Lab-0002

Permafrost Modeling - the Active Layer

Model
Kudryavtsev Model

Duration
1.5 hrs

Updated
2020/03/20

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This lab is part of the series Permafrost. Others in this series are:

• Permafrost Modeling - where does permafrost occur? (1 of 4)
• Permafrost Modeling - the Active Layer (2 of 4)
• Permafrost Modeling - making maps from gridded climate data (3 of 4)
• Permafrost Modeling - looking at future permafrost with climate models (4 of 4)

Contributor(s)

    Irina Overeem at CSDMS, University of Colorado.

    Mark Piper at CSDMS, University of Colorado.

    Kang Wang at East China Normal University.

    Scott Stewart at CSDMS, University of Colorado.

    Elchin Jafarov at Los Alamos National Labs, NM.

Introduction

The Kudryavtsev et al. (1974), or Ku model, presents an approximate solution of the Stefan problem. The model provides a steady-state solution under the assumption of sinusoidal air temperature forcing. It considers snow, vegetation, and soil layers as thermal damping to variation of air temperature. The layer of soil is considered to be a homogeneous column with different thermal properties in the frozen and thawed states. The main outputs are annual maximum frozen/thaw depth and mean annual temperature at the top of permafrost (or at the base of the active layer). It can be applied over a wide variety of climatic conditions.

Classroom organization
This lab is the second in a series of introduction to permafrost process modeling, designed for inexperienced users. In this second lesson, we explore the Kudryavstev model and learn to use this model in the CSDMS Python Modeling Tool (Pymt). We implemented the Kudryavstev model (as formulated in Anisimov et al.1997). It is dubbed the Ku-model.

This series of labs is designed for inexperienced modelers to gain some experience with running a numerical model, changing model inputs, and analyzing model output. Specifically, this lab looks at what controls soil temperature and active layer thickness and compares model output with observed longterm data collected at permafrost active layer thickness monitoring sites in Fairbanks and Barrow, Alaska.

Basic theory on the Kudryavstev model is presented in these slides File:KudryavtsevModel Lecture2.pptx

This lab will likely take ~ 1,5 hours to complete in the classroom. This time assumes you now have gained some familiarity with the Pymt and have learned how to set parameters, save runs, download data and look at output. If this is not the case, either start with Lab 1 in this series, or learn how to use Pymt here. The Pymt allows you to set up simulations and run notebooks.

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: csdms@colorado.edu

Download associated file: KudryavtsevModel Lecture2.pptx
Basic theory on the Kudryavstev model is presented in these slides

Learning objectives
Skills

• 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

Caption of the figure

Here are the instructions

Requirements
Software is required

References