mlsanityData to 14 Oct 2026

Decadal sink-source shifts of forest aboveground carbon since 1988

arXiv 2506.11879 ↗First posted 13 Jun 2025physics.geo-ph cs.LGcited by 0

Not on the map. Why?

Abstract (arXiv)

Forest ecosystems are vital to the global carbon cycle, yet their long-term aboveground carbon (AGC) dynamics remain uncertain. Here, we integrate multi-source satellite observations with probabilistic deep learning models to reconstruct a harmonized, uncertainty-aware global forest AGC record from 1988 to 2021 at 0.25-deg. We find that, although global forests sequestered 6.2 PgC, moist tropical and boreal forests have progressively transitioned toward carbon sources since the early 2000s. This shift coincides with a strengthening negative correlation between tropical AGC variability and atmospheric CO2 growth rates (r = -0.63 in 2011-2021), suggesting tropical forests increasingly modulate the global carbon cycle. Notably, in the Brazilian Amazon, the contribution of intact forests to the year-to-year variations in AGC losses increased from 33% in the 1990s to 76% in the 2010s, surpassing that of deforested areas (from 60% to 13%). Our findings highlight the vulnerability of carbon stocks in key biomes and provide a benchmark to track emerging sink-source shifts under anthropogenic climate change.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 5 of its 95 references that are in the map. First posted on arXiv 13 Jun 2025.

Builds on

5 of 95 references.

References · 5

  1. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,926
  2. 2017Axiomatic Attribution for Deep Networkscited by 3,384
  3. 2017What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?cited by 2,352
  4. 2022A high-resolution canopy height model of the Earthcited by 91
  5. 2019Country-wide high-resolution vegetation height mapping with Sentinel-2cited by 22

Built on by

Not cited yet.