Due to the complex topography and localized climate, active layer thickening and permafrost warming varied distinctly in different regions on the Qinghai-Tibet Plateau (QTP). Based on the borehole-temperature data at 93 sites from 2012 to 2018, we analyzed the temporal and spatial characteristics of active layer thickness, permafrost temperature, and relevant climatic factors in 8 typical geomorphological units on the QTP. The active layer thickened at 86 sites and thinned at 7 sites. The permafrost warmed at 89 sites and cooled at 4 sites. The median values of the annual increase rate of active layer thickness were from 0.04 to 0.13 m/a for the monitored regions. The highest rate reached 0.46 m/a, indicating severe permafrost degradation in local areas. The mean annual soil temperatures at a 6-m depth generally increased faster for cold permafrost, and the active layer thickened more significantly in warm permafrost sites. Among these regions, Kekexili Mountains showed a lower increase rate of active layer thickness, and the temperature rise of permafrost in the Fenghuoshan Mountains was more significant. The temporal change of snow cover duration was closely related to the active layer thickness variation in the northern permafrost regions on the QTP (Kunlunshan Mountains and Chumaerhe High Plain). In contrast, the temporal variation of freezing index was the dominant factor in the southern regions (Wuli Basin, Tongtianhe Basin, and Tanggula Mountains). No linear correlation between the temporal variations of climatical factors and active layer thickness variation was found for the regions in the middle of QTP (Kekexili Mountains, Beiluhe Basin, and Fenghuoshan Mountains ). The comprehensive effects of freezing index and snow cover duration result in the different relationships between air temperature variation and permafrost change in different regions on the QTP. These findings are beneficial for understanding the relationship between climate change and permafrost evolution.

The timing and duration of snow cover critically affect surface albedo, surface energy budgets, and hydrological processes. Previous studies using in-situ or satellite remote sensing data have mostly been site-specific (Siberia and the Tibetan Plateau), and remote sensing and/or modeling data include large uncertainties. Here, we used 1103 stations with long-term (1966-2012) ground-based snow measurements to investigate spatial and temporal variability in snow cover timing and duration and factors impacting those changes across the Eurasian continent. We found the earliest annual onset and latest disappearance of snow cover occurred along the Arctic coast, where the long-term (1971-2000) mean annual snow cover duration (SCD) was more than nine months which was the longest in this study. The shortest SCD, <= 10 days, was found in southern China. The first and last dates of snow cover (FD and LD, respectively), SCD, and the ratio of SCD to snow season length (RDL) were generally latitude dependent over the Eurasian Continent, while were elevation dependent on the Tibetan Plateau. During the period from 1966 through 2012, FD delayed and LD advanced by similar to 1 day/decade, and RDL increased by about 0.01/decade. The LD, SCD, and RDL anomalies (relative to the period 1971-2000) were also significantly correlated with latitude. Advances in LD and positive RDL were more significant in low-latitude regions, decreases in SCD were more significant in high-latitude regions. Changes in SCD were related to air temperature and snowfall in autumn and warming in spring. SCD specifically increased in the northern Xinjiang and northeastern China to increased snowfall. The significant reduction in SCD in southwestern Russia, the Tibetan Plateau and along the Yangtze River was mainly affected by climate warming. (C) 2020 Elsevier B.V. All rights reserved.

Depositions of light-absorbing particles (LAPs), such as black carbon (BC) and dust, on the snow surface modulate the snow albedo; therefore, they are considered key factors of snow-atmosphere interaction in the present-day climate system. However, their detailed roles have not yet been fully elucidated, mainly due to the lack of in-situ measurements. Here, we develop a new model chain NHM-Chem-SMAP, which is composed of a detailed regional meteorology-chemistry model and a multilayered physical snowpack model, and evaluate it using LAPs concentrations data measured at Sapporo, Japan during the 2011-2012 winter. NHM-Chem-SMAP successfully reproduces the in-situ measured seasonal variations in the mass concentrations of BC and dust in the surface snowpack. Furthermore, we find that LAPs from domestic and foreign sources played a role in shortening the snow cover duration by 5 and 10 days, respectively, compared to the completely pure snow condition.

Snow cover occupies large percentage of land surface in Tibetan Plateau. Snow cover duration (SCD) during non-growing seasons plays a critical role in regulating alpine vegetations phenology by affecting the energy budgets of land surface and soil moisture conditions. Different periods snow cover during non-growing season may have distinct effect on the vegetations phenology. Start of season (SOS) has been observed advanced under the ongoing climate change in the plateau, but it still remains unclear how the SCD alters the SOS. This study attempts to answer the following questions: (i) What is the pattern of spatial and temporal variations for SCD and grassland SOS? (ii) Which periods SCD plays a critical role in grasslands SOS? The remote sensing datasets from the Moderate Resolution Imaging Spectroradiometer (MODIS) were utilized to compute the SOS and SCD on the Tibetan Plateau over 200315. The Asymmetric Gaussian function was applied to extract SOS. We also explored the spatial pattern and temporal variation of SOS and SCD. Then, by using linear correlation coefficients, we investigated the driving effects of different periods non-growing season SCD on SOS. The non-growing season SCD slightly decreased during 200315, while SOS exhibited an overall advancing trend. Advanced trends in SOS were observed in the eastern plateau, and the delayed trends were mainly founded in western plateau. Snow cover area exhibited two separate peaks during autumn and late winter over the plateau. Extended SCD regions mainly distributed in middle-east of the plateau, while shrunken SCD distributed in other regions of the plateau. SCD of different seasons caused distinct effects on vegetation SOS. Lengthened autumn SCD advanced SOS over the eastern plateau. The slightly lengthened SCD postponed SOS over the western plateau. In the wet meadow regions, advanced SOS was positively associated with SCD during the entire non-growing season, whereas for the dry steppe, SCD over the preseason played a more dominant role. The SCD of previous autumn and winter also showed lag effect on SOS over meadow regions to a certain extent. This study confirmed the importance of SCD to phenological processes at the beginning of growing season and further suggested that role of SCD should be discriminated for different periods and for different heat-water conditions. With the lag effects and SCDs distinct effect of different seasons considered, predictions on the Tibetan Plateaus spring phenology could be improved.

Helicoverpa armigera causes serious damage to most crops around the world. However, the impacts of snow thickness on the H. armigera overwintering pupae are little known. A field experiment was employed in 2012-2015 at Urumqi, China. At soil depths of 5, 10, and 15 cm, overwintering pupae were embedded with four treatments: no snow cover (NSC), snow cover (SC), increasing snow thickness to 1.5 times the thickness of SC (ISSC-1), and to two times the thickness of SC (ISSC-2). Results suggested that snow cover and increasing snow thickness both significantly increased soil temperatures, which helped to decrease the mortality of overwintering pupae (MOP) of H. armigera. However, the MOP did not always decrease with increases in snow thickness. The MOPs in NSC and ISSC-1 were the highest and the lowest, respectively, though ISSC-2 had much thicker snow thickness than ISSC-1. A maximum snow thickness of 60 cm might lead to the lowest MOP. The longer the snow cover duration (SCD) at a soil depth of 10 cm in March and April was, the higher the MOP was. A thicker snow cover layer led to a higher soil moisture content (SMC) and a lower diurnal soil temperature range (DSTR). The highest and the lowest MOP were at a depth of 15 and 10 cm, respectively. The SMC at the depths of 10 and 15 cm had significant effects on MOP. A lower accumulated temperature (a 0 A degrees C) led to a higher MOP. The DSTR in March of approximately 4.5 A degrees C might cause the lowest MOP. The largest influence factor for the MOPs at depths of 5 and 10 cm and the combined data were the SCDs during the whole experimental period, and for the MOPs at a depth of 15 cm was the soil temperature in November.

Climate models project considerable ranges and uncertainties in future climatic changes. To assess the potential impacts of climatic changes on mountain permafrost within these ranges of uncertainty, this study presents a sensitivity analysis using a permafrost process model combined with climate input based on delta-change approaches. Delta values comprise a multitude of coupled air temperature and precipitation changes to analyse long-term, seasonal and seasonal extreme changes on a typical low-ice content mountain permafrost location in the Swiss Alps. The results show that seasonal changes in autumn (SON) have the largest impact on the near-surface permafrost thermal regime in the model, and lowest impacts in winter (DJF). For most of the variability, snow cover duration and timing are the most important factors, whereas maximum snow height only plays a secondary role unless maximum snow heights are very small. At least for the low-ice content site of this study, extreme events have only short-term effects and have less impact on permafrost than long-term air temperature trends.