Permafrost Modeling - where does permafrost occur?

1.5 hrs
Run online
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This lab is part of the series Permafrost. Others in this series are:

    Irina Overeem at CSDMS, University of Colorado.
    Mark Piper at CSDMS, University of Colorado.
    Kang Wang at East China Normal University.
    Elchin Jafarov at Los Alamos National Labs, NM.

What is permafrost and how do you make a first-order prediction about permafrost occurrence. This is lesson 1 in a mini-course on permafrost, this lab uses the Air Frost Number and annual temperature data to predict permafrost occurrence.

Classroom organization
This lab is the first in a series of introduction to permafrost process modeling, designed for inexperienced users. In this first lesson, we explore the Air Frost Number model and learn to use such models in the CSDMS Python Model tool (Pymt). We implemented a basic configuration of the Air Frost Number (as formulated by Nelson and Outcalt in 1987). This lab is designed to gain some experience with running a numerical model, changing model inputs, and analyzing model output. Specifically, this first lab looks at what controls permafrost occurrence and compares the occurrence of permafrost in Russia.

Basic theory on the Air Frost Number is presented in File:FrostNumberModel Lecture1.pptx.

This lab will likely take ~ 1,5 hours to complete in the classroom. This time assumes you are unfamiliar with the Pymt and need to learn setting parameters, and looking at output (otherwise it will be much faster).

If you have never used the Python Modeling Tool (Pymt), learn how to use it at: The Pymt allows you to set up simulations, and run them and then analyze data using Python.

You can create an account at CSDMS JupyterHub and test the Jupyter Notebook for this lab. Please follow the instruction at: If you are a faculty at an academic institution, it is possible to work with us to get temporary teaching accounts. Work directly with us by emailing:

Download associated file: FrostNumberModel Lecture1.pptx
Presentation describes basic theory on the Air Frost Number

Learning objectives
  • Familiarize with a basic configuration of the Air Frost Number Model
  • Make small changes to key input parameters
  • See how to write a loop for calculations over a time series
  • Hands-on experience with visualizing output in Python
Key concepts
  • What is the primary control on the occurrence of permafrost
  • Freezing and thawing day indices and how to approximate these
  • Where in Russia permafrost occurs
  • Critical evaluation of what the Air Frost number approximates

Lab notes
Figure 1. Screenshot of the pymt notebook
>> Open a new browser window and open the Pymt read the docs page at:


These labs are developed with support from NSF Grant 1503559, ‘Towards a Tiered Permafrost Modeling Cyberinfrastructure’ and are part of the Permafrost Modeling Toolbox,

  • Nelson, F.E., Outcalt, S.I., 1987. A computational method for prediction and prediction and regionalization of permafrost. Arct. Alp. Res. 19, 279–288.
  • Janke, J., Williams, M., Evans, A., 2012. A comparison of permafrost prediction models along a section of Trail Ridge Road, RMNP, CO. Geomorphology 138, 111-120.