Extreme Snowfall Research Articles

Habitat Characteristics Coincidence of Dead and Living Long-Tailed Gorals (Naemorhedus caudatus) According to Extreme Snowfall.

Simple SummaryThe long-tailed goral (Naemorhedus caudatus Milne-Edwards) is a critically endangered herbivore in South Korea. From March to June in 2010, 24 animals were found to have died due to heavy snowfall in the Wangpi Stream basin. In this study, we hypothesized that gorals that died due to snowfall are low-status individuals that lived in the sub-optimal or non-suitable areas. The results suggested that the sites where dead gorals were found were highly related to typical goral habitats. The optimal goral habitats could become uninhabitable following heavy snowfall. Most of the dead animals were pregnant females or were young, implying that they could not escape their primary habitats due to lower mobility. Thus, when there is a climate catastrophe, the optimal goral habitats should be considered for rescue and artificial feeding.The long-tailed goral (Naemorhedus caudatus) is a critically endangered herbivore in South Korea. Despite government efforts to recover the population through reintroduction programs, the animal remains vulnerable to heavy snowfall. From March to June 2010, 24 animals were found dead due to heavy snowfall in the Wangpi Stream basin. In this study, we hypothesized that gorals that died due to snowfall are low-status individuals that lived in the sub-optimal or non-suitable areas. Using the occurrence data from extensive field surveys from 2008 to 2010 in the Wangpi Stream and the carcass location data, we (1) defined the goral habitat characteristics and (2) compared the habitat characteristics between dead and living gorals using ensemble species distribution modeling. The results suggested that the sites where dead gorals were found were highly related to typical goral habitats. These results implied that the optimal goral habitats could become uninhabitable following heavy snowfall. Most of the dead animals were pregnant females or were young, implying that they could not escape their primary habitats due to lower mobility. Thus, when there is a climate catastrophe, the optimal goral habitats should be considered for rescue and artificial feeding.

Arctic sea-ice loss fuels extreme European snowfall

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.

A study of extreme snowfall during 2019 and 2020 across the Kashmir Himalaya

Snowfall pictures from different locations during 2019/2020 winter season.

Recent Increases in Winter Snowfall Provide Resilience to Very Small Glaciers in the Julian Alps, Europe

Very small glaciers (&lt;0.5 km2) account for more than 80% of the total number of glaciers and more than 15% of the total glacier area in the European Alps. This study seeks to better understand the impact of extreme snowfall events on the resilience of very small glaciers and ice patches in the southeastern European Alps, an area with the highest mean annual precipitation in the entire Alpine chain. Mean annual precipitation here is up to 3300 mm water equivalent, and the winter snow accumulation is approximately 6.80 m at 1800 m asl averaged over the period 1979–2018. As a consequence, very small glaciers and ice/firn patches are still present in this area at rather low altitudes (1830–2340 m). We performed repeated geodetic mass balance measurements on 14 ice bodies during the period 2006–2018 and the results show an increase greater than 10% increase in ice volume over this period. This is in accordance with several extreme winter snow accumulations in the 2000s, promoting a positive mass balance in the following years. The long-term evolution of these very small glaciers and ice bodies matches well with changes in mean temperature of the ablation season linked to variability of Atlantic Multidecadal Oscillation. Nevertheless, the recent behaviour of such residual ice masses in this area where orographic precipitation represents an important component of weather amplification is somehow different to most of the Alps. We analysed synoptic meteorological conditions leading to the exceptional snowy winters in the 2000s, which appear to be related to the influence and modification of atmospheric planetary waves and Arctic Amplification, with further positive feedbacks due to change in local sea surface temperature and its interactions with low level flows and the orography. Although further summer warming is expected in the next decades, we conclude that modification of storm tracks and more frequent occurrence of extreme snowfall events during winter are crucial in ensuring the resilience of small glacial remnants in maritime alpine sectors.

Causes of the extreme snowfall anomaly over the northeast Tibetan plateau in early winter 2018

An extremely heavy snowfall event occurred in early winter (November–December) 2018 over the northeast of Tibetan Plateau (NTP), with snowfall amounts ranking the highest since 1961. It is necessary to better understand the causes and reveal the physical mechanism of this extreme snowfall event. In this article, the features of this extreme snowfall event and its possible linkage with atmospheric circulation anomalies, and sea surface temperature (SST) anomaly effects are analyzed. The results show that in early winter 2018, (1) there was a similar Eurasian teleconnection (EU) pattern in the mid-latitudes with a periodic strengthening of the Siberian High, which favored northern cold airflow into the NTP, providing the suitable temperature conditions for snowfall. (2) Low-latitude meridional and mid-latitude zonal water vapor transport were both significantly greater than normal, causing much more water vapor flow than normal into the NTP region through the western and southern boundaries, leading to the second highest net water vapor input since 1961 and creating sufficient continuous water vapor conditions for extreme snowfall. (3) The baroclinicity and the instability of the atmospheric circulation, accompanied by abnormal cold and warm advection and vertical movement over the NTP, provided the dynamic atmospheric conditions for the snowfall event. (4) Under the background of warm El Nino Southern Oscillation (ENSO) event and the positive phase of the Tropical Indian Ocean dipole (IOD) SST, the EU pattern initiated by the tele-influence of IOD mode dominated the midlatitude region across Eurasia continent. The positive anomalies of EU teleconnection over western Asia guided the heavy water vapor flow into the NTP through the western and southern boundaries, respectively. In addition, the effects of ENSO/IOD caused the northern Indian Ocean and northwest Pacific to be dominated by anticyclones, creating more northward water vapor transport from the tropical region. Accordingly, sufficient water vapor flowed into the NTP along the eastern edge of the Tibetan Plateau, providing the moisture conditions for the severe snowfall event.

Spatiotemporal distribution of heavy and extreme snowfalls in the Transcarpathian region

This paper presents a spatiotemporal analysis of the dynamics of heavy and extreme snowfalls in the Transcarpathian region during 1990-2019. Data on snowfalls are obtained from the observation points of the state hydrometeorological network of the Transcarpathian Regional Center of Hydrometeorology. Also there are data included from the Pozhezhevska snow avalanche station (Ivano-Frankivsk region) as a representative observation point for the highland zone of the eastern part of the Transcarpathian region. The analysis took into account the date of snowfall, the amount of precipitation that fell during the snowfall,l and its duration. The recurrence of different indicators of snowfall distribution was calculated for each observation point separately for the specified thirty-year period. Some results were mapped by the isolines using kriging interpolation. Spatio-temporal heterogeneities and regularities in the distribution of heavy and extreme snowfalls have been revealed.

近40a青藏高原冬季极端降雪的变化特征分析

近40a青藏高原冬季极端降雪的变化特征分析

Integrating archival analysis, observational data, and climate projections to assess extreme event impacts in Alaska

Understanding potential risks, vulnerabilities, and impacts to weather extremes and climate change are key information needs for coastal planners and managers in support of climate adaptation. Assessing historical trends and potential socio-economic impacts is especially difficult in the Arctic given limitations on availability of weather observations and historical impacts. This study utilizes a novel interdisciplinary approach that integrates archival analysis, observational data, and climate model downscaling to synthesize information on historical and projected impacts of extreme weather events in Nome, Alaska. Over 300 impacts (1990–2018) are identified based on analyses of the Nome Nugget newspaper articles and Storm Data entries. Historical impacts centered on transportation, community activities, and utilities. Analysis of observed and ERA5 reanalysis data indicates that impacts are frequently associated with high wind, extreme low temperatures, heavy snowfall events, and winter days above freezing. Downscaled output (2020–2100) from two climate models suggests that there will be changes in the frequency and timing of these extreme weather events. For example, extreme cold temperature is projected to decrease through the 2040s and then rarely occurs afterwards, and extreme wind events show little change before the 2070s. Significantly, our findings also reveal that not all weather-related extremes will change monotonically throughout the twenty-first century, such as extreme snowfall events that will increase through the 2030s before declining in the 2040s. The dynamical nature of projected changes in extreme events has implications for climate adaptation planning.

Dynamical Downscaling for Southeast Alaska: Historical Climate and Future Projections

AbstractWarming temperatures across southeast Alaska are affecting the region’s energy and transportation sectors, marine ecosystems, and forest health. More frequent above-freezing temperatures lead a transition from snow- to rain-dominant precipitation regimes, accelerating glacial mass balance loss and a leading to a greater risk for warm-season drought. Southeast Alaska has steep topographical gradients, which necessitate the use of downscaled climate information to study historical and projected periods. This study used regional dynamical downscaling at 4-km spatial resolution with the Weather Research and Forecasting Model to assess historical (1981–2010) and projected (2031–60) climate states for southeast Alaska. These simulations were driven by one reanalysis (i.e., the Climate Forecast System Reanalysis) and two climate models (i.e., the Geophysical Fluid Dynamics Laboratory Climate Model, version 3, and the NCAR Community Climate System Model, version 4), which each included a historical simulation and a projected simulation. The future simulations used the representative concentration pathway 8.5 emissions scenario. Bias-corrected projections (2031–60 minus 1981–2010) indicated seasonal warming of 1°–3°C, increased precipitation during autumn (4%–12%) and winter (7%–12%), and decreased snowfall in all seasons (up to 60% in autumn). The average number of days annually with a minimum temperature below freezing dropped by more than 30. The average annual maximum consecutive 3-day precipitation amounts increased by 11%–16%, but analogous extreme snowfall amounts dropped by 5%–11%. The most substantial snow losses occurred at low-elevation and coastal locations; at many high elevations (e.g., above 1000 m), extreme snowfall amounts increased.

Evaluation and projection of snowfall changes in High Mountain Asia based on NASA’s NEX-GDDP high-resolution daily downscaled dataset

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.

An attempt to explain recent changes in European snowfall extremes

Abstract. The goal of this work is to investigate and explain recent changes in total and maximum yearly snowfall from daily data in light of current global warming or the interdecadal variability of atmospheric circulation. We focus on the period 1979–2018 and compare two different datasets: the ERA5 reanalysis data and the E-OBSv20.0 data, where snowfall is identified from rainfall by applying a threshold to temperature. We compute changes as differences from quantities computed for the periods 1999–2018 and 1979–1998. On the one hand, we show that the decline in average snowfall observed in almost all European regions is coherent with previous findings and can be linked to global warming. On the other hand, we observe contrasting changes in maxima and sometimes disagreement in the sign of changes in the two datasets. Coherent positive trends are found for a few countries in the Balkans. These have been investigated in details by looking at modifications in the atmospheric weather patterns as well as local thermodynamic factors concurring to large snowfall events. We link these changes to the stronger prevalence of Atlantic Ridge or blocking patterns associated with deeper cyclonic structures over the Adriatic and Tyrrhenian seas. These cyclones find warmer surfaces and large availability of humidity and convective available potential energy (CAPE), thus producing large snowfall amounts, enhanced by the Stau effect on the Balkan topography.

An Updated Synoptic Climatology of Lake Erie and Lake Ontario Heavy Lake-Effect Snow Events

Lake-effect snow (LES) storms pose numerous hazards, including extreme snowfall and blizzard conditions, and insight into the large-scale precursor conditions associated with LES can aid local forecasters and potentially allow risks to be mitigated. In this study, a synoptic climatology of severe LES events over Lakes Erie and Ontario was created using an updated methodology based on previous studies with similar research objectives. Principal component analysis (PCA) coupled with cluster analysis (CA) was performed on a case set of LES events from a study domain encompassing both lakes, grouping LES events with similar spatial characteristics into the primary composite structures for LES. Synoptic scale composites were constructed for each cluster using the North American Regional Reanalysis (NARR). Additionally, one case from each cluster was simulated using the Weather Research and Forecast (WRF) model to analyze mesoscale conditions associated with each of the clusters. Three synoptic setups were identified that consisted of discrepancies, mostly in the surface fields, from a common pattern previously identified as being conducive to LES, which features a dipole and upper-level low pressure anomaly located near the Hudson Bay. Mesoscale conditions associated with each composite support differing LES impacts constrained to individual lakes or a combination of both.

Performance of 4D-Var Data Assimilation on Extreme Snowfall Forecasts over the Western Himalaya Using WRF Model

The accurate predictions of extreme precipitation/snowfall events are very helpful in identifying the severe avalanche/landslide prone hazard areas in advance over high mountainous regions. The Weather Research and Forecasting model (WRF) version 3.9 has been used to investigate the performance of Four-Dimensional Variational data assimilation (4D-Var) on Three-Dimensional Variational data assimilation (3D-Var) by considering two extreme snowfall events (23–26 January 2017 and 05–08 February 2019) over the Western Himalaya (WH). The result shows that the 4D-Var performed better than the 3D-Var for both the events by analyzing domain-averaged error and sensitivity parameter analysis. The initial state model variable’s domain-averaged error analysis revealed that 4D-Var has great potential to improve the initial conditions than the 3D-Var from lower to the upper atmosphere. Sensitivity parameter analysis also supports 4D-Var has more sensitive than the 3D-var especially in the lower and upper atmosphere by changing temperature and moisture fields along with winds circulations. From statistical skill scores analysis, 4D-Var performed well to reproduce the extreme snowfall events than the 3D-Var over WH.

Climate warming drives Himalayan alpine plant growth and recruitment dynamics

Abstract Understanding how climate influences plant reproduction and growth at contrasting range limits is crucial for predicting how species' ranges may shift in response to ongoing climate change. Trees and shrubs have shown warming‐induced increases in performance at upper elevation limits but reduced performance at lower distributional limits due to warming‐driven drought limitation. Whether these differential responses are also valid for alpine forbs exposed to accelerated warming remains largely unknown. We examined climate signal recorded in annual growth and recruitment over the past 60 years in the alpine forb Potentilla pamirica in Western Himalayas, and tested whether the responses to recent climate warming differ between dry steppe, wet alpine and cold subnival zone within the species 5,250–5,900 m elevation range. We reconstructed recruitment and growth chronologies from 1,019 individuals spanning 1–73 years, and more than 21,500 annual growth rings. We identified contrasting climatic controls of recruitment and growth at opposite elevation range margins, as well as contrasting demographic trends identified from age distributions. In lower‐elevation steppes, recruitment increased with high late‐winter snowfall and decreased with high summer temperatures, while growth increased with high summer precipitation. Conversely, warm winters and summers in higher‐elevation alpine and subnival zones support growth and recruitment, while snowy winters reduce them, especially at their upper elevation limit. The age distribution revealed greater numbers of younger individuals, indicating healthy growing populations, in the alpine habitat, while evidence of ageing plant populations was observed in steppe and subnival zones. Accelerated warming since the 1990s reduced growth and recruitment in dry steppes while supporting plant performance in the alpine habitat. The recruitment in the subnival zone did not peak during the past warmest decade due to concomitant extreme snowfall events. Synthesis. Our results provide novel information on population‐specific climate dependency of plant recruitment, growth and population dynamics, suggesting the high vulnerability of high‐elevation Himalayan ecosystems to climate change. This is partly balanced by high species longevity and slow radial growth securing a long‐term population persistence. Continuing trends of extreme snowfall events at higher elevations and droughts at lower elevations may lead to species range contraction.

Reduced frequency and size of late-twenty-first-century snowstorms over North America

Understanding how snowstorms may change in the future is critical for estimating impacts on water resources and the Earth and socioeconomic systems that depend on them. Here we use snowstorms as a marker to assess the mesoscale fingerprint of climate change, providing a description of potential changes in winter weather event occurrence, character and variability in central and eastern North America under a high anthropogenic emissions pathway. Snowstorms are segmented and tracked using high-resolution, snow water equivalent output from dynamically downscaled simulations which, unlike global climate models, can resolve important mesoscale features such as banded snow. Significant decreases are found in the frequency and size of snowstorms in a pseudo-global warming simulation, including those events that produce the most extreme snowfall accumulations. Early and late boreal winter months show particularly robust proportional decreases in snowstorms and snow water equivalent accumulations. Predicting the impact of climate change on snowstorms is key for future water resource estimates. North American snowstorms are tracked in high-resolution warming simulations and exhibit robust decreases in storm count, snow water equivalent and areal footprint, particularly in shoulder seasons.

Warming shrivels future snowstorms

The influence of the changing climate on individual snowstorms has been uncertain, in part due to the use of coarse model simulations. Now, research employing more detailed simulations finds fewer and smaller snowstorms as a result of warming, with a reduction in the amount and extent of extreme snowfall.

Dynamic changes in snowfall extremes in the Songhua River Basin, Northeastern China

AbstractRainfall and snowfall differ greatly in terms of their effects on hydrological processes. Snowfall is usually regarded as snow water equivalent in studying precipitation extremes, without considering the difference between snowfall and rainfall. Although snowfall is a key indicator of global change, no generally accepted and no unified indices for the assessment of snowfall extremes currently exist. The objectives of this study are to identify a suite of extreme snowfall indices that can be used to describe extreme snowfall events, and to analyse the dynamic changes in the extreme snowfall indices that have occurred in the Songhua River Basin (SRB), China. The study employs a dataset that contains daily data from 60‐meteorological stations that cover a 55‐year period. The results include a suite of extreme snowfall indices that can be used to assess extreme snowfall events. These extreme snowfall indices include four comprehensive indices, four intensity indices, four grade indices and two date indices. The total snowfall, the number of snowfall days, the ratio of snowfall to total precipitation, the snowfall intensity (SNI), the amounts of extreme snowfall and very extreme snowfall display insignificant trends over the entire SRB. The changes in the ending snowfall date exhibit a significant advancing trend (p &lt; .001), while the changes in beginning snowfall date display a significant delaying trend (p &lt; .05), which have led to a reduced snowfall season length (p &lt; .001). These results provide a series of reference indices in describing snowfall extremes and they can enhance our understanding of the variations in snowfall that occur under global warming.

Snow disposal in Moscow

The main principle of the strategy of the integrated improvement the functioning of a city of the northern region and its infrastructure in winter is a systematic solution to the problem of industrial disposal of snow mass in various parts of the city road network, removal and utilization of snow. The implementation of the strategy in the capital of Russia - the city of Moscow is based on the use of the General snow disposal scheme. In connection with the meteorological conditions that have changed in recent years in Moscow, i.e. increase in the height of the snow cover and extension of the roadway paving subject to cleaning, the use of advanced technologies of snow collection and disposal, new types of deicing agents, the aim of the research was updating the General snow disposal scheme in Moscow. As a method of its implementation, we used a statistical analysis of the formation in recent years of the volume and average annual depth of the snow cover in the city of Moscow, an estimation of the heat capacity of sewers and a heating network for the utilization of snow mass. The article presents the results of the implementation of the updated General snow disposal scheme, gives suggestions for snow removal in extreme snowfalls.

Future Projection of Extreme Heavy Snowfall Events With a 5‐km Large Ensemble Regional Climate Simulation

AbstractWe have recently experienced several heavy snowfall events, but still do not sufficiently understand how global warming will impact changes in local extreme snowfall events. The analysis relevant to the extreme events requires ensemble experiments with high‐resolution regional climate modeling. In this study, we use a large number of ensemble warming projections downscaled dynamically to 5‐km grids to examine the differences in distribution and mechanism in extreme snowfall events over Japan between present and future climates. Japan has two typical snowfall patterns, the winter monsoon and the south coast cyclone patterns. The domain‐averaged amount of extreme snowfall in the south coast cyclone pattern regions appear to decrease with the rise in temperatures. Meanwhile, the winter monsoon pattern regions showed little change as the increases in extreme snowfall in the mountainous areas are canceled out by the decrease in the coastal areas. Based on the existing mechanism of normal snowfall, the increase in mountain snow is caused by a temporary intensification of cold air outbreaks. The amount of the cold air blowing from Japan Sea clearly increases only during the days preceding the extreme snowfall event, and as the cold dry air obtains more latent heat over the ocean, the precipitable water vapor increases. The winter monsoon then transports this air over land where the intensification of precipitation results from orographic updraft. This sequence of processes beginning with cold air outbreaks may have a stronger control over extreme snowfall phenomena on the warmer field.

An extreme flood caused by a heavy snowfall over the Indigirka River basin in Northeastern Siberia

AbstractFlooding is one of the greatest disasters that produces strong effects on the ecosystem and livelihoods of the local population. Flood frequency is expected to increase globally making its risk assessment an urgent issue. In spring‐summer 2017, an extreme flooding occurred in the Indigirka River lowland of Northeastern Siberia that inundated a large area. In this study, the extent and climatic drivers of the flooding were determined using the results of field observations, satellite images, and climate reanalysis dataset, and its possible effects on the ecosystem were discussed. In 2017, a significant lowland area of around 16,016 km2 was covered with water even in July, which was 5,217 km2 (around 4% of the total area) greater than the water‐covered area in 2015 when usual hydrological condition in the area was observed. The hydrographic signature obtained for the Indigirka River water level in 2017 was unusual. Although the water level rose sharply at the end of May (which was typical for the Arctic region), it did not fall afterwards and even increased again to an annual daily maximum value in the middle of July. The climate reanalysis dataset obtained for the temporal–spatial variations of snow water equivalent, snowmelt, and runoff over the lowland revealed that a large amount of snowmelt runoff in June and July 2017 produced a large water‐covered area and unusually high river water levels that lasted until summer. Snow depth from winter to spring was largest in 2017 over the period from 2009 to 2017, and the surface of the lower reach of the lowland was partially covered with snow even in the end of June due to the extreme snowfall that occurred in October 2016. Such unusual hydrological conditions waterlogged most trees over the lowland, which caused serious ecosystem devastation and changes in the material cycle.