Articles | Volume 23, issue 8
https://doi.org/10.5194/bg-23-2815-2026
https://doi.org/10.5194/bg-23-2815-2026
Research article
 | 
24 Apr 2026
Research article |  | 24 Apr 2026

A top-down evaluation of bottom-up estimates to reduce uncertainty in methane emissions from Arctic wetlands

Luana S. Basso, Goran Georgievski, Victor Brovkin, Christian Beer, Christian Rödenbeck, and Mathias Göckede

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Cited articles

Basso, L. S., Georgievski, G., Brovkin, V., Beer, C., Rödenbeck, C., and Göckede, M.: Estimates of Arctic-boreal wetland methane emissions from bottom-up and top-down approaches using the JSBACH v3.2 model and the Jena CarboScope Global Atmospheric Inversion System, Zenodo [data set], https://doi.org/10.5281/zenodo.19201813, 2026. 
Bergman, I., Klarqvist, M., and Nilsson, M.: Seasonal variation in rates of methane production from peat of various botanical origins: effects of temperature and substrate quality, FEMS Microbiol. Ecol., 33, 181–189, https://doi.org/10.1111/j.1574-6941.2000.tb00740.x, 2000. 
Bernard, J., Salmon, E., Saunois, M., Peng, S., Serrano-Ortiz, P., Berchet, A., Gnanamoorthy, P., Jansen, J., and Ciais, P.: Satellite-based modeling of wetland methane emissions on a global scale (SatWetCH4 1.0), Geosci. Model Dev., 18, 863–883, https://doi.org/10.5194/gmd-18-863-2025, 2025. 
Beven, K. J. and Kirkby, M. J.: A physically based, variable contributing area model of basin hydrology/Un modèle à base physique de zone d'appel variable de l'hydrologie du bassin versant, Hydrol. Sci. B., 24, 43–69, https://doi.org/10.1080/02626667909491834, 1979. 
Chinta, S., Gao, X., and Zhu, Q.: Machine Learning Driven Sensitivity Analysis of E3SM Land Model Parameters for Wetland Methane Emissions, J. Adv. Model. Earth Sy., 16, https://doi.org/10.1029/2023MS004115, 2024. 
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This study examines how combining atmospheric inversion with process-based modelling can reduce discrepancies in estimates of Arctic wetland CH4 emissions. We conducted a series of inversion experiments, each incorporating CH4 wetland fluxes from process-based models with different CH4 production parameterizations. Our results showed that no single parameterization captures the complexity of Arctic–Boreal emissions; instead, region-specific adjustments are needed to reduce discrepancies.
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