• Energy Research

Assessing potential future carbon loss from tropical forests is important for evaluating the efficacy of programmes for reducing emissions from deforestation and degradation (REDD). An exploration of results from 22 climate models in conjunction with a land surface scheme suggests that in the Americas, Africa and Asia, the resilience of tropical forests to climate change is higher than expected, although uncertainties are large.

The Hadley Centre coupled climate–carbon cycle model (HadCM3LC) predicts loss of the Amazon rainforest in response to future anthropogenic greenhouse gas emissions. In this study, the atmospheric component of HadCM3LC is used to assess the role of simulated changes in mid-twenty-first century sea surface temperature (SST) in Amazon Basin climate change. When the full HadCM3LC SST anomalies (SSTAs) are used, the atmosphere model reproduces the Amazon Basin climate change exhibited by HadCM3LC, including much of the reduction in Amazon Basin rainfall. This rainfall change is shown to be the combined effect of SSTAs in both the tropical Atlantic and the Pacific, with roughly equal contributions from each basin. The greatest rainfall reduction occurs from May to October, outside of the mature South American monsoon (SAM) season. This dry season response is the combined effect of a more rapid warming of the tropical North Atlantic relative to the south, and warm SSTAs in the tropical east Pacific. Conversely, a weak enhancement of mature SAM season rainfall in response to Atlantic SST change is suppressed by the atmospheric response to Pacific SST. This net wet season response is sufficient to prevent dry season soil moisture deficits from being recharged through the SAM season, leading to a perennial soil moisture reduction and an associated 30% reduction in annual Amazon Basin net primary productivity (NPP). A further 23% NPP reduction occurs in response to a 3.5°C warmer air temperature associated with a global mean SST warming.

Simulations with the Hadley Centre general circulation model (HadCM3), including carbon cycle model and forced by a ‘business-as-usual’ emissions scenario, predict a rapid loss of Amazonian rainforest from the middle of this century onwards. The robustness of this projection to both uncertainty in physical climate drivers and the formulation of the land surface scheme is investigated. We analyse how the modelled vegetation cover in Amazonia responds to (i) uncertainty in the parameters specified in the atmosphere component of HadCM3 and their associated influence on predicted surface climate. We then enhance the land surface description and (ii) implement a multilayer canopy light interception model and compare with the simple ‘big-leaf’ approach used in the original simulations. Finally, (iii) we investigate the effect of changing the method of simulating vegetation dynamics from an area-based model (TRIFFID) to a more complex size- and age-structured approximation of an individual-based model (ecosystem demography). We find that the loss of Amazonian rainforest is robust across the climate uncertainty explored by perturbed physics simulations covering a wide range of global climate sensitivity. The introduction of the refined light interception model leads to an increase in simulated gross plant carbon uptake for the present day, but, with altered respiration, the net effect is a decrease in net primary productivity. However, this does not significantly affect the carbon loss from vegetation and soil as a consequence of future simulated depletion in soil moisture; the Amazon forest is still lost. The introduction of the more sophisticated dynamic vegetation model reduces but does not halt the rate of forest dieback. The potential for human-induced climate change to trigger the loss of Amazon rainforest appears robust within the context of the uncertainties explored in this paper. Some further uncertainties should be explored, particularly with respect to the representation of rooting depth.

Abstract. We show that the distribution of anthropogenic carbon between the atmosphere, land surface, and ocean differs with the choice of projection scenario even for identical changes in mean global surface temperature. Warming thresholds occur later in lower-CO2-emissions scenarios and with less carbon in the three main reservoirs than in higher-CO2-emissions scenarios. At 2 ∘C of warming, the mean carbon allocation differs by up to 63 PgC between scenarios, which is equivalent to approximately 6 years of the current global total emissions. At the same warming level, higher-CO2-concentration scenarios have a lower combined ocean and land carbon allocation fraction of the total carbon compared to lower-CO2-concentration scenarios. The warming response to CO2, quantified as the equilibrium climate sensitivity, ECS, directly impacts the global warming level exceedance year and hence the carbon allocation. Low-ECS models have more carbon than high-ECS models at a given warming level because the warming threshold occurs later, allowing more emissions to accumulate. These results are important for carbon budgets and mitigation strategies as they impact how much carbon the ocean and land surface could absorb at a given warming level. Carbon budgeting will be key to reducing the impacts of anthropogenic climate change, and these findings could have critical consequences for policies aimed at reaching net zero.