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Warming response of peatland CO2 sink is sensitive to seasonality in warming trends

Nature Climate Change volume 12, pages 743–749 (2022)Cite this article

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Abstract

Peatlands have acted as net CO2 sinks over millennia, exerting a global climate cooling effect. Rapid warming at northern latitudes, where peatlands are abundant, can disturb their CO2 sink function. Here we show that sensitivity of peatland net CO2 exchange to warming changes in sign and magnitude across seasons, resulting in complex net CO2 sink responses. We use multiannual net CO2 exchange observations from 20 northern peatlands to show that warmer early summers are linked to increased net CO2 uptake, while warmer late summers lead to decreased net CO2 uptake. Thus, net CO2 sinks of peatlands in regions experiencing early summer warming, such as central Siberia, are more likely to persist under warmer climate conditions than are those in other regions. Our results will be useful to improve the design of future warming experiments and to better interpret large-scale trends in peatland net CO2 uptake over the coming few decades.

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Fig. 1: Spatial distribution and climatic conditions of studied peatland sites.
Fig. 2: Relationship between annual air-temperature anomalies and annual peatland net CO2 ecosystem exchange anomalies.
Fig. 3: Sensitivities (regression slopes) between (ΔNEE) and environmental drivers in different seasons.
Fig. 4: Estimated ΔNEE across northern latitudes (>45° N).
Fig. 5: Annual time series of estimated ΔNEE.

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Data availability

Monthly data used in this study can be accessed through the corresponding author’s GitHub repository61 (https://github.com/manuelhelbig/PeatlandNEE) and is available from the corresponding author upon request.

Code availability

All MATLAB code used in this study is made available through the corresponding author’s GitHub repository61 (https://github.com/manuelhelbig/PeatlandNEE). The software used to generate all results in this study is MATLAB 2016a.

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Acknowledgements

M.H., L.B.F. and O.S. acknowledge support from the Natural Sciences and Engineering Research Council Discovery Grants programme. P.J.H.’s contributions were supported by the US Department of Energy, Office of Science, Office of Biological and Environmental Research at Oak Ridge National Laboratory, which is managed by UT-Battelle, LLC, for DOE under contract DE-AC05-00OR22725. A.P. was funded by the Russian Foundation for Basic Research, Krasnoyarsk Territory, and Krasnoyarsk Regional Fund of Science, project no. 20-45-242908, and the Russian Science Foundation, project no. 21-17-00163. O.S. acknowledges funding by the Canada Research Chairs and the Canada Foundation for Innovation Leaders Opportunity Fund. M.U. was funded by Arctic Challenge for Sustainability II grant JPMXD1420318865 and KAKENHI (grant no. 19H05668). P.G.L.’s and G.K.’s contributions were supported by the Irish Government’s ERTDI Programme, grant no. 2001‐CC/CD‐(5/7) and the Irish Environmental Protection Agency CELTICFLUX project, grant no. 2001-CC-C2-M1. S.W. and F.J.W.P. were funded by Bioforsk, NILU—Norwegian Institute for Air Research and the Smithsonian Environmental Research Center, with funding from the Research Council of Norway (project NFR208424, GHG-NOR) and the Stiftelsen Fondet for Jord-og Myrundersøkelser. F.J.W.P. received additional support from the Research Council of Norway (grant no. 274711) and the Swedish Research Council (grant no. 2017-05268). P.A. acknowledges the Academy of Finland Flagship Programme for financial support of ‘Forest–Human–Machine Interplay—Building Resilience, Redefining Value Networks and Enabling Meaningful Experiences (UNITE)’ flagship (decision no. 337655) and the funding from the Swedish Research Council for Sustainable Development FORMAS (grant no. 2018-01820). E.-S.T. acknowledges Academy of Finland funding (grant codes 330840 and 337550). We acknowledge support from the Ministry of Transport and Communication, the Ministry of Education and Culture and the Academy of Finland through ICOS Finland. Funding for E.S.E. was provided by the US Geological Survey, Research Work Order 224 to the University of Alaska Fairbanks, the Bonanza Creek Long-Term Ecological Research Program funded by the National Science Foundation (NSF DEB-1026415, DEB-1636476) and the NSF Long-Term Research in Environmental Biology Program (NSF LTREB 2011276). C.H. acknowledges support from the Natural Environment Research Council award number NE/R016429/1 as part of the UK-SCAPE programme delivering National Capability. M.B.N., M.P., P.V., P.W. and J.R. acknowledge the support by the Swedish Research Council of the national research infrastructures ICOS Sweden and SITES (Swedish Infrastructure for Ecosystem Service). P.V. received additional support from the Swedish government-funded Strategic Research Area Biodiversity and Ecosystem Services in a Changing Climate, BECC. S.Z. and T.S.E.-M. acknowledge support by the Max Planck Society for the Advancement of Sciences, e.V., through the long-term project ZOTTO (EBIO 8015). We are grateful to the Liidlii Kue First Nation and Jean-Marie River First Nation for supporting observations at the Scotty Creek Research Station, which were part of the Arctic Boreal Vulnerability Experiment (ABoVE).

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Authors and Affiliations

  1. Department of Physics and Atmospheric Science, Dalhousie University, Halifax, Nova Scotia, Canada

    M. Helbig

  2. Department of Biology, Dalhousie University, Halifax, Nova Scotia, Canada

    T. Živković

  3. Bioeconomy and Environment, Natural Resources Institute Finland, Helsinki, Finland

    P. Alekseychik

  4. Finnish Meteorological Institute, Helsinki, Finland

    M. Aurela, J. Hattakka, T. Laurila & A. Lohila

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

    T. S. El-Madany & S. Zaehle

  6. Institute of Arctic Biology, University of Alaska Fairbanks, Fairbanks, AK, USA

    E. S. Euskirchen

  7. Department of Biological Sciences, University of Lethbridge, Lethbridge, Alberta, Canada

    L. B. Flanagan

  8. Department of Soil, Water, and Climate, University of Minnesota, Saint Paul, MN, USA

    T. J. Griffis

  9. Oak Ridge National Laboratory, Oak Ridge, TN, USA

    P. J. Hanson

  10. UK Center for Ecology and Hydrology, Edinburgh, UK

    C. Helfter

  11. Research Faculty of Agriculture, Hokkaido University, Sapporo, Japan

    T. Hirano

  12. Department of Geography and Environmental Studies, Carleton University, Ottawa, Canada

    E. R. Humphreys

  13. School of Engineering and Architecture and Environmental Research Institute, University College Cork, Cork, Ireland

    G. Kiely & P. G. Leahy

  14. USDA Forest Service, Northern Research Station, Grand Rapids, MN, USA

    R. K. Kolka & D. T. Roman

  15. Institute of Atmospheric and Earth System Research, University of Helsinki, Helsinki, Finland

    A. Lohila, I. Mammarella & T. Vesala

  16. Department of Forest Ecology and Management, Swedish University of Agricultural Sciences, Umeå, Sweden

    M. B. Nilsson & M. Peichl

  17. V. N. Sukachev Institute of Forest SB RAS, KSC SB RAS, Krasnoyarsk, Russian Federation

    A. Panov

  18. Centre for Biogeochemistry in the Anthropocene, Department of Geosciences, University of Oslo, Oslo, Norway

    F. J. W. Parmentier

  19. Department of Physical Geography and Ecosystem Science, Lund University, Lund, Sweden

    F. J. W. Parmentier, J. Rinne & P. Vestin

  20. Production Systems Unit, Natural Resources Institute Finland, Helsinki, Finland

    J. Rinne

  21. Département de géographie, Université de Montréal, Montréal, Quebec, Canada

    O. Sonnentag

  22. School of Forest Sciences, University of Eastern Finland, Joensuu, Finland

    E.-S Tuittila

  23. Graduate School of Agriculture, Osaka Metropolitan University, Osaka, Japan

    M. Ueyama

  24. Division of Environment and Natural Resources, Norwegian Institute of Bioeconomy Research, Ås, Norway

    S. Weldon

  25. Department of Earth Sciences, University of Gothenburg, Gothenburg, Sweden

    P. Weslien

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  1. M. Helbig
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  33. S. Zaehle
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Contributions

M.H. designed the study. M.H. and T.Ž. developed the methodology. M.A., P.A., T.S.E.-M., E.S.E., L.B.F., T.J.G., J.H., C.H., T.H., E.R.H., G.K., R.K.K., T.L., P.G.L., A.L., I.M., M.B.N., A.P., F.J.W.P., M.P., J.R., D.T.R., O.S., E.-S.T., M.U., T.V., P.V., S.W., P.W. and S.Z. contributed eddy covariance flux data, and P.J.H. contributed data from the peatland warming experiment. M.H. analysed the data and wrote the first draft. All authors contributed to data interpretation and commented on the manuscript at all stages.

Corresponding author

Correspondence to M. Helbig.

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Competing interests

The authors declare no competing interests.

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Peer review information

Nature Climate Change thanks Katharina Jentzsch and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Seasonal air temperature changes across northern latitudes.

Warming rates across northern latitudes between 1951-1970 and 2001-2020 for (a) winter [January to March], (b) spring [April to June], (c) summer [July to September], and (d) fall [October to December] (data: CRU TS v4.061).

Extended Data Fig. 2 Interannual variability of net ecosystem CO2 exchange and air and soil temperature.

Mean interannual variability in (a) net ecosystem CO2 exchange (NEE) and (b) air and soil temperature across 20 peatland sites. Interannual variability is shown as the standard deviation of monthly NEE and air and soil temperature. Shaded areas show the standard error of the interannual variability across all sites.

Extended Data Fig. 3 Seasonal relationships between environmental drivers and air temperature.

Estimated fixed effect (that is, monthly air temperature) slopes in linear mixed-effects regression models of (a) incoming shortwave radiation, (b) enhanced vegetation index [EVI], and (c) water table depth with sites as random effect. Linear mixed effect models are fitted separately to each period. Error bars show 95% confidence intervals of estimated slope parameters and black circles indicate statistical significance at ɑ ≤ 0.05.

Extended Data Fig. 4 Monthly relationships between air temperature and net ecosystem CO2 exchange.

Monthly estimated fixed effect (that is, monthly air temperature [Ta]) slopes in linear mixed-effects regression models of monthly net ecosystem CO2 exchange (NEE) with sites considered as random effect. Asterisks indicate the level of statistical significance (* p ≤ 0.05; ** p ≤ 0.01; *** p ≤ 0.001). The error bars represent the 95% confidence intervals of the estimated slope parameters.

Extended Data Fig. 5 Differences between seasonally varying and uniform warming impacts on net ecosystem CO2 exchange.

Differences in estimated change in annual peatland net ecosystem exchange (ΔNEE) between the period 1951 to 1970 and 2001 to 2020 resulting from seasonally varying and seasonally uniform warming for areas with ≥ 5% peatland extent. Green areas indicate larger net CO2 loss for seasonally uniform warming and brown areas indicate smaller net CO2 loss for seasonally uniform warming.

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Supplementary Table 1 and Figs. 1–5.

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Helbig, M., Živković, T., Alekseychik, P. et al. Warming response of peatland CO2 sink is sensitive to seasonality in warming trends. Nat. Clim. Chang. 12, 743–749 (2022). https://doi.org/10.1038/s41558-022-01428-z

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