EGU24-17224, updated on 11 Mar 2024
EGU General Assembly 2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Statistical Modelling of Permafrost Subsidence Based on High-resolution InSAR Data

Zhijun Liu1, Barbara Widhalm2, Annett Bartsch2, Thomas Kleinen1, and Victor Brovkin1
Zhijun Liu et al.
  • 1Max Planck Institute for Meteorology, Climate Dynamics, Hamburg, Germany (
  • 2b.geos, Research and Development, Korneuburg, Austria

The northern high latitudes are warming much faster than the rest of the planet. While gradual thaw of permafrost is accounted for in the recent generation of the Earth System Models (ESMs), consequences of the abrupt thaw of permafrost and the subsequent greenhouse gas release are not yet taken into consideration. However, an abrupt thaw of very small fraction of the northern permafrost region can lead to significant carbon release and subsequent global warming (Turetsky et al. 2020).

An in-depth analysis of fine-scale permafrost subsidence processes is crucial for improved representation of abrupt thawing in simulations. Currently, permafrost subsidence is only taken into consideration in a few models, where subsidence is described in a deterministic process-based approach. This approach overlooks the high spatial heterogeneity in fine-scale permafrost processes.

Recent advancements in satellite technology allow the acquisition of Interferometric Synthetic Aperture Radar (InSAR) data on permafrost vertical displacement at meter-scale resolution. We conducted a case study on the Yamal Peninsula, Russia, where we compare permafrost subsidence data from Sentinel-1 with various potential driving factors, including climate forcing data from ERA5-Land and geomorphology data from MERIT Hydro. A statistical approach is taken to analyse the relationships between different factors and their contributions to permafrost subsidence. The results demonstrate the high heterogeneity of permafrost subsidence in the form of probability distribution functions at ESM-scale resolution. Eventually, our study aims to obtain a parameterization for pan-Arctic permafrost subsidence that can be implemented into the ICON-ESM in order to close the gap in permafrost modelling between process- and ESM-scale.


Reference: Turetsky, M.R., Abbott, B.W., Jones, M.C. et al. Carbon release through abrupt permafrost thaw. Nat. Geosci. 13, 138–143 (2020).

How to cite: Liu, Z., Widhalm, B., Bartsch, A., Kleinen, T., and Brovkin, V.: Statistical Modelling of Permafrost Subsidence Based on High-resolution InSAR Data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17224,, 2024.