Abstract. The Tibetan Plateau (TP) has the largest areas of permafrost terrain in the mid-and low-latitude regions of the world. Some permafrost distribution maps have been compiled but, due to limited data sources, ambiguous criteria, inadequate validation, and deficiency of high-quality spatial data sets, there is high uncertainty in the mapping of the permafrost distribution on the TP. We generated a new permafrost map based on freezing and thawing indices from modified Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperatures (LSTs) and validated this map using various ground-based data sets. The soil thermal properties of five soil types across the TP were estimated according to an empirical equation and soil properties (moisture content and bulk density). The temperature at the top of permafrost (TTOP) model was applied to simulate the permafrost distribution. Permafrost, seasonally frozen ground, and unfrozen ground covered areas of 1.06 × 10 6 km 2 (0.97-1.15 × 10 6 km 2 , 90 % confidence interval) (40 %), 1.46 × 10 6 (56 %), and 0.03 × 10 6 km 2 (1 %), respectively, excluding glaciers and lakes. Ground-based observations of the permafrost distribution across the five investigated regions (IRs, located in the transition zones of the permafrost and seasonally frozen ground) and three highway transects (across the entire permafrost regions from north to south) were used to validate the model. Validation results showed that the kappa coefficient varied from 0.38 to 0.78 with a mean of 0.57 for the five IRs and 0.62 to 0.74 with a mean of 0.68 within the three transects. Compared with earlier studies, the TTOP modelling results show greater accuracy. The results provide more detailed information on the permafrost distribution and basic data for use in future research on the Tibetan Plateau permafrost.

The dynamics of permafrost (including the permafrost thermal state and active layer thicknesses (ALT)) across the Qinghai‐Tibetan Plateau (QTP) have not been well understood on a large scale. Here we simulate the ALT and permafrost thermal state using the Geophysical Institute Permafrost Lab version 2 (GIPL2) model across the QTP. Based on the single‐point simulations, the model is upscaled to the entire QTP. The upscaled model is validated with five investigated regions (IRs), including Wenquan (WQIR), Gaize (GZIR), Aerjin (AEJIR), Xikunlun (XKLIR), and Qinghai‐Tibetan Highway (G109IR). The results show that the modified GIPL2 model improves the accuracy of the permafrost thermal state simulations. Due to our simulated results on the QTP, the average ALT is of 2.30 m (2.21–2.40 m). The ALT decreases with an increase in the altitude and decreases from the southeast to the northwest. The ALT is thin in the central QTP, but it is thick in the high‐elevation mountain areas and some areas surrounding glaciers and lakes. The largest ALT is found in the border areas between permafrost and seasonally frozen ground regions. The simulated results of the MAGT (the mean annual ground temperature) indicate that most of the permafrost is substable, which is sensitive to climate warming. The simulated results would be of great significance on assessing the impacts of permafrost dynamics on local hydrology, ecology, and engineering construction.

Abstract. Permafrost has great influences on the climatic, hydrological, and ecological systems on the Qinghai–Tibet Plateau (QTP). The changing permafrost and its impact have been attracting great attention worldwide like never before. More observational and modeling approaches are needed to promote an understanding of permafrost thermal state and climatic conditions on the QTP. However, limited data on the permafrost thermal state and climate background have been sporadically reported in different pieces of literature due to the difficulties of accessing and working in this region where the weather is severe, environmental conditions are harsh, and the topographic and morphological features are complex. From the 1990s, we began to establish a permafrost monitoring network on the QTP. Meteorological variables were measured by automatic meteorological systems. The soil temperature and moisture data were collected from an integrated observation system in the active layer. Deep ground temperature (GT) was observed from boreholes. In this study, a comprehensive dataset consisting of long-term meteorological, GT, soil moisture, and soil temperature data was compiled after quality control from an integrated, distributed, and multiscale observation network in the permafrost regions of QTP. The dataset is helpful for scientists with multiple study fields (i.e., climate, cryospheric, ecology and hydrology, meteorology science), which will significantly promote the verification, development, and improvement of hydrological models, land surface process models, and climate models on the QTP. The datasets are available from the National Tibetan Plateau/Third Pole Environment Data Center (https://data.tpdc.ac.cn/en/disallow/789e838e-16ac-4539-bb7e-906217305a1d/, last access: 24 August 2021, https://doi.org/10.11888/Geocry.tpdc.271107, Lin et al., 2021).

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Frozen ground is an important component of the cryosphere, which exerts strong influences on regional ecology, hydrology and infrastructure engineering (W. Wang et al., 2018; Westermann et al., 2015). The Qinghai-Tibet Plateau (QTP) is underlain by typical high-altitude permafrost region, which is undergoing more dramatic climatic warming than its surrounding regions (Wang et al., 2019). A growing number of studies have reported the present status and predicted degradation of permafrost under various global warming scenarios (

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Warming permafrost on a global scale is projected to have significant impacts on engineering, hydrology and environmental quality. Greater warming trends are predicted on the Qinghai–Tibetan Plateau (QTP), but most models for mountain permafrost have not considered the effects of water phase change and the state of deep permafrost due to a lack of detailed information. To better understand historical and future permafrost change based on in situ monitoring and field investigations, a numerical heat conduction permafrost model was introduced which differentiated the frozen and thawed state of soil, and considered unfrozen water content in frozen soil, distribution of ground ice and geothermal heat flow. Simulations were conducted at two sites with validation by long‐term monitoring of ground temperature data. After forcing with reconstructed historical ground surface temperature series starting from 1966, the model predicted permafrost changes until 2100 under different RCP scenarios. The results indicate a slow thermal response of permafrost to climate warming at the two investigated sites. Even under the most radical warming scenario (RCP8.5), deepening of the permafrost table is not obvious before 2040. At both sites, the model indicates that shallow permafrost may disappear but deep permafrost may persist by 2100. Moreover, the simulation shows that the degradation modes may differ between zones of discontinuous and continuous permafrost. The main degradation mode of the site in the discontinuous zone appears to be upward thawing from the permafrost base, while that of the site in the continuous zone is downward thawing at the permafrost table with little change at the permafrost base.

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