Login / Signup

The enduring world forest carbon sink.

Yude PanRichard A BirdseyOliver L PhillipsRichard A HoughtonJingyun FangPekka E KauppiHeather KeithWerner A KurzAkihiko ItoSimon L LewisGert-Jan NabuursAnatoly Z ShvidenkoShoji HashimotoBas J W LerinkDmitry G SchepaschenkoAndrea D A CastanhoDaniel Murdiyarso
Published in: Nature (2024)
The uptake of carbon dioxide (CO 2 ) by terrestrial ecosystems is critical for moderating climate change 1 . To provide a ground-based long-term assessment of the contribution of forests to terrestrial CO 2 uptake, we synthesized in situ forest data from boreal, temperate and tropical biomes spanning three decades. We found that the carbon sink in global forests was steady, at 3.6 ± 0.4 Pg C yr -1 in the 1990s and 2000s, and 3.5 ± 0.4 Pg C yr -1 in the 2010s. Despite this global stability, our analysis revealed some major biome-level changes. Carbon sinks have increased in temperate (+30 ± 5%) and tropical regrowth (+29 ± 8%) forests owing to increases in forest area, but they decreased in boreal (-36 ± 6%) and tropical intact (-31 ± 7%) forests, as a result of intensified disturbances and losses in intact forest area, respectively. Mass-balance studies indicate that the global land carbon sink has increased 2 , implying an increase in the non-forest-land carbon sink. The global forest sink is equivalent to almost half of fossil-fuel emissions (7.8 ± 0.4 Pg C yr -1 in 1990-2019). However, two-thirds of the benefit from the sink has been negated by tropical deforestation (2.2 ± 0.5 Pg C yr -1 in 1990-2019). Although the global forest sink has endured undiminished for three decades, despite regional variations, it could be weakened by ageing forests, continuing deforestation and further intensification of disturbance regimes 1 . To protect the carbon sink, land management policies are needed to limit deforestation, promote forest restoration and improve timber-harvesting practices 1,3 .
Keyphrases
  • climate change
  • human health
  • carbon dioxide
  • healthcare
  • primary care
  • public health
  • machine learning
  • big data
  • single cell
  • medical education