Articles | Volume 21, issue 2
https://doi.org/10.5194/bg-21-335-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-21-335-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| Highlight paper
| 
19 Jan 2024
Research article | Highlight paper |
| 19 Jan 2024
| 19 Jan 2024
High-resolution spatial patterns and drivers of terrestrial ecosystem carbon dioxide, methane, and nitrous oxide fluxes in the tundra
Anna-Maria Virkkala
CORRESPONDING AUTHOR
Woodwell Climate Research Center, Falmouth, 149 Woods Hole Road, MA 02540-1644, USA
Department of Geosciences and Geography, University of Helsinki, Gustaf Hällströmin katu 2, 00014 Helsinki, Finland
Pekka Niittynen
Department of Biological and Environmental Science, University of Jyväskylä, P.O. Box 35, 40014 Jyväskylä, Finland
Julia Kemppinen
Geography Research Unit, University of Oulu, P.O. Box 8000, 90014 Oulu, Finland
Maija E. Marushchak
Department of Environmental and Biological Sciences, University of Eastern Finland, P.O. Box 1627, 70211 Kuopio, Finland
Carolina Voigt
Department of Environmental and Biological Sciences, University of Eastern Finland, P.O. Box 1627, 70211 Kuopio, Finland
Geert Hensgens
Department of Earth and Climate, Vrije Universiteit Amsterdam, De Boelelaan 1085, 1081 HV, Amsterdam, the Netherlands
Johanna Kerttula
Department of Environmental and Biological Sciences, University of Eastern Finland, P.O. Box 1627, 70211 Kuopio, Finland
Konsta Happonen
Youth Research Society, Kumpulantie 3 A, 00520 Helsinki, Finland
Vilna Tyystjärvi
Finnish Meteorological Institute, Climate System Research Unit, Erik Palménin aukio 1, 00560 Helsinki, Finland
Christina Biasi
Department of Environmental and Biological Sciences, University of Eastern Finland, P.O. Box 1627, 70211 Kuopio, Finland
Department of Ecology, University of Innsbruck, Sternwartstraße 15, 6020 Innsbruck, Austria
Jenni Hultman
Natural Resources Institute Finland, Latokartanonkaari 9, 00790 Helsinki, Finland
Department of Microbiology, University of Helsinki, Viikinkaari 9, 00014 Helsinki, Finland
Janne Rinne
Natural Resources Institute Finland, Latokartanonkaari 9, 00790 Helsinki, Finland
Miska Luoto
Department of Geosciences and Geography, University of Helsinki, Gustaf Hällströmin katu 2, 00014 Helsinki, Finland
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Cited
21 citations as recorded by crossref.
- Light and dark conditions control the nitrous oxide uptake and emission dynamics in a subarctic, nutrient-poor permafrost peatland N. Triches et al. https://doi.org/10.1038/s43247-026-03698-3
- WetCH4: a machine-learning-based upscaling of methane fluxes of northern wetlands during 2016–2022 Q. Ying et al. https://doi.org/10.5194/essd-17-2507-2025
- Discovery of Eremiobacterota with nifH homologues in tundra soil I. Pessi et al. https://doi.org/10.1111/1758-2229.13277
- An expert survey on chamber measurement techniques and data handling procedures for methane fluxes K. Jentzsch et al. https://doi.org/10.5194/essd-17-2331-2025
- Spatial modelling of polycyclic aromatic hydrocarbon distribution in a Canadian ice wedge polygon tundra landscape R. Lodi et al. https://doi.org/10.1016/j.scitotenv.2025.181156
- Environmental drivers constraining the seasonal variability in satellite-observed and modelled methane at northern high latitudes E. Kivimäki et al. https://doi.org/10.5194/bg-22-5193-2025
- A hybrid approach for optimizing soil greenhouse gas flux predictions: integrating machine learning outputs into hyperparameter-tuned regression trees M. RASTGOU et al. https://doi.org/10.1016/j.pedsph.2025.10.002
- Remote sensing of vegetation trends: A review of methodological choices and sources of uncertainty H. Darabi et al. https://doi.org/10.1016/j.rsase.2025.101500
- Practical guidelines for reproducible N2O flux chamber measurements in nutrient-poor ecosystems N. Triches et al. https://doi.org/10.5194/amt-18-3407-2025
- High-resolution remote sensing and machine-learning-based upscaling of methane fluxes: a case study in the Western Canadian tundra K. Ivanova et al. https://doi.org/10.5194/bg-23-233-2026
- Variation in Soil CO2 Fluxes across Land Cover Mosaic in Typical Tundra of the Taimyr Peninsula, Siberia A. Panov et al. https://doi.org/10.3390/atmos15060698
- Challenges in the use of local data for regional scale mapping of C and N stocks in the continuous permafrost zone at the Yukon Coastal Plain J. Wagner et al. https://doi.org/10.5194/soil-12-113-2026
- Long-term cessation of grazing reduces net carbon uptake in northern grassland and heathland C. Klopsch et al. https://doi.org/10.1016/j.agee.2026.110441
- Upscaling of soil methane fluxes from topographic attributes derived from a digital elevation model in a cold temperate mountain forest S. Paul et al. https://doi.org/10.5194/bg-23-683-2026
- The significant role of vegetation activity in regulating wetland methane emission in China B. Liang et al. https://doi.org/10.1016/j.envres.2025.120773
- Microclimate, an important part of ecology and biogeography J. Kemppinen et al. https://doi.org/10.1111/geb.13834
- A practical metric for estimating the current climate forcing of natural mires J. Rinne et al. https://doi.org/10.1088/1748-9326/add607
- Contrasting carbon dynamics in grazed and flood-prone grasslands on mineral and degraded peat soils A. Lindenberger et al. https://doi.org/10.1016/j.agee.2026.110531
- Svalbard winter warming is reaching melting point J. Bradley et al. https://doi.org/10.1038/s41467-025-60926-8
- Coarse land cover datasets bias Arctic-Boreal wetland methane budgets J. Hashemi et al. https://doi.org/10.1038/s43247-025-02963-1
- Spatiotemporal variability and environmental controls on aquatic methane emissions in an Arctic permafrost catchment M. Thayne et al. https://doi.org/10.5194/bg-23-477-2026
21 citations as recorded by crossref.
- Light and dark conditions control the nitrous oxide uptake and emission dynamics in a subarctic, nutrient-poor permafrost peatland N. Triches et al. https://doi.org/10.1038/s43247-026-03698-3
- WetCH4: a machine-learning-based upscaling of methane fluxes of northern wetlands during 2016–2022 Q. Ying et al. https://doi.org/10.5194/essd-17-2507-2025
- Discovery of Eremiobacterota with nifH homologues in tundra soil I. Pessi et al. https://doi.org/10.1111/1758-2229.13277
- An expert survey on chamber measurement techniques and data handling procedures for methane fluxes K. Jentzsch et al. https://doi.org/10.5194/essd-17-2331-2025
- Spatial modelling of polycyclic aromatic hydrocarbon distribution in a Canadian ice wedge polygon tundra landscape R. Lodi et al. https://doi.org/10.1016/j.scitotenv.2025.181156
- Environmental drivers constraining the seasonal variability in satellite-observed and modelled methane at northern high latitudes E. Kivimäki et al. https://doi.org/10.5194/bg-22-5193-2025
- A hybrid approach for optimizing soil greenhouse gas flux predictions: integrating machine learning outputs into hyperparameter-tuned regression trees M. RASTGOU et al. https://doi.org/10.1016/j.pedsph.2025.10.002
- Remote sensing of vegetation trends: A review of methodological choices and sources of uncertainty H. Darabi et al. https://doi.org/10.1016/j.rsase.2025.101500
- Practical guidelines for reproducible N2O flux chamber measurements in nutrient-poor ecosystems N. Triches et al. https://doi.org/10.5194/amt-18-3407-2025
- High-resolution remote sensing and machine-learning-based upscaling of methane fluxes: a case study in the Western Canadian tundra K. Ivanova et al. https://doi.org/10.5194/bg-23-233-2026
- Variation in Soil CO2 Fluxes across Land Cover Mosaic in Typical Tundra of the Taimyr Peninsula, Siberia A. Panov et al. https://doi.org/10.3390/atmos15060698
- Challenges in the use of local data for regional scale mapping of C and N stocks in the continuous permafrost zone at the Yukon Coastal Plain J. Wagner et al. https://doi.org/10.5194/soil-12-113-2026
- Long-term cessation of grazing reduces net carbon uptake in northern grassland and heathland C. Klopsch et al. https://doi.org/10.1016/j.agee.2026.110441
- Upscaling of soil methane fluxes from topographic attributes derived from a digital elevation model in a cold temperate mountain forest S. Paul et al. https://doi.org/10.5194/bg-23-683-2026
- The significant role of vegetation activity in regulating wetland methane emission in China B. Liang et al. https://doi.org/10.1016/j.envres.2025.120773
- Microclimate, an important part of ecology and biogeography J. Kemppinen et al. https://doi.org/10.1111/geb.13834
- A practical metric for estimating the current climate forcing of natural mires J. Rinne et al. https://doi.org/10.1088/1748-9326/add607
- Contrasting carbon dynamics in grazed and flood-prone grasslands on mineral and degraded peat soils A. Lindenberger et al. https://doi.org/10.1016/j.agee.2026.110531
- Svalbard winter warming is reaching melting point J. Bradley et al. https://doi.org/10.1038/s41467-025-60926-8
- Coarse land cover datasets bias Arctic-Boreal wetland methane budgets J. Hashemi et al. https://doi.org/10.1038/s43247-025-02963-1
- Spatiotemporal variability and environmental controls on aquatic methane emissions in an Arctic permafrost catchment M. Thayne et al. https://doi.org/10.5194/bg-23-477-2026
Saved (final revised paper)
Latest update: 15 Jun 2026
Editorial statement
Arctic greenhouse gas fluxes are key for climate feedback but the Arctic greenhouse gas balance is poorly constrained due to a limited understanding of the spatial variation in these fluxes. This study combines extensive chamber-based flux measurements and remote sensing data to develop a machine-learning model to predict greenhouse gas fluxes across a tundra landscape in Finland. The analysis revealed that the system was a net greenhouse gas sink and showed widespread CH4 uptake in upland vegetation types, almost surpassing the high wetland CH4 emissions at the landscape scale.
Arctic greenhouse gas fluxes are key for climate feedback but the Arctic greenhouse gas balance...
Short summary
Arctic greenhouse gas (GHG) fluxes of CO2, CH4, and N2O are important for climate feedbacks. We combined extensive in situ measurements and remote sensing data to develop machine-learning models to predict GHG fluxes at a 2 m resolution across a tundra landscape. The analysis revealed that the system was a net GHG sink and showed widespread CH4 uptake in upland vegetation types, almost surpassing the high wetland CH4 emissions at the landscape scale.
Arctic greenhouse gas (GHG) fluxes of CO2, CH4, and N2O are important for climate feedbacks. We...
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