IDEAS home Printed from https://ideas.repec.org/a/nat/natcli/v14y2024i3d10.1038_s41558-024-01933-3.html
   My bibliography  Save this article

Boreal–Arctic wetland methane emissions modulated by warming and vegetation activity

Author

Listed:
  • Kunxiaojia Yuan

    (Lawrence Berkeley National Laboratory)

  • Fa Li

    (University of Wisconsin Madison)

  • Gavin McNicol

    (University of Illinois Chicago)

  • Min Chen

    (University of Wisconsin Madison)

  • Alison Hoyt

    (Stanford University)

  • Sara Knox

    (The University of British Columbia
    McGill University)

  • William J. Riley

    (Lawrence Berkeley National Laboratory)

  • Robert Jackson

    (Stanford University)

  • Qing Zhu

    (Lawrence Berkeley National Laboratory)

Abstract

Wetland methane (CH4) emissions over the Boreal–Arctic region are vulnerable to climate change and linked to climate feedbacks, yet understanding of their long-term dynamics remains uncertain. Here, we upscaled and analysed two decades (2002–2021) of Boreal–Arctic wetland CH4 emissions, representing an unprecedented compilation of eddy covariance and chamber observations. We found a robust increasing trend of CH4 emissions (+8.9%) with strong inter-annual variability. The majority of emission increases occurred in early summer (June and July) and were mainly driven by warming (52.3%) and ecosystem productivity (40.7%). Moreover, a 2 °C temperature anomaly in 2016 led to the highest recorded annual CH4 emissions (22.3 Tg CH4 yr−1) over this region, driven primarily by high emissions over Western Siberian lowlands. However, current-generation models from the Global Carbon Project failed to capture the emission magnitude and trend, and may bias the estimates in future wetland CH4 emission driven by amplified Boreal–Arctic warming and greening.

Suggested Citation

  • Kunxiaojia Yuan & Fa Li & Gavin McNicol & Min Chen & Alison Hoyt & Sara Knox & William J. Riley & Robert Jackson & Qing Zhu, 2024. "Boreal–Arctic wetland methane emissions modulated by warming and vegetation activity," Nature Climate Change, Nature, vol. 14(3), pages 282-288, March.
    • Handle: RePEc:nat:natcli:v:14:y:2024:i:3:d:10.1038_s41558-024-01933-3
    DOI: 10.1038/s41558-024-01933-3
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41558-024-01933-3
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1038/s41558-024-01933-3?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Jakob Runge & Vladimir Petoukhov & Jonathan F. Donges & Jaroslav Hlinka & Nikola Jajcay & Martin Vejmelka & David Hartman & Norbert Marwan & Milan Paluš & Jürgen Kurths, 2015. "Identifying causal gateways and mediators in complex spatio-temporal systems," Nature Communications, Nature, vol. 6(1), pages 1-10, December.
    2. Jakob Runge & Sebastian Bathiany & Erik Bollt & Gustau Camps-Valls & Dim Coumou & Ethan Deyle & Clark Glymour & Marlene Kretschmer & Miguel D. Mahecha & Jordi Muñoz-Marí & Egbert H. Nes & Jonas Peters, 2019. "Inferring causation from time series in Earth system sciences," Nature Communications, Nature, vol. 10(1), pages 1-13, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Leto Peel & Tiago P. Peixoto & Manlio De Domenico, 2022. "Statistical inference links data and theory in network science," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    2. Bingbo Gao & Jianyu Yang & Ziyue Chen & George Sugihara & Manchun Li & Alfred Stein & Mei-Po Kwan & Jinfeng Wang, 2023. "Causal inference from cross-sectional earth system data with geographical convergent cross mapping," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    3. Sourav Mukherjee & Ashok Kumar Mishra & Jakob Zscheischler & Dara Entekhabi, 2023. "Interaction between dry and hot extremes at a global scale using a cascade modeling framework," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    4. Jakob Runge, 2023. "Modern causal inference approaches to investigate biodiversity-ecosystem functioning relationships," Nature Communications, Nature, vol. 14(1), pages 1-3, December.
    5. Se Ho Park & Seokmin Ha & Jae Kyoung Kim, 2023. "A general model-based causal inference method overcomes the curse of synchrony and indirect effect," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    6. Jajcay, Nikola, 2018. "Spatial and temporal scales of atmospheric dynamics," Thesis Commons ar8ks, Center for Open Science.
    7. Cai, Yunhao & Jing, Peng & Wang, Baihui & Jiang, Chengxi & Wang, Yuan, 2023. "How does “over-hype” lead to public misconceptions about autonomous vehicles? A new insight applying causal inference," Transportation Research Part A: Policy and Practice, Elsevier, vol. 175(C).
    8. Tejasvi Chauhan & Anjana Devanand & Mathew Koll Roxy & Karumuri Ashok & Subimal Ghosh, 2023. "River interlinking alters land-atmosphere feedback and changes the Indian summer monsoon," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    9. Albert C. Yang & Chung-Kang Peng & Norden E. Huang, 2022. "Reply To: Comments on identifying causal relationships in nonlinear dynamical systems via empirical mode decomposition," Nature Communications, Nature, vol. 13(1), pages 1-3, December.
    10. Ge, Xinlei & Lin, Aijing, 2023. "Symbolic convergent cross mapping based on permutation mutual information," Chaos, Solitons & Fractals, Elsevier, vol. 167(C).
    11. Timothy M. Lenton & Jesse F. Abrams & Annett Bartsch & Sebastian Bathiany & Chris A. Boulton & Joshua E. Buxton & Alessandra Conversi & Andrew M. Cunliffe & Sophie Hebden & Thomas Lavergne & Benjamin , 2024. "Remotely sensing potential climate change tipping points across scales," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    12. Andreas Koutsodendris & Vasilis Dakos & William J. Fletcher & Maria Knipping & Ulrich Kotthoff & Alice M. Milner & Ulrich C. Müller & Stefanie Kaboth-Bahr & Oliver A. Kern & Laurin Kolb & Polina Vakhr, 2023. "Atmospheric CO2 forcing on Mediterranean biomes during the past 500 kyrs," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    13. van Elteren, Casper & Quax, Rick & Sloot, Peter, 2022. "Dynamic importance of network nodes is poorly predicted by static structural features," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 593(C).
    14. Sebastiano Trevisani & Pietro Daniel Omodeo, 2021. "Earth Scientists and Sustainable Development: Geocomputing, New Technologies, and the Humanities," Land, MDPI, vol. 10(3), pages 1-17, March.
    15. Ramos, Antônio M.T. & Casagrande, Helder L. & Macau, Elbert E.N., 2020. "Investigation on the high-order approximation of the entropy bias," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 549(C).
    16. Liangfeng Zou & Yuanyuan Zha & Yuqing Diao & Chi Tang & Wenquan Gu & Dongguo Shao, 2023. "Coupling the Causal Inference and Informer Networks for Short-term Forecasting in Irrigation Water Usage," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(1), pages 427-449, January.
    17. Ma, Zhengjing & Mei, Gang, 2022. "A hybrid attention-based deep learning approach for wind power prediction," Applied Energy, Elsevier, vol. 323(C).
    18. Javier, Prince Joseph Erneszer A. & Liponhay, Marissa P. & Dajac, Carlo Vincienzo G. & Monterola, Christopher P., 2022. "Causal network inference in a dam system and its implications on feature selection for machine learning forecasting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcli:v:14:y:2024:i:3:d:10.1038_s41558-024-01933-3. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.