Articles | Volume 19, issue 10
https://doi.org/10.5194/tc-19-4211-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-19-4211-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
The thermal state of permafrost under climate change on the Qinghai–Tibet Plateau (1980–2022): a case study of the West Kunlun
Jianting Zhao
School of Geographical Sciences, Nanjing University of Information Science &Technology, Nanjing 210044, China
Department of Physical Geography and Ecosystem Science, Lund University, Lund 22362, Sweden
School of Geographical Sciences, Nanjing University of Information Science &Technology, Nanjing 210044, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 101408, China
Cryosphere Research Station on the Qinghai–Tibet Plateau, State Key Laboratory of Cryospheric Sciences, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
Zhe Sun
School of Geographical Sciences, Nanjing University of Information Science &Technology, Nanjing 210044, China
School of Geography and Planning, Nanning Normal University, Nanning 530001, China
Guojie Hu
Cryosphere Research Station on the Qinghai–Tibet Plateau, State Key Laboratory of Cryospheric Sciences, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
Cryosphere Research Station on the Qinghai–Tibet Plateau, State Key Laboratory of Cryospheric Sciences, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
Minxuan Xiao
School of Geographical Sciences, Nanjing University of Information Science &Technology, Nanjing 210044, China
Guangyue Liu
Cryosphere Research Station on the Qinghai–Tibet Plateau, State Key Laboratory of Cryospheric Sciences, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
Qiangqiang Pang
Cryosphere Research Station on the Qinghai–Tibet Plateau, State Key Laboratory of Cryospheric Sciences, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
Erji Du
Cryosphere Research Station on the Qinghai–Tibet Plateau, State Key Laboratory of Cryospheric Sciences, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
Zhibin Li
School of Geographical Sciences, Nanjing University of Information Science &Technology, Nanjing 210044, China
Xiaodong Wu
Cryosphere Research Station on the Qinghai–Tibet Plateau, State Key Laboratory of Cryospheric Sciences, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
Yao Xiao
Cryosphere Research Station on the Qinghai–Tibet Plateau, State Key Laboratory of Cryospheric Sciences, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
Lingxiao Wang
School of Geographical Sciences, Nanjing University of Information Science &Technology, Nanjing 210044, China
Wenxin Zhang
Department of Physical Geography and Ecosystem Science, Lund University, Lund 22362, Sweden
School of Geographical and Earth Sciences, University of Glasgow, Glasgow, G12 8QQ, UK
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The Third Pole regions are important components in the global permafrost, and the detailed spatial soil organic carbon data are the scientific basis for environmental protection as well as the development of Earth system models. Based on multiple environmental variables and soil profile data, this study use machine-learning approaches to evaluate the SOC storage and spatial distribution at a depth interval of 0–3 m in the frozen ground area of the Third Pole region.
Xiangfei Li, Tonghua Wu, Xiaodong Wu, Jie Chen, Xiaofan Zhu, Guojie Hu, Ren Li, Yongping Qiao, Cheng Yang, Junming Hao, Jie Ni, and Wensi Ma
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In this study, an ensemble simulation of 55296 scheme combinations for at a typical permafrost site on the Qinghai–Tibet Plateau (QTP) was conducted. The general performance of the Noah-MP model for snow cover events (SCEs), soil temperature (ST) and soil liquid water content (SLW) was assessed, and the sensitivities of parameterization schemes at different depths were investigated. We show that Noah-MP tends to overestimate SCEs and underestimate ST and topsoil SLW on the QTP.
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Short summary
We used the Moving-Grid Permafrost Model (MVPM) to simulate the permafrost thermal regime in West Kunlun (55,669 km², NW Qinghai–Tibet Plateau), driven by remote-sensing-based land surface temperature (LST; 1980–2022). The model showed high accuracy and stability. Despite ongoing warming (+0.40 °C per decade), permafrost extent remained stable, reflecting delayed deep responses. The permafrost thermal regime reveals altitude- and soil-dependent responses to climate change and offers valuable insights into thermal states in data-scarce regions.
We used the Moving-Grid Permafrost Model (MVPM) to simulate the permafrost thermal regime in...