Articles | Volume 16, issue 12
https://doi.org/10.5194/tc-16-4823-2022
© Author(s) 2022. 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-16-4823-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
05 Dec 2022
Research article |
| 05 Dec 2022
| 05 Dec 2022
Simulating the current and future northern limit of permafrost on the Qinghai–Tibet Plateau
Jianting Zhao
School of Geographical Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, China
Lin Zhao
CORRESPONDING AUTHOR
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–Xizang 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
Key Laboratory of Environment Change and Resources Use in Beibu Gulf, Ministry of Education, Nanning Normal University, Nanning 530001, China
Fujun Niu
State Key Laboratory of Frozen Soil Engineering, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
Guojie Hu
Cryosphere Research Station on the Qinghai–Xizang 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–Xizang Plateau, State Key Laboratory of Cryospheric Sciences,
Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
Guangyue Liu
Cryosphere Research Station on the Qinghai–Xizang 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–Xizang Plateau, State Key Laboratory of Cryospheric Sciences,
Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
Chong Wang
School of Geographical Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, China
Lingxiao Wang
School of Geographical Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, China
Yongping Qiao
Cryosphere Research Station on the Qinghai–Xizang Plateau, State Key Laboratory of Cryospheric Sciences,
Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
Jianzong Shi
Cryosphere Research Station on the Qinghai–Xizang Plateau, State Key Laboratory of Cryospheric Sciences,
Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
Yuxin Zhang
School of Geographical Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, China
Junqiang Gao
School of Mathematics and Statistics, Nanjing University of Information Science & Technology, Nanjing 210044, China
Yuanwei Wang
School of Geographical Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, China
School of Geographical Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, China
Wenjun Yu
School of Hydrology and Water Resources, Nanjing University of Information Science & Technology, Nanjing 210044, China
Huayun Zhou
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 101408, China
Cryosphere Research Station on the Qinghai–Xizang Plateau, State Key Laboratory of Cryospheric Sciences,
Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
Zanpin Xing
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 101408, China
Cryosphere Research Station on the Qinghai–Xizang 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
Luhui Yin
School of Geographical Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, China
Shengfeng Wang
School of Geographical Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, China
Related authors
Jianting Zhao, Lin Zhao, Ze Sun, Guojie Hu, Defu Zou, Minxuan Xiao, Guangyue Liu, Qiangqiang Pang, Erji Du, Zhibin Li, Xiaodong Wu, Yao Xiao, Lingxiao Wang, and Wenxin Zhang
EGUsphere, https://doi.org/10.5194/egusphere-2024-3956, https://doi.org/10.5194/egusphere-2024-3956, 2025
Short summary
Short summary
The thermal regime is a key indicator of permafrost evolution. We quantitatively analyzed the spatiotemporal dynamics of the permafrost status in western Tibet since the 1980s, based on numerical simulations using the enhanced, model-forcing-driven Moving-Grid Permafrost Model. Our simulated results indicated that slow and lagged response of permafrost to climate warming, which closely linked to historical thermal conditions.
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.
Xianmin Ke, Wei Wang, Fujun Niu, Zeyong Gao, Wenkang Huang, and Huake Cao
EGUsphere, https://doi.org/10.5194/egusphere-2025-864, https://doi.org/10.5194/egusphere-2025-864, 2025
Short summary
Short summary
Measurements of the permafrost distribution are often limited to seasonally frozen soil or permafrost at a few borehole locations, and the detection of deep permafrost and sublake taliks in the QTP has rarely been attempted. We used ERT, TEM, and ground temperature measurement (GTM) methods to investigate permafrost structure and sublake talik morphologies. We determined the current permafrost structure and found that permafrost below three thermokarst lakes has thawed completely.
Jianting Zhao, Lin Zhao, Ze Sun, Guojie Hu, Defu Zou, Minxuan Xiao, Guangyue Liu, Qiangqiang Pang, Erji Du, Zhibin Li, Xiaodong Wu, Yao Xiao, Lingxiao Wang, and Wenxin Zhang
EGUsphere, https://doi.org/10.5194/egusphere-2024-3956, https://doi.org/10.5194/egusphere-2024-3956, 2025
Short summary
Short summary
The thermal regime is a key indicator of permafrost evolution. We quantitatively analyzed the spatiotemporal dynamics of the permafrost status in western Tibet since the 1980s, based on numerical simulations using the enhanced, model-forcing-driven Moving-Grid Permafrost Model. Our simulated results indicated that slow and lagged response of permafrost to climate warming, which closely linked to historical thermal conditions.
Xiaoqing Peng, Guangshang Yang, Oliver W. Frauenfeld, Xuanjia Li, Weiwei Tian, Guanqun Chen, Yuan Huang, Gang Wei, Jing Luo, Cuicui Mu, and Fujun Niu
Earth Syst. Sci. Data, 16, 2033–2045, https://doi.org/10.5194/essd-16-2033-2024, https://doi.org/10.5194/essd-16-2033-2024, 2024
Short summary
Short summary
It is important to know about the distribution of thermokarst landscapes. However, most work has been done in the permafrost regions of the Qinghai–Tibetan Plateau, except for the Qilian Mountains in the northeast. Here we used satellite images and field work to investigate and analyze its potential driving factors. We found a total of 1064 hillslope thermokarst (HT) features in this area, and 82 % were initiated in the last 10 years. These findings will be significant for the next predictions.
Chenhao Chai, Lei Wang, Deliang Chen, Jing Zhou, Hu Liu, Jingtian Zhang, Yuanwei Wang, Tao Chen, and Ruishun Liu
Hydrol. Earth Syst. Sci., 26, 4657–4683, https://doi.org/10.5194/hess-26-4657-2022, https://doi.org/10.5194/hess-26-4657-2022, 2022
Short summary
Short summary
This work quantifies future snow changes and their impacts on hydrology in the upper Salween River (USR) under SSP126 and SSP585 using a cryosphere–hydrology model. Future warm–wet climate is not conducive to the development of snow. The rain–snow-dominated pattern of runoff will shift to a rain-dominated pattern after the 2040s under SSP585 but is unchanged under SSP126. The findings improve our understanding of cryosphere–hydrology processes and can assist water resource management in the USR.
Zhuoxuan Xia, Lingcao Huang, Chengyan Fan, Shichao Jia, Zhanjun Lin, Lin Liu, Jing Luo, Fujun Niu, and Tingjun Zhang
Earth Syst. Sci. Data, 14, 3875–3887, https://doi.org/10.5194/essd-14-3875-2022, https://doi.org/10.5194/essd-14-3875-2022, 2022
Short summary
Short summary
Retrogressive thaw slumps are slope failures resulting from abrupt permafrost thaw, and are widely distributed along the Qinghai–Tibet Engineering Corridor. The potential damage to infrastructure and carbon emission of thaw slumps motivated us to obtain an inventory of thaw slumps. We used a semi-automatic method to map 875 thaw slumps, filling the knowledge gap of thaw slump locations and providing key benchmarks for analysing the distribution features and quantifying spatio-temporal changes.
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.
T. Qu, H. Zhang, F. Niu, X. Shi, and Z. Li
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 887–891, https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-887-2020, https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-887-2020, 2020
Wenfeng Huang, Bin Cheng, Jinrong Zhang, Zheng Zhang, Timo Vihma, Zhijun Li, and Fujun Niu
Hydrol. Earth Syst. Sci., 23, 2173–2186, https://doi.org/10.5194/hess-23-2173-2019, https://doi.org/10.5194/hess-23-2173-2019, 2019
Short summary
Short summary
Up to now, little has been known on ice thermodynamics and lake–atmosphere interaction over the Tibetan Plateau during ice-covered seasons due to a lack of field data. Here, model experiments on ice thermodynamics were conducted in a shallow lake using HIGHTSI. Water–ice heat flux was a major source of uncertainty for lake ice thickness. Heat and mass budgets were estimated within the vertical air–ice–water system. Strong ice sublimation occurred and was responsible for water loss during winter.
Lu Gao, Jianhui Wei, Lingxiao Wang, Matthias Bernhardt, Karsten Schulz, and Xingwei Chen
Earth Syst. Sci. Data, 10, 2097–2114, https://doi.org/10.5194/essd-10-2097-2018, https://doi.org/10.5194/essd-10-2097-2018, 2018
Short summary
Short summary
High-resolution temperature data sets are important for the Chinese Tian Shan, which has a complex ecological environment system. This study presents a unique high-resolution (1 km, 6-hourly) air temperature data set for this area from 1979 to 2016 based on a robust statistical downscaling framework. The strongest advantage of this method is its independence of local meteorological stations due to a model internal, vertical lapse rate scheme. This method was validated for other mountains.
Defu Zou, Lin Zhao, Yu Sheng, Ji Chen, Guojie Hu, Tonghua Wu, Jichun Wu, Changwei Xie, Xiaodong Wu, Qiangqiang Pang, Wu Wang, Erji Du, Wangping Li, Guangyue Liu, Jing Li, Yanhui Qin, Yongping Qiao, Zhiwei Wang, Jianzong Shi, and Guodong Cheng
The Cryosphere, 11, 2527–2542, https://doi.org/10.5194/tc-11-2527-2017, https://doi.org/10.5194/tc-11-2527-2017, 2017
Short summary
Short summary
The area and distribution of permafrost on the Tibetan Plateau are unclear and controversial. This paper generated a benchmark map based on the modified remote sensing products and validated it using ground-based data sets. Compared with two existing maps, the new map performed better and showed that permafrost covered areas of 1.06 × 106 km2. The results provide more detailed information on the permafrost distribution and basic data for use in future research on the Tibetan Plateau permafrost.
Related subject area
Discipline: Frozen ground | Subject: Numerical Modelling
The thermal state of permafrost in under climate change on the Qinghai-Tibet Plateau from 1980 to 2022: A case study of the West Kunlun
Modelling the effect of free convection on permafrost melting rates in frozen rock clefts
Coupled thermo–geophysical inversion for permafrost monitoring
Simulating ice segregation and thaw consolidation in permafrost environments with the CryoGrid community model
Investigating the thermal state of permafrost with Bayesian inverse modeling of heat transfer
Representation of soil hydrology in permafrost regions may explain large part of inter-model spread in simulated Arctic and subarctic climate
Evaluating simplifications of subsurface process representations for field-scale permafrost hydrology models
Strong increase in thawing of subsea permafrost in the 22nd century caused by anthropogenic climate change
Lateral thermokarst patterns in permafrost peat plateaus in northern Norway
A method for solving heat transfer with phase change in ice or soil that allows for large time steps while guaranteeing energy conservation
Effects of multi-scale heterogeneity on the simulated evolution of ice-rich permafrost lowlands under a warming climate
Evaluating permafrost physics in the Coupled Model Intercomparison Project 6 (CMIP6) models and their sensitivity to climate change
Pathways of ice-wedge degradation in polygonal tundra under different hydrological conditions
Thaw processes in ice-rich permafrost landscapes represented with laterally coupled tiles in a land surface model
Observation and modelling of snow at a polygonal tundra permafrost site: spatial variability and thermal implications
The physical properties of coarse-fragment soils and their effects on permafrost dynamics: a case study on the central Qinghai–Tibetan Plateau
Jianting Zhao, Lin Zhao, Ze Sun, Guojie Hu, Defu Zou, Minxuan Xiao, Guangyue Liu, Qiangqiang Pang, Erji Du, Zhibin Li, Xiaodong Wu, Yao Xiao, Lingxiao Wang, and Wenxin Zhang
EGUsphere, https://doi.org/10.5194/egusphere-2024-3956, https://doi.org/10.5194/egusphere-2024-3956, 2025
Short summary
Short summary
The thermal regime is a key indicator of permafrost evolution. We quantitatively analyzed the spatiotemporal dynamics of the permafrost status in western Tibet since the 1980s, based on numerical simulations using the enhanced, model-forcing-driven Moving-Grid Permafrost Model. Our simulated results indicated that slow and lagged response of permafrost to climate warming, which closely linked to historical thermal conditions.
Amir Sedaghatkish, Frédéric Doumenc, Pierre-Yves Jeannin, and Marc Luetscher
The Cryosphere, 18, 4531–4546, https://doi.org/10.5194/tc-18-4531-2024, https://doi.org/10.5194/tc-18-4531-2024, 2024
Short summary
Short summary
We developed a model to simulate the natural convection of water within frozen rock crevices subject to daily warming in mountain permafrost regions. Traditional models relying on conduction and latent heat flux typically overlook free convection. The results reveal that free convection can significantly accelerate the melting rate by an order of magnitude compared to conduction-based models. Our results are important for assessing the impact of climate change on mountain infrastructure.
Soňa Tomaškovičová and Thomas Ingeman-Nielsen
The Cryosphere, 18, 321–340, https://doi.org/10.5194/tc-18-321-2024, https://doi.org/10.5194/tc-18-321-2024, 2024
Short summary
Short summary
We present the results of a fully coupled modeling framework for simulating the ground thermal regime using only surface measurements to calibrate the thermal model. The heat conduction model is forced by surface ground temperature measurements and calibrated using the field measurements of time lapse apparent electrical resistivity. The resistivity-calibrated thermal model achieves a performance comparable to the traditional calibration of borehole temperature measurements.
Juditha Aga, Julia Boike, Moritz Langer, Thomas Ingeman-Nielsen, and Sebastian Westermann
The Cryosphere, 17, 4179–4206, https://doi.org/10.5194/tc-17-4179-2023, https://doi.org/10.5194/tc-17-4179-2023, 2023
Short summary
Short summary
This study presents a new model scheme for simulating ice segregation and thaw consolidation in permafrost environments, depending on ground properties and climatic forcing. It is embedded in the CryoGrid community model, a land surface model for the terrestrial cryosphere. We describe the model physics and functionalities, followed by a model validation and a sensitivity study of controlling factors.
Brian Groenke, Moritz Langer, Jan Nitzbon, Sebastian Westermann, Guillermo Gallego, and Julia Boike
The Cryosphere, 17, 3505–3533, https://doi.org/10.5194/tc-17-3505-2023, https://doi.org/10.5194/tc-17-3505-2023, 2023
Short summary
Short summary
It is now well known from long-term temperature measurements that Arctic permafrost, i.e., ground that remains continuously frozen for at least 2 years, is warming in response to climate change. Temperature, however, only tells half of the story. In this study, we use computer modeling to better understand how the thawing and freezing of water in the ground affects the way permafrost responds to climate change and what temperature trends can and cannot tell us about how permafrost is changing.
Philipp de Vrese, Goran Georgievski, Jesus Fidel Gonzalez Rouco, Dirk Notz, Tobias Stacke, Norman Julius Steinert, Stiig Wilkenskjeld, and Victor Brovkin
The Cryosphere, 17, 2095–2118, https://doi.org/10.5194/tc-17-2095-2023, https://doi.org/10.5194/tc-17-2095-2023, 2023
Short summary
Short summary
The current generation of Earth system models exhibits large inter-model differences in the simulated climate of the Arctic and subarctic zone. We used an adapted version of the Max Planck Institute (MPI) Earth System Model to show that differences in the representation of the soil hydrology in permafrost-affected regions could help explain a large part of this inter-model spread and have pronounced impacts on important elements of Earth systems as far to the south as the tropics.
Bo Gao and Ethan T. Coon
The Cryosphere, 16, 4141–4162, https://doi.org/10.5194/tc-16-4141-2022, https://doi.org/10.5194/tc-16-4141-2022, 2022
Short summary
Short summary
Representing water at constant density, neglecting cryosuction, and neglecting heat advection are three commonly applied but not validated simplifications in permafrost models to reduce computation complexity at field scale. We investigated this problem numerically by Advanced Terrestrial Simulator and found that neglecting cryosuction can cause significant bias (10%–60%), constant density primarily affects predicting water saturation, and ignoring heat advection has the least impact.
Stiig Wilkenskjeld, Frederieke Miesner, Paul P. Overduin, Matteo Puglini, and Victor Brovkin
The Cryosphere, 16, 1057–1069, https://doi.org/10.5194/tc-16-1057-2022, https://doi.org/10.5194/tc-16-1057-2022, 2022
Short summary
Short summary
Thawing permafrost releases carbon to the atmosphere, enhancing global warming. Part of the permafrost soils have been flooded by rising sea levels since the last ice age, becoming subsea permafrost (SSPF). The SSPF is less studied than the part on land. In this study we use a global model to obtain rates of thawing of SSPF under different future climate scenarios until the year 3000. After the year 2100 the scenarios strongly diverge, closely connected to the eventual disappearance of sea ice.
Léo C. P. Martin, Jan Nitzbon, Johanna Scheer, Kjetil S. Aas, Trond Eiken, Moritz Langer, Simon Filhol, Bernd Etzelmüller, and Sebastian Westermann
The Cryosphere, 15, 3423–3442, https://doi.org/10.5194/tc-15-3423-2021, https://doi.org/10.5194/tc-15-3423-2021, 2021
Short summary
Short summary
It is important to understand how permafrost landscapes respond to climate changes because their thaw can contribute to global warming. We investigate how a common permafrost morphology degrades using both field observations of the surface elevation and numerical modeling. We show that numerical models accounting for topographic changes related to permafrost degradation can reproduce the observed changes in nature and help us understand how parameters such as snow influence this phenomenon.
Niccolò Tubini, Stephan Gruber, and Riccardo Rigon
The Cryosphere, 15, 2541–2568, https://doi.org/10.5194/tc-15-2541-2021, https://doi.org/10.5194/tc-15-2541-2021, 2021
Short summary
Short summary
We present a new method to compute temperature changes with melting and freezing – a fundamental challenge in cryosphere research – extremely efficiently and with guaranteed correctness of the energy balance for any time step size. This is a key feature since the integration time step can then be chosen according to the timescale of the processes to be studied, from seconds to days.
Jan Nitzbon, Moritz Langer, Léo C. P. Martin, Sebastian Westermann, Thomas Schneider von Deimling, and Julia Boike
The Cryosphere, 15, 1399–1422, https://doi.org/10.5194/tc-15-1399-2021, https://doi.org/10.5194/tc-15-1399-2021, 2021
Short summary
Short summary
We used a numerical model to investigate how small-scale landscape heterogeneities affect permafrost thaw under climate-warming scenarios. Our results show that representing small-scale heterogeneities in the model can decide whether a landscape is water-logged or well-drained in the future. This in turn affects how fast permafrost thaws under warming. Our research emphasizes the importance of considering small-scale processes in model assessments of permafrost thaw under climate change.
Eleanor J. Burke, Yu Zhang, and Gerhard Krinner
The Cryosphere, 14, 3155–3174, https://doi.org/10.5194/tc-14-3155-2020, https://doi.org/10.5194/tc-14-3155-2020, 2020
Short summary
Short summary
Permafrost will degrade under future climate change. This will have implications locally for the northern high-latitude regions and may well also amplify global climate change. There have been some recent improvements in the ability of earth system models to simulate the permafrost physical state, but further model developments are required. Models project the thawed volume of soil in the top 2 m of permafrost will increase by 10 %–40 % °C−1 of global mean surface air temperature increase.
Jan Nitzbon, Moritz Langer, Sebastian Westermann, Léo Martin, Kjetil Schanke Aas, and Julia Boike
The Cryosphere, 13, 1089–1123, https://doi.org/10.5194/tc-13-1089-2019, https://doi.org/10.5194/tc-13-1089-2019, 2019
Short summary
Short summary
We studied the stability of ice wedges (massive bodies of ground ice in permafrost) under recent climatic conditions in the Lena River delta of northern Siberia. For this we used a novel modelling approach that takes into account lateral transport of heat, water, and snow and the subsidence of the ground surface due to melting of ground ice. We found that wetter conditions have a destabilizing effect on the ice wedges and associated our simulation results with observations from the study area.
Kjetil S. Aas, Léo Martin, Jan Nitzbon, Moritz Langer, Julia Boike, Hanna Lee, Terje K. Berntsen, and Sebastian Westermann
The Cryosphere, 13, 591–609, https://doi.org/10.5194/tc-13-591-2019, https://doi.org/10.5194/tc-13-591-2019, 2019
Short summary
Short summary
Many permafrost landscapes contain large amounts of excess ground ice, which gives rise to small-scale elevation differences. This results in lateral fluxes of snow, water, and heat, which we investigate and show how it can be accounted for in large-scale models. Using a novel model technique which can account for these differences, we are able to model both the current state of permafrost and how these landscapes change as permafrost thaws, in a way that could not previously be achieved.
Isabelle Gouttevin, Moritz Langer, Henning Löwe, Julia Boike, Martin Proksch, and Martin Schneebeli
The Cryosphere, 12, 3693–3717, https://doi.org/10.5194/tc-12-3693-2018, https://doi.org/10.5194/tc-12-3693-2018, 2018
Short summary
Short summary
Snow insulates the ground from the cold air in the Arctic winter, majorly affecting permafrost. This insulation depends on snow characteristics and is poorly quantified. Here, we characterize it at a carbon-rich permafrost site, using a recent technique that retrieves the 3-D structure of snow and its thermal properties. We adapt a snowpack model enabling the simulation of this insulation over a whole winter. We estimate that local snow variations induce up to a 6 °C spread in soil temperatures.
Shuhua Yi, Yujie He, Xinlei Guo, Jianjun Chen, Qingbai Wu, Yu Qin, and Yongjian Ding
The Cryosphere, 12, 3067–3083, https://doi.org/10.5194/tc-12-3067-2018, https://doi.org/10.5194/tc-12-3067-2018, 2018
Short summary
Short summary
Coarse-fragment soil on the Qinghai–Tibetan Plateau has different thermal and hydrological properties to soils commonly used in modeling studies. We took soil samples and measured their physical properties in a laboratory, which were used in a model to simulate their effects on permafrost dynamics. Model errors were reduced using the measured properties, in which porosity played an dominant role.
Cited articles
Bense, V., 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, F030361-18, https://doi.org/10.1029/2011JF002143, 2012.
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.
Cao, Y., Sheng, Y., Wu, J., Li, J., Ning, Z., Hu, X., Feng, Z., and Wang,
S.: Influence of upper boundary conditions on simulated ground temperature
field in permafrost regions, J. Glaciol. Geocryol., 36, 802–810, 2014 (in Chinese).
Chang, Y., Lyu, S., Luo, S., Li, Z., Fang, X., Chen, B., Chen, S., Li, R.,
and Chen, S.: Estimation of permafrost on the Tibetan Plateau under current
and future climate conditions using the CMIP5 data, Int. J. Climatol., 38,
5659–5676, https://doi.org/10.1002/joc.5770, 2018.
Cheng, G.: Influences of local factors on permafrost occurrence and their
implications for Qinghai-Xizang Railway design, Sci. China D,
47, 704–709, https://doi.org/10.1007/BF02893300, 2004.
Cheng, G. and Jin, H.: Permafrost and groundwater on the Qinghai Tibet
Plateau and in Northeast China, Hydrogeol. J., 21, 5–23,
https://doi.org/10.1007/s10040-012-0927-2, 2013.
Cheng, G. and Wu, T.: Responses of permafrost to climate change and their
environmental significance, Qinghai-Tibet Plateau, J. Geophys. Res., 112,
1–10, https://doi.org/10.1029/2006JF000631, 2007.
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).
Cheng, G. D.: Problems on zonation of high-altitude permafrost, Acta Geogr.
Sin., 39, 185–193, 1984.
Construction Ministry of PRC: Code for design of soil and foundation of
building in frozen soil region, China Architecture and Building
Press, Beijing, China, 2011 (in Chinese).
Ehlers, T., Chen, D., Appel, E., Bolch, T., Chen, F., Diekmann, B., Dippold,
M., Giese, M., Guggenberger, G., Lai, H., Li, X., Liu, J., Liu, Y., Ma, Y.,
Miehe, G., Mosbrugger, V., Mulch, A., Piao, S., Schwalb, A., Thompson, L.,
Su, Z., Sun, H., Yao, T., Yang, X., Yang, K., and Zhu L.: Past, present, and
future geo-biosphere interactions on the Tibetan Plateau and implications
for permafrost, Earth Sci. Rev., 234, 104197,
https://doi.org/10.1016/j.earscirev.2022.104197, 2022.
Fewster, R., Morris, P., Ivanovic, R., Swindles, G., Peregon, A., and Smith,
C.: Imminent loss of climate space for permafrost peatlands in Europe and
Western Siberia, Nat. Clim. Change, 12, 373–379,
https://doi.org/10.1038/s41558-022-01296-7, 2022.
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, 117, D05106, J.
Geophys. Res.-Atmos., 117, D05106, https://doi.org/10.1029/2011JD016545, 2012.
Hipp, T., Etzelmüller, B., Farbrot, H., Schuler, T. V., and Westermann, S.: Modelling borehole temperatures in Southern Norway – insights into permafrost dynamics during the 20th and 21st century, The Cryosphere, 6, 553–571, https://doi.org/10.5194/tc-6-553-2012, 2012.
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., Wu, X., Li, R., Wu, T., Xie, C., Pang, Q., and Zou, D.:
Comparison of the thermal conductivity parameterizations for a freeze-thaw
algorithm with a multi-layered soil in permafrost regions, Catena, 156,
244–251, https://doi.org/10.1016/j.catena.2017.04.011, 2017.
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: 29 November 2022), 2021.
IPCC: Special report on the ocean and cryosphere in a changing climate,
https://archive.ipcc.ch/srocc/ (last access: 29 November 2022), 2019.
Iturbide, M., Gutiérrez, J. M., Alves, L. M., Bedia, J., Cerezo-Mota, R., Cimadevilla, E., Cofiño, A. S., Di Luca, A., Faria, S. H., Gorodetskaya, I. V., Hauser, M., Herrera, S., Hennessy, K., Hewitt, H. T., Jones, R. G., Krakovska, S., Manzanas, R., Martínez-Castro, D., Narisma, G. T., Nurhati, I. S., Pinto, I., Seneviratne, S. I., van den Hurk, B., and Vera, C. S.: An update of IPCC climate reference regions for subcontinental analysis of climate model data: definition and aggregated datasets, Earth Syst. Sci. Data, 12, 2959–2970, https://doi.org/10.5194/essd-12-2959-2020, 2020 (data available at: https://interactive-atlas.ipcc.ch, last access: 29 November 2022).
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 Edith, G.: Hole-filled seamless SRTM
data V4, Tech. rep., International Centre for Tropical Agriculture (CIAT),
Cali, Columbia, https://srtm.csi.cgiar.org/ (last access: 29 November 2022), 2008.
Jin, H., Li, S., Cheng, G., Wang, S., and Li, X.: Permafrost and climatic
change in China, Global Planet. Change, 26, 387–404,
https://doi.org/10.1016/S0921-8181(00)00051-5, 2000.
Jin, H., Zhao, L., Wang, S., and Jin, R.: Thermal regimes and degradation
modes of permafrost along the Qinghai-Tibet Highway, Sci. China D, 49, 1170–1183, https://doi.org/10.1007/s11430-006-2003-z, 2006.
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, 2011.
Jin, H., Wu, Q., and Romanovsky, V.: Degrading permafrost and its impacts,
Adv. Clim. Change Res., 12, 1–5, https://doi.org/10.1016/j.accre.2021.01.007, 2021.
Lawrence, D., Slater, A., and Swenson, S: 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.
Lee, H., Swenson, S., Slater, A., and Lawrence, D.: Effects of excess
ground ice on projections of permafrost in a warming climate, Environ. Res.
Lett., 9, 124006, https://doi.org/10.1088/1748-9326/9/12/124006, 2014.
Li, D., Chen, J., Meng, Q., Liu, D., Fang, J., and Liu, J.: Numeric
simulation of permafrost degradation in the eastern Tibetan Plateau,
Permafrost Periglac., 19, 93–99,
https://doi.org/10.1002/ppp.611, 2008.
Li, R., Zhao, L., Ding, Y., Wu, T., Xiao, Y., Du, E., Liu, G., and Qiao, Y.:
Temporal and spatial variations of the active layer along the Qinghai-Tibet
Highway in a permafrost region, Chinese Sci. Bull., 57, 4609–4616,
https://doi.org/10.1007/s11434-012-5323-8, 2012.
Li, S., Cheng, G., and Guo, D.: The future thermal regime of numerical
simulating permafrost on the Qinghai-Xizang (Tibet) Plateau, China, under a
warming climate, Sci. China D, 39, 434–441, 1996.
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, 2015b.
Li, X., Cheng, G., Jin, H., Kang, E., Che, T., Jin, R., Wu, L., Nan, Z.,
Wang, J., and Shen, Y.: Cryospheric Change in China, Global Planet. Change,
62, 210–218, https://doi.org/10.1016/j.gloplacha.2008.02.001, 2008.
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.
Lu, Q., Zhao, D., and Wu, S.: Simulated responses of permafrost distribution
to climate change on the Qinghai–Tibet Plateau, Sci. Rep.-UK, 7, 3845,
https://doi.org/10.1038/s41598-017-04140-7, 2017.
Luo, J., Niu, F., Lin, Z., Liu, M., and Yin, G.: Variations in the northern
permafrost boundary over the last four decades in the Xidatan region,
Qinghai–Tibet Plateau, J. Mt. Sci., 15, 765–778,
https://doi.org/10.1007/s11629-017-4731-2, 2018.
Miner, K., Turetsky, M., Malina, E., Bartsch, A., Tamminen, J., McGuire, A.,
Fix, A., Sweeney, C., Elder, C., and Miller, C.: Permafrost carbon emissions
in a changing Arctic, Nat. Rev. Earth Environ., 3, 55–67,
https://doi.org/10.1038/s43017-021-00230-3, 2022.
Nan, Z., Gao, Z., Li, S., and Wu, T.: Permafrost changes in the northern
limit of permafrost on the Qinghai-Tibet plateau in the last 30 years, J.
Geogr. Sci., 58, 817–823, 2003.
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.
Nitze, I., Grosse, G., Jones, B. M., Romanovsky, V. E., and Boike, J.:
Remote sensing quantifies widespread abundance of permafrost region
disturbances across the Arctic and Subarctic, Nat. Commun., 9, 5423,
https://doi.org/10.1038/s41467-018-07663-3, 2018.
Obu, J., Westermann, S., Bartsch, A., Berdnikov, N., Christiansen, H.,
Dashtseren, A., Delaloye, R., Elberling, B., Etzelmüller, B., Kholodov,
A., Khomutov, A., Kääb, A., Leibmanc, M., Lewkowicz, A., Panda, S.,
Romanovsky, V., Way, R., Westergaard-Nielsen, A., Wu, T., Yamkhin, J., and
Zou, D.: Northern Hemisphere permafrost map based on TTOP modelling for
2000–2016 at 1 km2 scale, Earth-Sci. Rev., 193, 299–316,
https://doi.org/10.1016/j.earscirev.2019.04.023, 2019.
Qin, D.: Glossary of cryosphere science, Meteorological Press,
Beijing, China, ISBN 9787502959920, 2014 (in Chinses).
Ran, Y., Li, X., Cheng, G., Zhang, T., Wu, Q., Jin, H., and Jin, R.:
Distribution of permafrost in China: an overview of existing permafrost
maps, Permafrost Periglac., 23,
322–333, https://doi.org/10.1002/ppp.1756, 2012.
Riseborough, D., Shiklomanov, N., Etzelmüller B, Gruber, S., and
Marchenko, S.: Recent advances in permafrost modelling, Permafrost Periglac., 19, 137–156, https://doi.org/10.1002/ppp.615, 2008.
Schädel, C., Bader, M., Schuur, E., Biasi, C., Bracho, R., Čapek,
P., Baets, S., Baets, S., Diáková, K., Ernakovich, J., Aragones, C.,
Graham, D., Hartley, I., Iversen, C., Kane, E., Knoblauch, C., Lupascu, M.,
Martikainen, P., Natali, S., Norby, R., O'Donnell, J., Chowdhury,T.,
Šantrůčková, H., Shaver, G., Sloan, V., Treat, C., Turetsky,
M., Waldrop, M., and Wickland, K.: Potential carbon emissions dominated by
carbon dioxide from thawed permafrost soils, Nature Clim.
Change, 6, 950–953, https://doi.org/10.1038/nclimate3054, 2016.
Schiesser, W.: The Numerical Method of Lines: Integration of Partial
Differential Equations, Academic Press, San Diego, USA, 212, 1991.
Schuur, E. and Abbott, B.: High risk of permafrost
thaw, Nature, 480, 32–33, https://doi.org/10.1038/480032a, 2011.
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.
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.
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.
Wang, C., Wang, Z., Kong, Y. Zhang, F., Yang, K., and Zhang, T.: Most of the
Northern Hemisphere Permafrost Remains under Climate Change, Sci.
Rep.-UK, 9, 3295, https://doi.org/10.1038/s41598-019-39942-4, 2019.
Wang, S., Jin, H., Li, S., and Zhao, L.: Permafrost degradation on the
Qinghai-Xizang (Tibet) Plateau and its environmental impacts, Permafrost Periglac., 11, 43–53,
https://doi.org/10.1002/(SICI)1099-1530(200001/03)11:1<43::AID-PPP332>3.0.CO;2-H, 2000.
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., 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.
Willmott, C. and Matsuura, K.: Advantages of the mean absolute error (MAE)
over the root mean square error (RMSE) in assessing average model
performance, Clim. Res., 30, 79–82, https://doi.org/10.3354/cr030079, 2005.
Wu, J., Sheng, Y., Wu, Q., and Wen, Z.: Processes and modes of permafrost
degradation on the Qinghai-Tibet Plateau, Sci. China D, 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, D13108, https://doi.org/10.1029/2007JD009539, 2008.
Wu, Q., Zhang, T., and Liu, Y.: Permafrost temperatures and thickness on the
Qinghai-Tibet Plateau, Global Planet. Change., 72, 32–38,
https://doi.org/10.1016/j.gloplacha.2010.03.001, 2010.
Wu, T., Li, S., Cheng, G., and Nan, Z.: Using ground-penetrating radar to
detect permafrost degradation in the northern limit of permafrost on the
Tibetan plateau, Cold Reg. Sci. Technol., 41, 211–219,
https://doi.org/10.1016/j.coldregions.2004.10.006, 2005.
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.
Xu, Y., Shen, Y., and Wu, Z.: Spatial and Temporal Variations of Land
Surface Temperature Over the Tibetan Plateau Based on Harmonic Analysis, Mt.
Res. Dev., 33, 85–94,
https://doi.org/10.1659/MRD-JOURNAL-D-12-00090.1, 2013.
Yang, M., Nelson, F. E., Shiklomanov, N. I., Guo, D., and Wan, G.:
Permafrost degradation and its environmental effects on the Tibetan Plateau:
A review of recent research, Earth-Sci. Rev., 103, 31–44, 2010.
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.
Yershov, E.: Principles of Geocryology, Lanzhou University Press, Lanzhou, China, ISBN 9787311048570, 2016 (in
Chinse).
Yi, S., Wang, X., Qin, Y., Xiang, B., and Ding, Y.: Responses of alpine
grassland on Qinghai–Tibetan plateau to climate warming and permafrost
degradation: a modeling perspective, Environ. Res. Lett., 9, 074014,
https://doi.org/10.1088/1748-9326/9/7/074014, 2014.
Yin, G., Luo, J., Niu, F., Lin, Z., and Liu, M.: Thermal regime and
variations in the island permafrost near the northern permafrost boundary in
Xidatan, Qinghai–Tibet Plateau, Front. Earth Sci., 560, 708630,
https://doi.org/10.3389/feart.2021.708630, 2021.
Yue, G., Zhao, L., Zhao, Y., Du, E., Wang, Q., Wang, Z., and Qiao, Y.:
Relationship between soil properties in permafrost active layer and surface
vegetation in Xidatan on the Qinghai-Tibetan Plateau, J. Geogr. Sci., 35,
565–573, https://doi.org/10.7522/j.issn.1000-0240.2013.0065, 2013.
Zhang, G., Nan, Z., Hu, N., Yin, Z., and Zhao, L., Cheng, G., and Mu, C.: Qinghai-Tibet Plateau permafrost at
risk in the late 21st Century, Earth's Future, 10, e2022EF002652,
https://doi.org/10.1029/2022EF002652, 2022.
Zhang, Y.: Integration dataset of Tibet Plateau boundary, National Tibetan
Plateau Data Center, https://doi.org/10.11888/Geogra.tpdc.270099, 2019.
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.
Zhao, L. and Sheng, Y.: Permafrost survey manual, Science
Press, Beijing, 13–14, ISBN 9787030581334, 2015 (in Chinses).
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. 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., 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, 2021 (data available at: https://data.tpdc.ac.cn/en/disallow/789e838e-16ac-4539-bb7e-906217305a1d/, last access: 29 November 2022).
Zhou, Y., Guo, D., Qiu, G., Cheng, G., and Li, S.: China Permafrost, Science
Press, Beijing, 145–151, ISBN 7030082850, 2000.
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
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.
Permafrost has been warming and thawing globally; this is especially true in boundary regions....
