in 10–13% of the Amazon basin. Although our findings do not indicate that the projected rainfall changes for the end of the twenty-first century will lead to complete Amazon dieback, they suggest that frequent extreme drought events have the potential to destabilize large parts of the Amazon forest. peerReviewed
  • References (2)

    We thank Ruud van der Ent for developing and sharing the atmospheric-moisture tracking model used in this study, Egbert van Nes for providing code for Supplementary Fig. 1 and Brigitte Mueller for providing the precipitation multi-data set used in this study. We also thank Hermann Behling, A.J. Han Dolman, Jonathan F. Donges, Dieter Gerten, Milena Holmgren, Patrick Keys, Peter Puetz, Thomas Kneib, Marten Scheffer, Kirsten Thonicke and Liubov Tupikina for discussion and comments. We are thankful to Pierre Manceaux for his contribution to the design of the figures and to Alison Schlums for proofread. This paper was developed within the scope of the IRTG 1740/TRP 2011/50151-0, funded by the DFG/FAPESP. D.C.Z. acknowledges the financial support from EU-FP7 ROBIN project under grant agreement 283093. H.M.J.B. acknowledges the financial support from FAPESP through grants number 11/50151-0 and 13/50510-5. L.W.-E. acknowledges the financial support from The Swedish Research Council Formas through grant number 1364115. A.S. acknowledges the financial support from SENSE Research School, A.R. the EU-FP7 project AMAZALERT (ProjectID 282664) and M.H. the project Microsoft/FAPESP 2013/50169-1, and C.F.S. the financial support by the German Federal Ministry for the Environment, Nature Conservation and Nuclear Safety (16_II_148_Global_A_IMPACT).

    r The Author(s) 2017

  • Related Research Results (1)
  • Similar Research Results (1)
  • Software (1)
  • Metrics
    No metrics available
Share - Bookmark