Permafrost in High Mountain Asia (HMA) is becoming increasingly vulnerable to thaw due to climate change. However, the lack of either in situ ground surface or borehole temperature data beyond the Tibetan Plateau prevents comprehensive assessments of its impact on the regional hydrologic cycle and local cascading hazards. Although past studies have generated estimates of permafrost extent in Central Asia, many are limited to the Tibetan Plateau, excluding the more remote reaches of the Tien Shan, Pamirs, and Himalayas. By leveraging surface temperatures from both the Moderate Resolution Imaging Spectroradiometer (MODIS) and Atmospheric Infra-Red Sounder (AIRS), this study advances further understanding of remotely sensed permafrost occurrence at high altitudes, which are prone to error due to frequent cloud cover. We demonstrate that the fusion of MODIS and AIRS products can accurately estimate long-term thermal regimes of the subsurface, with reported correlation coefficients of 0.773 and 0.560, RMSEs of 0.890 degree celsius and 0.680 degree celsius, and biases of 0.003 degree celsius and 0.462 degree celsius, respectively, for the ground surface and the depth of zero annual amplitude, during a reference period of 2003-2016. Furthermore, we provide a range of possible permafrost extents based on established equations for calculating the temperature at the top of the permafrost to demonstrate temperature sensitivity to soil moisture and snow cover. The MODIS-AIRS product is recommended to be a robust source of ground temperature estimates, which may be sufficient for inferring mountain permafrost presence in HMA. Incorporating the influence of soil moisture and snow depth, although limited by biased estimates, also produces estimates of permafrost regional areas comparable to previously reported permafrost indices. A total permafrost area of 1.69 (+/- 0.32) million km(2) is estimated for the entire HMA, across 15 mountain subregions.

Polar amplification appears in response to greenhouse gas forcing, which has become a focus of climate change research. However, polar amplification has not been systematically investigated over the Earth's three poles (the Arctic, Antarctica, and the Third Pole). An index of polar amplification is employed, and the annual and seasonal variations of land surface temperature over the Earth's three poles are examined using MODIS (Moderate Resolution Imaging Spectroradiometer) observations for the period 2001-2018. As expected, the warming of the Arctic is most conspicuous, followed by the Third Pole, and is weakest in Antarctica. Compared to the temperature changes for the global land region, positive polar amplification appears in the Arctic and the Third Pole on an annual scale, whereas Antarctic amplification disappears, with a negative amplification index of -0.72. The polar amplification for the Earth's three poles shows seasonal differences. Strong Arctic amplification appears in boreal spring and winter, with a surface warming rate of more than 3.40 times the global mean for land regions. In contrast, the amplification of the Third Pole is most conspicuous in boreal summer. The two poles located in the Northern Hemisphere have the weakest amplification in boreal autumn. Differently from the positive amplification for the Arctic and the Third Pole in all seasons, the faster variations in Antarctic temperature compared to the globe only appear in austral autumn and winter, and the amplification signal is negative in these seasons, with an amplification index of -1.68 and -2.73, respectively. In the austral winter, the strong negative amplification concentrates on West Antarctica and the coast of East Antarctica, with an absolute value of amplification index higher than 5 in general. Generally, the polar amplification is strongest in the Arctic except from June to August, and Antarctic amplification is the weakest among the Earth's three poles. The Earth's three poles are experiencing drastic changes, and the potential influence of climate change should receive attention.

The high-resolution permafrost distribution maps have a closer relationship with engineering applications in cold regions because they are more relative to the real situation compared with the traditional permafrost zoning mapping. A particle swarm optimization algorithm was used to obtain the index eta with 30 m resolution and to characterize the distribution probability of permafrost at the field scale. The index consists of five environmental variables: slope position, slope, deviation from mean elevation, topographic diversity, and soil bulk density. The downscaling process of the surface frost number from a resolution of 1000 m to 30 m is achieved by using the spatial weight decomposition method and index eta. We established the regression statistical relationship between the surface frost number after downscaling and the temperature at the freezing layer that is below the permafrost active layer base. We simulated permafrost temperature distribution maps with 30 m resolution in the four periods of 2003-2007, 2008-2012, 2013-2017, and 2018-2021, and the permafrost area is, respectively, 28.35 x 10(4) km(2), 35.14 x 10(4) km(2), 28.96 x 10(4) km(2), and 25.21 x 10(4) km(2). The proportion of extremely stable permafrost (< -5.0 degrees C), stable permafrost (-3.0 similar to -5.0 degrees C), sub-stable permafrost (-1.5 similar to -3.0 degrees C), transitional permafrost (-0.5 similar to -1.5 degrees C), and unstable permafrost (0 similar to -0.5 degrees C) is 0.50-1.27%, 6.77-12.45%, 29.08-33.94%, 34.52-39.50%, and 19.87-26.79%, respectively, with sub-stable, transitional, and unstable permafrost mainly distributed. Direct and indirect verification shows that the permafrost temperature distribution maps after downscaling still have high reliability, with 83.2% of the residual controlled within the range of +/- 1 degrees C and the consistency ranges from 83.17% to 96.47%, with the identification of permafrost sections in the highway engineering geological investigation reports of six highway projects. The maps are of fundamental importance for engineering planning and design, ecosystem management, and evaluation of the permafrost change in the future in Northeast China.

Satellite-derived Land Surface Temperature (LST) dynamics have been increasingly used to study various geophysical processes. This review provides an extensive overview of the applications of LST in the context of global change. By filtering a selection of relevant keywords, a total of 164 articles from 14 international journals published during the last two decades were analyzed based on study location, research topic, applied sensor, spatio-temporal resolution and scale and employed analysis methods. It was revealed that China and the USA were the most studied countries and those that had the most first author affiliations. The most prominent research topic was the Surface Urban Heat Island (SUHI), while the research topics related to climate change were underrepresented. MODIS was by far the most used sensor system, followed by Landsat. A relatively small number of studies analyzed LST dynamics on a global or continental scale. The extensive use of MODIS highly determined the study periods: A majority of the studies started around the year 2000 and thus had a study period shorter than 25 years. The following suggestions were made to increase the utilization of LST time series in climate research: The prolongation of the time series by, e.g., using AVHRR LST, the better representation of LST under clouds, the comparison of LST to traditional climate change measures, such as air temperature and reanalysis variables, and the extension of the validation to heterogenous sites.

The second-generation Modern-ERA Retrospective analysis for Research and Applications (MERRA-2) land surface temperature (LST) dataset has been widely used for permafrost mapping in specific areas; however, its accuracy and application need to be evaluated over China. In this study, the MERRA-2 LST was evaluated against meteorological observations and three other reanalysis datasets including the first-generation MERRA, Japanese 55-year Reanalysis (JRA-55), and European Centre for Medium-Range Weather Forecasts (ECMWF) Interim Reanalysis (ERA-Interim), using multiple statistical methods over the period from 1980 to 2018. The results revealed that the MERRA-2 LST generally exhibited cold bias compared to meteorological observations while performing better than the JRA55, ERA-Interim, and MERRA datasets in China, particularly in high-altitude permafrost regions. The comparison indicated that the time series trends for the MERRA-2 LST was consistent with that observed until 2000, and noticeably amplified cold bias, particularly for the period after 2005, was observed. Moreover, two correction methods were proposed and compared to reduce the error range for the MERRA-2 dataset, which was caused by the difference in elevation and land cover types. Calibrated results demonstrated that the linear regression method (Method1) between the elevation difference and mean bias error (MBE) for the LST performed well with root mean square error (RMSE) ranged from 2.15 to 5.97 ?C to 1.09-2.53 ?C. Moreover, in comparison with the MODIS LST dataset, the results showed that the adjusted MERRA-2 LST was in good agreement with smaller RMSEs against the observations. The surface frost number model was used for mapping the permafrost distribution over China based on the daily adjusted MERRA-2 LST dataset. According to the simulation results, the permafrost extent had a slightly continued degradation trend with a rate of 3-5% per decade over the past 39 years. The simulated permafrost area over China for the years 2010-2018 was approximately 1.63 x 106 km2, which accounts for 16.9% of mainland China. Thus, the adjusted MERRA-2 LST with high spatial-temporal consistency is the optimal choice to investigate permafrost distribution on a large scale.

The monitoring of permafrost is important for assessing the effects of global environmental changes and maintaining and managing social infrastructure, and remote sensing is increasingly being used for this wide-area monitoring. However, the accuracy of the conventional method in terms of temperature factor and soil factor needs to be improved. To address these two issues, in this study, we propose a new model to evaluate permafrost with a higher accuracy than the conventional methods. In this model, the land surface temperature (LST) is used as the upper temperature of the active layer of permafrost, and the temperature at the top of permafrost (TTOP) is used as the lower temperature. The TTOP value is then calculated by a modified equation using precipitation-evapotranspiration (PE) factors to account for the effect of soil moisture. This model, referred to as the TTOP-LST zero-curtain (TLZ) model, allows us to analyze subsurface temperatures for each layer of the active layer, and to evaluate the presence or absence of the zero-curtain effect through a time series analysis of stratified subsurface temperatures. The model was applied to the Qinghai-Tibetan Plateau and permafrost was classified into seven classes based on aspects such as stability and seasonality. As a result, it was possible to map the recent deterioration of permafrost in this region, which is thought to be caused by global warming. A comparison with the mean annual ground temperature (MAGT) model using local subsurface temperature data showed that the average root mean square error (RMSE) value of subsurface temperatures at different depths was 0.19 degrees C, indicating the validity of the TLZ model. A similar analysis based on the TLZ model is expected to enable detailed permafrost analysis in other areas.

The Mongolian Plateau is one of the regions most sensitive to climate change, the more obvious increase of temperature in 21st century here has been considered as one of the important causes of drought and desertification. It is very important to understand the multi-year variation and occurrence characteristics of drought in the Mongolian Plateau to explore the ecological environment and the response mechanism of surface materials to climate change. This study examines the spatio-temporal variations in drought and its frequency of occurrence in the Mongolian Plateau based on the Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI) (1982-1999) and the Moderate-resolution Imaging Spectroradiometer (MODIS) (2000-2018) datasets; the Temperature Vegetation Dryness Index (TVDI) was used as a drought evaluation index. The results indicate that drought was widespread across the Mongolian Plateau between 1982 and 2018, and aridification incremented in the 21st century. Between 1982 and 2018, an area of 164.38 x 10(4) km(2)/yr suffered from drought, accounting for approximately 55.28% of the total study area. An area of approximately 150.06 x 10(4) km(2) (51.43%) was subject to more than 160 droughts during 259 months of the growing seasons between 1982 and 2018. We observed variable frequencies of drought occurrence depending on land cover/land use types. Drought predominantly occurred in bare land and grassland, both of which accounting for approximately 79.47% of the total study area. These terrains were characterized by low vegetation and scarce precipitation, which led to frequent and extreme drought events. We also noted significant differences between the areal distribution of drought, drought frequency, and degree of drought depending on the seasons. In spring, droughts were widespread, occurred with a high frequency, and were severe; in autumn, they were localized, frequent, and severe; whereas, in summer, droughts were the most widespread and frequent, but less severe. The increase in temperature, decrease in precipitation, continuous depletion of snow cover, and intensification of human activities have resulted in a water deficit. More severe droughts and aridification have affected the distribution and functioning of terrestrial ecosystems, causing changes in the composition and distribution of plants, animals, microorganisms, conversion between carbon sinks and carbon sources, and biodiversity. We conclude that regional drought events have to be accurately monitored, whereas their occurrence mechanisms need further exploration, taking into account nature, climate, society and other influencing factors.

The spatial distribution of permafrost and associated mean annual ground temperature (MAGT) and active layer thickness (ALT) are crucial data for hydrological studies. In this paper, we present the current state of knowledge on the spatial distribution of the permafrost properties of 29 river basins in Mongolia. The MAGT and ALT values are estimated by applying TTOP and Kudryavtsev methods. The main input of both methods is the spatially distributed surface temperature. We used the 8-day land surface temperature (LST) data from the day- and night-time Aqua and Terra images of the moderate resolution imaging spectroradiometer (MODIS). The gaps of the MODIS LST data were filled by spatial interpolation. Next, an LST model was developed based on 34 observational borehole data using a panel regression analysis (Baltagi, Econometric analysis of panel data, 3 edn, Wiley, New York, 2005). The model was applied for the whole country and covered the period from August 2012 to August 2013. The results show that the permafrost covers 26.3% of the country. The average MAGT and ALT for the permafrost region is - 1.6 degrees C and 3.1 m, respectively. The MAGT above -2 degrees C (warm permafrost) covers approximately 67% of the total permafrost area. The permafrost area and distribution in cold and warm permafrost varies highly over the country, in particular in regions where the river network is highly developed. High surface temperatures associated with climate change would result in changes of permafrost conditions, and, thus, would impact the surface water availability in these regions. The data on permafrost conditions presented in this paper can be used for further research on changes in the hydrological conditions of Mongolia.

The scientific community has widely reported the impacts of climate change on the Central Himalaya. To qualify and quantify these effects, long-term land surface temperature observations in both the daytime and nighttime, acquired by the Moderate Resolution Imaging Spectroradiometer from 2000 to 2017, were used in this study to investigate the spatiotemporal variations and their changing mechanism. Two periodic parameters, the mean annual surface temperature (MAST) and the annual maximum temperature (MAXT), were derived based on an annual temperature cycle model to reduce the influences from the cloud cover and were used to analyze their trend during the period. The general thermal environment represented by the average MAST indicated a significant spatial distribution pattern along with the elevation gradient. Behind the clear differences in the daytime and nighttime temperatures at different physiographical regions, the trend test conducted with the Mann-Kendall (MK) method showed that most of the areas with significant changes showed an increasing trend, and the nighttime temperatures exhibited a more significant increasing trend than the daytime temperatures, for both the MAST and MAXT, according to the changing areas. The nighttime changing areas were more widely distributed (more than 28%) than the daytime changing areas (around 10%). The average change rates of the MAST and MAXT in the daytime are 0.102 degrees C/yr and 0.190 degrees C/yr, and they are generally faster than those in the nighttime (0.048 degrees C/yr and 0.091 degrees C/yr, respectively). The driving force analysis suggested that urban expansion, shifts in the courses of lowland rivers, and the retreat of both the snow and glacier cover presented strong effects on the local thermal environment, in addition to the climatic warming effect. Moreover, the strong topographic gradient greatly influenced the change rate and evidenced a significant elevation-dependent warming effect, especially for the nighttime LST. Generally, this study suggested that the nighttime temperature was more sensitive to climate change than the daytime temperature, and this general warming trend clearly observed in the central Himalayan region could have important influences on local geophysical, hydrological, and ecological processes.

Surface temperature is critical for the simulation of climate change impacts on the ecology, environment, and particularly permafrost in the cryosphere. Virtually, surface temperatures are different in the near-surface air temperature (T-a) measured at a screen-height of 1.5-2 m, the land surface temperature (LST) on the top canopy layer, and the ground surface temperature (GST) 0-5 cm beneath the surface cover. However, not enough attention has been concentrated on the difference in these surface temperatures. This study aims at quantifying the distinction of surface temperatures by the comparisons and numerical simulations of observational field data collected in a discontinuous permafrost region on the northeastern Qinghai-Tibet Plateau (QTP). We compared the hourly, seasonal and yearly differences between T omega, IST, GST, and ground temperatures, as well as the freezing and thawing indices, the N-factors, and the surface and thermal offsets derived from these temperatures. The results showed that the peak hourly LST was reached earliest, closely followed by the hourly T-a. Mean annual LST (MALST) was moderately comparable to mean annual T-a (MAAT), and both were lower than mean annual GST (MAGST). Surface offsets (MAGST-MAAT) were all within 3.5 degrees C, which are somewhat consistent with other parts of the QTP but smaller than those in the Arctic and Subarctic regions with dense vegetation and thick, long-duration snow cover. Thermal offsets, the mean annual differences between the ground surface and the permafrost surface, were within -0.3 degrees C, and one site was even reversed, which may be relevant to equally thawed to frozen thermal conductivities of the soils. Even with identical T-a (comparable to MAAT of -3.27 and -3.17 degrees C), the freezing and thawing processes of the active layer were distinctly different, due to the complex influence of surface characteristics and soil, textures. Furthermore, we employed the Geophysical Institute Permafrost Lab (GIPL) model to numerically simulate the dynamics of ground temperature driven by T-a, LST, and GST, respectively. Simulated results demonstrated that GST was a reliable driving indicator for the thermal regime of frozen ground, even if no thermal effects of surface characteristics were taken into account. However, great biases of mean annual ground temperatures, being as large as 3 degrees C, were induced on the basis of simulations with LST and T-a when the thermal effect of surface characteristics was neglected. We conclude that quantitative calculation of the thermal effect of surface characteristics on GST is indispensable for the permafrost simulations based on the T-a datasets and the LST products-that derived from thermal infrared remote sensing.