Abrupt loss and uncertain recovery from fires of Amazon forests under low climate mitigation scenarios

Abstract

Tropical forests contribute a major sink for anthropogenic carbon emissions essential to slowing down the buildup of atmospheric CO₂ and buffering climate change impacts. However, the response of tropical forests to more frequent weather extremes and long-recovery disturbances like fires remains uncertain. Analyses of field data and ecological theory raise concerns about the possibility of the Amazon crossing a tipping point leading to catastrophic tropical forest loss. In contrast, climate models consistently project an enhanced tropical sink. Here, we show a heterogeneous response of Amazonian carbon stocks in GFDL-ESM4.1, an Earth System Model (ESM) featuring dynamic disturbances and height-structured tree-grass competition. Enhanced productivity due to CO₂ fertilization promotes increases in forest biomass that, under low emission scenarios, last until the end of the century. Under high emissions, positive trends reverse after 2060, when simulated fires prompt forest loss that results in a 40% decline in tropical forest biomass by 2100. Projected fires occur under dry conditions associated with El Niño Southern Oscillation and the Atlantic Multidecadal Oscillation, a response observed under current climate conditions, but exacerbated by an overall decline in precipitation. Following the initial disturbance, grassland dominance promotes recurrent fires and tree competitive exclusion, which prevents forest recovery. EC-Earth3-Veg, an ESM with a dynamic vegetation model of similar complexity, projected comparable wildfire forest loss under high emissions but faster postfire recovery rates. Our results reveal the importance of complex nonlinear responses to assessing climate change impacts and the urgent need to research postfire recovery and its representation in ESMs.

Keywords: earth system model; forest recovery; tropical forest; wildfires.

Fig. 1.

Emergence of alternative states and hysteresis in the structure of tropical vegetation along a gradient of water availability. The schematic highlights key mechanisms implemented in the dynamic land model LM4.1 embedded in GFDL-ESM4.1. Low precipitation regimes favor the dominance of grasslands and savannas where seasonal fuel accumulation promotes recurrent fires that keep a state of arrested succession. High precipitation regimes converge toward a high tree cover state where the closed tree canopy inhibits grasses, reduces evaporative water loss, and increases transpiration to enhance moisture recycling at regional scales. Fire and humidity feedback mechanisms reinforce the resilience of each state and result in their coexistence at intermediate precipitation levels, where the dominant formation becomes contingent to past conditions. After a string of wet years, trees may be able to displace grasses, form a closed canopy, and reach a new alternative equilibrium. As conditions become drier, a closed forest canopy resiliently keeps humidity and prevents its own collapse until disturbances like fires prompt an abrupt transition to the low cover state.

Fig. 2.

(A) Left: Distribution of total tree biomass in the Neotropics and in the Paleotropics at the end of GFDL-ESM4.1 simulations during the historical period (the boxplots show the median and central interquartile range). Right: Projected trends in biomass under scenarios SSP1-2.6 and SSP5-8.5 (values normalized to the initial stock). (B) Projected trends in total tree biomass in the Neotropics based on GFDL-ESM4.1 global simulations under CMIP6 emission scenarios SSP1-2.6 and SSP5-8.5. Each line corresponds to the dynamics of natural tropical forests in an individual grid cell location (i.e., tiles that were unaffected by changes in land use). Trajectories showing a decrease in total biomass are highlighted with a purple hue. Flames along the abscissa indicate years with high carbon emissions due to fires. The complementary SI Appendix, Fig. S4 highlights the relationship between trends in tree biomass and changes in precipitation. Right: Distribution (as probability density function) of tree biomass by the end of the simulation for grid cell locations showing increasing or decreasing trends. (C) Relationship between tree biomass and mean annual precipitation (MAP, mm) at the start (2015 to 2034) and at the end (2081 to 2100) of simulations under scenario SSP5-8.5. The reference lines represent the probability of different vegetation types (treeless, savanna, and forest) estimated by ref. based on remote sensing observations of vegetation cover and precipitation over South America. The background gray area delimits a bistability zone where the probability that forest is the dominant vegetation type is between 0.1 and 0.9. SI Appendix, Fig. S5 depicts the trajectories of each grid cell on tree biomass-MAP coordinates.

Fig. 3.

Seasonal changes in GFDL-ESM4.1 fire suppression factors for soil moisture ( $f_{\theta }$ ) and relative humidity ( $f_{\mathit{rh}}$ ) under scenario SSP5-8.5 for a representative grid cell located in the Amazon. Each panel corresponds to a different period during the simulation and the green paths show the seasonal cycle of $f_{\theta }$ and $f_{\mathit{rh}}$ during each year. The background color provides the combined effect of both factors on fire probability; i.e., as the green path moves toward the Upper Right corner of each panel, fires become more likely. See SI Appendix for details on the fire suppression factors.

Fig. 4.

(A–C) Time series combining GFDL-ESM4.1 historical (1980 to 2014) and SSP5-8.5 (2015 to 2100) simulations for (A) the ONI (°C) and the AMO (°C), and (B) precipitation during the dry season (October–March, mm y⁻¹; the dashed line shows the ensemble mean across n = 21 CMIP6 ESMs, see SI Appendix, Table S2), and (C) the rate of release of carbon lost through fires to the atmosphere (fire carbon emissions, kg C m⁻² y⁻¹) averaged (mean and 80%CI) over the Amazon. (D and E) Maps of the maximum absolute correlation coefficient r between annual changes in fire carbon emissions and lagged values of (D) ONI and (E) AMO climate teleconnection indices. The box in (D) limits the area used to summarize time series data presented in panels (B and  C). Insets provide reference maps based on remote sensing observations (1997 to 2014). See SI Appendix, Table S1 and Figs. S10–S18 for additional information about the simulation of fires in GFDL-ESM4.1 and in other CMIP6 ESMs.

Fig. 5.

Distribution of the relative rates of biomass increase ( $r_{\mathrm{B}\mathrm{m}}$ , y⁻¹) simulated by GFDL-ESM4.1, EC-Earth3-Veg, and other CMIP6 ESMs (CESM2, CNRM-ESM2-1, MRI-ESM2-0, and NorESM2-LM; see SI Appendix, Table S1 for further details) under scenario SSP5-8.5. Relative rates were calculated as the natural logarithm of the ratio of annual woody biomass between consecutive years during the period 2015 to 2100. (A) Empirical cumulative distribution function of simulated $r_{\mathrm{B}\mathrm{m}}$ . (B) Relationship between $r_{\mathrm{B}\mathrm{m}}$ and carbon emissions to the atmosphere due to fires (fire emissions, kg C m⁻² y⁻¹) in EC-Earth3-Veg and GFDL-ESM4.1 models.