Abstract
This study develops a novel general framework to project the permafrost fate with rigorous uncertainty quantification to assess dominant sources. Borehole temperature records from three sites in the Russian western Arctic are used to constrain the uncertainty of a high-fidelity freeze-thaw model. Projections from 9 Global Climate Models (GCM) are stochastically downscaled to generate future trajectories of surface ground heat flux. Under the two emission scenarios SSP2-4.5 and SSP5-8.5, the projected average thawing depths by 2100 vary from 0.4 to 14.4 m or 2.1 to 17.7 m, and the increase in the top 10 m average temperature from 2015 to 2100 is 1.2–2.7°C or 1.9–3.0°C. The results show that the freeze-thaw model uncertainty can sometimes dominate over that of GCM outputs, calling for site-specific information to improve model accuracy. The framework is applicable for understanding permafrost degradation and related uncertainties at larger scales.
| Original language | English |
|---|---|
| Article number | e2024JF008168 |
| Journal | Journal of Geophysical Research: Earth Surface |
| Volume | 130 |
| Issue number | 10 |
| DOIs | |
| State | Published - Oct 2025 |
| Externally published | Yes |
Funding
This research was sponsored by the National Science Foundation (NSF) Office of Polar Programs Grants 1725654 (University of Michigan), 1724868 (Kansas State University), 1724633 (Georgia Tech), and 1724786 (Ohio State University), respectively. The NSF Navigating the New Arctic Program Track‐I Grants 2126792, 2126793, 2126797, 2126798 to the same co‐authors facilitated this work. V. Ivanov and V. Mazepa acknowledge the support from project RUB1‐7032‐EK‐11 funded by the U.S. Civilian Research & Development Foundation. V. Mazepa acknowledges the partial support from Grant RFBR‐19‐05‐00756 from the Russian Foundation for Basic Research. A. Vasiliev (project No. FWRZ‐2021‐0005), V. Valdayskikh (project No. FEUZ‐2024‐0011), and A. Sokolov (project No. FUWU‐2022‐0009) were supported by the state assignment of the Ministry of Science and Higher Education of the Russian Federation. D. Xu was supported by the U.S. Department of Energy (DOE), Office of Science, Biological and Environmental Research (BER) through the Earth System Model Development (ESMD) program area as part of the multi‐program collaborative integrated Coastal Modeling (ICoM) project (Grant KP1703110/75415). The team also appreciates the contribution of the Python Toolkit for Uncertainty Quantification (PyTUQ) from the Uncertainty Quantification group in Sandia National Laboratories. K. Sargsyan acknowledges the support by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research, Scientific Discovery through Advanced Computing (SciDAC) program. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC., a wholly owned subsidiary of Honeywell International, Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE‐NA‐0003525. We acknowledge the World Climate Research Programme, which, through its Working Group on Coupled Modeling, coordinated and promoted CMIP6. We thank the climate modeling groups for producing and making available their model output, the Earth System Grid Federation (ESGF) for archiving the data and providing access, and the multiple funding agencies who support CMIP6 and ESGF. This research was sponsored by the National Science Foundation (NSF) Office of Polar Programs Grants 1725654 (University of Michigan), 1724868 (Kansas State University), 1724633 (Georgia Tech), and 1724786 (Ohio State University), respectively. The NSF Navigating the New Arctic Program Track-I Grants 2126792, 2126793, 2126797, 2126798 to the same co-authors facilitated this work. V. Ivanov and V. Mazepa acknowledge the support from project RUB1-7032-EK-11 funded by the U.S. Civilian Research & Development Foundation. V. Mazepa acknowledges the partial support from Grant RFBR-19-05-00756 from the Russian Foundation for Basic Research. A. Vasiliev (project No. FWRZ-2021-0005), V. Valdayskikh (project No. FEUZ-2024-0011), and A. Sokolov (project No. FUWU-2022-0009) were supported by the state assignment of the Ministry of Science and Higher Education of the Russian Federation. D. Xu was supported by the U.S. Department of Energy (DOE), Office of Science, Biological and Environmental Research (BER) through the Earth System Model Development (ESMD) program area as part of the multi-program collaborative integrated Coastal Modeling (ICoM) project (Grant KP1703110/75415). The team also appreciates the contribution of the Python Toolkit for Uncertainty Quantification (PyTUQ) from the Uncertainty Quantification group in Sandia National Laboratories. K. Sargsyan acknowledges the support by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research, Scientific Discovery through Advanced Computing (SciDAC) program. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC., a wholly owned subsidiary of Honeywell International, Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA-0003525. We acknowledge the World Climate Research Programme, which, through its Working Group on Coupled Modeling, coordinated and promoted CMIP6. We thank the climate modeling groups for producing and making available their model output, the Earth System Grid Federation (ESGF) for archiving the data and providing access, and the multiple funding agencies who support CMIP6 and ESGF.
Keywords
- Bayesian downscaling
- parameter inference
- permafrost degradation
- uncertainty quantification