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Field-based tree mortality constraint reduces estimates of model-projected forest carbon sinks.

Kailiang YuPhilippe CiaisSonia I SeneviratneZhihua LiuHan Y H ChenJonathan BarichivichCraig D AllenHui YangYuanyuan HuangAshley P Ballantyne
Published in: Nature communications (2022)
Considerable uncertainty and debate exist in projecting the future capacity of forests to sequester atmospheric CO 2 . Here we estimate spatially explicit patterns of biomass loss by tree mortality (LOSS) from largely unmanaged forest plots to constrain projected (2015-2099) net primary productivity (NPP), heterotrophic respiration (HR) and net carbon sink in six dynamic global vegetation models (DGVMs) across continents. This approach relies on a strong relationship among LOSS, NPP, and HR at continental or biome scales. The DGVMs overestimated historical LOSS, particularly in tropical regions and eastern North America by as much as 5 Mg ha -1 y -1 . The modeled spread of DGVM-projected NPP and HR uncertainties was substantially reduced in tropical regions after incorporating the field-based mortality constraint. The observation-constrained models show a decrease in the tropical forest carbon sink by the end of the century, particularly across South America (from 2 to 1.4 PgC y -1 ), and an increase in the sink in North America (from 0.8 to 1.1 PgC y -1 ). These results highlight the feasibility of using forest demographic data to empirically constrain forest carbon sink projections and the potential overestimation of projected tropical forest carbon sinks.
Keyphrases
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  • deep learning