{"title":"The decline in tropical land carbon sink drove high atmospheric CO<sub>2</sub> growth rate in 2023.","authors":"Yanchen Gui, Kai Wang, Zhe Jin, Heyuan Wang, Hanzhi Deng, Xiangyi Li, Xiangjun Tian, Tao Wang, Wei Chen, Tengjiao Wang, Shilong Piao","doi":"10.1093/nsr/nwae365","DOIUrl":null,"url":null,"abstract":"<p><p>Atmospheric CO<sub>2</sub> 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 CO<sub>2</sub> 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<sup>-1</sup>), with minor contributions from a small reduction in ocean carbon sink (184 TgC year<sup>-1</sup>) and a slight increase in fossil fuel emissions (24 TgC year<sup>-1</sup>). 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<sup>-1</sup>) and AI simulations (1578 ± 666 TgC year<sup>-1</sup>) showing similar declines in the tropics. We further linked this tropical decline to the detrimental impact of El Niño-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.</p>","PeriodicalId":18842,"journal":{"name":"National Science Review","volume":"11 12","pages":"nwae365"},"PeriodicalIF":16.3000,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11646126/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"National Science Review","FirstCategoryId":"103","ListUrlMain":"https://doi.org/10.1093/nsr/nwae365","RegionNum":1,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/12/1 0:00:00","PubModel":"eCollection","JCR":"Q1","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
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 Niño-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.
期刊介绍:
National Science Review (NSR; ISSN abbreviation: Natl. Sci. Rev.) is an English-language peer-reviewed multidisciplinary open-access scientific journal published by Oxford University Press under the auspices of the Chinese Academy of Sciences.According to Journal Citation Reports, its 2021 impact factor was 23.178.
National Science Review publishes both review articles and perspectives as well as original research in the form of brief communications and research articles.