Articles | Volume 17, issue 8
https://doi.org/10.5194/tc-17-3505-2023
https://doi.org/10.5194/tc-17-3505-2023
Research article
 | 
24 Aug 2023
Research article |  | 24 Aug 2023

Investigating the thermal state of permafrost with Bayesian inverse modeling of heat transfer

Brian Groenke, Moritz Langer, Jan Nitzbon, Sebastian Westermann, Guillermo Gallego, and Julia Boike

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Cited articles

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Barrow 2: N. Meadow Lake No.2/NML-2, https://permafrost.gi.alaska.edu/site/br2 (last access: 6 July 2022), 2021. a, b, c
Berliner, L. M.: Physical-Statistical Modeling in Geophysics, J. Geophys. Res.-Atmos., 108, 8776, https://doi.org/10.1029/2002JD002865, 2003. a, b
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Biskaborn, B. K., Lanckman, J.-P., Lantuit, H., Elger, K., Streletskiy, D. A., Cable, W. L., and Romanovsky, V. E.: The new database of the Global Terrestrial Network for Permafrost (GTN-P), Earth Syst. Sci. Data, 7, 245–259, https://doi.org/10.5194/essd-7-245-2015, 2015. a, b
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Short summary
It is now well known from long-term temperature measurements that Arctic permafrost, i.e., ground that remains continuously frozen for at least 2 years, is warming in response to climate change. Temperature, however, only tells half of the story. In this study, we use computer modeling to better understand how the thawing and freezing of water in the ground affects the way permafrost responds to climate change and what temperature trends can and cannot tell us about how permafrost is changing.
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