Articles | Volume 11, issue 3
https://doi.org/10.5194/essd-11-1263-2019
https://doi.org/10.5194/essd-11-1263-2019
Review article
 | 
22 Aug 2019
Review article |  | 22 Aug 2019

Monthly gridded data product of northern wetland methane emissions based on upscaling eddy covariance observations

Olli Peltola, Timo Vesala, Yao Gao, Olle Räty, Pavel Alekseychik, Mika Aurela, Bogdan Chojnicki, Ankur R. Desai, Albertus J. Dolman, Eugenie S. Euskirchen, Thomas Friborg, Mathias Göckede, Manuel Helbig, Elyn Humphreys, Robert B. Jackson, Georg Jocher, Fortunat Joos, Janina Klatt, Sara H. Knox, Natalia Kowalska, Lars Kutzbach, Sebastian Lienert, Annalea Lohila, Ivan Mammarella, Daniel F. Nadeau, Mats B. Nilsson, Walter C. Oechel, Matthias Peichl, Thomas Pypker, William Quinton, Janne Rinne, Torsten Sachs, Mateusz Samson, Hans Peter Schmid, Oliver Sonnentag, Christian Wille, Donatella Zona, and Tuula Aalto

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Cited articles

Aalto, J., Karjalainen, O., Hjort, J., and Luoto, M.: Statistical Forecasting of Current and Future Circum-Arctic Ground Temperatures and Active Layer Thickness, Geophys. Res. Lett., 45, 4889–4898, https://doi.org/10.1029/2018GL078007, 2018. 
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Short summary
Here we develop a monthly gridded dataset of northern (> 45 N) wetland methane (CH4) emissions. The data product is derived using a random forest machine-learning technique and eddy covariance CH4 fluxes from 25 wetland sites. Annual CH4 emissions from these wetlands calculated from the derived data product are comparable to prior studies focusing on these areas. This product is an independent estimate of northern wetland CH4 emissions and hence could be used, e.g. for process model evaluation.
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