The deposition of light absorbing impurities (LAIs) (e.g., black carbon (BC), organic carbon (OC), mineral dust (MD)) on snow is an important attribution to accelerate snowmelt across the northern Xinjiang, China. At present, there is still a lack of understanding of the LAIs concentration, elution and enrichment process in snow cover over Xinjiang. Based on these, continuously sampling during two years carried out to investigate the concentrations, impacts and potential sources of LAIs in snow at Kuwei Station in the southern Altai Mountains. The average concentrations of BC, OC and MD in the surface snow were 2787 +/- 2334 ng g(-1), 6130 +/- 6127 ng g(-1), and 70.03 +/- 62.59 mu g g(-1), respectively, which dramatically increased along with snowmelt intensified, reflecting a significant enrichment process of LAIs at the snow surface. Besides, high LAIs concentrations also found in the subsurface and melting layers of the snowpit, reflecting the elution and redistribution of LAIs. With the simulation of the SNow ICe Aerosol Radiative model, BC was the main dominant factor in reducing snow albedo and radiative forcing (RF), its impact was more remarkable in the snowmelt period. The average contribution rates of BC, MD and BC + MD to snow albedo reduction increased by 20.0 +/- 1.9%, 13.0 +/- 0.2%, and 20.5 +/- 2.3% in spring compared with that in winter; meanwhile, the corresponding average RFs increased by 15.8 +/- 3.4 W m(-2), 4.7 +/- 0.3 W m(-2) and 16.4 +/- 3.2 W m(-2), respectively. Changes in the number of snowmelt days caused by BC and MD decreased by 3.0 +/- 0.4 d to 8.3 +/- 1.3 d. It indicated that surface enrichment of LAIs during snow melting might accelerate snowmelt further. Weather Research and Forecasting Chemistry model showed that the resident emission was the main potential source of BC and OC in snow. This implied that the mitigation of intensive snowmelt needs to mainly reduce resident emission of LAIs in the future. (C) 2020 Elsevier Ltd. All rights reserved.

Snow properties and their changes are crucial to better understanding of hydrological processes, soil thermal regimes, and surface energy balances. Reliable data and information on snow depth and snow water equivalent (SWE) are also crucial for water resource assessments and socio-economic development at local and regional scales. However, these data are extremely limited and unreliable in northern Xinjiang, China. This study thus aims to investigate spatial variations of snow depth, SWE, and snow density based on winter snowfield surveys during 2015 through 2017 in the Altai Mountains, northwestern China. The results indicated that snow depth (25-114 cm) and SWE (40-290 mm) were greater in the alpine Kanas-Hemu region, and shallow snow accumulated (9-42 cm for snow depth, 26-106 mm for SWE) on the piedmont sloping plain. While there was no remarkable regional difference in the distribution of snow density. Snow property distributions were strongly controlled by topography and vegetation. Elevation and latitude were the most important factors affecting snow depth and SWE, while snow density was strongly affected by longitude across the Altai Mountains in China. The influence of topography on snow property distributions was spatially heterogenous. Mean snow depth increased from 13.7 to 31.2 cm and SWE from 28.5 to 79.9 mm, respectively, with elevation increased from 400 to 1000 m a.s.l. on the piedmont sloping plain. Snow depth decreased to about 15.1 cm and SWE to about 28.5 mm from 1000 to 1800 m a.s.l., then again increased to about 98.1 cm and 271.7 mm on peaks (2000 m a.s.l.) in the alpine Kanas-Hemu. Leeward slopes were easier to accumulate snow cover, especially on north-, east-, and southeast-facing slopes. Canopy interception was also the cause of the difference in snow distribution. Snow depth, SWE, and snow density in forests were reduced by 8%-53%, 2%-67% and -4% to +48%, respectively, compared with surrounding open areas. Especially when snow depth was less than 40 cm, snow depth and SWE differences in forests were more exaggerated. This study provides a basic data set of spatial distributions and variations of snow depth, SWE and snow density in the Altai Mountains, which can be used as an input parameter in climate or hydrological models. These first-hand observations will help to better understand the relationship between snow, topography and climate in mountainous regions across northern China and other high-mountain Asian regions.

Snow cover and seasonally frozen ground (SFG) are the key cryospheric elements on the southern edge of Altai Mountains (SEAM). Quantifying the thermal effect of snow cover on the frozen ground remains challenging. Utilizing the datasets observed at Altai Kuwei Snow Station (AKSS) and by National Meteorological Stations of China Meteorological Administration (CMA), we evaluated the thermal effect of snow cover on SFG regime. The results observed by AKSS indicated that the energy exchange between the ground and atmosphere was significantly insulated by snow cover, resulting in a considerable temperature offset between the snow surface and the ground below. This offset reached a maximum of 12.8 degrees C for a snow depth of 50 cm, but decreased for snowpack depths of >70 cm, whereas the snow temperature lapse rate was systematically steeper in the upper snowpack than at depth. Snow cover was the dominating driver of inter-annual differences in the SFG regime, as represented by the annual maximum freezing depth and soil heat flux. The observed average soil heat loss rate increased from 2.68 to 5.86 W/m(2) on two occasions when the average snow depth decreased from 61.2 cm to 13.7 cm, resulting in an increase in maximum freezing depth of SFG from 69 cm to >250 cm soil depth. The results observed by CMA also demonstrate how snow cover controlled the SFG regime by warming the ground and inhibiting freezing of the soil column. Snow cover caused a 44.5-cm decline of annual maximum freezing depth during 1961-2015 period. SFG degradation between 1961 and 2015 was accompanied by increases in both air temperature and snow cover, with the former playing the dominant role. The correlation between snow cover and the ground-atmosphere temperature offset provides a new empirical method of evaluating the effective thermal effect of snow cover on SFG.

The timing and extent of the last glaciation in the Altai Mountains are key to understanding climate change in this critical region. However, robust glacial chronologies are sparse across the Altai Mountains, especially in the Chinese Altai, impeding the correlation of glacial events and examination of the possible climate forcing mechanisms. Here, we report twenty new Be-10 exposure-ages obtained from two moraines in the headwater area of the Xiaokelanhe River, Chinese Altai. The inner latero-frontal moraine yields exposure-ages ranging from 16.60 +/- 1.00 to 20.41 +/- 1.15 ka (n = 5), reflecting a limited advance during the global Last Glacial Maximum (LGM). The morpho-stratigraphically older moraine remnants have exposure-ages of 14.36 +/- 0.94-38.98 +/- 2.23 ka (n = 15). The tentatively determined moraine age of 34.10 +/- 4.99 ka suggests that the local LGM in the Xiaokelanhe River likely occurred during Marine Isotope Stage (MIS) 3 or earlier. From a compilation of the 20 new, and 79 previously published exposure-ages, we observe at least three distinct glacial events during the last glacial, with the local LGM occurring prior to MIS 2. A comparison between the timing of glacial activities and climate proxies suggests a potential combination of summer solar insolation, North Atlantic climate oscillations, and atmospheric CO2 levels, as triggers for glacial movements during the last glacial cycle. Precipitation delivered by the mid-latitude westerlies may have also contributed to glacial advances during MIS 3. These correlations remain tentative however, due to limited chronological control.

Drifting snow is a significant factor in snow redistribution and cascading snow incidents. However, field observations of drifting snow are relatively difficult due to limitations in observation technology, and drifting snow observation data are scarce. The FlowCapt sensor is a relatively stable sensor that has been widely used in recent years to obtain drifting snow observations. This study presents the results from two FlowCapt sensors that were employed to obtain field observations of drifting snow during the 2017-2018 snow season in the southern Altai Mountains, Central Asia, where the snow cover is widely distributed. The results demonstrate that the FlowCapt sensor can successfully acquire stable field observations of drifting snow. Drifting snow occurs mainly within the height range of 80-cm zone above the snow surface, which accounts for 97.73% of the total snow mass transport. There were three typical snowdrift events during the 2017-2018 observation period, and the total snowdrift flux caused during these key events accounted for 87.5% of the total snow mass transport. Wind speed controls the occurrence of drifting snow, and the threshold wind speed (friction velocity) for drifting snow is approximately 3.0 m/s (0.15 m/s); the potential for drifting snow increases rapidly above 3.0 m/s, with drifting snow essentially being inevitable for wind speeds above 7.0 m/s. Similarly, the snowdrift flux is also controlled by wind speed. The observed maximum snowdrift flux reaches 192.00 g/(m(2)center dot s) and the total snow transport is 584.9 kg/m during the snow season. Although drifting snow will lead to a redistribution of the snow mass, any accumulation or loss of the snow mass is also affected synergistically by other factors, such as topography and snow properties. This study provides a paradigm for establishing a field observation network for drifting snow monitoring in the southern Altai Mountains and bridges the gaps toward elucidating the mechanisms of drifting snow in the Altai Mountains of Central Asia. A broader network of drifting snow observations will provide key data for the prevention and control of drifting snow incidents, such as the design height of windbreak fences installed on both sides of highways.

Optimizing the functions and services provided by the mountain cryosphere will maximize its benefits and minimize the negative impacts experienced by the populations that live and work in the cryosphere-fed regions. The high sensitivity of the mountain cryosphere to climate change highlights the importance of evaluating cryospheric changes and any cascading effects if we are to achieve regional sustainable development goals (SDGs). The southern Altai Mountains (SAM), which are located in the arid to semi-arid region of central Asia, are vulnerable to ecological and environmental changes as well as to developing economic activities in northern Xinjiang, China. Furthermore, cryospheric melting in the SAM serves as a major water resource for northeastern Kazakhstan. Here, we systematically investigate historical cryospheric changes and possible trends in the SAM and also discover the opportunities and challenges on regional water resources management arising from these changes. The warming climate and increased solid precipitation have led to inconsistent trends in the mountain cryosphere. For example, mountain glaciers, seasonally frozen ground (SFG), and river ice have followed significant shrinkage trends as evidenced by the accelerated glacier melt, shallowed freezing depth of SFG, and thinned river ice with shorter durations, respectively. In contrast, snow accumulation has increased during the cold season, but the duration of snow cover has remained stable because of the earlier onset of spring melting. The consequently earlier melt has changed the timing of surface runoff and water availability. Greater interannual fluctuations in snow cover have led to more frequent transitions between snow cover hazards (snowstorm and snowmelt flooding) and snow droughts, which pose challenges to hydropower, agriculture, aquatic life, the tail-end lake environment, fisheries, and transboundary water resource management. Increasing the reservoir capacity to regulate interannual water availability and decrease the risk associated with hydrological hazards related to extreme snowmelt may be an important supplement to the regulation and supply of cryospheric functions in a warmer climate.

This study presents the long-term temperature monitoring in the Russian Altai Mountains. In contrast to the Mongolian and Chinese parts, the modern temperature regime of the Russian Altai remains unclear. The complexity of a comprehensive understanding of permafrost conditions in the Russian Altai is related to the high dis of the terrain, the paucity of the latest observational data, and the sparse population of permafrost areas. The general objective of this study is to determine the temperature regime on the surface, in the active layer, and in the zero annual amplitude (ZAA) layer, based on the known patterns of permafrost distribution in the region. Using automatic measuring equipment (loggers), we obtained information on the temperature of frozen and thawed ground within the altitudes from 1484 to 2879 m a. s. l. during the period from 2014 to 2020. An array of 15 loggers determined the temperature regime of bare and vegetated areas within watersheds, slopes, and valleys. N-factor parameters and surface temperature are similar to those in the Mongolian Altai, but the mean annual ground temperature at the depth of 1 m has a wide range of fluctuations (more than 32 degrees C) based on research results, and we allocated it into three groups based on altitudinal zonality. Snow cover has a strong influence on the temperature regime, but the determination of the fine-scale variability requires additional study. Ground temperature regime during the observation period remained stable, but continued monitoring allows a more detailed assessment of the response to climatic changes.

Snowmelt water is a vital freshwater resource in the Altai Mountains of northwestern China. Yet its seasonal hydrological cycle characteristics could change under a warming climate and more rapid spring snowmelt. Here, we simulated snowmelt runoff dynamics in the Kayiertesi River catchment, from 2000 to 2016, by using an improved hydrological distribution model that relied on high-resolution meteorological data acquired from the National Centers for Environmental Prediction (Fnl-NCEP) that were downscaled using the Weather Research Forecasting model. Its predictions were compared to observed runoff data, which confirmed the simulations' reliability. Our results show the model performed well, in general, given its daily validation Nash-Sutcliffe efficiency (NSE) of 0.62 (from 2013 to 2015) and a monthly NSE score of 0.68 (from 2000 to 2010) for the studied river basin of the Altai Mountains. In this river basin catchment, snowfall accounted for 64.1% of its precipitation and snow evaporation for 49.8% of its total evaporation, while snowmelt runoff constituted 29.3% of the annual runoff volume. Snowmelt's contribution to runoff in the Altai Mountains can extend into non-snow days because of the snowmelt water retained in soils. From 2000 to 2016, the snow-to-rain ratio decreased rapidly, however, the snowmelt contribution remained relatively stable in the study region. Our findings provide a sound basis for making snowmelt runoff predictions, which could be used prevent snowmelt-induced flooding, as well as a generalizable approach applicable to other remote, high-elevation locations where high-density, long-term observational data are currently lacking. How snowmelt contributes to water dynamics and resources in cold regions is garnering greater attention. Our proposed model is thus timely perhaps, enabling more comprehensive assessments of snowmelt contributions to hydrological processes in those alpine regions characterized by seasonal snow cover.

The deposition of light absorbing impurities (LAIs) (e.g., black carbon (BC), organic carbon (OC), mineral dust (MD)) on snow is an important attribution to accelerate snowmelt across the northern Xinjiang, China. At present, there is still a lack of understanding of the LAIs concentration, elution and enrichment process in snow cover over Xinjiang. Based on these, continuously sampling during two years carried out to investigate the concentrations, impacts and potential sources of LAIs in snow at Kuwei Station in the southern Altai Mountains. The average concentrations of BC, OC and MD in the surface snow were 2787 +/- 2334 ng g(-1), 6130 +/- 6127 ng g(-1), and 70.03 +/- 62.59 mu g g(-1), respectively, which dramatically increased along with snowmelt intensified, reflecting a significant enrichment process of LAIs at the snow surface. Besides, high LAIs concentrations also found in the subsurface and melting layers of the snowpit, reflecting the elution and redistribution of LAIs. With the simulation of the SNow ICe Aerosol Radiative model, BC was the main dominant factor in reducing snow albedo and radiative forcing (RF), its impact was more remarkable in the snowmelt period. The average contribution rates of BC, MD and BC + MD to snow albedo reduction increased by 20.0 +/- 1.9%, 13.0 +/- 0.2%, and 20.5 +/- 2.3% in spring compared with that in winter; meanwhile, the corresponding average RFs increased by 15.8 +/- 3.4 W m(-2), 4.7 +/- 0.3 W m(-2) and 16.4 +/- 3.2 W m(-2), respectively. Changes in the number of snowmelt days caused by BC and MD decreased by 3.0 +/- 0.4 d to 8.3 +/- 1.3 d. It indicated that surface enrichment of LAIs during snow melting might accelerate snowmelt further. Weather Research and Forecasting Chemistry model showed that the resident emission was the main potential source of BC and OC in snow. This implied that the mitigation of intensive snowmelt needs to mainly reduce resident emission of LAIs in the future. (C) 2020 Elsevier Ltd. All rights reserved.

Melt-albedo feedback on glaciers is recognized as important processes for understanding glacier behavior and its sensitivity to climate change. This study selected the Muz Taw Glacier in the Altai Mountains to investigate the spatiotemporal variations in albedo and their linkages with mass balance, which will improve our knowledge of the recent acceleration of regional glacier shrinkage. Based on the Landsat-derived albedo, the spatial distribution of ablation-period albedo was characterized by a general increase with elevation, and significant east-west differences at the same elevation. The gap-filling MODIS values captured a nonsignificant negative trend of mean ablation-period albedo since 2000, with a total decrease of approximately 4.2%. From May to September, glacier-wide albedo exhibited pronounced V-shaped seasonal variability. A significant decrease in annual minimum albedo was found from 2000 to 2021, with the rate of approximately -0.30% yr(-1) at the 99% confidence level. The bivariate relationship demonstrated that the change of ablation-period albedo explained 82% of the annual mass-balance variability. We applied the albedo method to estimate annual mass balance over the period 2000-2015. Combined with observed values, the average mass balance was -0.82 +/- 0.32 m w.e. yr(-1) between 2000 and 2020, with accelerated mass loss.