Decadal sink-source shifts of forest aboveground carbon since 1988
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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.
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References · 5
- 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,926
- 2017Axiomatic Attribution for Deep Networkscited by 3,384
- 2017What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?cited by 2,352
- 2022A high-resolution canopy height model of the Earthcited by 91
- 2019Country-wide high-resolution vegetation height mapping with Sentinel-2cited by 22
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