The comprehensive understanding of the occurred changes of permafrost, including the changes of mean annual ground temperature (MAGT) and active layer thickness (ALT), on the Qinghai-Tibet Plateau (QTP) is critical to project permafrost changes due to climate change. Here, we use statistical and machine learning (ML) modeling approaches to simulate the present and future changes of MAGT and ALT in the permafrost regions of the QTP. The results show that the combination of statistical and ML method is reliable to simulate the MAGT and ALT, with the root-mean-square error of 0.53 degrees C and 0.69 m for the MAGT and ALT, respectively. The results show that the present (2000-2015) permafrost area on the QTP is 1.04 x 10(6) km(2) (0.80-1.28 x 10(6) km(2)), and the average MAGT and ALT are -1.35 +/- 0.42 degrees C and 2.3 +/- 0.60 m, respectively. According to the classification system of permafrost stability, 37.3% of the QTP permafrost is suffering from the risk of disappearance. In the future (2061-2080), the near-surface permafrost area will shrink significantly under different Representative Concentration Pathway scenarios (RCPs). It is predicted that the permafrost area will be reduced to 42% of the present area under RCP8.5. Overall, the future changes of MAGT and ALT are pronounced and region-specific. As a result, the combined statistical method with ML requires less parameters and input variables for simulation permafrost thermal regimes and could present an efficient way to figure out the response of permafrost to climatic changes on the QTP.

Permafrost in the Qinghai-Tibet Plateau (QTP) is sensitive to climate warming, but the associated degradation risk still lacks accurate evaluation. To address this issue, machine learning (ML) models are established to simulate the mean annual ground temperature (MAGT) and active layer thickness (ALT), and climate data from shared socioeconomic pathways (SSPs) are prepared for evaluation in the future period. Based on the projections, permafrost is expected to remain relatively stable under the SSP1-2.6 scenario, and large-scale permafrost degradation will occur after the 2050s, resulting in area losses of 30.15% (SSP2-4.5), 58.96% (SSP3-7.0), and 65.97% (SSP5-8.5) in the 2090s relative to the modeling period (2006-2018). The average permafrost MAGT (ALT) is predicted to increase by 0.50 degrees C (59 cm), 0.67 degrees C (89 cm), and 0.79 degrees C (97 cm) in the 2090s with respect to the modeling period under the SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios, respectively. Permafrost in the Qilian Mountains and Three Rivers Source region are fragile and vulnerable to degradation. In the future period, permafrost on the sunny slopes is more prone to degradation and the sunny-shade slope effect of permafrost distribution will be further enhanced under climate warming. The lower limit of permafrost distribution is expected to rise by about 100 m in the 2050s under the SSP2-4.5 scenario. These findings can provide valuable insights about future permafrost changes in the QTP. In the past decades, the Qinghai-Tibet Plateau (QTP) warmed at more than twice the global average, and permafrost degradation within this process has become widely acknowledged. To project the possible changes, a combination of climate data from global climate model, machine learning model, and permafrost field observation data were used, based on a comprehensive review of previous studies. The findings indicate that permafrost in the QTP is not expected to undergo significant degradation under the SSP1-2.6 scenario. However, noticeable permafrost degradation is projected to occur after the 2050s under the SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios, particularly in the Qilian Mountains and Three Rivers Source region. It is predicted that permafrost on sunny slopes is more susceptible to degradation under climate warming, and the permafrost area difference between the sunny and shade slopes will be further expanded. The mean annual air temperature of the QTP will rise by about 1.5 degrees C in the 2050s under the SSP2-4.5 scenario relative to the average between 2006 and 2018, which may lead to a 100 m rise on the low limit of permafrost distribution. Permafrost area of the Qinghai-Tibet Plateau (QTP) is expected to lose by 30.15% (SSP2-4.5) to 65.97% (SSP5-8.5) in the 2090sPermafrost in the Qilian Mountains and Three Rivers Source region are fragile and vulnerable to degradationThe lower limit of permafrost distribution in the QTP is forecasted to rise by about 100 m in the 2050s under the SSP2-4.5 scenario

Permafrost degradation poses serious threats to both natural and human systems through its influence on ecological-hydrological processes, infrastructure stability, and the climate system. The Arctic and the Third Pole (Tibetan Plateau, TP hereafter) are the two northern regions on Earth with the most extensive permafrost areas. However, there is a lack of systematic comparisons of permafrost characteristics and its climate and ecoenvironment between these two regions and their susceptibility to disturbances. This study provides a comprehensive review of the climate, ecosystem characteristics, ground temperature, permafrost extent, and active-layer thickness, as well as the past and future changes in permafrost in the Arctic and the TP. The potential consequences associated with permafrost degradation are also examined. Lastly, possible connections between the two regions through land-ocean-atmosphere interactions are explored. Both regions have experienced dramatic warming in recent decades, characterized by Arctic amplification and elevation-dependent warming on the TP. Permafrost temperatures have increased more rapidly in the Arctic than on the TP, and will likely be reinforced under a future high emission scenario. Near-surface permafrost extents are projected to shrink in both regions in the coming decades, with a more dramatic decline in the TP. The active layer on the TP is thicker and has substantially deepened, and is projected to thicken more than in the Arctic. Widespread permafrost degradation increases geohazard risk and has already wielded considerable effects on the human and natural systems. Permafrost changes have also exerted a pronounced impact on the climate system through changes in permafrost carbon and land-atmosphere interactions. Future research should involve comparative studies of permafrost dynamics in both regions that integrate long-term observations, high-resolution satellite measurements, and advanced Earth System models, with emphasis on linkages between the two regions.

The comprehensive understanding of the occurred changes of permafrost, including the changes of mean annual ground temperature (MAGT) and active layer thickness (ALT), on the Qinghai-Tibet Plateau (QTP) is critical to project permafrost changes due to climate change. Here, we use statistical and machine learning (ML) modeling approaches to simulate the present and future changes of MAGT and ALT in the permafrost regions of the QTP. The results show that the combination of statistical and ML method is reliable to simulate the MAGT and ALT, with the root-mean-square error of 0.53 degrees C and 0.69 m for the MAGT and ALT, respectively. The results show that the present (2000-2015) permafrost area on the QTP is 1.04 x 10(6) km(2) (0.80-1.28 x 10(6) km(2)), and the average MAGT and ALT are -1.35 +/- 0.42 degrees C and 2.3 +/- 0.60 m, respectively. According to the classification system of permafrost stability, 37.3% of the QTP permafrost is suffering from the risk of disappearance. In the future (2061-2080), the near-surface permafrost area will shrink significantly under different Representative Concentration Pathway scenarios (RCPs). It is predicted that the permafrost area will be reduced to 42% of the present area under RCP8.5. Overall, the future changes of MAGT and ALT are pronounced and region-specific. As a result, the combined statistical method with ML requires less parameters and input variables for simulation permafrost thermal regimes and could present an efficient way to figure out the response of permafrost to climatic changes on the QTP.