Snow Research Articles

The influence of atmospheric circulation on the seasonal dynamics of the chemical composition of snow cover in the Pechora-Ilychsky reserve

The influence of atmospheric circulation on the seasonal dynamics of the chemical composition of snow cover in the Pechora-Ilychsky reserve

Comparison of hunting site strategies of the common buzzard Buteo buteo in open landscapes and along expressways.

The expansion of human activities in their many forms increases the frequency, diversity, and scale of human-wildlife interactions. One such negative form is the expansion of road infrastructure, causing road kill and traffic-related noise as well as habitat loss and fragmentation. Even so, habitats around road infrastructure are attractive foraging areas that attract certain bird species. We assessed the impact of road infrastructure on the foraging strategies of the common buzzard Buteo buteo. Birds were observed during two winter seasons in two land-use types, along an expressway and an open agricultural landscape. Individual birds were tracked for a 10-min sequence as a separate sample was analysed. The material, covering 1,220 min along the expressway, and 1,100 min in the agricultural landscape, was collected. Time spent by buzzards on medium-height sites was higher along the expressway than in farmland. Buzzards changed their hunting sites following the mean wind speed. Also, they more often changed their sites along the expressway than in farmland. The land-use types, snow cover, and the mean wind speed mediated the number of attacks on prey. These results illustrate the high plasticity of the buzzards' behaviour, which can adapt their hunting strategies to both foraging locations (expressway and farmland) and weather conditions. Roadsides along expressways are attractive foraging areas for this diurnal raptor, so reducing the risk of vehicle collisions with this and other birds of prey may require targeted planning efforts.

Variation in Glacier Albedo on the Tibetan Plateau between 2001 and 2022 Based on MODIS Data

Albedo is a primary driver of the glacier surface energy balance and consequent melting. As glacier albedo decreases, it further accelerates glacier melting. Over the past 20 years, glaciers on the Tibetan Plateau have experienced significant melting. However, our understanding of the variations in glacier albedo and its driving factors in this region remains limited. This study used MOD10A1 data to examine the average characteristics and variations in glacier albedo on the Tibetan Plateau from 2001 to 2022; the MOD10A1 snow cover product, developed at the National Snow and Ice Data Center, was employed to analyze spatiotemporal variations in surface albedo. The results indicate that the albedo values of glaciers on the Tibetan Plateau predominantly range between 0.50 and 0.60, with distinctly higher albedo in spring and winter, and lower albedo in summer and autumn. Glacier albedo on the Tibetan Plateau decreased at an average linear regression rate of 0.06 × 10−2 yr−1 over the past two decades, with the fastest declines occurring in autumn at an average rate of 0.18 × 10−2 yr−1, contributing to the prolongation of the melting period. Furthermore, significant variations in albedo change rates with altitude were found near the snowline, which is attributed to the transformation of the snow and ice surface. The primary factors affecting glacier albedo on the Tibetan Plateau are temperature and snowfall, whereas in the Himalayas, black carbon and dust primarily influence glacier albedo. Our findings reveal a clear decrease in glacier albedo on the Tibetan Plateau and demonstrate that seasonal and spatial variations in albedo and temperature are the most important driving factors. These insights provide valuable information for further investigation into surface albedo and glacier melt.

Severe winter conditions in the Citlaltepetl-Cofre de Perote mountain range, 2021-2022

The central border that divides the states of Puebla and Veracruz is characterized by being the highest territorial limit in Mexico; it is composed of three of the highest mountains in the country. This border reaches a length of 100 km, forming a watershed that continuously exceeds 3000 m in altitude. Due to its elevation, winter freezing conditions are frequent; however, due to changing climate patterns, in recent winter seasons, severe frosts and snowfalls have occurred at elevations well below the main peaks, affecting the health of thousands of vulnerable residents of both states surrounding this mountain range. Because the 2021-2022 winter season was one of the most intense in all North America, with severe impacts in Mexico as a result of freezing temperatures and snowfall, this work analyzes the winter conditions in 2021-2022 through the monitoring of frontal systems and the analysis of data from automatic meteorological stations near the study area. From the records, the lower limit of surface freezing was estimated for each arrival of cold air masses; the occurrence of snowfall was determined by remote sensing, and the area of snow cover was also calculated. At the same time, the population affected by freezing weather conditions was estimated. The surface freezing starts at elevations as low as 2259 m, covering an area of up to 2949 km2, which corresponds to the entire study region, affecting 139 towns in Veracruz and 163 in Puebla.

Seasonal snow–atmosphere modeling: let's do it

Abstract. Mountain snowpack forecasting relies on accurate mass and energy input information in relation to the snowpack. For this reason, coupled snow–atmosphere models, which downscale input fields to the snow model using atmospheric physics, have been developed. These coupled models are often limited in the spatial and temporal extents of their use by computational constraints. In addressing this challenge, we introduce HICARsnow, an intermediate-complexity coupled snow–atmosphere model. HICARsnow couples two physics-based models of intermediate complexity to enable basin-scale snow and atmospheric modeling at seasonal timescales. To showcase the efficacy and capability of HICARsnow, we present results from its application to a high-elevation basin in the Swiss Alps. The simulated snow depth is compared throughout the snow season to aerial lidar data. The model shows reasonable agreement with observations from peak accumulation through late-season melt-out, representing areas of high snow accumulation due to redistribution processes, as well as melt patterns caused by interactions between radiation and topography. HICARsnow is also found to resolve preferential deposition, with model outputs suggesting that parameterizations of the process using surface wind fields may only be inappropriate under certain atmospheric conditions. The two-way coupled model also improves surface air temperatures over late-season snow, demonstrating added value for the atmospheric model as well. Differences between observations and model outputs during the accumulation season indicate a poor representation of redistribution processes away from exposed ridges and steep terrain and a low bias in albedo at high elevations during the ablation season. Overall, HICARsnow shows great promise for applications in operational snow forecasting and in studying the representation of snow accumulation and ablation processes.

Internal Variability Dominated the Extreme Cold Wave Over North America in December 2022

AbstractIn December 2022, North America experienced an unprecedented extreme cold event. However, the underlying physical mechanisms of this cold wave, and the extent to which it is driven by internal variability or external forcing, are not fully understood. Using ERA5 reanalysis data and the HadGEM3‐A‐N216 attribution simulations, we identified internal variability as the main cause, contributing −5.14 K to surface air temperature (SAT) anomalies in North America. External forcing slightly mitigated the cold by 0.42 K. An internally generated wave train from the North Pacific, influenced in combination by Pacific‐North American (PNA) and North Pacific Oscillation (NPO) teleconnection patterns, initiated this intense cyclonic event, contributing −2.18 K and −2.12 K to SAT anomalies, respectively. La Niña‐like sea surface temperature anomalies amplified this wave train and resultant cold wave. Additionally, excessive snow cover in the previous November also intensified the December cold anomalies by enhancing surface albedo and reducing solar radiation.

Improved estimation of daily blue-sky snow shortwave albedo from MODIS data and reanalysis information

Snow albedo is a key geophysical parameter that controls the energy exchanges between the atmosphere and Earth's surfaces and has been widely utilized in climatic and environmental change studies. However, recent studies have demonstrated that current albedo satellite products still have large uncertainties in snow-covered areas. In this study, we estimated the blue-sky shortwave albedo of snow surfaces using the eXtreme Gradient Boosting (XGBoost) algorithm with Moderate Resolution Imaging Spectroradiometer (MODIS) top-of-atmosphere (TOA) reflectance values, ERA-5 land reanalysis snow parameters (e.g., snow cover, snow density and snow depth water equivalent) and in situ measurements. In the XGBoost model, the MODIS MCD43 albedo values were input as prior knowledge, and the random sample validation results showed that the R2 and root mean square error (RMSE) values of this model were approximately 0.953 and 0.044, respectively. The typical sites for independent validation were subjected to in situ measurements at the UPE_L, AWS5, and CA_ARB sites. Finally, the retrieved XGBoost albedo values were compared with the official NASA MODIS (MCD43, collection 6), the Global Land Surface Satellite (GLASS), and the National Oceanic and Atmospheric Administration (NOAA) Visible Infrared Imaging Radiometer Suite (VIIRS) SURFALB albedo products. The validation results indicated that the proposed approach achieved much greater accuracy (RMSE = 0.052, bias = 0.002) than did the corresponding official MODIS (RMSE = 0.087, bias = −0.033), GLASS (RMSE = 0.089, bias = −0.031) and VIIRS SURFALB albedo (RMSE = 0.100, bias = −0.032) products. The improved shortwave albedo captured the rapid temporal changes in surface snow conditions.

Snow Cover Extraction from Landsat 8 OLI Based on Deep Learning with Cross-Scale Edge-Aware and Attention Mechanism

Snow cover distribution is of great significance for climate change and water resource management. Current deep learning-based methods for extracting snow cover from remote sensing images face challenges such as insufficient local detail awareness and inadequate utilization of global semantic information. In this study, a snow cover extraction algorithm integrating cross-scale edge perception and an attention mechanism on the U-net model architecture is proposed. The cross-scale edge perception module replaces the original jump connection of U-net, enhances the low-level image features by introducing edge detection on the shallow feature scale, and enhances the detail perception via branch separation and fusion features on the deep feature scale. Meanwhile, parallel channel and spatial attention mechanisms are introduced in the model encoding stage to adaptively enhance the model’s attention to key features and improve the efficiency of utilizing global semantic information. The method was evaluated on the publicly available CSWV_S6 optical remote sensing dataset, and the accuracy of 98.14% indicates that the method has significant advantages over existing methods. Snow extraction from Landsat 8 OLI images of the upper reaches of the Irtysh River was achieved with satisfactory accuracy rates of 95.57% (using two, three, and four bands) and 96.65% (using two, three, four, and six bands), indicating its strong potential for automated snow cover extraction over larger areas.

Skillful Long-Lead Seasonal Predictions in the Summertime Northern Hemisphere Midlatitudes

Abstract In contrast to boreal winter when extratropical seasonal predictions benefit greatly from ENSO-related teleconnections, our understanding of forecast skill and sources of predictability in summer is limited. Based on 40 years of hindcasts of the Canadian Seasonal to Interannual Prediction System, version 3 (CanSIPSv3), this study shows that predictions for the Northern Hemisphere summer surface air temperature are skillful more than 6 months in advance in several midlatitude regions, including eastern Europe–Middle East, central Siberia–Mongolia–North China, and the western United States. These midlatitude regions of statistically significant predictive skill appear to be connected to each other through an upper-tropospheric circumglobal wave train. Although a large part of the forecast skill for the surface air temperature and 500-hPa geopotential height is attributable to the linear trend associated with global warming, there is significant long-lead seasonal forecast skill related to interannual variability. Two additional idealized hindcast experiments are performed to help shed light on sources of the long-lead forecast skill using one of the CanSIPSv3 models and its uncoupled version. It is found that tropical ENSO-related sea surface temperature (SST) anomalies contribute to the forecast skill in the western United States, while land surface conditions in winter, including snow cover and soil moisture, in the Siberian and western U.S. regions have a delayed or long-lasting impact on the atmosphere, which leads to summer forecast skill in these regions. This implies that improving land surface initial conditions and model representation of land surface processes is crucial for the further development of a seasonal forecasting system. Significance Statement Useful seasonal predictions in the boreal summer midlatitude regions are of great value. In this study, we show that predictions for the boreal summer season are skillful more than 6 months in advance in several midlatitude regions, including eastern Europe–Middle East, central Siberia–Mongolia–North China, and the western United States. The forecast skill in these regions is associated with a circumglobal teleconnection atmospheric circulation pattern. Sources of the long-lead forecast skill include the global warming–related trend and anomalies in the ocean and land surface initial conditions. It is found that the wintertime snow cover and soil moisture in the Siberian and western U.S. regions have a delayed or long-lasting impact on the atmosphere, which leads to summer forecast skill.

PSXI-2 Impact of flexible prepartum supplementation strategies on cow-calf and subsequent progeny feedlot performance

Abstract Implementation of minimal supplemental protein strategies during late gestation may have the potential to minimize any negative postweaning progeny performance while decreasing feed costs. A 3-yr study was conducted to evaluate the effects of mid- to late-gestation supplementation strategies on reproduction, cow body weight (BW), and calf performance in May-calving mature cows grazing upland native range. Cows received one of 3 supplementation strategies: 1) 0.227 kg/d of a 30% CP distillers-based supplement (HalfSupp), 2) 0.454 kg/d of a 30% CP distillers-based supplement (Supp), or 3) brief and intermittent supplementation of Supp at 0.454 kg/cow based on periods of acute environmental stress, such as snow cover, wet winter events, and is best described as flexible supplementation (Flex). Supplementation was initiated in December each year and terminated approximately 30-d prior to the start of the calving season in April. Across all 3 yr, average supplement feeding rate was 25.5, 50.9, and 5.4 kg/cow during the 112-d supplemental period, which resulted in average yearly prepartum supplemental costs of $27.30, 54.60, and 1.99 for HalfSupp, Supp, and Flex; respectively. After weaning, steers grazed sub-irrigated meadow with a dried distiller grain supplement until May. In May, all steers grazed sub-irrigated meadow without additional supplement until August/September when steers were shipped to West Central Research and Extension Center (WCREC). Steers were placed in a GrowSafe feeding system for the rest of the finishing feeding period. Each year, steers were slaughtered at a commercial facility (Tyson Fresh Meats, Lexington, NE) when estimated visually to have 1.3 cm fat thickness over the 12th rib as a group. Cow BW was not different (P = 0.86) at the initiation of supplementation in Jan. However, cows fed Half had a lighter (P < 0.01) BW at pre-calving and tended (P = 0.08) to have a lighter BW at pre-breeding than their counterparts. After a 45-d breeding season, pregnancy rates were not influenced (P = 0.91) by prepartum supplementation strategies. Prepartum supplementation strategies did not influence (P > 0.56) preweaning calf BW at birth, pre-breeding, and weaning. In addition, steer backgrounding BW was also not influenced (P > 0.63) by prepartum supplementation strategy of their dam. Upon entry into the feedlot, steer BW was not impacted (P > 0.84) by prepartum supplementation strategy of their dam. Feedlot performance (BW, average daily gain, and feed efficiency) was not influenced (P > 0.59) by prepartum supplementation strategy of their dam. After harvest, all carcass characteristics were not impacted (P > 0.11) by the prepartum supplementation strategy of the dam. Considering the cost for prepartum supplementation, feeding a protein supplement just during environmentally stressful periods during gestation appears to be a viable alternative to more conventional methods and reduces winter feed costs.

Winter atmospheric deposition of trace elements in the Arkhangelsk region (NW Russia): Insights into environmental effects

Atmospheric precipitation is an important indicator of ecological status. Snow cover accumulates all atmospheric precipitation that occurs during the cold season and releases large amounts of matter into surface water streams during spring snowmelt. We measured chemical composition of suspended solids (>0.45 μm) and dissolved matter (<0.45 μm) of atmospheric precipitation sampled on a monthly basis in 4 stations of Arkhangelsk region in cold seasons of 2018–2021. The fluxes of insoluble particles increase consistently in the spring months. In winter, the largest fluxes of suspended solids from the atmosphere were observed in the urbanized areas (in Severodvinsk and Onega). Snow in Onega showed a significant enrichment in Mn as well as Rb, Sr, Cs, Ba and Pb, which is related to boiler house emissions. Severodvinsk snow is enriched in V, Ni in dissolved fraction and Al, V, Cu, Sr, Pb in suspended solids. The environmental risk assessment shows that Onega station is affected to high and extremely polluted conditions for Mn concentration and Severodvinsk station is affected to multi-element pollution from moderate to significant levels. Comparison with the composition of the river runoff of the Northern Dvina showed that the concentrations of Cu, Zn, Cd and Pb in the snow are equal to or even higher than the river concentrations, indicating that atmospheric precipitation accumulated during winter can significantly affect the geochemistry of river runoff with respect to these elements.

Climate change and the climate reliability of hosts in the second century of the Winter Olympic Games

ABSTRACT In the year the world first surpassed the 1.5°C dangerous global warming threshold set out in the Paris Climate Agreement, the international community celebrates the 100th anniversary of the Olympic Winter Games and considers its future in a warmer world. In the largest study to date, the climate reliability of 93 locations to host Olympic and Paralympic Winter Games (OWG and PWG) snow sports is examined. Under a more probable mid-range emission scenario (RCP4.5, SSP2-45), 52 locations remain climate-reliable for the OWG in the 2050s and 46 in the 2080s. The scheduling of the PWG in March put it at higher risk, with only 22 climate-reliable locations in the 2050s and 16 in the 2080s. When a more stringent minimum snow depth requirement was applied, the number of reliable locations declined slightly for both OWG and PWG, signifying the importance of advanced sustainable snowmaking as an adaptation strategy. While it is inevitable that climate change will impact the geography and development of winter sports to some degree, a reassuring finding is that even with a diminished pool of potential host locations, with continued adaptation, the OWG-PWG can endure as a genuinely global celebration of sport.

Snow depth retrieval method for PolSAR data using multi-parameters snow backscattering model

Snow depth (SD) is a crucial property of snow, its spatial and temporal variation is important for global change, snowmelt runoff simulation, disaster prediction, and freshwater storage estimation. Polarimetric Synthetic Aperture Radar (PolSAR) can precisely describe the backscattering of the target and emerge as an effective tool for SD retrieval. The backscattering component of dry snow is mainly composed of volume scattering from the snowpack and surface scattering from the snow-ground interface. However, the existing method for retrieving SD using PolSAR data has the problems of over-reliance on in-situ data and ignoring surface scattering from the snow-ground interface. We proposed a novel SD retrieval method for PolSAR data by fully considering the primary backscattering components of snow and through multi-parameter estimation to solve the snow backscattering model. Firstly, a snow backscattering model was formed by combining the small permittivity volume scattering model and the Michigan semi-empirical surface scattering model to simulate the different scattering components of snow, and the corresponding backscattering coefficients were extracted using the Yamaguchi decomposition. Then, the snow permittivity was calculated through generalized volume parameters and the extinction coefficient was further estimated through modeling. Finally, the snow backscattering model was solved by these parameters to retrieve SD. The proposed method was validated by Ku-band UAV SAR data acquired in Altay, Xinjiang, and the accuracy was evaluated by in-situ data. The correlation coefficient, root mean square error, and mean absolute error are 0.80, 4.49 cm, and 3.95 cm, respectively. Meanwhile, the uncertainties generated by different SD, model parameters estimation, solution method, and underlying surface are analyzed to enhance the generality of the proposed method.

Climate Teleconnections Influencing Historical Variations, Trends, and Shifts in Snow Cover Days in Finland

Climate Teleconnections Influencing Historical Variations, Trends, and Shifts in Snow Cover Days in Finland

396 Effects of allocation timing on cattle performance and forage utilization when swath grazing a sorghum-sudangrass hybrid in eastern Nebraska

Abstract Swath, or windrow, grazing is the process of cutting hay, leaving it in windrows, and allowing cattle to graze these windrows in the winter. This grazing system reduces winter feeding costs by mitigating the need to bale and transport hay, while having the potential for increased utilization compared with grazing the forage standing. This study aimed to determine the effect of varying allocation timing on cattle performance and forage utilization. A sorghum-sudangrass hybrid (Sorghum× drummondii) was planted in June 2023, cut in early November, and grazed from mid-November 2023 to mid-January 2024. A 16-ha field was divided based on seeding rate with one-half seeded at the recommended rate of 39.2 kg/ha (REC) and one-half with a double rate of 78.4 kg/ha (DOU). The field was subdivided into a total of 8 paddocks (4 per seeding rate) with either 10 (n = 4) or 11 (n = 4) windrows. Forage was cut and windrows were gathered from a 4.57 m width in the field. Prior to grazing, a 0.30 m length of three swaths in each paddock was sampled to determine initial forage mass. A total of 60 steers [initial body weight (BW) = 240 ± 8.3 kg] were stratified by BW and assigned to one of 8 paddocks (n = 7 or 8 calves) to result in equal BW per swathed area. Allocations were offered in full once-per week (1X) or, one-half of the total forage was allocated twice weekly (2X). Post-graze samples were collected to estimate utilization and allocation amount was adjusted as needed based on visual appraisal of post grazing biomass. Cattle were pulled from the field due to snow cover after a 64-d grazing period. The area grazed was used to calculate the animal unit months/ha (AUM) achieved for each paddock. Pre-grazing forage mass did not differ between allocation timing (P = 0.89) or among seeding rates (P = 0.10; REC = 6,720 ± 545 kg/ha, DOU = 8,534.0 ± 545 kg/ha). There was an allocation timing by seeding rate interaction (P &amp;lt; 0.01) for forage offered with 1X (10.62 kgּ steer-1ּ d-1) being offered more (P &amp;lt; 0.01) than 2X (8.43 kg/steer/d) within REC, but no difference (P = 0.31) in forage offered due to allocation within DOU (8.31 kgּ steer-1ּ d-1). However, post grazing biomass (P = 0.35) and utilization (59 ± 8%) were not affected by allocation timing (P = 0.48). Average daily gain (ADG) also did not differ (P = 0.93) due to allocation timing (0.20 ± 0.07 kg). Most notably, there was a significant allocation effect (P = 0.05) on AUM/ha with 1X (14.51 ± 1.00) being lesser than 2X (16.82 ± 1.00). However, there was no significant difference (P = 0.30) in forage disappearance (291 ± 61 kg/AUM) due to allocation timing. This indicates that more frequent allocations can lead to increased carrying capacity. Increasing the frequency of allocation is a simple management practice producers can implement when swath grazing sorghum-sudangrass to increase carrying capacity.

Which global reanalysis dataset has better representativeness in snow cover on the Tibetan Plateau?

Which global reanalysis dataset has better representativeness in snow cover on the Tibetan Plateau?

The cooperative effects of November Arctic sea ice and Eurasian snow cover on the Eurasian surface air temperature in January–February

AbstractDue to their significant influence on large‐scale atmospheric circulation and climate anomalies, the variability of Arctic sea ice and Eurasian snow cover during late autumn and their combined effects have garnered increasing attention. This study aims to investigate the physical mechanism underlying the covariation among the Barents‐Kara Seas (BKS) sea ice concentration (SIC), Eurasian snow cover extent (SCE) and the ensuing winter Eurasian surface air temperature (SAT). The statistics results of singular value decomposition suggest a significant linkage between the decreased BKS SIC, zonal “negative–positive” dipole SCE anomalies over Eurasia in November and cold Eurasian SAT in January–February (JF). Observational diagnosis analyses about the meridional moisture, heat transport and surface heat flux demonstrate that subpolar Eurasian anticyclonic circulation plays a crucial role in connecting the predominant modes of SIC and SCE. Furthermore, the BKS SIC and Eurasian SCE anomalies can jointly excite upward‐propagating planetary waves into the stratosphere, while simultaneously reducing the subpolar meridional temperature gradient. This results in westerly wind deceleration and favours the continuous planetary wave propagation. Consequently, the stratospheric polar vortex is significantly weakened, along with negative Northern Annular Mode anomalies propagating downward from the stratosphere to troposphere. Negative‐phase Arctic Oscillation anomalies correspondingly develop during JF, resulting in widespread cold anomalies over the Eurasian continent. These results are further confirmed by numerical sensitivity experiments from the Community Atmosphere Model forced by the above mentioned SIC and SCE anomalies. The empirical hindcast model analyses further suggest that the prediction skill of JF Eurasian SAT is enhanced when both the November BKS SIC and Eurasian SCE signals are considered.

First results of the polar regional climate model RACMO2.4

Abstract. The next version of the polar Regional Atmospheric Climate Model (referred to as RACMO2.4p1) is presented in this study. The principal update includes embedding of the package of physical parameterizations of the Integrated Forecast System (IFS) cycle 47r1. This constitutes changes in the precipitation, convection, turbulence, aerosol and surface schemes and includes a new cloud scheme with more prognostic variables and a dedicated lake model. Furthermore, the standalone IFS radiation physics module ecRad is incorporated into RACMO, and a multilayer snow module for non-glaciated regions is introduced. Other updates involve the introduction of a fractional land–ice mask, new and updated climatological data sets (such as aerosol concentrations and leaf area index), and the revision of several parameterizations specific to glaciated regions. As a proof of concept, we show first results for Greenland, Antarctica and a region encompassing the Arctic. By comparing the results with observations and the output from the previous model version (RACMO2.3p3), we show that the model performs well regarding the surface mass balance, surface energy balance, temperature, wind speed, cloud content and snow depth. The advection of snow hydrometeors strongly impacts the ice sheet's local surface mass balance, particularly in high-accumulation regions such as southeast Greenland and the Antarctic Peninsula. We critically assess the model output and identify some processes that would benefit from further model development.

Elevation‐dependent warming and possible‐driving mechanisms over global highlands

AbstractElevation‐dependent warming (EDW) has been a topic of intense debate due to limited observed data in global highland areas. This study aims to fill this gap by utilizing CRU and ERA5 datasets from 1981 to 2021 to explore the trends of climate change and its elevation dependency. The anomalies of temperature indicators (Tmean, Tmax, and Tmin) in both ERA5 and CRU showed significant warming trends over global highlands. Moreover, the response of temperature indicators to elevation across global highlands is not spatially uniform. The linear regression model based on the elevation showed significant warming signals for the temperature indicators at various elevations over the global highlands. On a regional scale, Tmean and Tmax predominantly showed linear EDW over EU highlands, while Tmean in Asian highlands exhibited EDW signals at 4–5 km. Tmin showed EDW at 2.5–5.5 km with ERA5 and 3–5 km with CRU. In the Andes, EDW was prominent at 2.5–4 km. Overall, EDW signals are evident in all studied regions but vary across them. While assessing the driving factors, the results of this study indicate that total column water vapour (TCWV), snow depth (SD), snow albedo, and normalized difference vegetation index (NDVI) correlated positively with the temperature indicators. These findings emphasize the significance of elevation‐specific interactions between environmental factors and temperature in forecasting temperature changes in mountainous areas. Additionally, temperature exhibited coherence with teleconnection indices from the Atlantic and Pacific Oceans. Asian and European (EU) highlands exhibited interzonal coherence with the Pacific and Atlantic Oceans, while North American (NA) highlands showed coherence, followed by South American (SA) highlands. These findings provide a comprehensive understanding of EDW and its implications for highland regions globally, including the potential for more severe depletion of snow/ice resources in a warmer future.

Summer snow on Arctic sea ice modulated by the Arctic Oscillation

Since the 1970s, Arctic sea ice has undergone unprecedented change, becoming thinner, less extensive and less resilient to summer melt. Snow’s high albedo greatly reduces solar absorption in sea ice and the upper ocean, which mitigates sea–ice melt and ocean warming. However, the drivers of summertime snow depth variability are unknown. The Arctic Oscillation is a mode of natural climate variability, influencing Arctic snowfall and air temperatures. Thus, it may affect summertime snow conditions on Arctic sea ice. Here we examine the role of the Arctic Oscillation in summer snow depth variability on Arctic sea ice in 1980–2020 using atmospheric reanalysis, snow modelling and satellite data. The positive phase leads to greater snow accumulation, ranging up to ~4.5 cm near the North Pole, and higher surface albedo in summer. There are more intense, frequent Arctic cyclones, cooler temperatures aloft and greater snowfall relative to negative and neutral phases; these conditions facilitate a more persistent summer snow cover, which may lessen sea-ice melt and ocean warming. The Arctic Oscillation influence on summertime snow weakens after 2007, which suggests that future warming and Arctic sea-ice loss might modify the relationship between the Arctic Oscillation and snow on Arctic sea ice.