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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.

Prior studies have shown that above-normal sea surface temperatures (SSTs) enhance snowfall over Korea. Here, we show that heavy snow is also associated with below-normal East Sea SSTs, and we investigate the dynamical response of the atmosphere to this surface condition using observations and numerical modeling. The results indicate that anomalous southeasterly/easterly winds are induced by heavy snowfall-related cold SST anomalies, and consequently, the moisture flux is converged. The existence of the southeasterly winds and the accompanied moisture flux convergence appear to be instrumental in producing the heavy snowfall events. The anomalous southeasterly/easterly winds associated with heavy snowfall-related cold SST anomalies reduce the climatological northwesterly/westerly winds, leading to relatively warm and wet conditions over the east coast of Korea that are favorable for forming and intensifying snowfall events in the region.

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.

The increasing frequency and intensity of heavy snowfall events due to climate change pose significant risks to agricultural facilities, particularly lightweight structures such as greenhouses. This study develops a probabilistic risk management framework using a Markov Chain approach to analyse snow load dynamics under varying climatic conditions, incorporating the effects of snow density changes due to temperature fluctuations. Time-series meteorological data from 99 weather stations across South Korea over the past 20 years were utilised to calculate hourly snow loads. Cluster analysis was employed to classify snow load states, and transition probabilities between states were derived to construct Markov transition matrices. The framework evaluates failure probabilities of various greenhouse specifications, highlighting the influence of structural thresholds and regional snowfall patterns. Areas prone to heavy snowfall exhibited significantly higher failure probabilities compared to regions with milder conditions. National-scale failure probability maps were developed to provide actionable insights for disaster mitigation, emphasising the importance of region-specific risk management strategies. Results demonstrate the critical role of snow density in snow load evolution and its implications for structural safety. The proposed methodology facilitates early warning systems, resource allocation, and policy recommendations, supporting a proactive approach to disaster risk reduction. This study underscores the necessity of integrating probabilistic models with structural safety assessments to enhance the resilience of agricultural facilities against extreme snowfall events, offering a robust tool for sustainable agricultural practices.

Snowfall is the primary contributor to Antarctic surface mass balance. Identifying regional‐scale mechanisms that drive heavy snowfall provides context for changes in Antarctic surface mass balance in a warmer climate. We compare drivers of top snowfall days across five Antarctic regions using machine learning and traditional synoptic diagnostics. A convolutional neural network identifies top snow days with an accuracy of 92%–94% per region when trained on just atmospheric moisture and low‐level meridional wind, highlighting the importance of atmospheric river‐like structures to top Antarctic snowfall days. The network's skill depends mainly on low‐level wind in East Antarctica and atmospheric moisture in West Antarctica, suggesting that dynamic processes are comparatively more important in driving East Antarctic top snowfall days. We leverage the quasi‐geostrophic omega equation to identify mechanisms for ascent and snowfall production, and we find that East Antarctic top snowfall days feature stronger synoptic‐scale forcing for ascent compared to West Antarctica.

While the Arctic region has been warming strongly in recent decades, anomalously large snowfall in recent winters has affected large parts of North America, Europe, and east Asia. Here we demonstrate that the decrease in autumn Arctic sea ice area is linked to changes in the winter Northern Hemisphere atmospheric circulation that have some resemblance to the negative phase of the winter Arctic oscillation. However, the atmospheric circulation change linked to the reduction of sea ice shows much broader meridional meanders in midlatitudes and clearly different interannual variability than the classical Arctic oscillation. This circulation change results in more frequent episodes of blocking patterns that lead to increased cold surges over large parts of northern continents. Moreover, the increase in atmospheric water vapor content in the Arctic region during late autumn and winter driven locally by the reduction of sea ice provides enhanced moisture sources, supporting increased heavy snowfall in Europe during early winter and the northeastern and midwestern United States during winter. We conclude that the recent decline of Arctic sea ice has played a critical role in recent cold and snowy winters.

Water years (WY) 2017 and 2023 were anomalously wet for California, each alleviating multiyear drought. In both cases, this was unexpected given La Niña conditions, with most seasonal forecasts favoring drier‐than‐normal winters. We analyze over seven decades of precipitation and snow records along with mid‐tropospheric circulation to identify recurring weather patterns driving California precipitation and Sierra Nevada snowpack. Tropical forcing by ENSO causes subtle but important differences in these wet weather patterns, which largely drives the canonical seasonal ENSO‐precipitation relationship. However, the seasonal frequency of these weather patterns is not strongly modulated by ENSO and remains a primary source of uncertainty for seasonal forecasting. Seasonal frequency of ENSO‐independent weather patterns was a major cause of anomalous precipitation in WY2017, record‐setting snow in WY2023, and differences in precipitation outcome during recent El Niño winters 1983, 1998, and 2016. Improved understanding of recurrent atmospheric weather patterns could help to improve seasonal precipitation forecasts. Plain Language Summary In 2017 and 2023, California experienced unexpectedly wet conditions despite predictions of dry winters due to La Niña. In 2016, seasonal predictions in California favored wet conditions due to the very strong El Niño, but the season was normal‐to‐dry statewide. Understanding relationships between El Niño/La Niña and recurring atmospheric weather patterns driving individual storms is needed to improve seasonal forecasts. We studied historical relationships between weather patterns that bring rain and snow to the region and the El Niño Southern Oscillation (ENSO). We find ENSO influences important characteristics of weather patterns once they make landfall in California, making El Niño storms generally wetter in coastal southern California and Desert Southwest. However, ENSO does not strongly affect how often these patterns occur in a season, which makes seasonal precipitation forecasts challenging. The frequency of certain weather patterns not tied to ENSO played important roles in the unusual rainfall of 2017, the heavy snowfall of 2023, and the drier than expected winter of 2016. Understanding these weather patterns provides operationally and scientifically relevant context for future seasonal forecasts by highlighting that while ENSO only minimally influences the frequency of certain impactful storm types, it does change the precipitation characteristics of these storms. Key Points Weather regime type and frequency are key drivers of winter seasons with anomalous precipitation and/or snow accumulation in California ENSO does not modulate the seasonal frequency of weather regimes impacting the coast, presenting a challenge for seasonal forecasting ENSO modulates synoptic circulation characteristics of key weather regimes which produces the canonical ENSO‐precipitation relationship

During the 2023/2024 winter, eastern China experienced frequent cold extremes, heavy snowfall, and freezing rain, modulated by the circulation regime of enhanced atmospheric intraseasonal oscillation (ISO). Our results show these severe weather events can be attributed to two pathways of El Niño's influence. The strong ISO cold events were characterized by an amplified Siberian High, developed from a North Atlantic (NA)‐originating Rossby wave train. The El Niño modulated the NA jet stream by strengthening, extending it eastward, and increasing its southwest–northeast tilt, which reinforced cold‐air circulation regime via interactions between the seasonal background flow and ISO perturbations. Meanwhile, El Niño strengthened the western North Pacific anticyclone, enhancing moisture transport for freezing rain formation. The two pathways of El Niño–Southern Oscillation (ENSO) impacts can be found in most ENSO years, implying that ENSO is an important source of subseasonal forecast of extreme cold‐wet events in China.

Analyses of observation data from 1961 to 2014 by using the empirical orthogonal function (EOF) method indicate that the primary mode (a monosign pattern) of heavy snowfall over northern China in winter shows evident variations from a negative polarity to a positive polarity in the mid-1990s. Associated with this decadal change, the southward displacement of the polar front jet stream and northward shift of the subtropical jet stream in the upper troposphere are apparent. Accordingly, a negative height anomaly dominates the region from Lake Balkhash to Lake Baikal and a positive height anomaly occupies the midlatitudes of the North Pacific in the middle troposphere. Such anomalous patterns in the middle and high troposphere correspond approximately to the northern mode of the East Asian winter monsoon (EAWM) and may favor the interaction of cold air with moist airflows over northern China, which helps increase local heavy snowfall. Further investigation shows that the shift in the Atlantic multidecadal oscillation (AMO) from a cold phase to a warm phase in the 1990s may also play a role, through its linkage to the above atmospheric circulations with the aid of a downstream propagation of wave train that emanates from the Atlantic Ocean.

Heavy snowfall is a critical part of hydrological systems and has frequently occurred over Central Asia in recent three decades. The study focuses on the dominant synoptic circulation pattern of heavy snowfall, Central Asian vortices (CAVs), to explore the multi-timescale features and possible influencing factors during cold seasons. The frequency of CAVs in cold seasons shows the “midwinter suppression-like” pattern, which is high in late autumn and early spring but low in winter. The distribution of CAVs is mainly concentrated in the north of Kazakhstan and from the Caspian Sea to the Lake Balkhash, which has caused increased intensity and affected areas of heavy snowfall since the 1980s. The background circulation of CAVs is related to various forcing factors, among which the most important are the North Pacific Victoria mode (VM) and midlatitude North Atlantic anomaly (MNA). VM could stimulate anomalous circumglobal wave train from North Pacific to Central Asia, thereby strengthening cyclonic anomalies over northwestern Central Asia and providing conducive conditions for CAV development. During this process, MNA plays a role in replenishing the wave energy for the circumglobal wave train over North Atlantic and helps the occurrence of CAV heavy snowfall as well. On the shorter timescale, CAVs are modulated by the intraseasonal variation of VM. Within 2.5 weeks before CAV heavy snowfall days, the wave train from North Pacific connects with the downstream wave train, which leads to anomalous wave energy converging in Central Asia and favors the formation of CAVs and related heavy snowfall.