Surface air temperatures can directly affect the thermal state of permafrost in the permafrost region of the Northern Hemisphere (PRONH). It is necessary to understand the trends in air temperatures and consider actual CMIP future scenario output instead of a linear temperature increase over different permafrost regions. In this study, air temperatures from 23 models of the sixth coupled‐model intercomparison project (CMIP6) are evaluated against observational data from the PRONH. It is shown that most of the models reasonably represent the dominant characteristics of air temperature variations. Under three different future scenarios, air temperature and warming trends are examined using an optimal model ensemble. Results show that mean annual air temperature (MAAT) is higher in sporadic‐permafrost and isolated‐permafrost regions than in continuous‐permafrost regions. MAAT warming rates were 0.10°C/decade and 0.35°C/decade from 1900 to 2014 and from 1980 to 2014, respectively, and were 0.09°C/decade, 0.38°C/decade, and 0.95°C/decade from 2015 to 2100 under the low‐ to high‐emission scenarios. Air temperature varies considerably between the high‐altitude, transitional, and high‐latitude permafrost regions (HLR), and warming trends are the greatest in HLR under future scenarios. Moreover, warming trends in different permafrost classes vary little from the historical values from 1900 to 2014, and with a gradual increase from isolated‐permafrost regions to continuous‐permafrost regions under future scenarios. The results suggested that air temperatures in the PRONH warmed approximately 1.6 times faster than global air temperatures from 1980 to 2014 and under the three future scenarios.

Landslides are one of the major geohazards in the Qinghai-Tibet Plateau, and have recently increased in both frequency and size. SAR interferometry (InSAR) has been widely applied in landslide research, but studies on monitoring small-scale landslides are rare. In this study, we investigated the performance of Small Baseline Subsets method (SBAS) in monitoring small-scale landslide and further developed a new deformation model to obtain the absolute deformation time series. The results showed that SBAS could well capture the small-scale landslide characteristics including spatiotemporal abnormal displacement and progressive failure processes. The newly developed absolute deformation model further detected the process of landslide details, such as instances of noticeable creeps induced by rainfall and snowmelt. Finally, a conceptual model of the kinematics-based failure mechanism for small-scale landslide was proposed. This study extended the monitoring capability of InSAR and improved our knowledge on the deformation in the frozen ground regions.

Soil texture data are the basic input parameters for many Earth System Models. As the largest middle–low altitude permafrost regions on the planet, the land surface processes on the Qinghai–Tibet Plateau can affect regional and even global water and energy cycles. However, the spatial distribution of soil texture data on the plateau is largely unavailable due to the difficulty of obtaining field data. Based on collection data from field surveys and environmental factors, we predicted the spatial distribution of clay, silt, and sand contents at a 1 km resolution, from 0–5, 5–15, 15–30, 30–60, 60–100, and 100–200 cm soil depth layers. The random forest models were constructed to predict the soil texture according to the relationships between environmental factors and soil texture data. The results showed that the soil particles of the QTP are dominated by sand, which accounts for more than 70% of the total particles. As for the spatial distribution, silt and clay contents are high in the southeast plateau, and low values of silt and clay mainly appeared in the northwest plateau. Climate and NDVI values are the most important factors that affect the spatial distribution of soil texture on the QTP. The results of this study provide the soil texture data at different depths for the whole plateau at a spatial resolution of 1 km, and the dataset can be used as an input parameter for many Earth System Models.