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.
Research article
| 
02 Oct 2025
Research article |
| 02 Oct 2025
| 02 Oct 2025
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
Related authors
Jianting Zhao, Lin Zhao, Zhe Sun, Fujun Niu, Guojie Hu, Defu Zou, Guangyue Liu, Erji Du, Chong Wang, Lingxiao Wang, Yongping Qiao, Jianzong Shi, Yuxin Zhang, Junqiang Gao, Yuanwei Wang, Yan Li, Wenjun Yu, Huayun Zhou, Zanpin Xing, Minxuan Xiao, Luhui Yin, and Shengfeng Wang
The Cryosphere, 16, 4823–4846, https://doi.org/10.5194/tc-16-4823-2022, https://doi.org/10.5194/tc-16-4823-2022, 2022
Short summary
Short summary
Permafrost has been warming and thawing globally; this is especially true in boundary regions. We focus on the changes and variability in permafrost distribution and thermal dynamics in the northern limit of permafrost on the Qinghai–Tibet Plateau (QTP) by applying a new permafrost model. Unlike previous papers on this topic, our findings highlight a slow, decaying process in the response of permafrost in the QTP to a warming climate, especially regarding areal extent.
Yuqi Zhu, Chao Liu, Rui Liu, Hanxi Wang, Xiangwen Wu, Zihao Zhang, Shuying Zang, and Xiaodong Wu
SOIL, 11, 793–809, https://doi.org/10.5194/soil-11-793-2025, https://doi.org/10.5194/soil-11-793-2025, 2025
Short summary
Short summary
Water-soluble organic carbon (WSOC) is crucial in boreal forests, but its behavior across soil depths is poorly understood. Our study found that shallow soils contain complex WSOC molecules with high biodegradability, while deeper soils have simpler molecules that are also highly biodegradable. These results reveal the significant role of WSOC in carbon cycling across soil layers, improving our understanding of carbon dynamics in boreal ecosystems impacted by climate change.
Marielle Saunois, Adrien Martinez, Benjamin Poulter, Zhen Zhang, Peter A. Raymond, Pierre Regnier, Josep G. Canadell, Robert B. Jackson, Prabir K. Patra, Philippe Bousquet, Philippe Ciais, Edward J. Dlugokencky, Xin Lan, George H. Allen, David Bastviken, David J. Beerling, Dmitry A. Belikov, Donald R. Blake, Simona Castaldi, Monica Crippa, Bridget R. Deemer, Fraser Dennison, Giuseppe Etiope, Nicola Gedney, Lena Höglund-Isaksson, Meredith A. Holgerson, Peter O. Hopcroft, Gustaf Hugelius, Akihiko Ito, Atul K. Jain, Rajesh Janardanan, Matthew S. Johnson, Thomas Kleinen, Paul B. Krummel, Ronny Lauerwald, Tingting Li, Xiangyu Liu, Kyle C. McDonald, Joe R. Melton, Jens Mühle, Jurek Müller, Fabiola Murguia-Flores, Yosuke Niwa, Sergio Noce, Shufen Pan, Robert J. Parker, Changhui Peng, Michel Ramonet, William J. Riley, Gerard Rocher-Ros, Judith A. Rosentreter, Motoki Sasakawa, Arjo Segers, Steven J. Smith, Emily H. Stanley, Joël Thanwerdas, Hanqin Tian, Aki Tsuruta, Francesco N. Tubiello, Thomas S. Weber, Guido R. van der Werf, Douglas E. J. Worthy, Yi Xi, Yukio Yoshida, Wenxin Zhang, Bo Zheng, Qing Zhu, Qiuan Zhu, and Qianlai Zhuang
Earth Syst. Sci. Data, 17, 1873–1958, https://doi.org/10.5194/essd-17-1873-2025, https://doi.org/10.5194/essd-17-1873-2025, 2025
Short summary
Short summary
Methane (CH4) is the second most important human-influenced greenhouse gas in terms of climate forcing after carbon dioxide (CO2). A consortium of multi-disciplinary scientists synthesise and update the budget of the sources and sinks of CH4. This edition benefits from important progress in estimating emissions from lakes and ponds, reservoirs, and streams and rivers. For the 2010s decade, global CH4 emissions are estimated at 575 Tg CH4 yr-1, including ~65 % from anthropogenic sources.
Defu Zou, Lin Zhao, Guojie Hu, Erji Du, Guangyue Liu, Chong Wang, and Wangping Li
Earth Syst. Sci. Data, 17, 1731–1742, https://doi.org/10.5194/essd-17-1731-2025, https://doi.org/10.5194/essd-17-1731-2025, 2025
Short summary
Short summary
This study provides baseline data of permafrost temperature at 15 m depth on the Qinghai–Tibetan Plateau (QTP) over the period 2010–2019 at a spatial resolution of nearly 1 km, using 231 borehole records and a machine learning method. The average MAGT15 m of the QTP permafrost was −1.85 °C, with 90 % of the values ranging from −5.1 to −0.1 °C and 51.2 % exceeding −1.5 °C. The data can serve as a crucial boundary condition for deeper permafrost assessments and a reference for model simulations.
Yong Yang, Huaiwei Sun, Jingfeng Wang, Wenxin Zhang, Gang Zhao, Weiguang Wang, Lei Cheng, Lu Chen, Hui Qin, and Zhanzhang Cai
Earth Syst. Sci. Data, 17, 1191–1216, https://doi.org/10.5194/essd-17-1191-2025, https://doi.org/10.5194/essd-17-1191-2025, 2025
Short summary
Short summary
Traditional methods for estimating ocean heat flux often introduce large uncertainties due to complex parameterizations. To tackle this issue, we developed a novel framework based on maximum entropy production (MEP) theory. By incorporating heat storage effects and refining the Bowen ratio, we enhanced the MEP method's accuracy. This research derives a new long-term global ocean latent heat flux dataset that offers high accuracy, enhancing our understanding of ocean energy dynamics.
Mengge Lu, Huaiwei Sun, Yong Yang, Jie Xue, Hongbo Ling, Hong Zhang, and Wenxin Zhang
Hydrol. Earth Syst. Sci., 29, 613–625, https://doi.org/10.5194/hess-29-613-2025, https://doi.org/10.5194/hess-29-613-2025, 2025
Short summary
Short summary
Our study explores how ecosystems recover after flash droughts. Using vegetation and soil moisture data, we found that recovery takes about 37.5 d on average (longer in central and southern regions) in China. Factors like post-drought radiation and temperature affect recovery, with extreme temperatures prolonging it. Herbaceous plants recover faster than forests. Our findings aid water resource management and drought monitoring on a large scale, offering insights into ecosystem resilience.
Zhen Zhang, Benjamin Poulter, Joe R. Melton, William J. Riley, George H. Allen, David J. Beerling, Philippe Bousquet, Josep G. Canadell, Etienne Fluet-Chouinard, Philippe Ciais, Nicola Gedney, Peter O. Hopcroft, Akihiko Ito, Robert B. Jackson, Atul K. Jain, Katherine Jensen, Fortunat Joos, Thomas Kleinen, Sara H. Knox, Tingting Li, Xin Li, Xiangyu Liu, Kyle McDonald, Gavin McNicol, Paul A. Miller, Jurek Müller, Prabir K. Patra, Changhui Peng, Shushi Peng, Zhangcai Qin, Ryan M. Riggs, Marielle Saunois, Qing Sun, Hanqin Tian, Xiaoming Xu, Yuanzhi Yao, Yi Xi, Wenxin Zhang, Qing Zhu, Qiuan Zhu, and Qianlai Zhuang
Biogeosciences, 22, 305–321, https://doi.org/10.5194/bg-22-305-2025, https://doi.org/10.5194/bg-22-305-2025, 2025
Short summary
Short summary
This study assesses global methane emissions from wetlands between 2000 and 2020 using multiple models. We found that wetland emissions increased by 6–7 Tg CH4 yr-1 in the 2010s compared to the 2000s. Rising temperatures primarily drove this increase, while changes in precipitation and CO2 levels also played roles. Our findings highlight the importance of wetlands in the global methane budget and the need for continuous monitoring to understand their impact on climate change.
Ana Maria Roxana Petrescu, Glen P. Peters, Richard Engelen, Sander Houweling, Dominik Brunner, Aki Tsuruta, Bradley Matthews, Prabir K. Patra, Dmitry Belikov, Rona L. Thompson, Lena Höglund-Isaksson, Wenxin Zhang, Arjo J. Segers, Giuseppe Etiope, Giancarlo Ciotoli, Philippe Peylin, Frédéric Chevallier, Tuula Aalto, Robbie M. Andrew, David Bastviken, Antoine Berchet, Grégoire Broquet, Giulia Conchedda, Stijn N. C. Dellaert, Hugo Denier van der Gon, Johannes Gütschow, Jean-Matthieu Haussaire, Ronny Lauerwald, Tiina Markkanen, Jacob C. A. van Peet, Isabelle Pison, Pierre Regnier, Espen Solum, Marko Scholze, Maria Tenkanen, Francesco N. Tubiello, Guido R. van der Werf, and John R. Worden
Earth Syst. Sci. Data, 16, 4325–4350, https://doi.org/10.5194/essd-16-4325-2024, https://doi.org/10.5194/essd-16-4325-2024, 2024
Short summary
Short summary
This study provides an overview of data availability from observation- and inventory-based CH4 emission estimates. It systematically compares them and provides recommendations for robust comparisons, aiming to steadily engage more parties in using observational methods to complement their UNFCCC submissions. Anticipating improvements in atmospheric modelling and observations, future developments need to resolve knowledge gaps in both approaches and to better quantify remaining uncertainty.
Jie Zhang, Elisabeth Larsen Kolstad, Wenxin Zhang, Iris Vogeler, and Søren O. Petersen
Biogeosciences, 20, 3895–3917, https://doi.org/10.5194/bg-20-3895-2023, https://doi.org/10.5194/bg-20-3895-2023, 2023
Short summary
Short summary
Manure application to agricultural land often results in large and variable N2O emissions. We propose a model with a parsimonious structure to investigate N transformations around such N2O hotspots. The model allows for new detailed insights into the interactions between transport and microbial activities regarding N2O emissions in heterogeneous soil environments. It highlights the importance of solute diffusion to N2O emissions from such hotspots which are often ignored by process-based models.
Jianting Zhao, Lin Zhao, Zhe Sun, Fujun Niu, Guojie Hu, Defu Zou, Guangyue Liu, Erji Du, Chong Wang, Lingxiao Wang, Yongping Qiao, Jianzong Shi, Yuxin Zhang, Junqiang Gao, Yuanwei Wang, Yan Li, Wenjun Yu, Huayun Zhou, Zanpin Xing, Minxuan Xiao, Luhui Yin, and Shengfeng Wang
The Cryosphere, 16, 4823–4846, https://doi.org/10.5194/tc-16-4823-2022, https://doi.org/10.5194/tc-16-4823-2022, 2022
Short summary
Short summary
Permafrost has been warming and thawing globally; this is especially true in boundary regions. We focus on the changes and variability in permafrost distribution and thermal dynamics in the northern limit of permafrost on the Qinghai–Tibet Plateau (QTP) by applying a new permafrost model. Unlike previous papers on this topic, our findings highlight a slow, decaying process in the response of permafrost in the QTP to a warming climate, especially regarding areal extent.
Jie Zhang, Wenxin Zhang, Per-Erik Jansson, and Søren O. Petersen
Biogeosciences, 19, 4811–4832, https://doi.org/10.5194/bg-19-4811-2022, https://doi.org/10.5194/bg-19-4811-2022, 2022
Short summary
Short summary
In this study, we relied on a properly controlled laboratory experiment to test the model’s capability of simulating the dominant microbial processes and the emissions of one greenhouse gas (nitrous oxide, N2O) from agricultural soils. This study reveals important processes and parameters that regulate N2O emissions in the investigated model framework and also suggests future steps of model development, which have implications on the broader communities of ecosystem modelers.
Lingxiao Wang, Lin Zhao, Huayun Zhou, Shibo Liu, Erji Du, Defu Zou, Guangyue Liu, Yao Xiao, Guojie Hu, Chong Wang, Zhe Sun, Zhibin Li, Yongping Qiao, Tonghua Wu, Chengye Li, and Xubing Li
The Cryosphere, 16, 2745–2767, https://doi.org/10.5194/tc-16-2745-2022, https://doi.org/10.5194/tc-16-2745-2022, 2022
Short summary
Short summary
Selin Co has exhibited the greatest increase in water storage among all the lakes on the Tibetan Plateau in the past decades. This study presents the first attempt to quantify the water contribution of ground ice melting to the expansion of Selin Co by evaluating the ground surface deformation since terrain surface settlement provides a
windowto detect the subsurface ground ice melting. Results reveal that ground ice meltwater contributed ~ 12 % of the lake volume increase during 2017–2020.
Tonghua Wu, Changwei Xie, Xiaofan Zhu, Jie Chen, Wu Wang, Ren Li, Amin Wen, Dong Wang, Peiqing Lou, Chengpeng Shang, Yune La, Xianhua Wei, Xin Ma, Yongping Qiao, Xiaodong Wu, Qiangqiang Pang, and Guojie Hu
Earth Syst. Sci. Data, 14, 1257–1269, https://doi.org/10.5194/essd-14-1257-2022, https://doi.org/10.5194/essd-14-1257-2022, 2022
Short summary
Short summary
We presented an 11-year time series of meteorological, active layer, and permafrost data at the Mahan Mountain relict permafrost site in northeastern Qinghai-Tibet Plateau. From 2010 to 2020, the increasing rate of active layer thickness was 1.8 cm-year and the permafrost temperature showed slight changes. The release of those data would be helpful to understand the impacts of climate change on permafrost in relict permafrost regions and to validate the permafrost models and land surface models.
Yi Zhao, Zhuotong Nan, Hailong Ji, and Lin Zhao
The Cryosphere, 16, 825–849, https://doi.org/10.5194/tc-16-825-2022, https://doi.org/10.5194/tc-16-825-2022, 2022
Short summary
Short summary
Convective heat transfer (CHT) is important in affecting thermal regimes in permafrost regions. We quantified its thermal impacts by contrasting the simulation results from three scenarios in which the Simultaneous Heat and Water model includes full, partial, and no consideration of CHT. The results show the CHT commonly happens in shallow and middle soil depths during thawing periods and has greater impacts in spring than summer. The CHT has both heating and cooling effects on the active layer.
Xiaowen Wang, Lin Liu, Yan Hu, Tonghua Wu, Lin Zhao, Qiao Liu, Rui Zhang, Bo Zhang, and Guoxiang Liu
Nat. Hazards Earth Syst. Sci., 21, 2791–2810, https://doi.org/10.5194/nhess-21-2791-2021, https://doi.org/10.5194/nhess-21-2791-2021, 2021
Short summary
Short summary
We characterized the multi-decadal geomorphic changes of a low-angle valley glacier in the East Kunlun Mountains and assessed the detachment hazard influence. The observations reveal a slow surge-like dynamic pattern of the glacier tongue. The maximum runout distances of two endmember avalanche scenarios were presented. This study provides a reference to evaluate the runout hazards of low-angle mountain glaciers prone to detachment.
Lin Zhao, Defu Zou, Guojie Hu, Tonghua Wu, Erji Du, Guangyue Liu, Yao Xiao, Ren Li, Qiangqiang Pang, Yongping Qiao, Xiaodong Wu, Zhe Sun, Zanpin Xing, Yu Sheng, Yonghua Zhao, Jianzong Shi, Changwei Xie, Lingxiao Wang, Chong Wang, and Guodong Cheng
Earth Syst. Sci. Data, 13, 4207–4218, https://doi.org/10.5194/essd-13-4207-2021, https://doi.org/10.5194/essd-13-4207-2021, 2021
Short summary
Short summary
Lack of a synthesis dataset of the permafrost state has greatly limited our understanding of permafrost-related research as well as the calibration and validation of RS retrievals and model simulation. We compiled this dataset, including ground temperature, active layer hydrothermal regimes, and meteorological indexes based on our observational network, and we summarized the basic changes in permafrost and its climatic conditions. It is the first comprehensive dataset on permafrost for the QXP.
Lihui Luo, Yanli Zhuang, Mingyi Zhang, Zhongqiong Zhang, Wei Ma, Wenzhi Zhao, Lin Zhao, Li Wang, Yanmei Shi, Ze Zhang, Quntao Duan, Deyu Tian, and Qingguo Zhou
Earth Syst. Sci. Data, 13, 4035–4052, https://doi.org/10.5194/essd-13-4035-2021, https://doi.org/10.5194/essd-13-4035-2021, 2021
Short summary
Short summary
We implement a variety of sensors to monitor the hydrological and thermal deformation between permafrost slopes and engineering projects in the hinterland of the Qinghai–Tibet Plateau. We present the integrated observation dataset from the 1950s to 2020, explaining the instrumentation, processing, data visualisation, and quality control.
Dong Wang, Tonghua Wu, Lin Zhao, Cuicui Mu, Ren Li, Xianhua Wei, Guojie Hu, Defu Zou, Xiaofan Zhu, Jie Chen, Junmin Hao, Jie Ni, Xiangfei Li, Wensi Ma, Amin Wen, Chengpeng Shang, Yune La, Xin Ma, and Xiaodong Wu
Earth Syst. Sci. Data, 13, 3453–3465, https://doi.org/10.5194/essd-13-3453-2021, https://doi.org/10.5194/essd-13-3453-2021, 2021
Short summary
Short summary
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
Geosci. Model Dev., 14, 1753–1771, https://doi.org/10.5194/gmd-14-1753-2021, https://doi.org/10.5194/gmd-14-1753-2021, 2021
Short summary
Short summary
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.
Cited articles
Allen, D. M., Michel, F. A., and Judge, A. S.: The permafrost regime in the Mackenzie Delta, Beaufort Sea region, N.W.T. and its significance to the reconstruction of the paleoclimatic history, J. Quaternary Sci., 3, 3–13, https://doi.org/10.1002/jqs.3390030103, 1988.
Baldwin, J. and Vecchi, G.: Influence of the Tian Shan on arid extratropical Asia, J. Climate, 29, 5741–5762, https://doi.org/10.1175/JCLI-D-15-0490.1, 2016.
Bense, V. F., Kooi, H., Ferguson, G., and Read, T.: Permafrost degradation as a control on hydrogeological regime shifts in a warming climate, J. Geophys. Res.-Earth, 117, F03026, https://doi.org/10.1029/2011JF002143, 2012.
Biskaborn, B. K., Smith, S. L., Noetzli, J., Matthes, H., Vieira, G., Streletskiy, D. A., Schoeneich, P., Romanovsky, V. E., Lewkowicz, A. G., Abramov, A., Allard, M., Boike, J., Cable, W. L., Christiansen, H. H., Delaloye, R., Diekmann, B., Drozdov, D., Etzelmüller, B., Grosse, G., Guglielmin, M., Ingeman-Nielsen, T., Isaksen, K., Ishikawa, M., Johansson, M., Johannsson, H., Joo, A., Kaverin, D., Kholodov, A., Konstantinov, P., Kröger, T., Lambiel, C., Lanckman, J.-P., Luo, D., Malkova, G., Meiklejohn, I., Moskalenko, N., Oliva, M., Phillips, M., Ramos, M., Sannel, A. B. K., Sergeev, D., Seybold, C., Skryabin, P., Vasiliev, A., Wu, Q., Yoshikawa, K., Zheleznyak, M., and Lantuit, H.: Permafrost is warming at a global scale, Nat. Commun., 10, 264, https://doi.org/10.1038/s41467-018-08240-4, 2019.
Boike, J., Georgi, C., Kirilin, G., Muster, S., Abramova, K., Fedorova, I., Chetverova, A., Grigoriev, M., Bornemann, N., and Langer, M.: Thermal processes of thermokarst lakes in the continuous permafrost zone of northern Siberia – observations and modeling (Lena River Delta, Siberia), Biogeosciences, 12, 5941–5965, https://doi.org/10.5194/bg-12-5941-2015, 2015.
Breiman, L.: Random forests, Mach. Learn., 45, 5–32, https://doi.org/10.1023/A:1010933404324, 2001.
Burke, E. J., Zhang, Y., and Krinner, G.: Evaluating permafrost physics in the Coupled Model Intercomparison Project 6 (CMIP6) models and their sensitivity to climate change, The Cryosphere, 14, 3155–3174, https://doi.org/10.5194/tc-14-3155-2020, 2020.
Buteau, S., Fortier, R., Delisle, G., and Allard, M.: Numerical simulation of the impacts of climate warming on a permafrost mound, Permafrost Periglac., 15, 41–57, https://doi.org/10.1002/ppp.474, 2004.
Cable, W. L., Romanovsky, V. E., and Jorgenson, M. T.: Scaling-up permafrost thermal measurements in western Alaska using an ecotype approach, The Cryosphere, 10, 2517–2532, https://doi.org/10.5194/tc-10-2517-2016, 2016.
Cannon, F., Carvalho, L. M. V., Jones, C., and Norris, J.: Winter westerly disturbance dynamics and precipitation in the Western Himalaya and Karakoram: A wave-tracking approach, Theor. Appl. Climatol., 125, 27–44, https://doi.org/10.1007/s00704-015-1489-8, 2016.
Cao, Z., Nan, Z., Hu, J., Chen, Y., and Zhang, Y.: A new 2010 permafrost distribution map over the Qinghai–Tibet Plateau based on subregion survey maps: a benchmark for regional permafrost modeling, Earth Syst. Sci. Data, 15, 3905–3930, https://doi.org/10.5194/essd-15-3905-2023, 2023.
Cao, B., Gruber, S., Zheng, D., and Li, X.: The ERA5-Land soil temperature bias in permafrost regions, The Cryosphere, 14, 2581–2595, https://doi.org/10.5194/tc-14-2581-2020, 2020.
Che, T., Xin, L., Jin, R., Armstrong, R., and Zhang, T.: Snow depth derived from passive microwave remote-sensing data in China, Ann. Glaciol., 49, 145–154, https://doi.org/10.3189/172756408787814690, 2008.
Chen, H., Nan, Z., Zhao, L., Ding, Y., Chen, J., and Pang, Q.: Noah modelling of the permafrost distribution and characteristics in the West Kunlun area, Qinghai–Tibet Plateau, China, Permafrost Periglac., 26, 160–174, https://doi.org/10.1002/ppp.1841, 2015.
Cheng, G., Zhao, L., Li, R., Wu, X., Sheng, Y., Hu, G., Zou, D., Jin, H., Li, X., and Wu, Q.: Characteristic, changes and impacts of permafrost on Qinghai–Tibet Plateau, Chin. Sci. Bull., 64, 2783–2795, https://doi.org/10.1360/TB-2019-0191, 2019 (in Chinese with English abstract).
Copernicus Climate Change Service (C3S): ERA5-Land hourly data from 1950 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.e2161bac, 2019.
Dai, Y., Shangguan, W., Wei, N., Xin, Q., Yuan, H., Zhang, S., Liu, S., Lu, X., Wang, D., and Yan, F.: A review of the global soil property maps for Earth system models, SOIL, 5, 137–158, https://doi.org/10.5194/soil-5-137-2019, 2019.
Dobiński, W. and Kasprzak, M.: Permafrost Base Degradation: Characteristics and Unknown Thread with Specific Example from Hornsund, Svalbard, Front. Earth Sci., 10, 802157, https://doi.org/10.3389/feart.2022.802157, 2022.
Fiddes, J., Endrizzi, S., and Gruber, S.: Large-area land surface simulations in heterogeneous terrain driven by global data sets: application to mountain permafrost, The Cryosphere, 9, 411–426, https://doi.org/10.5194/tc-9-411-2015, 2015.
Guo, D. and Wang, H.: CMIP5 permafrost degradation projection: a comparison among different regions, J. Geophys. Res.-Atmos., 121, 4499–4517, https://doi.org/10.1002/2015JD024108, 2016.
Guo, D., Wang, H., and Li, D.: A projection of permafrost degradation on the Tibetan Plateau during the 21st century, J. Geophys. Res.-Atmos., 117, D05106, https://doi.org/10.1029/2011JD016545, 2012.
Guo, D., Wang, H., and Wang, A.: Sensitivity of historical simulation of the permafrost to different atmospheric forcing data sets from 1979 to 2009, J. Geophys. Res.-Atmos., 122, 12269–12286, https://doi.org/10.1002/2017JD027477, 2017.
Guo, W., Liu, S., Xu, J., Wu, L., Shangguan, D., Yao, X., Wei, J., Bao, W., Yu, P., and Liu, Q.: The second Chinese glacier inventory: data, methods and results, J. Glaciol., 61, 357–372, 2015.
Hachem, S., Duguay, C. R., and Allard, M.: Comparison of MODIS-derived land surface temperatures with ground surface and air temperature measurements in continuous permafrost terrain, The Cryosphere, 6, 51–69, https://doi.org/10.5194/tc-6-51-2012, 2012.
Harp, D. R., Atchley, A. L., Painter, S. L., Coon, E. T., Wilson, C. J., Romanovsky, V. E., and Rowland, J. C.: Effect of soil property uncertainties on permafrost thaw projections: a calibration-constrained analysis, The Cryosphere, 10, 341–358, https://doi.org/10.5194/tc-10-341-2016, 2016.
Hengl, T., Mendes de Jesus, J., Heuvelink, G. B., Ruiperez Gonzalez, M., Kilibarda, M., Blagotić, Shangguan, W., Wright, M. N., Geng, X., Marschallinger, B. B., Guevara, M., Vargas, R., MacMillan, R. A., Batjes, N. H., Leenaars, J. Ribeiro, E., Wheeler, I., Mantel, S., and Kempen, B.: SoilGrids250m: Global gridded soil information based on machine learning, PLoS One, 12, e0169748, https://doi.org/10.1371/journal.pone.0169748, 2017.
Hjort, J., Streletskiy, D., Doré, G., Wu, Q., Bjella, K., and Luoto, M.: Impacts of permafrost degradation on infrastructure, Nat. Rev. Earth Environ., 3, 24–38, https://doi.org/10.1038/s43017-021-00247-8, 2022.
Hu, G., Zhao, L., Li, R., Wu, X., Wu, T., Xie, C., Zhu, X., and Su, Y.: Variations in soil temperature from 1980 to 2015 in permafrost regions on the Qinghai-Tibetan Plateau based on observed and reanalysis products, Geoderma, 337, 893–905, https://doi.org/10.1016/j.geoderma.2018.10.044, 2019.
Hu, G., Zhao, L., Li, R., Park, H., Wu, X., Su, Y., Guggenberger, G., Wu, T., Zou, D., Zhu, X., Zhang, W., Wu, Y., and Hao, J: Water and heat coupling processes and its simulation in frozen soils: Current status and future research directions, Catena, 222, 106844, https://doi.org/10.1016/j.catena.2022.106844, 2023a.
Hu, J., Zhao, L., Wang, C., Hu, G., Zou, D., Xing, Z., Jiao, M., Qiao, Y., Liu, G., and Du, E.: Applicability evaluation and correction of CLDAS surface temperature products in permafrost region of Qinghai-Tibet Plateau, Climate Change Research, 20, 10-2-5, https://doi.org/10.12006/j.issn.1673-1719.2023.033, 2024 (in Chinese with English abstract).
Hu, S., He, L., and Wang, J.: Heat flow in the continental area of China: a new data set, Earth Planet. Sc. Lett., 179, 407–419, https://doi.org/10.1016/S0012-821X(00)00126-6, 2000.
Hu, Y., Liu, L., Huang, L., Zhao, L., Wu, T., Wang, X., and Cai, J.: Mapping and characterizing rock glaciers in the arid Western Kunlun Mountains supported by InSAR and deep learning, J. Geophys. Res.-Earth, 128, e2023JF007206, https://doi.org/10.1029/2023JF007206, 2023b.
IPCC: Climate change 2021: the physical science basis, https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_Full_Report.pdf (last access: 12 September 2025), 2021.
IPCC: Special report on the ocean and cryosphere in a changing climate, https://www.ipcc.ch/srocc/ (last access: 12 September 2025), 2019.
Jafarov, E. E., Marchenko, S. S., and Romanovsky, V. E.: Numerical modeling of permafrost dynamics in Alaska using a high spatial resolution dataset, The Cryosphere, 6, 613–624, https://doi.org/10.5194/tc-6-613-2012, 2012.
Jarvis, A., Reuter, H., Nelson, A., and Guevara, E.: Hole-filled seamless SRTM data V4, Tech. rep., International Centre for Tropical Agriculture (CIAT), Cali, Colombia, https://srtm.csi.cgiar.org/ (last access: 20 May 2025), 2008.
Jiao, M., Zhao, L., Wang, C., Hu, G., Li, Y., Zhao, J., Zou, D., Xing, Z., Qiao, Y., Liu, G., Du, E., Xiao, M., and Hou, Y.: Spatiotemporal variations of soil temperature at 10 and 50 cm depths in permafrost regions along the Qinghai-Tibet engineering corridor, Remote Sens., 15, 455, https://doi.org/10.3390/rs15020455, 2023.
Jin, H., Yu, Q., Wang, S., and Lü, L.: Changes in permafrost environments along the Qinghai–Tibet engineering corridor induced by anthropogenic activities and climate warming, Cold Reg. Sci. Technol., 53, 317–333, https://doi.org/10.1016/j.coldregions.2007.07.005, 2008.
Jin, H., Luo, D., Wang, S., Lü, L., and Wu, J.: Spatiotemporal variability of permafrost degradation on the Qinghai-Tibet Plateau, Sci. Cold Arid Reg., 3, 281–305, https://doi.org/10.3724/SP.J.1226.2011.00281, 2011.
Jin, H., Wu, Q., and Romanovsky, V.: E. Degrading permafrost and its impacts, Adv. Clim. Chang. Res., 12, 1–5, https://doi.org/10.1016/j.accre.2021.01.007, 2021.
Karlsson, K.-G., Riihelä, A., Trentmann, J., Stengel, M., Solodovnik, I., Meirink, J. F., Devasthale, A., Jääskeläinen, E., Kallio-Myers, V., Eliasson, S., Benas, N., Johansson, E., Stein, D., Finkensieper, S., Håkansson, N., Akkermans, T., Clerbaux, N., Selbach, N., Schröder, M., and Hollmann, R.: CLARA-A3: CM SAF cLoud, Albedo and surface RAdiation dataset from AVHRR data – Edition 3, Satellite Application Facility on Climate Monitoring [data set], https://doi.org/10.5676/EUM_SAF_CM/CLARA_AVHRR/V003, 2023.
Koven, C., William J., and Alex S.: Analysis of Permafrost Thermal Dynamics and Response to Climate Change in the CMIP5 Earth System Models, J. Climate, 26, 1877–1900, https://doi.org/10.1175/JCLI-D-12-00228.1, 2013.
Lachenbruch, A. H. and Marshall, B. V.: Changing Climate: Geothermal Evidence from Permafrost in the Alaskan Arctic, Science, 234, 689–696, https://doi.org/10.1126/science.234.4777.689, 1986.
Lafrenière, M. and Lamoureux, S.: Effects of changing permafrost conditions on hydrological processes and fluvial fluxes, Earth-Sci. Rev., 191, 212–223, https://doi.org/10.1016/j.earscirev.2019.02.018, 2019.
Langer, M., Westermann, S., Heikenfeld, M., Dorn, W., and Boike, J.: Satellite-based modeling of permafrost temperatures in a tundra lowland landscape, Remote Sens. Environ., 135, 12–24, https://doi.org/10.1016/j.rse.2013.03.011, 2013.
Langer, M., Nitzbon, J., Groenke, B., Assmann, L.-M., Schneider von Deimling, T., Stuenzi, S. M., and Westermann, S.: The evolution of Arctic permafrost over the last 3 centuries from ensemble simulations with the CryoGridLite permafrost model, The Cryosphere, 18, 363–385, https://doi.org/10.5194/tc-18-363-2024, 2024.
Lawrence, D. M., Slater, A. G., Romanovsky, V. E., and Nicolsky, D. J.: Sensitivity of a model projection of near-surface permafrost degradation to soil column depth and representation of soil organic matter, J. Geophys. Res.-Earth Surface, 113, https://doi.org/10.1029/2007JF000883, 2008.
Lawrence, D. M., Slater, A. G., and Swenson, S. C.: Simulation of Present-Day and Future Permafrost and Seasonally Frozen Ground Conditions in CCSM4, J. Climate, 25, 2207–2225, https://doi.org/10.1175/JCLI-D-11-00334.1, 2012.
Li, S. and Cheng, G.: Map of Frozen Ground on Qinghai-Xizang Plateau, Gansu Culture Press, Lanzhou, 1996.
Li, S. and Li, S.: Significance and research on the two boreholes of Tianshuihai in the west Kunlun Mountains, J. Glaciol. Geocryol., 13, 133–137, 1991 (in Chinese with English abstract).
Li, K., Chen, J., Zhao, L., Zhang, X., Pang, Q., Fang, H., and Liu, G.: Permafrost distribution in a typical area of the western Kunlun Mountains derived from a comprehensive survey, J. Glaciol. Geocryol., 34, 1325–1332, 2012 (in Chinese with English abstract).
Li, N., Cuo, L., Zhang, Y., and Ding, J.: The synthesis of potential factors contributing to the asynchronous warming between air and shallow ground since the 2000s on the Tibetan Plateau, Geoderma, 441, 116753, https://doi.org/10.1016/j.geoderma.2023.116753, 2024.
Li, W., Zhao, L., Wu, X., Wang, S., Nan, Z., Fang, H., and Shi, W.: Distribution of Soils and Landform Relationships in Permafrost Regions of the Western Qinghai-Xizang (Tibetan) Plateau, China, Soil. Sci., 179, 348–357, https://doi.org/10.1097/SS.0000000000000075, 2014.
Li, W., Zhao, L., Wu, X., Zhao, Y., Fang, H., and Shi, W.: Distribution of soils and landform relationships in the permafrost regions of Qinghai-Xizang (Tibetan) Plateau, Chinese Sci. Bull., 60, 2216–2226, https://doi.org/10.1360/N972014-01206, 2015.
Liu, G., Xie, C., Zhao, L., Xiao, Y., Wu, T., Wang, W., and Liu, W.: Permafrost warming near the northern limit of permafrost on the Qinghai–Tibetan Plateau during the period from 2005 to 2017, A case study in the Xidatan area, Permafrost Periglac., 32, 323–334, https://doi.org/10.1002/ppp.2089, 2020.
Liang, S., Cheng, C., Jia, K., Jiang, B., Liu, Q., Xiao, Z., Yao, Y., Yuan, W., Zhang, X., Zhao, X., and Zhou, J.: The Global LAnd Surface Satellite (GLASS) products suite, B. Am. Meteorol. Soc., https://doi.org/10.1175/BAMS-D-18-0341.1, 2020.
Marchenko, S. S., Bjella, K., Nicolsky, D. J., and Romanovsky, V. E.: Modeling Dynamics of Permafrost Degradation and their Impact on Ecosystems Across Entire Alaska: Arctic and Subarctic Engineering Design Tool (Part-1), Preprints, 2024030927, https://doi.org/10.20944/preprints202403.0927.v2, 2024.
Miner, K., D'Andrilli, J., Mackelprang, R., Edwards, A., Malaska, M., Waldrop, M., and Miller, C.: Emergent biogeochemical risks from Arctic permafrost degradation. Nat. Clim. Change, 11, 809–819, https://doi.org/10.1038/s41558-021-01162-y, 2021.
Muñoz-Sabater, J., Dutra, E., Agustí-Panareda, A., Albergel, C., Arduini, G., Balsamo, G., Boussetta, S., Choulga, M., Harrigan, S., Hersbach, H., Martens, B., Miralles, D. G., Piles, M., Rodríguez-Fernández, N. J., Zsoter, E., Buontempo, C., and Thépaut, J.-N.: ERA5-Land: a state-of-the-art global reanalysis dataset for land applications, Earth Syst. Sci. Data, 13, 4349–4383, https://doi.org/10.5194/essd-13-4349-2021, 2021.
Ni, J., Wu, T., Zhu, X., Hu, G., Zou, D., Wu, X., Li, R., Xie, C., Qiao, Y., Pang, Q., Hao, J., and Yang, C.: Simulation of the present and future projection of permafrost on the Qinghai-Tibet Plateau with statistical and machine learning models, J. Geophys. Res.-Atmos, 126, e2020JD033402, https://doi.org/10.1029/2020JD033402, 2021
Nicolsky, D. J., Romanovsky, V. E., and Tipenko, G. S.: Using in-situ temperature measurements to estimate saturated soil thermal properties by solving a sequence of optimization problems, The Cryosphere, 1, 41–58, https://doi.org/10.5194/tc-1-41-2007, 2007.
Nicolsky, D. J., Romanovsky, V. E., Panda, S. K., Marchenko, S. S., and Muskett, R. R.: Applicability of the ecosystem type approach to model permafrost dynamics across the Alaska North Slope, J. Geophys. Res.-Earth, 122, 50–75, https://doi.org/10.1002/2016JF003852, 2017.
Nitzbon, J., Westermann, S., Langer, M., Martin, L., Strauss, L., Laboor, S., and Boike, J.: Fast response of cold ice-rich permafrost in northeast Siberia to a warming climate, Nat. Commun., 11, 2201, https://doi.org/10.1038/s41467-020-15725-8, 2020.
O'Neill, H., Burn, C., Allard, M., Arenson, L., Bunn, M., Connon, R., Kokelj, S., LeBlanc, A., Morse, P., and Smith, S.: Permafrost thaw and northern development, Nat. Clim. Change, 10, 722–723, https://doi.org/10.1038/s41558-020-0862-5, 2020.
Orsolini, Y., Wegmann, M., Dutra, E., Liu, B., Balsamo, G., Yang, K., de Rosnay, P., Zhu, C., Wang, W., Senan, R., and Arduini, G.: Evaluation of snow depth and snow cover over the Tibetan Plateau in global reanalyses using in situ and satellite remote sensing observations, The Cryosphere, 13, 2221–2239, https://doi.org/10.5194/tc-13-2221-2019, 2019.
Qin, R. and Zhang, F.: HRLT: A high-resolution (1 day, 1 km) and long-term (1961–2019) gridded dataset for temperature and precipitation across China, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.941329, 2022.
Qin, R., Zhao, Z., Xu, J., Ye, J.-S., Li, F.-M., and Zhang, F.: HRLT: a high-resolution (1 d, 1 km) and long-term (1961–2019) gridded dataset for surface temperature and precipitation across China, Earth Syst. Sci. Data, 14, 4793–4810, https://doi.org/10.5194/essd-14-4793-2022, 2022.
Schiesser, W.: The Numerical Method of Lines: Integration of Partial Differential Equations, Academic Press, San Diego, USA, Vol. 212, ISBN 0-12-624130-9, 1991.
Schuur, E., McGuire, A., Schadel, C., Grosse, G., Harden, J., Hayes, D., Hugelius, G., Koven, C., Kuhry, P., Lawrence, D., Natali, S., Olefeldt, D., Romanovsky, V., Schaefer, K., Turetsky, M., Treat, C., and Vonk, J.: Climate change and the permafrost carbon feedback, Nature, 520, 171–179, https://doi.org/10.1038/nature14338, 2015.
Shangguan, W., Dai, Y., Liu, B., Zhu., A., Duan, Q., Wu, L., Ji, D., Ye, A., Yuan, H., Zhang, Q., Chen, D., Chen, M., Chu, J., Dou, Y., Guo, J., Li, H., Li, J., Liang, L., Liang, X., Liu, H., Liu, S., Miao, C., and Zhang, Y.: A China data set of soil properties for land surface modeling, J. Adv. Model. Earth Sy., 5, 212–224, https://doi.org/10.1002/jame.20026, 2013.
Sjöberg, Y., Coon, E., Sannel, A., Pannetier, R., Harp, D., Frampton, A., Painter, S., and Lyon, S. W.: Thermal effects of groundwater flow through subarctic fens: a case study based on field observations and numerical modeling, Water Resour. Res., 52, 1591–1606, https://doi.org/10.1002/2015WR017571, 2016.
Saha, S., Moorthi, S., Pan, H.-L., Wu, X., Wang, J., Nadiga, S., Tripp, P., Kistler, R., Woollen, J., Behringer, D., Liu, H., Stokes, D., Grumbine, R., Gayno, G., Wang, J., Hou, Y.-T., Chuang, H.-Y., Juang, H.-M. H., Sela, J., Iredell, M., Treadon, R., Kleist, D., Van Delst, P., Keyser, D., Derber, J., Ek, M., Meng, J., Wei, H., Yang, R., Lord, S., van den Dool, H., Kumar, A., Wang, W., Long, C., Chelliah, M., Xue, Y., Huang, B., Schemm, J.-K., Ebisuzaki, W., Lin, R., Xie, P., Chen, M., Zhou, S., Higgins, W., Zou, C.-Z., Liu, Q., Chen, Y., Han, Y., Cucurull, L., Reynolds, R. W., Rutledge, G., and Goldberg, M.: The NCEP Climate Forecast System Reanalysis, B. Am. Meteorol. Soc., 91, 1015–1058, https://doi.org/10.1175/2010BAMS3001.1, 2010a.
Saha, S., et al.: NCEP Climate Forecast System Reanalysis (CFSR) 6-hourly Products, January 1979 to December 2010. Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory [data set], https://doi.org/10.5065/D69K487J, 2010b.
Slater, A. G. and Lawrence, D. M.: Diagnosing present and future permafrost from climate models, J. Climate, 26, 5608e5623, https://doi.org/10.1175/jcli-d-12-00341.1, 2013
Smith, S., O'Neill, H., Isaksen, K., Noetzli, J., and Romanovsky, V.: The changing thermal state of permafrost, Nat. Rev. Earth Environ., 3, 10–23, https://doi.org/10.1038/s43017-021-00240-1, 2022.
Su, B., Huang, J., Gemmer, M., Jian, D., Tao, H., Jiang, T., and Zhao, C.: Statistical downscaling of CMIP5 multi-model ensemble for projected changes of climate in the Indus River Basin, Atmos. Res., 178, 138–149, https://doi.org/10.1016/j.atmosres.2016.03.023 ,2016.
Sun, Z., Zhao, L., Hu, G., Qiao, Y., Du, E., Zou, D., and Xie, C.: Modeling permafrost changes on the Qinghai-Tibetan plateau from 1966 to 2100: a case study from two boreholes along the Qinghai-Tibet engineering corridor, Permafrost Periglac., 32, 156–171, https://doi.org/10.1002/ppp.2022, 2019.
Sun, Z., Zhao, L., Hu, G., Zhou, H., Liu, S., Qiao, Y., Du, E., Zou, D., and Xie, C.: Numerical simulation of thaw settlement and permafrost changes at three sites along the Qinghai-Tibet Engineering Corridor in a warming climate, Geophys. Res. Lett., 49, e2021GL097334, https://doi.org/10.1029/2021GL097334, 2022.
Sun, Z., Zhao, L., Hu, G., Zhou, H., Liu, S., Qiao, Y., Du, E., Zou, D., and Xie, C.: Effects of Ground Subsidence on Permafrost Simulation Related to Climate Warming, Atmosphere, 15, 12, https://doi.org/10.3390/atmos15010012, 2023.
Walvoord, M. and Kurylyk, B.: Hydrologic Impacts of Thawing Permafrost – A Review, Vadose Zone J., 15, vzj2016.01.0010, https://doi.org/10.2136/vzj2016.01.0010, 2016.
Wang, L., Zhao, L., Zhou, H., Liu, S., Hu, G., Li, Z., Wang, C., and Zhao, J.: Evidence of ground ice melting detected by InSAR and in situ monitoring over permafrost terrain on the Qinghai-Xizang (Tibet) Plateau, Permafrost Periglac., 34, 52–67, https://doi.org/10.1002/ppp.2171, 2023.
Wang, T., Wang, N., and Li, S.: Map of the glaciers, frozen ground and desert in China, 1:4000000, Chinese Map Press, Beijing, China, SSN 7503139889, 2006.
Wang, W., Rinke, A., Moore, J. C., Cui, X., Ji, D., Li, Q., Zhang, N., Wang, C., Zhang, S., Lawrence, D. M., McGuire, A. D., Zhang, W., Delire, C., Koven, C., Saito, K., MacDougall, A., Burke, E., and Decharme, B.: Diagnostic and model dependent uncertainty of simulated Tibetan permafrost area, The Cryosphere, 10, 287–306, https://doi.org/10.5194/tc-10-287-2016, 2016.
Wang, Z., Wang, Q., Zhao, L., Wu, X., Yue, G., Zou, D., Nan, Z., Liu, G., Pang, Q., Fang, H., Wu, T., Shi, J., Jiao, K., Zhao, Y., and Zhang, L.: Mapping the vegetation distribution of the permafrost zone on the Qinghai-Tibet Plateau, J. Mt. Sci., 13, 1035–1046, 2016.
Westermann, S., Schuler, T. V., Gisnås, K., and Etzelmüller, B.: Transient thermal modeling of permafrost conditions in Southern Norway, The Cryosphere, 7, 719–739, https://doi.org/10.5194/tc-7-719-2013, 2013.
Westermann, S., Østby, T. I., Gisnås, K., Schuler, T. V., and Etzelmüller, B.: A ground temperature map of the North Atlantic permafrost region based on remote sensing and reanalysis data, The Cryosphere, 9, 1303–1319, https://doi.org/10.5194/tc-9-1303-2015, 2015.
Westermann, S., Langer, M., Boike, J., Heikenfeld, M., Peter, M., Etzelmüller, B., and Krinner, G.: Simulating the thermal regime and thaw processes of ice-rich permafrost ground with the land-surface model CryoGrid 3, Geosci. Model Dev., 9, 523–546, https://doi.org/10.5194/gmd-9-523-2016, 2016.
Westermann, S., Peter, M., Langer, M., Schwamborn, G., Schirrmeister, L., Etzelmüller, B., and Boike, J.: Transient modeling of the ground thermal conditions using satellite data in the Lena River delta, Siberia, The Cryosphere, 11, 1441–1463, https://doi.org/10.5194/tc-11-1441-2017, 2017.
Wu, J. and Gao, X. J.: A gridded daily observation dataset over China region and comparison with other datasets, Chinese Journal of Geophysics, 56, 1102–1111, https://doi.org/10.6038/cjg20130406, 2013 (in Chinese with English abstract).
Wu, J., Sheng, Y., Wu, Q., and Wen, Z.: Processes and modes of permafrost degradation on the Qinghai-Tibet Plateau, Sci. China Ser. D-Earth Sci., 53, 150–158, https://doi.org/10.1007/s11430-009-0198-5, 2010.
Wu, Q. and Zhang, T.: Recent permafrost warming on the Qinghai-Tibetan Plateau, J. Geophys. Res., 113, 1–22, 2008.
Wu, X., Nan, Z., Zhao, S., Zhao, L., and Cheng, G.: Spatial modeling of permafrost distribution and properties on the Qinghai-Tibet Plateau, Permafrost Periglac., 29, 86–99, https://doi.org/10.1002/ppp.1971, 2018.
Xiao, Y., Zhao, L., Dai, Y., Li, R., Pang, Q., and Yao, J.: Representing permafrost properties in CoLM for the Qinghai–Xizang (Tibetan) plateau, Cold Reg. Sci. Technol., 87, 68–77, https://doi.org/10.1016/j.coldregions.2012.12.004, 2013.
Xing, Z., Zhao, L., Fan, L., Hu, G., Zou, D., Wang, C., Liu, S., Du, E., Xiao, Y., Li, R., Liu, G., Qiao, Y., and Shi, J.: Changes in the ground surface temperature in permafrost regions along the Qinghai–Tibet engineering corridor from 1900 to 2014: a modified assessment of CMIP6, Adv. Clim. Chang. Res., 14, 85–96, https://doi.org/10.1016/j.accre.2023.01.007, 2023.
Yang, S., Li, R., Wu, T., Hu, G., Xiao, Y., Du, Y., Zhu, X., Ni, J., Ma, J., Zhang, Y., and Shi, J.: Evaluation of reanalysis soil temperature and soil moisture products in permafrost regions on the Qinghai Tibetan Plateau, Geoderma, 377, 114583, https://doi.org/10.1016/j.geoderma.2020.114583, 2020.
Yao, T., Xue, Y., Chen, D., Chen, F., Thompson, L., Cui, P., Koike, T., Lau, W. K., Lettenmaier, D., Mosbrugger, V., Zhang, R., Xu, B., Dozier, J., Gillespie, T., Gu, Y., Kang, S., Piao, S., Sugimoto, S., Ueno, K., Wang, L., Wang, W., Zhang, F., Sheng, Y., Guo, W., Yang, X., Ma, Y., Shen, S. S. P., Su, Z., Chen, F., Liang, S., Liu, Y., Singh, V. P., Yang, K., Yang, D., Zhao, X., Qian, Y., Zhang, Y., and Li, Q.: Recent Third Pole's Rapid Warming Accompanies Cryospheric Melt and Water Cycle Intensification and Interactions between Monsoon and Environment: Multidisciplinary Approach with Observations, Modeling, and Analysis, B. Am. Meteorol. Soc., 100, 423–444, https://doi.org/10.1175/BAMS-D-17-0057.1, 2019.
Yan, D., Ma, N., and Zhang, Y.: Development of a fine-resolution snow depth product based on the snow cover probability for the Tibetan Plateau: Validation and spatial–temporal analyses, J. Hydrol., 604, 127027, https://doi.org/10.1016/j.jhydrol.2021.127027, 2022.
Yi, Y., Kimball, J. S., Chen, R. H., Moghaddam, M., Reichle, R. H., Mishra, U., Zona, D., and Oechel, W. C.: Characterizing permafrost active layer dynamics and sensitivity to landscape spatial heterogeneity in Alaska, The Cryosphere, 12, 145–161, https://doi.org/10.5194/tc-12-145-2018, 2018.
You, Q., Cai, Z., Pepin, N., Chen, D., Ahrens, B., Jiang, Z., Wu, F., Kang, S., Zhang, R., Wu, T., Wang, P., Li, M., Zou, Z., Gao, Y., Zhai, P., and Zhang, Y.: Warming amplification over the Arctic Pole and Third Pole: Trends, mechanisms and consequences, Earth Sci. Rev., 217, 103625, https://doi.org/10.1016/j.earscirev.2021.103625, 2021.
Zhang, G., Chen, W., and Xie, H.: Tibetan Plateau's Lake level and volume changes from NASA's ICESat/ICESat-2 and Landsat missions, Geophys. Res. Lett., 46, 13107–13118, https://doi.org/10.1029/2019GL084912, 2019.
Zhang, T.: Influence of the seasonal snow cover on the ground thermal regime: An overview, Rev. Geophys., 43, RG4002, https://doi.org/10.1029/2004RG000157, 2005.
Zhang, Y., Wang, X., Fraser, R., Olthof, I., Chen, W., Mclennan, D., Ponomarenko, S., and Wu, W.: Modelling and mapping climate change impacts on permafrost at high spatial resolution for an Arctic region with complex terrain, The Cryosphere, 7, 1121–1137, https://doi.org/10.5194/tc-7-1121-2013, 2013.
Zhang, Y., Olthof, I., Fraser, R., and Wolfe, S. A.: A new approach to mapping permafrost and change incorporating uncertainties in ground conditions and climate projections, The Cryosphere, 8, 2177–2194, https://doi.org/10.5194/tc-8-2177-2014, 2014.
Zhao, J., Zhao, L., Sun, Z., Niu, F., Hu, G., Zou, D., Liu, G., Du, E., Wang, C., Wang, L., Qiao, Y., Shi, J., Zhang, Y., Gao, J., Wang, Y., Li, Y., Yu, W., Zhou, H., Xing, Z., Xiao, M., Yin, L., and Wang, S.: Simulating the current and future northern limit of permafrost on the Qinghai–Tibet Plateau, The Cryosphere, 16, 4823–4846, https://doi.org/10.5194/tc-16-4823-2022, 2022.
Zhao, L. and Sheng, Y.: Permafrost survey manual, Science Press, Beijing, 13–14, ISBN 9787030581334, 2015.
Zhao, L. and Sheng, Y.: Permafrost and environment changes on the Qinghai-Tibetan Plateau, Beijing, China: Science Press, ISBN 9787030581334, 2019.
Zhao, L., Ding, Y., Liu, G., Wang, S., and Jin, H.: Estimates of the reserves of ground ice in permafrost regions on the Tibetan plateau, J. Glaciol. Geocryol., 32, 1–9, 2010a.
Zhao, L., Wu, Q., Marchenko, S., and Sharkhuu, N.: Thermal state of permafrost and active layer in Central Asia during the international polar year, Permafrost Periglac., 21, 198–207, https://doi.org/10.1002/ppp.688, 2010b.
Zhao, L., Wu, T., Xie, C., Li, R., Wu, X., Yao, J., Yue, G., and Xiao, Y.: Support geoscience research, environmental management, and engineering construction with investigation and monitoring on permafrost in the Qinghai-Tibet plateau, China, Bull. Chin. Acad. Sci. 32, 1159–1168, https://doi.org/10.16418/j.issn.1000-3045.2017.10.015, 2017 (in Chinese with English abstract).
Zhao, L., Hu, G., Zou, D., Wu, X., Ma, L., Sun, Z., Yuan, L., Zhou, H., and Liu, S.: Permafrost Changes and Its Effects on Hydrological Processes on Qinghai-Tibet Plateau, Bull. Chin. Acad. Sci., 34, 1233–1246, https://doi.org/10.16418/j.issn.1000-3045.2019.11.006, 2019.
Zhao, L., Zou, D. Hu, G., Du, E., Pang, Q., Xiao, Y., Li, R., Sheng, Y., Wu, X., Sun, Z., Wang, L., Wang, C., Ma, L., Zhou, H., and Liu, S.: Changing climate and the permafrost environment on the Qinghai-Tibet (Xizang) Plateau, Permafrost Periglac., 31, 396–405, https://doi.org/10.1002/ppp.2056, 2020.
Zhao, L., Zou, D., Hu, G., Wu, T., Du, E., Liu, G., Xiao, Y., Li, R., Pang, Q., Qiao, Y., Wu, X., Sun, Z., Xing, Z., Sheng, Y., Zhao, Y., Shi, J., Xie, C., Wang, L., Wang, C., and Cheng, G.: A synthesis dataset of permafrost thermal state for the Qinghai–Tibet (Xizang) Plateau, China, Earth Syst. Sci. Data, 13, 4207–4218, https://doi.org/10.5194/essd-13-4207-2021, 2021a.
Zhao, L., Hu, G., Zou, D., Wu, T., Du, E., Liu, G., Xiao, Y., Li, R., Pang, Q., Qiao, Y., Wu, X., Sun, Z., Xing, Z., Zhao, Y., Shi, J., Xie, C., Wang, L., Wang, C., and Cheng, G.: A synthesis dataset of permafrost for the Qinghai-Xizang (Tibet) Plateau, China (2002–2018), National Tibetan Plateau / Third Pole Environment Data Center [data set], https://doi.org/10.11888/Geocry.tpdc.271107, 2021b.
Zhao, L., Hu, G., Liu, G., Zou, D., Wang, Y., Xiao, Y., Du, E., Wang, C., Xing, Z., Sun, Z., Zhao, Y., Liu, S., Zhang, Y., Wang, L., Zhou, H., and Zhao, J.: Investigation, Monitoring, and Simulation of Permafrost on the Qinghai-Tibet Plateau: A Review, Permafrost Periglac., 35, 412–422, https://doi.org/10.1002/ppp.2227, 2024.
Zhou, C. and Cheng, W.: 1:1,000,000 geomorphological map of Western China, National Tibetan Plateau / Third Pole Environment Data Center [data set], https://doi.org/10.11888/Geogra.tpdc.270104, 2019.
Zou, D., Pang, Q., Zhao, L., Wang, L., Hu, G., Du, E., Liu, G., Liu, S., and Liu, Y.: Estimation of Permafrost Ground Ice to 10 m Depth on the Qinghai-Tibet Plateau, Permafrost Periglac., https://doi.org/10.1002/ppp.2226, 2024.
Zou, D., Zhao, L., Wu, T., Wu, X., Pang, Q., and Wang, Z.: Modeling ground surface temperature by means of remote sensing data in high-altitude areas: test in the central Tibetan Plateau with application of moderate-resolution imaging spectroradiometer Terra/Aqua land surface temperature and ground based infrared radiometer, J. Appl. Remote Sens., 8, 083516, https://doi.org/10.1117/1.JRS.8.083516, 2014.
Zou, D., Zhao, L., Sheng, Y., Chen, J., Hu, G., Wu, T., Wu, J., Xie, C., Wu, X., Pang, Q., Wang, W., Du, E., Li, W., Liu, G., Li, J., Qin, Y., Qiao, Y., Wang, Z., Shi, J., and Cheng, G.: A new map of permafrost distribution on the Tibetan Plateau, The Cryosphere, 11, 2527–2542, https://doi.org/10.5194/tc-11-2527-2017, 2017.
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...
