IDEAS home Printed from https://ideas.repec.org/a/nat/natcli/v15y2025i2d10.1038_s41558-024-02234-5.html
   My bibliography  Save this article

Wildfires offset the increasing but spatially heterogeneous Arctic–boreal CO2 uptake

Author

Listed:
  • Anna-Maria Virkkala

    (Woodwell Climate Research Center)

  • Brendan M. Rogers

    (Woodwell Climate Research Center)

  • Jennifer D. Watts

    (Woodwell Climate Research Center)

  • Kyle A. Arndt

    (Woodwell Climate Research Center)

  • Stefano Potter

    (Woodwell Climate Research Center)

  • Isabel Wargowsky

    (Woodwell Climate Research Center)

  • Edward A. G. Schuur

    (Northern Arizona University
    Northern Arizona University)

  • Craig R. See

    (Northern Arizona University)

  • Marguerite Mauritz

    (University of Texas at El Paso)

  • Julia Boike

    (Alfred Wegener Institute Helmholtz Center for Polar and Marine Research
    Humboldt University)

  • M. Syndonia Bret-Harte

    (University of Alaska Fairbanks)

  • Eleanor J. Burke

    (Met Office Hadley Centre)

  • Arden Burrell

    (Woodwell Climate Research Center)

  • Namyi Chae

    (Korea University)

  • Abhishek Chatterjee

    (California Institute of Technology)

  • Frederic Chevallier

    (Université Paris-Saclay)

  • Torben R. Christensen

    (Aarhus University
    University of Oulu)

  • Roisin Commane

    (Columbia University)

  • Han Dolman

    (Royal Netherlands Institute for Sea Research
    Vrije Universiteit)

  • Colin W. Edgar

    (University of Alaska Fairbanks)

  • Bo Elberling

    (University of Copenhagen)

  • Craig A. Emmerton

    (University of Alberta)

  • Eugenie S. Euskirchen

    (University of Alaska Fairbanks)

  • Liang Feng

    (University of Edinburgh)

  • Mathias Göckede

    (Max Planck Institute for Biogeochemistry)

  • Achim Grelle

    (Linnaeus University)

  • Manuel Helbig

    (Dalhousie University
    Université de Montréal
    GFZ German Research Centre for Geosciences)

  • David Holl

    (Universität Hamburg)

  • Järvi Järveoja

    (Swedish University of Agricultural Sciences)

  • Sergey V. Karsanaev

    (Division of Federal Research Centre “The Yakut Scientific Centre of the Siberian Branch of the Russian Academy of Sciences”)

  • Hideki Kobayashi

    (Japan Agency for Marine-Earth Science and Technology)

  • Lars Kutzbach

    (Universität Hamburg)

  • Junjie Liu

    (California Institute of Technology)

  • Ingrid T. Luijkx

    (Wageningen University)

  • Efrén López-Blanco

    (Aarhus University
    Greenland Institute of Natural Resources)

  • Kyle Lunneberg

    (San Diego State University)

  • Ivan Mammarella

    (University of Helsinki)

  • Maija E. Marushchak

    (University of Eastern Finland)

  • Mikhail Mastepanov

    (Aarhus University
    University of Oulu)

  • Yojiro Matsuura

    (Tsukuba)

  • Trofim C. Maximov

    (Division of Federal Research Centre “The Yakut Scientific Centre of the Siberian Branch of the Russian Academy of Sciences”)

  • Lutz Merbold

    (Agroscope)

  • Gesa Meyer

    (Université de Montréal
    Environment and Climate Change Canada)

  • Mats B. Nilsson

    (Swedish University of Agricultural Sciences)

  • Yosuke Niwa

    (National Institute for Environmental Studies
    Meteorological Research Institute)

  • Walter Oechel

    (San Diego State University)

  • Paul I. Palmer

    (University of Edinburgh)

  • Sang-Jong Park

    (Korea Polar Research Institute)

  • Frans-Jan W. Parmentier

    (University of Oslo)

  • Matthias Peichl

    (Swedish University of Agricultural Sciences)

  • Wouter Peters

    (Wageningen University
    Groningen University)

  • Roman Petrov

    (Division of Federal Research Centre “The Yakut Scientific Centre of the Siberian Branch of the Russian Academy of Sciences”)

  • William Quinton

    (Wilfrid Laurier University)

  • Christian Rödenbeck

    (Max Planck Institute for Biogeochemistry)

  • Torsten Sachs

    (GFZ German Research Centre for Geosciences
    Technische Universität Braunschweig)

  • Christopher Schulze

    (Université de Montréal
    University of Alberta)

  • Oliver Sonnentag

    (Université de Montréal)

  • Vincent L. Louis

    (University of Alberta)

  • Eeva-Stiina Tuittila

    (University of Eastern Finland)

  • Masahito Ueyama

    (Osaka Metropolitan University)

  • Andrej Varlagin

    (Russian Academy of Sciences)

  • Donatella Zona

    (San Diego State University)

  • Susan M. Natali

    (Woodwell Climate Research Center)

Abstract

The Arctic–Boreal Zone is rapidly warming, impacting its large soil carbon stocks. Here we use a new compilation of terrestrial ecosystem CO2 fluxes, geospatial datasets and random forest models to show that although the Arctic–Boreal Zone was overall an increasing terrestrial CO2 sink from 2001 to 2020 (mean ± standard deviation in net ecosystem exchange, −548 ± 140 Tg C yr−1; trend, −14 Tg C yr−1; P

Suggested Citation

  • Anna-Maria Virkkala & Brendan M. Rogers & Jennifer D. Watts & Kyle A. Arndt & Stefano Potter & Isabel Wargowsky & Edward A. G. Schuur & Craig R. See & Marguerite Mauritz & Julia Boike & M. Syndonia Br, 2025. "Wildfires offset the increasing but spatially heterogeneous Arctic–boreal CO2 uptake," Nature Climate Change, Nature, vol. 15(2), pages 188-195, February.
  • Handle: RePEc:nat:natcli:v:15:y:2025:i:2:d:10.1038_s41558-024-02234-5
    DOI: 10.1038/s41558-024-02234-5
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41558-024-02234-5
    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/s41558-024-02234-5?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. Kuhn, Max, 2008. "Building Predictive Models in R Using the caret Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i05).
    2. Zhihua Liu & John S. Kimball & Ashley P. Ballantyne & Nicholas C. Parazoo & Wen J. Wang & Ana Bastos & Nima Madani & Susan M. Natali & Jennifer D. Watts & Brendan M. Rogers & Philippe Ciais & Kailiang, 2022. "Respiratory loss during late-growing season determines the net carbon dioxide sink in northern permafrost regions," Nature Communications, Nature, vol. 13(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. Prabal Das & D. A. Sachindra & Kironmala Chanda, 2022. "Machine Learning-Based Rainfall Forecasting with Multiple Non-Linear Feature Selection Algorithms," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(15), pages 6043-6071, December.
    2. Paulo Infante & Gonçalo Jacinto & Anabela Afonso & Leonor Rego & Pedro Nogueira & Marcelo Silva & Vitor Nogueira & José Saias & Paulo Quaresma & Daniel Santos & Patrícia Góis & Paulo Rebelo Manuel, 2023. "Factors That Influence the Type of Road Traffic Accidents: A Case Study in a District of Portugal," Sustainability, MDPI, vol. 15(3), pages 1-16, January.
    3. Ephrem Habyarimana & Faheem S Baloch, 2021. "Machine learning models based on remote and proximal sensing as potential methods for in-season biomass yields prediction in commercial sorghum fields," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-23, March.
    4. Crespo, Cristian, 2020. "Two become one: improving the targeting of conditional cash transfers with a predictive model of school dropout," LSE Research Online Documents on Economics 123139, London School of Economics and Political Science, LSE Library.
    5. Alexander Wettstein & Gabriel Jenni & Ida Schneider & Fabienne Kühne & Martin grosse Holtforth & Roberto La Marca, 2023. "Predictors of Psychological Strain and Allostatic Load in Teachers: Examining the Long-Term Effects of Biopsychosocial Risk and Protective Factors Using a LASSO Regression Approach," IJERPH, MDPI, vol. 20(10), pages 1-20, May.
    6. Tang, Kayu & Parsons, David J. & Jude, Simon, 2019. "Comparison of automatic and guided learning for Bayesian networks to analyse pipe failures in the water distribution system," Reliability Engineering and System Safety, Elsevier, vol. 186(C), pages 24-36.
    7. Daifeng Xiang & Gangsheng Wang & Jing Tian & Wanyu Li, 2023. "Global patterns and edaphic-climatic controls of soil carbon decomposition kinetics predicted from incubation experiments," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    8. Joel Podgorski & Oliver Kracht & Luis Araguas-Araguas & Stefan Terzer-Wassmuth & Jodie Miller & Ralf Straub & Rolf Kipfer & Michael Berg, 2024. "Groundwater vulnerability to pollution in Africa’s Sahel region," Nature Sustainability, Nature, vol. 7(5), pages 558-567, May.
    9. Tranos, Emmanouil & Incera, Andre Carrascal & Willis, George, 2022. "Using the web to predict regional trade flows: data extraction, modelling, and validation," OSF Preprints 9bu5z, Center for Open Science.
    10. Štefan Lyócsa & Petra Vašaničová & Branka Hadji Misheva & Marko Dávid Vateha, 2022. "Default or profit scoring credit systems? Evidence from European and US peer-to-peer lending markets," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-21, December.
    11. Marcos Rodrigues & Fermín Alcasena & Pere Gelabert & Cristina Vega‐García, 2020. "Geospatial Modeling of Containment Probability for Escaped Wildfires in a Mediterranean Region," Risk Analysis, John Wiley & Sons, vol. 40(9), pages 1762-1779, September.
    12. Natalia Pardo-Lorente & Anestis Gkanogiannis & Luca Cozzuto & Antoni Gañez Zapater & Lorena Espinar & Ritobrata Ghose & Jacqueline Severino & Laura García-López & Rabia Gül Aydin & Laura Martin & Mari, 2024. "Nuclear localization of MTHFD2 is required for correct mitosis progression," Nature Communications, Nature, vol. 15(1), pages 1-23, December.
    13. Andrea Lazzari & Simone Giovinazzo & Giovanni Cabassi & Massimo Brambilla & Carlo Bisaglia & Elio Romano, 2025. "Evaluating Urban Sewage Sludge Distribution on Agricultural Land Using Interpolation and Machine Learning Techniques," Agriculture, MDPI, vol. 15(2), pages 1-13, January.
    14. Giovanny Pillajo-Quijia & Blanca Arenas-Ramírez & Camino González-Fernández & Francisco Aparicio-Izquierdo, 2020. "Influential Factors on Injury Severity for Drivers of Light Trucks and Vans with Machine Learning Methods," Sustainability, MDPI, vol. 12(4), pages 1-28, February.
    15. Zander S. Venter & Adam Sadilek & Charlotte Stanton & David N. Barton & Kristin Aunan & Sourangsu Chowdhury & Aaron Schneider & Stefano Maria Iacus, 2021. "Mobility in Blue-Green Spaces Does Not Predict COVID-19 Transmission: A Global Analysis," IJERPH, MDPI, vol. 18(23), pages 1-12, November.
    16. G. Brooke Anderson & Keith W. Oleson & Bryan Jones & Roger D. Peng, 2018. "Classifying heatwaves: developing health-based models to predict high-mortality versus moderate United States heatwaves," Climatic Change, Springer, vol. 146(3), pages 439-453, February.
    17. Van Belle, Jente & Guns, Tias & Verbeke, Wouter, 2021. "Using shared sell-through data to forecast wholesaler demand in multi-echelon supply chains," European Journal of Operational Research, Elsevier, vol. 288(2), pages 466-479.
    18. Jun Wang & Jinyong Huang & Yunlong Hu & Qianwen Guo & Shasha Zhang & Jinglin Tian & Yanqin Niu & Ling Ji & Yuzhong Xu & Peijun Tang & Yaqin He & Yuna Wang & Shuya Zhang & Hao Yang & Kang Kang & Xinchu, 2024. "Terminal modifications independent cell-free RNA sequencing enables sensitive early cancer detection and classification," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    19. Ali Al-Ramini & Mohammad A Takallou & Daniel P Piatkowski & Fadi Alsaleem, 2022. "Quantifying changes in bicycle volumes using crowdsourced data," Environment and Planning B, , vol. 49(6), pages 1612-1630, July.
    20. Jing Sun & Yue Liu & Jianhui Zhao & Bin Lu & Siyun Zhou & Wei Lu & Jingsun Wei & Yeting Hu & Xiangxing Kong & Junshun Gao & Hong Guan & Junli Gao & Qian Xiao & Xue Li, 2024. "Plasma proteomic and polygenic profiling improve risk stratification and personalized screening for colorectal cancer," Nature Communications, Nature, vol. 15(1), pages 1-10, December.

    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:15:y:2025:i:2:d:10.1038_s41558-024-02234-5. 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.