Air temperature increases thermally degrade permafrost, which has widespread impacts on engineering design, resource development, and environmental protection in cold regions. This study evaluates the potential thermal degradation of permafrost over the Qinghai–Tibet Plateau (QTP) from the 1960s to the 2000s using estimated decadal mean annual air temperatures (MAATs) by integrating remote-sensing-based estimates of mean annual land surface temperatures (MASTs), leaf area index (LAI) and fractional snow cover values, and decadal mean MAAT date from 152 weather stations with a geographically weighted regression (GWR). The results reflect a continuous rise of approximately 0.04 °C a⁻¹ in the decadal mean MAAT values over the past half century. A thermal-condition classification matrix is used to convert modelled MAATs to permafrost thermal type. Results show that the climate warming has led to a thermal degradation of permafrost in the past half century. The total area of thermally degraded permafrost is approximately 153.76 × 10⁴ km², which corresponds to 88 % of the permafrost area in the 1960s. The thermal condition of 75.2 % of the very cold permafrost, 89.6 % of the cold permafrost, 90.3 % of the cool permafrost, 92.3 % of the warm permafrost, and 32.8 % of the very warm permafrost has been degraded to lower levels of thermal condition. Approximately 49.4 % of the very warm permafrost and 96 % of the likely thawing permafrost has degraded to seasonally frozen ground. The mean elevations of the very cold, cold, cool, warm, very warm, and likely thawing permafrost areas increased by 88, 97, 155, 185, 161, and 250 m, respectively. The degradation mainly occurred from the 1960s to the 1970s and from the 1990s to the 2000s. This degradation may lead to increased risks to infrastructure, reductions in ecosystem resilience, increased flood risks, and positive climate feedback effects. It therefore affects the well-being of millions of people and sustainable development at the Third Pole.

Monitoring the thermal state of permafrost (TSP) is important in many environmental science and engineering applications. However, such data are generally unavailable, mainly due to the lack of ground observations and the uncertainty of traditional physical models. This study produces novel permafrost datasets for the Northern Hemisphere (NH), including predictions of the mean annual ground temperature (MAGT) at the depth of zero annual amplitude (DZAA) (approximately 3 to 25 m) and active layer thickness (ALT) with 1 km resolution for the period of 2000–2016, as well as estimates of the probability of permafrost occurrence and permafrost zonation based on hydrothermal conditions. These datasets integrate unprecedentedly large amounts of field data (1002 boreholes for MAGT and 452 sites for ALT) and multisource geospatial data, especially remote sensing data, using statistical learning modeling with an ensemble strategy. Thus, the resulting data are more accurate than those of previous circumpolar maps (bias = 0.02±0.16 ^∘C and RMSE = 1.32±0.13 ^∘C for MAGT; bias = 2.71±16.46 cm and RMSE = 86.93±19.61 cm for ALT). The datasets suggest that the areal extent of permafrost (MAGT ≤0 ^∘C) in the NH, excluding glaciers and lakes, is approximately 14.77 (13.60–18.97) × 10⁶ km² and that the areal extent of permafrost regions (permafrost probability >0) is approximately 19.82×10⁶ km². The areal fractions of humid, semiarid/subhumid, and arid permafrost regions are 51.56 %, 45.07 %, and 3.37 %, respectively. The areal fractions of cold ($\le -\mathrm{3.0}$ ^∘C), cool (−3.0 ^∘C to −1.5 ^∘C), and warm ($>-\mathrm{1.5}$ ^∘C) permafrost regions are 37.80 %, 14.30 %, and 47.90 %, respectively. These new datasets based on the most comprehensive field data to date contribute to an updated understanding of the thermal state and zonation of permafrost in the NH. The datasets are potentially useful for various fields, such as climatology, hydrology, ecology, agriculture, public health, and engineering planning. All of the datasets are published through the National Tibetan Plateau Data Center (TPDC), and the link is https://doi.org/10.11888/Geocry.tpdc.271190 (Ran et al., 2021a).