首页|Low latency carbon budget analysis reveals a large decline of the land carbon sink in 2023

Low latency carbon budget analysis reveals a large decline of the land carbon sink in 2023

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In 2023,the CO2 growth rate was 3.37±0.11 ppm at Mauna Loa,which was 86％above that of the previous year and hit a record high since observations began in 1958,while global fossil fuel CO2 emissions only increased by 0.6％±0.5％.This implies an unprecedented weakening of land and ocean sinks,and raises the question of where and why this reduction happened.Here,we show a global net land CO2 sink of 0.44±0.21 GtC yr-1,which is the weakest since 2003.We used dynamic global vegetation models,satellite fire emissions,an atmospheric inversion based on OCO-2 measurements and emulators of ocean biogeochemical and data-driven models to deliver a fast-track carbon budget in 2023.Those models ensured consistency with previous carbon budgets.Regional flux anomalies from 2015 to 2022 are consistent between top-down and bottom-up approaches,with the largest abnormal carbon loss in the Amazon during the drought in the second half of2023(0.31±0.19 GtC yr-1),extreme fire emissions of 0.58±0.10 GtC yr-1 in Canada and a loss in Southeast Asia(0.13±0.12 GtC yr-1).Since 2015,land CO2 uptake north of 20°N had declined by half to 1.13±0.24 GtC yr-1 in 2023.Meanwhile,the tropics recovered from the 2015-2016 El Nino carbon loss,gained carbon during the La Nina years(2020-2023),then switched to a carbon loss during the 2023 El Nino(0.56±0.23 GtC yr-1).The ocean sink was stronger than normal in the equatorial eastern Pacific due to reduced upwelling from La Nina's retreat in early 2023 and the development of El Nino later.Land regions exposed to extreme heat in 2023 contributed a gross carbon loss of 1.73 GtC yr-1,indicating that record warming in 2023 had a strong negative impact on the capacity of terrestrial ecosystems to mitigate climate change.

Global Carbon BudgetEl Nino 2023artificial intelligence emulators of models

Piyu Ke、Philippe Ciais、Stephen Sitch、Wei Li、Ana Bastos、Zhu Liu、Yidi Xu、Xiaofan Gui、Jiang Bian、Daniel S.Goll、Yi Xi、Wanjing Li、Michael O'Sullivan、Jefferson Goncalves De Souza、Pierre Friedlingstein、Frédéric Chevallier

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Department of Earth System Science,Tsinghua University,Beijing 100084,China

Faculty of Environment,Science and Economy,University of Exeter,Exeter EX4 4QF,United Kingdom

Laboratoire des Sciences du Climat et de I'Environnement,University Paris Saclay CEA CNRS,Gif sur Yvette 91191,France

Institute for Earth System Science and Remote Sensing,Leipzig University,Leipzig 04103,Germany

Department of Biogeochemical Integration,Max Planck Institute for Biogeochemistry,Jena 07745,Germany

Machine learning group,Microsoft research,Beijing 100080,China

Laboratoire de Météorologie Dynamique,IPSL,CNRS,ENS,Université PSL,Sorbonne Université,École Polytechnique,Paris 75005,France

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2024

10.1093/nsr/nwae367

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ISSN：
年,卷(期)：2024.11(12)