Abstract
Ground heat flux (G0) is a key component of the land-surface energy balance of high-latitude regions. Despite its crucial role in controlling permafrost degradation due to global warming, G0 is sparsely measured and not well represented in the outputs of global scale model simulation. In this study, an analytical heat transfer model is tested to reconstruct G0 across seasons using soil temperature series from field measurements, Global Climate Model, and climate reanalysis outputs. The probability density functions of ground heat flux and of model parameters are inferred using available G0 data (measured or modeled) for snow-free period as a reference. When observed G0 is not available, a numerical model is applied using estimates of surface heat flux (dependent on parameters) as the top boundary condition. These estimates (and thus the corresponding parameters) are verified by comparing the distributions of simulated and measured soil temperature at several depths. Aided by state-of-the-art uncertainty quantification methods, the developed G0 reconstruction approach provides novel means for assessing the probabilistic structure of the ground heat flux for regional permafrost change studies.
| Original language | English |
|---|---|
| Article number | e2023EA003435 |
| Journal | Earth and Space Science |
| Volume | 11 |
| Issue number | 3 |
| DOIs | |
| State | Published - Mar 2024 |
| Externally published | Yes |
Funding
This research is 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. V. Valdayskikh was supported by the state task of the Ministry of Education and Science of the Russian Federation, project no. FEUZ 2023‐0019. The team is highly grateful for the design of field monitoring stations to Yuriy Trubnikov. The field support by Vyacheslav Osokin, Grigoriy Popov, and Andrey Baryshnikov is acknowledged. The team also appreciates the development of the UQ Toolkit (UQTk) from the Uncertainty Quantification group in Sandia National Laboratories. 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 Modelling, 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. The authors also thank the three anonymous reviewers for their valuable comments that improved this manuscript. This research is 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. V. Valdayskikh was supported by the state task of the Ministry of Education and Science of the Russian Federation, project no. FEUZ 2023-0019. The team is highly grateful for the design of field monitoring stations to Yuriy Trubnikov. The field support by Vyacheslav Osokin, Grigoriy Popov, and Andrey Baryshnikov is acknowledged. The team also appreciates the development of the UQ Toolkit (UQTk) from the Uncertainty Quantification group in Sandia National Laboratories. 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 Modelling, 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. The authors also thank the three anonymous reviewers for their valuable comments that improved this manuscript.
| Funders | Funder number |
|---|---|
| Earth System Grid Federation | CMIP6 |
| National Science Foundation | |
| U.S. Department of Energy | |
| Office of Polar Programs | 1725654 |
| CRDF Global | RFBR‐19‐05‐00756 |
| National Nuclear Security Administration | DE‐NA‐0003525 |
| Ohio State University | 2126793, 2126792, 2126798, 2126797, RUB1-7032-EK-11 |
| University of Michigan | 1724868 |
| Kansas State University | 1724786, 1724633 |
| Russian Foundation for Basic Research | |
| Ministry of Education and Science of the Russian Federation | FEUZ 2023-0019 |
Keywords
- ground heat flux
- parameter inference
- permafrost
- soil temperature
- surrogate modeling