首页|The decline in tropical land carbon sink drove high atmospheric CO2 growth rate in 2023

The decline in tropical land carbon sink drove high atmospheric CO2 growth rate in 2023

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Atmospheric CO2 growth rate(CGR),reflecting the carbon balance between anthropogenic emissions and net uptake from land and ocean,largely determines the magnitude and speed of global warming.The CGR at Mauna Loa Baseline Observatory reached a record high in 2023.We quantified major components of the global carbon balance for 2023,by developing a framework that integrated fossil fuel CO2 emissions data and an atmospheric inversion from the Global ObservatioN-based system for monitoring Greenhouse GAses(GONGGA)with two artificial intelligence(AI)models derived from dynamic global vegetation models.We attributed the record high CGR increase in 2023 compared to 2022 primarily to the large decline in land carbon sink(1803±197 TgC year-1),with minor contributions from a small reduction in ocean carbon sink(184 TgC year-1)and a slight increase in fossil fuel emissions(24 TgC year-1).At least 78％of the global decline in land carbon sink was contributed by the decline in tropical sink,with GONGGA inversion(1354 TgC year-1)and AI simulations(1578±666 TgC year-1)showing similar declines in the tropics.We further linked this tropical decline to the detrimental impact of El Nino-induced anomalous warming and drying on vegetation productivity in water-limited Sahel and southern Africa.Our successful attribution of CGR increase within a framework combining atmospheric inversion and AI simulations enabled near-real-time tracking of the global carbon budget,which had a one-year reporting lag.

carbon budgetland carbon sinkatmospheric CO2 growth rateartificial intelligenceEl Nino

Yanchen Gui、Kai Wang、Zhe Jin、Heyuan Wang、Hanzhi Deng、Xiangyi Li、Xiangjun Tian、Tao Wang、Wei Chen、Tengjiao Wang、Shilong Piao

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Institute of Carbon Neutrality,Sino-French Institute for Earth System Science,College of Urban and Environmental Sciences,Peking University,Beijing 100871,China

State Key Laboratory of Tibetan Plateau Earth System,Resources and Environment(TPESRE),Institute of Tibetan Plateau Research,Chinese Academy of Sciences,Beijing 100101,China

School of Computer Science,Peking University,Beijing 100871,China

Institute of Computational Social Science,Peking University(Qingdao),Qingdao 266555,China

University of Chinese Academy of Sciences,Beijing 101408,China

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2024

10.1093/nsr/nwae365

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ISSN：
年,卷(期)：2024.11(12)