Releasing global forests from human management: How much more carbon could be stored?

Abstract

Carbon storage in forests is a cornerstone of policy-making to prevent global warming from exceeding 1.5°C. However, the global impact of management (for example, harvesting) on the carbon budget of forests remains poorly quantified. We integrated global maps of forest biomass and management with machine learning to show that by removing human intervention, under current climatic conditions and carbon dioxide (CO 2 ) concentration, existing global forests could increase their aboveground biomass by up to 44.1 (error range: 21.0 to 63.0) petagrams of carbon. This is an increase of 15 to 16% over current levels, equating to about 4 years of current anthropogenic CO 2 emissions. Therefore, without strong reductions in emissions, this strategy holds low mitigation potential, and the forest sink should be preserved to offset residual carbon emissions rather than to compensate for present emissions levels.

Climate policies for carbon neutrality should not rely on the uncertain increase of carbon stocks in existing forests

“…We establish the long-term persistence of the global forest carbon sink by estimating the above-ground biomass carrying capacity (BCC) under present and future climatic conditions (Roebroek 2024). BCC is the biomass of a forest 'under prevailing environmental conditions and natural disturbance regimes, but excluding anthropogenic disturbance' (Keith et al 2009, Roebroek et al 2023. Under the assumption that human influences (e.g.…”

“…The estimates of BCC are made with a modular machine learning model framework. It is based on a simplified model definition, expanding on previous work (Roebroek et al 2023). It describes changes in BCC as a product of growth potential (B p , potential biomass), natural disturbance regime ((1 − D), fraction of potential biomass that is expected to be sustainable under given climate conditions) and CO 2 fertilization (F CO2 , a factor adapting potential biomass and the disturbance regime to enhanced CO 2 scenarios, systematically larger than 1):…”

“…Hyperparameters were chosen with a grid-based 5-fold cross validation approach, minimizing the pinball loss (with alpha set to 0.995) for the predictions of PRB. This procedure is described in more detail in Roebroek et al (2023) and visualized in figure 1.…”

“…The original model description in Roebroek et al (2023) was purely based on current environmental conditions, for which various predictors are available. In order to transpose the model framework to future climate scenarios, the set of environmental predictors had to be adapted.…”

Projecting future forest microclimate using a land surface model

“…Forests play an important role in shaping climate conditions from local to global scales by mediating greenhouse gas, water and energy fluxes between the land and the atmosphere (Davin and Noblet-Ducoudré 2010, Teuling et al 2017, O'Connor et al 2021, Roebroek et al 2023. In addition, tree canopies create distinct climatic conditions in the forest understory, which are typically referred to as the forest microclimate (Geiger et al 2009).…”