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High Mountain Asia (HMA), which includes the Tibetan Plateau, Tienshan Mountains and surrounding region, has abundant snowfall and a long period of snow cover annually. The headwaters of many prominent Asian rivers depend in part on HMA meltwater. In this study, we evaluate projected changes in mean snowfall (Smean), snowfall days (Sd), and snowfall fraction (Sf) for the years 2070-2099 relative to 1976-2005, under the Representative Concentration Pathway 4.5 (RCP4.5) and 8.5 (RCP8.5) emission scenarios. An evaluation of the results shows that while NASA's NEX-GDDP (National Aeronautics and Space Administration Earth Exchange Global Daily Downscaled Projections) high-resolution daily downscaled dataset can successfully capture the distribution of mean snowfall climatology, it has a strong bias for extreme snowfall indices. In general, the projected increase of temperature under RCP4.5 and RCP8.5-especially in winter-will result in a decrease in snowfall amount (−18.9%, −32.8%), fewer snowfall days (−29.6%, −47.3%), and less precipitation falling as snow (−26.7%, −42.3%). Furthermore, under high emission scenarios, rain-dominated regions are projected to expand 53.9%, while snow-dominated areas will only account for 17.9% of the entire HMA. Spatially, snowfall shows a more robust decline in eastern HMA (e.g. East Tienshan, East Kun Lun, Qilian, South and Eastern Tibet, and Hengduan) than in western HMA (e.g. Hissar Alay, Pamir, and Karakoram). This difference can be attributed to various environmental factors, such as climatology, elevation influences, and the unique seasonal recycle between the two regions.

This paper analyzes the proportion of snowfall days relative to precipitation days, in order to assess the impact of changing temperatures on snowfall, while minimizing the impact of variations in precipitation frequency and intensity. We analyzed the ratio of snowfall days to precipitation days for up to 100 years at 76 meteorological stations, spanning elevations from 200 to 2700 m asl in Switzerland. Our results show clear decreasing trends in snowfall days relative to precipitation days. These decreases are connected to increasing temperatures. The decrease in snowfall days was stronger at lower elevations, i.e., at locations with temperatures closer to the melting point. We observed a baseline seasonal temperature threshold of −2.7°C ± 0.8°C in winter and −3.8°C ± 0.6°C in spring, above which the decrease in snowfall days grew rapidly. From these observations, we developed an empirical model that can be used to evaluate the impact of future temperature increases on snowfall, independent of changes in the frequency and intensity of precipitation events. Key Points This paper assesses the impact of changing temperatures on snowfall The decrease grows rapidly above a winter temperature of −2.7 deg C +/− 0.8 deg C The impact of future temperature increases on snowfall can be evaluated

The loss of Arctic sea-ice has been implicated with severe cold and snowy mid-latitude winters. However, the mechanisms and a direct link remain elusive due to limited observational evidence. Here we present atmospheric water vapour isotope measurements from Arctic Finland during ‘the Beast from the East’—a severe anticyclonic outbreak that brought heavy snowfall and freezing across Europe in February 2018. We find that an anomalously warm Barents Sea, with a 60% ice-free surface, supplied up to 9.3 mm d−1 moisture flux to this cold northeasterly airflow. We demonstrate that approximately 140 gigatonnes of water was evaporated from the Barents Sea during the event, potentially supplying up to 88% of the corresponding fresh snow over northern Europe. Reanalysis data show that from 1979 to 2020, net March evaporation across the Barents Sea increased by approximately 70 kg per square metre of sea-ice lost (r2 = 0.73, P < 0.01), concurrent with a 1.6 mm (water equivalent) per year increase in Europe’s maximum snowfall. Our analysis directly links Arctic sea-ice loss with increased evaporation and extreme snowfall, and signifies that by 2080, an Atlantified ice-free Barents Sea will be a major source of winter moisture for continental Europe.

Using station data and Regional Climate Model version 4 (RegCM4) simulations under the representative concentration pathway 4.5 (RCP4.5) scenario, this article addresses historical and future changes of the wintertime snowfall over China. The observational results generally show a decrease in the frequency and an increase in the mean intensity of snowfalls in northwestern China (NWC), northeastern China (NEC), the eastern Tibetan Plateau (ETP), and southeastern China (SEC) since the 1960s. The total amount of wintertime snowfall, however, has increased in NWC, NEC, and ETP but decreased in SEC. The decrease in snow days is primarily due to the reduction of light snowfall events. The increase in the total amount is primarily explained by increases in heavy snowfalls, and the corresponding decrease is the result of decreases in light-to-heavy snowfalls. The RegCM4 ensemble, which can well simulate the observed snowfall climatology, projects that the snow days will be further reduced by the end of the twenty-first century relative to 1986–2005, primarily owing to the decline of light snowfall events. The total amount is projected to increase in NWC but decrease in the other three subregions. The increase in the total amount in NWC is attributed to increases in heavy and large snowfalls. Decreases in light snowfalls play a leading role in the decrease of the total amount in NEC. In ETP and SEC, the decrease in the total amount is the result of overall decreases in light-to-heavy snowfalls. The mechanism for such changes is an interesting topic to study in the future.

Retrievals of falling snow from space-based observations represent key inputs for understanding and linking Earth’s atmospheric, hydrological, and energy cycles. This work quantifies and investigates causes of differences among the first stable falling snow retrieval products from the Global Precipitation Measurement (GPM) Core Observatory satellite and CloudSat’s Cloud Profiling Radar (CPR) falling snow product. An important part of this analysis details the challenges associated with comparing the various GPM and CloudSat snow estimates arising from different snow–rain classification methods, orbits, resolutions, sampling, instrument specifications, and algorithm assumptions. After equalizing snow–rain classification methodologies and limiting latitudinal extent, CPR observes nearly 10 (3) times the occurrence (accumulation) of falling snow as GPM’s Dual-Frequency Precipitation Radar (DPR). The occurrence disparity is substantially reduced if CloudSat pixels are averaged to simulate DPR radar pixels and CPR observations are truncated below the 8-dBZ reflectivity threshold. However, even though the truncated CPR- and DPR-based data have similar falling snow occurrences, average snowfall rate from the truncated CPR record remains significantly higher (43%) than the DPR, indicating that retrieval assumptions (microphysics and snow scattering properties) are quite different. Diagnostic reflectivity (Z)–snow rate (S) relationships were therefore developed at Ku and W band using the same snow scattering properties and particle size distributions in a final effort to minimize algorithm differences. CPR–DPR snowfall amount differences were reduced to ~16% after adopting this diagnostic Z–S approach.

Global surface snowfall rate estimation is crucial for hydrological and meteorological applications but is still a challenging task. A novel approach is developed to comprehensively use passive microwave, infrared data and physical constraints in deep‐learning neural networks with an attention module for retrieving surface snowfall rate (PCSSR‐DNNWA). The PCSSR‐DNNWA consistently outperforms traditional approaches in predicting surface snowfall rates with a correlation coefficient of ∼0.76, mean error of ∼−0.02 mm/hr, and root mean squared error of ∼0.21 mm/hr. It is found that graupel water path is of vital importance with largest contributions in retrieving surface snowfall rate. By integrating the physical constraints, the algorithm of PCSSR‐DNNWA opens a new avenue for retrieving the surface snowfall rate from satellites since some predictors are intelligently considered, resulting in an increased accuracy, interpretability, and computational efficiency. Plain Language Summary Comprehensively monitoring surface snowfall on Earth can effectively be achieved through space‐borne instruments. However, estimating surface snowfall from space is a challenging task as the signals measured by space sensors are indirectly related to surface snowfall rate. In this study, a novel deep learning algorithm is developed based on deep neural networks, which is more accurate, interpretable and computationally efficient, compared with traditional approaches, in estimating surface snowfall rate using observations from various space‐borne sensors and physically relevant parameters. Key Points Physical constraints greatly improve the ability of surface snowfall rate retrieval Attention module in deep neural networks could intelligently adjust the weights of predictors

Heavy snowfall events in the Ural region have drawn significant attention due to their substantial frequency, the region’s relatively high population density and its developed network of roads and power lines. This study summarizes the main characteristics of the hazardous heavy snowfall (HHS) events (≥20 mm 12 h[sup.−1]) that have occurred in the Ural region between 1981 and 2025, as well as in related synoptic-scale environments, for the first time. The dataset consists of 116 HHS reports, with 12-hourly snowfall intensities ranging from 20 mm to 47.6 mm. The main characteristics of these events (snowfall amount, spatial distribution, inter-annual and seasonal variability and trends, associated weather phenomena, and related damage) are examined based on the data from weather stations, the ERA5 reanalysis, scientific literature, and media reports. While there is no statistically significant trend in HHS events, the frequency of the most damaging late spring and early autumn snowfalls has decreased. Using 72 h backward trajectories according to the NOAA HYSPLIT model and the ERA5 reanalysis, we classified the HHS events into five types according to air mass origin, and performed a composite analysis for each type. The main finding is that 46% of HHS reports are related to cyclones forming over the Caspian and Aral seas, resulting in a higher frequency of HHS events to the east of the Ural Mountains compared to the western part of the region.

Central Asian vortex (CAV) is a kind of typical regional cyclonic vortex in the North Hemisphere and its important component, Central Asian moist vortex (CAMV), is one of the key systems causing Central Asian heavy precipitation. This study focused on the main characteristics and interannual forcing sources of January CAMV, which has led to rapid increase of Central Asian heavy snowfall during several decades, mainly discussing about the individual and cooperative effect of boreal surface thermal anomalies on CAMVs. The results indicate that the January CAMV demonstrates significantly interannual variability in its annual activity days, moreover, this interannual variability has been experiencing decadal enhancement since 2004, which explains the decadal increase in contemporaneous Central Asian heavy snowfall amount. The “northern moist vortex” (NMV) featuring the low vortex in the northern Central Asia accompanying the European and Tibetan Plateau Ridge, represents the typical circulation configuration of January CAMV. The interannual frequency of NMV is highly related to preceding North Pacific Victoria mode (VM), mid-latitude North Atlantic sea surface temperature (MNA) as well as the land-sea temperature contrast (LSTC) between Africa and eastern North Atlantic. Among them, VM and MNA could respectively stimulate circumglobal and Atlantic-Eurasian wave train structures into northern Central Asia. While, LSTC could excite anomalous lower-level cyclonic circulation over the Mediterranean entrance, accelerating and lifting the entrance of African-Asian jet stream via the way of “eddy-driven jet stream”, thereby strengthening the docking of the African-Asian jet stream with the North Atlantic jet stream and enhancing the waveguide effect of two jet streams. When these three surface thermal factors coexist on the interannual scale, VM tends to play a leading role for contributing more than 50% to NMVs in three factors. While, MNA and LSTC play unneglected roles of “relay” to further modulate NMVs, and the cooperative effect of multiple surface thermal anomalies is the key to impacting January CAMVs.

Frequent heavy snowfall in Xinjiang plays an important role in the land water cycle. In this study, 18 groups of simulation experiments are conducted on the heavy snowfall event in Xinjiang during 9–13 December of 2015 using the Weather Research and Forecasting (WRF) model. In these experiments, the combination of six land surface parameterization schemes (the Noah scheme, Noah-MP scheme, RUC scheme, CLM4 scheme, PX scheme, and TD scheme) with three microphysical parameterization schemes (the WSM6 scheme, Thompson scheme, and Lin scheme) are adopted, where the observed snowfall data are used for performance evaluation. Results show that the simulated snowfall intensity and snowfall range in different areas are very sensitive to the selection of the land surface scheme. The snowfall in southern Xinjiang is overestimated by almost all six schemes, where the Noah-MP scheme performs more reasonably than the others. The Noah scheme shows its advantage in northwestern Xinjiang. The three different microphysical schemes vary significantly in producing snowfall amount. The WSM6 scheme produced the largest snowfall amount, and the Lin scheme resulted in the smallest snowfall amount. In addition, the accumulated snowfall amounts above 10 mm are generally underestimated by all six land surface schemes, while the accumulated snowfall amounts below 10 mm are overestimated by most of the schemes. The Noah-MP scheme performs the best in the simulation of the snowfall amount in the whole region. However, the Noah scheme shows an advantage in areas with a large snowfall amount.

Although Earth system models (ESMs) tend to overestimate historical land surface warming, they also overestimate snow amounts in the Northern Hemisphere. By combining ground-based datasets and ESMs, we find that this paradoxical phenomenon is predominantly driven by an overestimation of light snowfall frequency. Using spatially distributed emergent constraints, we show that this paradox persists in mid- (2041–2060) and long-term (2081–2100) projections, affecting more than half of the Northern Hemisphere’s land surface. ESMs underestimate the frequency of freezing days by 12–19% and overestimate snow water equivalent by 28–34%. Constrained projections indicate that the raw ESM outputs overestimate future Northern Hemisphere snowmelt water by 12–16% across 53–60% of the Northern Hemisphere’s land surface. This snowmelt water overprediction implies that the amount of water available in the future for agriculture, industry, ecosystems and domestic use may be lower than unadjusted ESM projections suggest. Many climate models overestimate the snow amount in the Northern Hemisphere despite strong warming. Here the authors find that light snowfall and snow melting processes drive this mismatch and use these relationships to constrain future projections of snow water resources.