Preprints
https://doi.org/10.5194/egusphere-2022-630
https://doi.org/10.5194/egusphere-2022-630
 
25 Jul 2022
25 Jul 2022
Status: this preprint is open for discussion.

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

Brian Groenke1,3, Moritz Langer1,4, Jan Nitzbon2,4, Sebastian Westermann5, Guillermo Gallego3,6, and Julia Boike1,4 Brian Groenke et al.
  • 1Permafrost Research Section, Alfred Wegener Institute Helmholtz Center for Polar and Marine Research, Potsdam, Germany
  • 2Paleoclimate Research Section, Alfred Wegener Institute Helmholtz Center for Polar and Marine Research, Bremerhaven, Germany
  • 3Department of Electrical Engineering and Computer Science, Technical University of Berlin, Germany
  • 4Department of Geography, Humboldt University, Berlin, Germany
  • 5Department of Geosciences, University of Oslo, Oslo, Norway
  • 6Einstein Center Digital Future and Science of Intelligence Excellence Cluster, Berlin, Germany

Abstract. Long-term measurements of permafrost temperatures do not provide a complete picture of the Arctic subsurface thermal regime. Regions with warmer permafrost often show little to no long-term change in ground temperature due to the uptake and release of latent heat during freezing and thawing. Thus, regions where the least warming is observed may also be the most vulnerable to permafrost degradation. Since direct measurements of ice and liquid water contents in the permafrost layer are not widely available, thermal modeling of the subsurface plays a crucial role in understanding how permafrost responds to changes in the local energy balance. In this work, we first analyze trends in observed air and permafrost temperatures at four sites within the continuous permafrost zone, where we find substantial variation in the apparent relationship between long-term changes in permafrost temperatures (0.02 K yr−1 to 0.16 K yr−1) and air temperature (0.09 K yr−1 to 0.11 K yr−1). We then apply recently developed Bayesian inversion methods to link observed changes in borehole temperatures to unobserved changes in latent heat and thaw depth using a transient model of heat conduction with phase change. Our results suggest that the degree to which recent warming trends correlate with permafrost thaw and variations in latent heat is heavily dependent on both local soil properties as well as historical climatology. At the warmest site, a nine meter borehole near Ny-Ålesund, Svalbard, modeled annual maximum thaw depth increases by an average of (12 ± 1) cm K−1 rise in mean annual ground temperature. In stark contrast, modeled thaw rates for a borehole on Samoylov Island in the Lena River Delta (northeastern Siberia) appear far less sensitive to temperature change, with an almost negligible increase of (1 ± 1) cm K−1. Although our study is limited to just four sites, the results urge caution in the interpretation and comparison of warming trends in Arctic boreholes, indicating substantial uncertainty in their implications for the current and future thermal state of permafrost.

Brian Groenke et al.

Status: open (until 19 Sep 2022)

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Brian Groenke et al.

Model code and software

Study code repository Brian Groenke https://gitlab.awi.de/sparcs/analysis/boreholetrendstudy

Transient heat conduction model: CryoGrid.jl (v0.10.3) Brian Groenke, Jan Nitzbon https://doi.org/10.5281/zenodo.6801740

Brian Groenke et al.

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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 two 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.