High-resolution spatial patterns and drivers of terrestrial ecosystem carbon dioxide, methane, and nitrous oxide fluxes in the tundra

Virkkala, Anna-Maria; Niittynen, Pekka; Kemppinen, Julia; Marushchak, Maija E.; Voigt, Carolina; Hensgens, Geert; Kerttula, Johanna; Happonen, Konsta; Tyystjärvi, Vilna; Biasi, Christina; Hultman, Jenni; Rinne, Janne; Luoto, Miska

doi:https://doi.org/10.5194/bg-21-335-2024

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.

https://doi.org/10.5194/bg-21-335-2024

© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 21, issue 2

Research article

| Highlight paper

|

19 Jan 2024

Research article | Highlight paper |

| 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, Pekka Niittynen, Julia Kemppinen, Maija E. Marushchak, Carolina Voigt, Geert Hensgens, Johanna Kerttula, Konsta Happonen, Vilna Tyystjärvi, Christina Biasi, Jenni Hultman, Janne Rinne, and Miska Luoto

Supplement

https://doi.org/10.5194/bg-21-335-2024-supplement

Data sets

Data and code for "High-resolution spatial patterns and drivers of terrestrial ecosystem carbon dioxide, methane, and nitrous oxide fluxes in the tundra" [Data set]. Anna-Maria Virkkala, Pekka Niittynen, Julia Kemppinen, Maija E. Marushchak, Carolina Voigt, Geert Hensgens, Johanna Kerttula, Konsta Happonen, Vilna Tyystjärvi, Christina Biasi, Jenni Hultman, Janne Rinne, and Miska Luoto https://doi.org/10.5281/zenodo.8369550

Model code and software

Data and code for "High-resolution spatial patterns and drivers of terrestrial ecosystem carbon dioxide, methane, and nitrous oxide fluxes in the tundra" [Data set]. Anna-Maria Virkkala, Pekka Niittynen, Julia Kemppinen, Maija E. Marushchak, Carolina Voigt, Geert Hensgens, Johanna Kerttula, Konsta Happonen, Vilna Tyystjärvi, Christina Biasi, Jenni Hultman, Janne Rinne, and Miska Luoto https://doi.org/10.5281/zenodo.8369550

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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.