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PI-Submitted Research Highlights for
Terrestrial Ecosystem Science Program

New Free Online Modeling Tool Broadens Permafrost Research

Elchin Jafarov


26 March 2019

User-friendly permafrost modeling toolbox aids students and researchers.

The Science 
Researchers provided new online modeling tools to aid the study of permafrost, which is thawing rapidly due to climate change but many of the dynamics are unknown. Permafrost covers a quarter of the land in the Northern Hemisphere and stores vast amounts of organic carbon that could contribute to climate warming. A team developed online, easily accessible permafrost process models—the Permafrost Modeling Toolbox (PMT)—and educational materials and provided online labs for use by students, scientists, and stakeholders. Complex, resource-intensive model development remained a barrier to permafrost research, until now.

The Impact
Permafrost—ground that stays frozen for more than two consecutive years—stores twice as much carbon as currently exists in Earth’s atmosphere; most of it has been frozen for up to hundreds of thousands of years. There is an urgent need to better understand and predict the thawing dynamics, climate feedbacks, and profound influences on hydrology and infrastructure. In addition to greenhouse gas and toxic metal releases, and altered groundwater flow, thawing permafrost significantly damages roads and infrastructure as it buckles beneath structures. Permafrost data are critically important for scientists, engineers, policymakers, indigenous communities and the general public. Evaluating current and future conditions requires modeling, which often requires code development and extensive computational resources. The PMT provides open-source numerical models of permafrost dynamics and additional Earth surface processes, and they are designed for users ranging from students studying thermal processes to industrial or academic researchers assessing environmental systems and climate feedbacks.

The toolbox currently includes three permafrost models of increasing complexity: (1) an empirical model (Air Frost Number model) that predicts the likelihood of permafrost occurring at a given location, (2) an analytical-empirical model (Kudryavtsev model) that provides solutions to thermodynamic equations, and (3) a numerical heat flow model (Geophysical Institute Permafrost Lab model). Interfaces allow information to be passed between models.

The PMT includes sets of sample inputs representing a variety of conditions and locations to enable immediate use of different permafrost models. Easy-to-use user interfaces and open-source, online access make PMT accessible to a broad audience well beyond the permafrost research community and supports linkages between permafrost dynamics and hydrological or landscape change.

Applications include calculating permafrost across Arctic sites, analyzing historic warming trends, mapping predicted permafrost, and comparing models with different complexities.

The PMT is part of a PermaModel collaboration between researchers at Los Alamos National Laboratory and the University of Colorado. The models are available through the Community Surface Dynamics Modeling System (CSDMS), an academic, industrial and government Earth modeling partnership.

BER Program Manager
Daniel Stover, SC-23.1

Principal Investigators
Elchin Jafarov,
Irina Overeem,

This work was supported by the National Science Foundation (award number 1503559) and by the Next-Generation Ecosystem Experiments (NGEE)–Arctic project, funded by the Office of Biological and Environmental Research within the U.S. Department of Energy Office of Science.

Overeem, I., E. Jafarov, K. Wang, K. Schaefer, S. Stewart, G. Clow, M. Piper, and Y. Elshorbany. “A modeling toolbox for permafrost landscapes.” Eos 99  2018). [DOI:10.1029/2018EO105155]

Related Links

This work was supported by the National Science Foundation (Award no. 1503559) and by the Next-Generation Ecosystem Experiments (NGEE) Arctic project, funded by the US Department of Energy, Office of Biological and Environmental Research.

Jafarov, Elchin (Los Alamos National Laboratory); and Overeem, Irina (University of Colorado)


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