23 Mar 2022
23 Mar 2022
Status: this preprint is open for discussion.

Evidence of Amazon rainforest dieback in CMIP6 models

Isobel Parry, Paul Ritchie, and Peter Cox Isobel Parry et al.
  • College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK, EX4 4QE

Abstract. Amazon forest dieback is seen as a potential tipping point under climate change. These concerns are partly based-on an early coupled climate-carbon cycle simulation, that produced unusually strong drying and warming in Amazonia. In contrast, the 5th generation Earth System Models (CMIP5) produced few examples of Amazon dieback under climate change. Here we examine results from seven 6th generation models (CMIP6) which include vegetation dynamics, and in some cases interactive forest fires. Although these models typically project increases in area-mean forest carbon across Amazonia under CO2-induced climate change, five of the seven models also produce abrupt reductions in vegetation carbon which indicate localised dieback events. The Northern South America region (NSA), which contains most of the rainforest, is especially vulnerable in the models. These dieback events, some of which are mediated by fire, are preceded by an increase in the amplitude of the seasonal cycle in near surface temperature, which is consistent with more extreme dry seasons. Based-on the ensemble mean of the detected dieback events we estimate that 7 +/-5 % of the NSA region will experience abrupt downward shifts in vegetation carbon per °C of global warming above 1.5 °C.

Isobel Parry et al.

Status: open (until 08 May 2022)

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Isobel Parry et al.

Isobel Parry et al.

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Short summary
Abrupt dieback of the Amazon rainforest resulting from drying under global warming has long been recognised as a potential future tipping point. The latest generation of climate models show that multiple localised abrupt shifts can occur in the vegetation carbon under increased levels of CO2 . Many detected abrupt shifts are proceeded by an increase in the temperature seasonal cycle amplitude, offering a promising early warning signal for Amazon forest dieback.