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Empirical estimates of regional carbon budgets imply reduced global soil heterotrophic respiration.

Philippe CiaisYitong YaoThomas GasserAlessandro BacciniYilong WangRonny LauerwaldShu-Shi PengAna BastosWei LiPeter A RaymondJosep G CanadellGlen P PetersRob J AndresJinfeng ChangChao YueA Johannes DolmanVanessa HaverdJens HartmannGoulven LaruelleAlexandra G KoningsAnthony W KingYi LiuSebastiaan LuyssaertFabienne MaignanPrabir K PatraAnna PeregonPierre RegnierJulia PongratzBenjamin PoulterAnatoly ShvidenkoRiccardo ValentiniRong WangGrégoire BroquetYi YinJakob ZscheischlerBertrand GuenetDaniel S GollAshley-P BallantyneHui YangChunjing QiuDan Zhu
Published in: National science review (2020)
Resolving regional carbon budgets is critical for informing land-based mitigation policy. For nine regions covering nearly the whole globe, we collected inventory estimates of carbon-stock changes complemented by satellite estimates of biomass changes where inventory data are missing. The net land-atmospheric carbon exchange (NEE) was calculated by taking the sum of the carbon-stock change and lateral carbon fluxes from crop and wood trade, and riverine-carbon export to the ocean. Summing up NEE from all regions, we obtained a global 'bottom-up' NEE for net land anthropogenic CO2 uptake of -2.2 ± 0.6 PgC yr-1 consistent with the independent top-down NEE from the global atmospheric carbon budget during 2000-2009. This estimate is so far the most comprehensive global bottom-up carbon budget accounting, which set up an important milestone for global carbon-cycle studies. By decomposing NEE into component fluxes, we found that global soil heterotrophic respiration amounts to a source of CO2 of 39 PgC yr-1 with an interquartile of 33-46 PgC yr-1-a much smaller portion of net primary productivity than previously reported.
Keyphrases
  • climate change
  • public health
  • machine learning
  • mental health
  • particulate matter
  • wastewater treatment
  • deep learning