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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 fifth-generation Earth system models (Phase 5 of the Coupled Model Intercomparison Project, CMIP5) produced few examples of Amazon dieback under climate change. Here we examine results from seven sixth-generation models (Phase 6 of the Coupled Model Intercomparison Project, CMIP6), which include interactive vegetation carbon 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 (NSA) region, 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 for every degree of global warming past 1.5 ∘ C. [ABSTRACT FROM AUTHOR]

Climate change has substantially increased the frequency of extreme droughts in the Amazon basin, generating concern about impacts on the world's largest tropical forest, which contributes about one‐seventh of the global vegetation carbon sink. Most research to understand drought impacts has focused on the immediate influences of such events, neglecting post‐drought effects on ecosystems recovery. Since ecological processes are influenced by antecedent conditions, we analyzed whether extreme droughts affect vegetation growth (i.e., net primary productivity, NPP) recovery. Here, we evaluated the NPP in the Amazon basin from 2003 to 2020, a period in which drought frequency was almost double the decadal incidence of the last century. We show that NPP does respond to the coupled impacts of individual droughts and the post‐drought impacts during ecosystem recovery. In particular, our results reveal that the ecosystems undergoing recovery show NPP about 13% lower than reference values based on the pre‐drought state or in areas undisturbed by drought. NPP deficits have consistently increased with the extreme droughts of 2005, 2010, and 2015 due to the combined effects of disturbances magnitude and the length of recovery. If the expected increase in drought frequency and intensity does occur, reduced recovery may lead the Amazon Forest to an alternative ecosystem state with lower carbon uptake, contributing to a warming global climate. Plain Language Summary: Extreme droughts cause significant impacts on forest systems. Although drought disturbances are widely quantified, few studies address the impacts of post‐drought effects during the recovery period. In this work, we show that forests undergoing post‐drought recovery show lower levels of productivity than undisturbed systems. Recovery debts vary with drought intensity and the recovery time after each event. Global warming is projected to increase drought intensity and frequency worldwide and recovery debts may promote a decrease in forests carbon uptake. Key Points: Forests undergoing post‐drought recovery show lower levels of productivity than undisturbed systemsRecovery debts vary not only with the degree of intensity of the drought but also with the recovery time after each event [ABSTRACT FROM AUTHOR]

A combined GCM analogue model and GCM land surface representation is used to investigate the influences of climatology and land surface parameterisation on modelled Amazonian vegetation change. This modelling structure (called IMOGEN) captures the main features of the changes in surface climate as estimated by a GCM with enhanced atmospheric greenhouse gas concentrations. Advantage is taken of IMOGEN's computational speed which allows multiple simulations to be carried out to assess the robustness of the GCM results. The timing of forest dieback is found to be sensitive to the initial ''pre-industrial'' climate, as well as uncertainties in the representation of land-atmosphere CO2 exchange. Changing from a Q10 form for plant dark and maintanence respiration (as used in the coupled GCM runs) to a respiration proportional to maximum photosynthesis, reduces the biomass lost from Amazonia in the 21st century. Replacing the GCM control climate (which has about 25% too little rain in the annual mean over Amazonia) with an observed climatology increases the CO2 concentration at which rainfall drops to critical levels, and thereby further delays the dieback. On the other hand, calibration of the canopy photosynthesis model against Amazonian flux data tends to lead to earlier forest dieback. Further advances are required in both GCM rainfall simulation and land-surface process representation before a clearer picture will emerge on the timing of possible Amazonian forest dieback. However, it seems likely that these advances will overall lead to projections of later forest dieback as GCM control climates become more realistic. [ABSTRACT FROM AUTHOR]

A suite of simulations with the HadCM3LC coupled climate-carbon cycle model is used to examine the various forcings and feedbacks involved in the simulated precipitation decrease and forest dieback. Rising atmospheric CO2 is found to contribute 20% to the precipitation reduction through the physiological forcing of stomatal closure, with 80% of the reduction being seen when stomatal closure was excluded and only radiative forcing by CO2 was included. The forest dieback exerts two positive feedbacks on the precipitation reduction; a biogeophysical feedback through reduced forest cover suppressing local evaporative water recycling, and a biogeochemical feedback through the release of CO2 contributing to an accelerated global warming. The precipitation reduction is enhanced by 20% by the biogeophysical feedback, and 5% by the carbon cycle feedback from the forest dieback. This analysis helps to explain why the Amazonian precipitation reduction simulated by HadCM3LC is more extreme than that simulated in other GCMs; in the fully-coupled, climate-carbon cycle simulation, approximately half of the precipitation reduction in Amazonia is attributable to a combination of physiological forcing and biogeophysical and global carbon cycle feedbacks, which are generally not included in other GCM simulations of future climate change. The analysis also demonstrates the potential contribution of regional-scale climate and ecosystem change to uncertainties in global CO2 and climate change projections. Moreover, the importance of feedbacks suggests that a human-induced increase in forest vulnerability to climate change may have implications for regional and global scale climate sensitivity. [ABSTRACT FROM AUTHOR]

The first GCM climate change projections to include dynamic vegetation and an interactive carbon cycle produced a very significant amplification of global warming over the 21st century. Under the IS92a ''business as usual'' emissions scenario CO2 concentrations reached about 980 ppmv by 2100, which is about 280 ppmv higher than when these feedbacks were ignored. The major contribution to the increased CO2 arose from reductions in soil carbon because global warming is assumed to accelerate respiration. However, there was also a lesser contribution from an alarming loss of the Amazonian rainforest. This paper describes the phenomenon of Amazonian forest dieback under elevated CO2 in the Hadley Centre climate-carbon cycle model. [ABSTRACT FROM AUTHOR]