Drivers and uncertainty of Amazon carbon sink long-term and interannual variability in CMIP6 models

The Amazon basin rainforest is a critical component of the climate system, currently representing 25% of terrestrial carbon gains and storing 150 to 200 billion tonnes of carbon. Whether the Amazon rainforest will remain a net carbon sink is an open scientific question: while its future stability and functioning may be compromised by climate change and anthropogenic pressures, Earth System Models (ESM) divergence in the projections undermines their reliability to simulate its future evolution. In this study, we examined the contribution of different climatic drivers behind the long-term and interannual variability evolution of the carbon sink within the Amazon basin using eleven CMIP6 ESMs, shedding light on the main factors contributing to inter-model diversity. By adopting the carbon-cycle feedback framework with C4MIP experiments, our results underscore the dominant role of CO2 fertilization in driving long-term Amazon carbon sink trend and uncertainty. We also address the variability of carbon fluxes at the interannual timescale using a multivariate predictive model on historical and ssp585 ScenarioMIP simulations. With this respect, we emphasize the contribution of GPP modulation by shortwave incoming radiation as dominating NBP divergence across the ESMs ensemble. Additionally, we demonstrate that temperature-driven anomalies will be the main mechanism responsible for the higher Amazon carbon sink sensitivity to the El Nino Southern Oscillation (ENSO) under sustained global warming, predominantly as a result of the amplification of NBP sensitivity to temperature anomalies. Being the representation of terrestrial carbon cycle processes still one of the main uncertainties undermining ESMs projections, we therefore advocate for increased focus from modelling groups towards a more accurate and consistent representation of land processes and parameterizations, which will hopefully lead to reduced uncertainties in simulations coming from the next generation of ESMs.