EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Climate extremes relevant for permafrost degradation

Goran Georgievski1,2, Stefan Hagemann1, Dmitry Sein2, Dmitry Drozdov3,4, Andrew Gravis4, Vladimir Romanovsky5, Dmitry Nicolsky5, Alexandru Onaca6, Florina Ardelean6, Marinela Chețan6, and Andrei Dornik6
Goran Georgievski et al.
  • 1Helmholtz-Zentrum Geesthacht, Institute of Coastal Research, Geesthacht, Germany (
  • 2Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research
  • 3Tyumen State Universit
  • 4Earth Cryosphere Institute, Siberian Branch, Russian Academy of Sciences
  • 5University of Alaska, Geophysical Institute Permafrost Laboratory
  • 6West University of Timisoara

During the past several decades, Arctic regions warmed almost twice as much as the global average temperature. Simultaneously in the high northern latitudes, observations indicate a decline in permafrost extend and landscape modifications due to permafrost degradation. Climate projections suggest an accelerated soil warming, and consequently deepening of the active layer thickness in the near future. Except air temperature, two other parameters i.e. precipitation and snow depth are the most important climatic parameters affecting the thermal state and extend of the permafrost. The key research question of this study is whether or not certain climatic conditions can be identified that can be considered as an extreme event relevant for permafrost degradation. Here we apply data mining techniques on meteorological re-analysis to develop a coherent framework for the identification of extreme climate conditions relevant for active soil layer deepening and a decline of permafrost extend.
Several key types of events have been classified based on various combinations of temperature, precipitation and snow depth statistics. Then, the respective events have been identified in ERA-Interim reanalysis and evaluated against in situ observations in West Siberia region. The evaluation proved that the developed algorithm could successfully detect relevant extreme climate conditions in meteorological re-analysis dataset. It also indicated possibilities to improve the algorithm by refining definitions of extreme events. Refinement of algorithm is currently work in progress as well as the evaluation against satellite observations and a hierarchy of numerical models. Nevertheless, the method is applicable for all kinds of gridded climatological datasets that contain air temperature, precipitation and snow depth.

This work is funded in the frame of ERA-Net plus Russia. TSU is supported by MOSC RF # 14.587.21.0048 (RFMEFI58718X0048), AWI and HZG are supported by BMBF (Grant no. 01DJ18016A and 01DJ18016B), and WUT by a grant of the Romanian National Authority for Scientific Research and Innovation, CCDI-UEFISCDI, project number ERANET-RUS-PLUS-SODEEP, within PNCD III

How to cite: Georgievski, G., Hagemann, S., Sein, D., Drozdov, D., Gravis, A., Romanovsky, V., Nicolsky, D., Onaca, A., Ardelean, F., Chețan, M., and Dornik, A.: Climate extremes relevant for permafrost degradation, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-16115,, 2020


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  • CC1: Comment on EGU2020-16115, Rúna Magnússon, 05 May 2020

    Dear Goran,

    Very interesting work - I did not manage to comment during the session earlier, which is why I am trying this way. 

    I wondered how you interpret these identified key events that appear influential in determining active layer thickness. High snow height followed by a warm summer would seem to be an effect of high soil moisture and high temperature, resulting in high heat flux? But how would you interpret the role of a warm summer followed by early and high snow height? What is the mechanism there that causes increased active layer thickness? 

    I would also be interested to hear what data mining techniques you have used to characterize and identify key events, if you're willing to elaborate. 

    Thank you in advance,
    Rúna Magnússon (Wageningen University)

    • AC1: Reply to CC1, Goran Georgievski, 05 May 2020

      Dear Rúna,

      Thank you for your comment and questions. Definition of extreme climate is given at the slide 3 which coincide with 3th minute of my presentation/talk. Here is how are they defined:
      1) Warm summer (3 consecutive months with mean temperature higher than JJA climatology), followed by early deep snow (3 consecutive months afterwards with mean snow depth higher than SON climatology)
      2) Warm summer (3 consecutive months with mean temperature higher than JJA climatology) preceded by late deep snow (3 consecutive months with mean snow depth higher than MAM climatology)
      3) Precipitation twice as high as the monthly climatology
      4) Annual mean temperature - 2 degrees higher than previous 5 years running mean

      Suggestions for those classification/definitions came from the experts working in field and doing permafrost process-study modelling. Therefore, to answer your question about underlying mechanisms, now I can only speculate, but I prefer talk to my project partners. However, definitions are slightly adopted to fit available data, which is here ERA-Interim meteorological reanalysis. Algorithm is written in Python and on basis of Boolean logic, searches for grid-boxes for which these conditions are satisfied.

      • CC2: Reply to AC1, Rúna Magnússon, 05 May 2020

        Dear Goran,

        thank you for your reply, interesting. I understand. For my own research site (lowland tundra, north-eastern Siberia) we have a similar expectation for the effectof warm summers preceded by high snowfall and summers with high precipitation on permafrost. We never looked into early snowfall preceded by a warm summer, which is why it caught my interest. 

        Again, thank you for your reply!