Snow depth is one of the key physical parameters for understanding land surface energy balance, soil thermal regime, water cycle, and assessing water resources from local community to regional industrial water supply. Previous studies by using in situ data are mostly site specific; data from satellite remote sensing may cover a large area or global scale, but uncertainties remain large. The primary objective of this study is to investigate spatial variability and temporal change in snow depth across the Eurasian continent. Data used include long-term (1966–2012) ground-based measurements from 1814 stations. Spatially, long-term (1971–2000) mean annual snow depths of >20 cm were recorded in northeastern European Russia, the Yenisei River basin, Kamchatka Peninsula, and Sakhalin. Annual mean and maximum snow depth increased by 0.2 and 0.6 cm decade⁻¹ from 1966 through 2012. Seasonally, monthly mean snow depth decreased in autumn and increased in winter and spring over the study period. Regionally, snow depth significantly increased in areas north of 50° N. Compared with air temperature, snowfall had greater influence on snow depth during November through March across the former Soviet Union. This study provides a baseline for snow depth climatology and changes across the Eurasian continent, which would significantly help to better understanding climate system and climate changes on regional, hemispheric, or even global scales.

A comprehensive and hemispheric-scale snow cover and snow depth analysis is a prerequisite for all related processes and interactions investigation on regional and global surface energy and water balance, weather and climate, hydrological processes, and water resources. However, such studies were limited by the lack of data products and/or valid snow retrieval algorithms. The overall objective of this study is to investigate the variation characteristics of snow depth across the Northern Hemisphere from 1992 to 2016. We developed long-term Northern Hemisphere daily snow depth (NHSnow) datasets from passive microwave remote sensing data using the support vector regression (SVR) snow depth retrieval algorithm. NHSnow is evaluated, along with GlobSnow and ERA-Interim/Land, for its accuracy across the Northern Hemisphere against meteorological station snow depth measurements. The results show that NHSnow performs comparably well with a relatively high accuracy for snow depth with a bias of −0.6 cm, mean absolute error of 16 cm, and root mean square error of 20 cm when benchmarked against the station snow depth measurements. The analysis results show that annual average snow depth decreased by 0.06 cm per year from 1992 to 2016. In the three seasons (autumn, winter, and spring), the areas with a significant decreasing trend of seasonal maximum snow depth are larger than those with a significant increasing trend. Additionally, snow cover days decreased at the rate of 0.99 day per year during 1992–2016. This study presents that the variation trends of snow cover days are, in part, not consistent with the variation trends of the annual average snow depth, of which approximately 20% of the snow cover areas show the completely opposite variation trends for these two indexes over the study period. This study provides a new perspective in snow depth variation analysis, and shows that rapid changes in snow depth have been occurring since the beginning of the 21st century, accompanied by dramatic climate warming.

In this study, we compiled a high-quality, in situ observational dataset to evaluate snow depth simulations from 22 CMIP6 models across high-latitude regions of the Northern Hemisphere over the period 1955–2014. Simulated snow depths have low accuracy (RMSE = 17–36 cm) and are biased high, exceeding the observed baseline (1976–2005) on average (18 ± 16 cm) across the study area. Spatial climatological patterns based on observations are modestly reproduced by the models (normalized root-mean-square deviations of 0.77 ± 0.20). Observed snow depth during the cold season increased by about 2.0 cm over the study period, which is approximately 11% relative to the baseline. The models reproduce decreasing snow depth trends that contradict the observations, but they all indicate a precipitation increase during the cold season. The modeled snow depths are insensitive to precipitation but too sensitive to air temperature; these inaccurate sensitivities could explain the discrepancies between the observed and simulated snow depth trends. Based on our findings, we recommend caution when using and interpreting simulated changes in snow depth and associated impacts.