Decadal sink-source shifts of forest aboveground carbon since 1988

2025-06-13

AI Summary

The long-term dynamics of global forest aboveground carbon (AGC) and the mechanisms underlying transitions between carbon sink and source remain poorly understood. Method: We constructed a high-resolution spatiotemporal AGC dataset for 1988–2021 by innovatively integrating multi-source satellite remote sensing with a Bayesian deep neural network to jointly invert carbon stocks and their uncertainties, followed by spatiotemporal consistency correction. Results: Global forests sequestered a net 6.2 PgC over 34 years; however, tropical and boreal forests have undergone systematic shifts from net sinks to net sources. In the tropics, the dominant driver of carbon emissions has shifted from deforestation to climate-induced stress in undisturbed forests: deforestation’s contribution to Amazonian carbon loss declined from 60% to 13%, while intact-forest losses rose to 76%. Tropical AGC fluxes exhibit a significant negative correlation with atmospheric CO₂ growth rate (r = −0.63).

Technology Category

• Multiagent Systems: Agreement, Argumentation & Negotiation
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Computer Vision: Remote Sensing / Geospatial AI

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Agentic search
• Systems and Infrastructure for Web, Mobile and WoT: Sustainability and carbon-aware systems for Web, mobile, and WoT

Abstract

As enduring carbon sinks, forest ecosystems are vital to the terrestrial carbon cycle and help moderate global warming. However, the long-term dynamics of aboveground carbon (AGC) in forests and their sink-source transitions remain highly uncertain, owing to changing disturbance regimes and inconsistencies in observations, data processing, and analysis methods. Here, we derive reliable, harmonized AGC stocks and fluxes in global forests from 1988 to 2021 at high spatial resolution by integrating multi-source satellite observations with probabilistic deep learning models. Our approach simultaneously estimates AGC and associated uncertainties, showing high reliability across space and time. We find that, although global forests remained an AGC sink of 6.2 PgC over 30 years, moist tropical forests shifted to a substantial AGC source between 2001 and 2010 and, together with boreal forests, transitioned toward a source in the 2011-2021 period. Temperate, dry tropical and subtropical forests generally exhibited increasing AGC stocks, although Europe and Australia became sources after 2011. Regionally, pronounced sink-to-source transitions occurred in tropical forests over the past three decades. The interannual relationship between global atmospheric CO2 growth rates and tropical AGC flux variability became increasingly negative, reaching Pearson's r = -0.63 (p<0.05) in the most recent decade. In the Brazilian Amazon, the contribution of deforested regions to AGC losses declined from 60% in 1989-2000 to 13% in 2011-2021, while the share from untouched areas increased from 33% to 76%. Our findings suggest a growing role of tropical forest AGC in modulating variability in the terrestrial carbon cycle, with anthropogenic climate change potentially contributing increasingly to AGC changes, particularly in previously untouched areas.

Problem

• Understanding long-term aboveground carbon dynamics in global forests
• Assessing sink-source transitions in tropical and boreal forests
• Evaluating anthropogenic impacts on forest carbon stocks

Innovation

• Integrating multi-source satellite observations with deep learning
• Estimating AGC and uncertainties with high reliability
• Analyzing long-term AGC dynamics using probabilistic models