22 Dec 2020

22 Dec 2020

Review status: a revised version of this preprint is currently under review for the journal TC.

Thermal erosion patterns of permafrost peat plateaus in northern Norway

Léo C. P. Martin1,2, Jan Nitzbon3,4, Johanna Scheer1,5, Kjetil S. Aas1, Trond Eiken1, Moritz Langer3,4, Simon Filhol1, Bernd Etzelmüller1, and Sebastian Westermann1,6 Léo C. P. Martin et al.
  • 1Department of Geosciences, University of Oslo, Blindern, 0316 Oslo, Norway
  • 2Faculty of Geosciences, Utrecht University, Utrecht, The Netherlands
  • 3Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, Telegrafenberg A45, 14473 Potsdam, Germany
  • 4Geography Department, Humboldt-Universität zu Berlin, Unter den Linden 6, 10099 Berlin, Germany
  • 5Technical University of Denmark, Anker Engelunds Vej 1, Lyngby, Denmark
  • 6Center for Biogeochemistry in the Antropocene, Oslo, Norway

Abstract. Subarctic peatlands underlain by permafrost contain significant amounts of organic carbon and our ability to quantify the evolution of such permafrost landscapes in numerical models is critical to provide robust predictions of the environmental and climatic changes to come. Yet, the accuracy of large-scale predictions is so far hampered by small-scale physical processes that create a high spatial variability of surface ground thermal regime and thus of permafrost degradation patterns. In this regard, a better understanding of the small-scale interplay between microtopography and lateral fluxes of heat, water and snow can be achieved by field monitoring and process-based numerical modeling.

Here, we quantify the topographic changes of the Šuoššjávri peat plateau (Northern Norway) over a three-years period using repeated drone-based high-resolution photogrammetry. Our results show that edge degradation is the main process through which thermal erosion occurs and represents about 80 % of measured subsidence, while most of the inner plateau surface exhibits no detectable subsidence. Based on detailed investigation of eight zones of the plateau edge, we show that this edge degradation corresponds to a volumetric loss of 0.13 ± 0.07 m3 yr−1 m−1 (cubic meter per year and per meter of plateau circumference).

Using the CryoGrid land surface model, we show that these degradation patterns can be reproduced in a modeling framework that implements lateral redistribution of snow, subsurface water and heat, as well as ground subsidence due to melting of excess ice. We reproduce prolonged climate-driven edge degradation that is consistent with field observations and present a sensitivity test of the plateau degradation on snow depth over the plateau. Small snow depth variations (from 0 to 30 cm) result in highly different degradation behavior, from stability to fast degradation.

These results represent a new step in the modeling of climate-driven landscape development and permafrost degradation in highly heterogeneous landscapes such as peat plateaus. Our approach provides a physically based quantification of permafrost thaw with a new level of realism, notably, regarding feedback mechanisms between the dynamical topography and the lateral fluxes through which a small modification of the snow depth result in dramatic modifications of the permafrost degradation intensity. In this regard, these results also highlight the major control of snow pack characteristics on the ground thermal regime and the potential improvement that accurate snow representation and prediction could bring to projections of permafrost degradation.

Léo C. P. Martin et al.

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Léo C. P. Martin et al.

Léo C. P. Martin et al.


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Short summary
It is important to understand how permafrost landscapes respond to climate changes because their thaw can contribute to global warming. We investigate how a common permafrost morphology degrades using both field observations of the surface elevation and numerical modeling. We show that numerical models accounting for topographic changes related to permafrost degradation can reproduce the observed changes in the Nature and help to understand how parameters such as snow influence this phenomenon.