Heavy Snowfall Research Articles

Integration of Snow and Architectural Design in Heavy Snowfall Areas

Integration of Snow and Architectural Design in Heavy Snowfall Areas

Snow Blower Application at Backhoe Loaders

During heavy snowfalls in winter, clearing large piles of snow on the roads is a very laborious process. It is necessary to take quick action to make the closed roads usable and not to stop the flow of life. Snow blowers are needed when equipment such as snow knives are not sufficient. However, since these types of machines are specially produced abroad, their costs are high. The Türkiye General Directorate of Highways and Hidromek Company have carried out a series of studies in order to popularize the use of snow blower. It is foreseen that if snow blower adaptation can be provided to backhoe loader construction machines that are widely used all over the world and especially in Türkiye, access to this equipment will be easier. Thus, technological dependence on foreign sources will also be reduced. As a result of the studies, the design of a snow blower with a capacity of 2000 tons/hour and its adaptation to the backhoe loader machine were achieved. Due to the need for such a high snow spraying capacity, radical changes were made in the main lines of the machine. Placing a power unit instead of the excavator part of the machine and placing a snow rotary attachment on the front arm was deemed the most appropriate solution.

Projection and identification of vulnerable areas due to heavy snowfall using machine learning and K-means clustering with RCP scenarios

Heavy snowfall is a natural disaster that causes extensive damage in South Korea. Therefore, predicting heavy snowfall occurrence, identifying vulnerable areas, and establishing response plans to reduce risk are crucial. In this study, to project heavy snowfall, meteorological and geographic data from the past 30 years were collected, and four machine learning algorithms were trained and compared: multiple linear regression, support vector regression, random forest regressor (RFR), and extreme gradient boosting. We observed that the RFR model (R2 = 0.64) demonstrated the most optimal performance in projecting snowfall compared to other models. Representative concentration pathway (RCP) scenario data was input into the RFR model to generate projection data up to 2100. Projection results of more than 48.2 cm based on heavy snowfall events in the past 20 years were observed 17 times in RCP2.6, 19 times in RCP4.5, 16 times in RCP6.0, and 17 times in RCP8.5. The annual GIS-based projected snowfall images for the RCP8.5 scenario were classified into five distinct groups using K-means clustering. These groups were then further divided based on the vulnerability of regions, including Gangwon-do, Jeollabuk-do, and northern Gyeonggi-do. Our study can aid decision-making on policies related to heavy snowfall disaster prevention standards, snow removal plans, budgeting, and the establishment of mid- to long-term climate change adaptation plans for government, public institutions and private organizations.

Control of the temperature signal in Antarctic proxies by snowfall dynamics

Abstract. Antarctica, the coldest and driest continent, is home to the largest ice sheet, whose mass is predominantly recharged by snowfall. A common feature of polar regions is the warming associated with snowfall, as moist oceanic air and cloud cover increase the surface temperature. Consequently, snow that accumulates on the ice sheet is deposited under unusually warm conditions. Here we use a polar-oriented regional atmospheric model to study the statistical difference between average and snowfall-weighted temperatures. During snowfall, the warm anomaly scales with snowfall amount, with the strongest sensitivity occurring at low-accumulation sites. Heavier snowfall in winter helps to decrease the annual snowfall-weighted temperature, but this effect is overwritten by the event-scale warming associated with precipitating atmospheric systems, which particularly contrast with the extremely cold conditions that occur in winter. Consequently, the seasonal range of snowfall-weighted temperature is reduced by 20 %. On the other hand, the annual snowfall-weighted temperature shows 80 % more interannual variability than the annual temperature due to the irregularity of snowfall occurrence and its associated temperature anomaly. Disturbances of the apparent annual temperature cycle and interannual variability have important consequences for the interpretation of water isotopes in precipitation, which are deposited with snowfall and commonly used for paleotemperature reconstructions from ice cores.

Climatic Characteristics of Heavy Snowfall and the Water Vapor Transport Characteristics in Typical Snowfall Events in Hunan Province of China

Due to the unique topography and geographical location, severe snowfall is the main disastrous weather in winter in the Hunan Province of China. Based on the daily precipitation data in Hunan Province from 1961 to 2021, the regional heavy snowfall processes are classified by using the synoptic diagnostic method. In addition, the water vapor transport characteristics of typical heavy snowfall processes are analyzed by the hybrid single-particle Lagrangian integrated trajectory (HYSPLIT) air mass backward trajectory model. Then, the responses of the differences in water vapor transport to heavy snowfall under different weather situations are discussed. The results show that the spatial distribution of climatic mean heavy snowfall days in Hunan Province is extremely uneven, and the heavy snowfall days decrease from north to south, with the most in the Dongting Lake area and the least in the Nanling Mountains. In the past decades, snowstorms mainly occur in local areas, and there are fewer widespread snowstorms. The frequency of heavy snowfall days generally shows a decreasing trend, with three peaks all appearing before 1990. After the 2010s, the number of days and stations of heavy snowfall decreased noticeably, and so did the number of regional heavy snowfall processes. This result indicates that global warming has remarkable effects on the snowstorm events in Hunan Province. Heavy snowfall mainly occurs from December to February, and peaks from mid-January to early February. Over the past 61 years, more than 50% of heavy snowstorm events occurred after 2000. According to the main weather systems affecting regional heavy snowfall processes, these weather processes in Hunan Province can be classified into three categories: southern branch trough (SBT) type, blocking high collapse (BHC) type, and stepped trough type. Among them, the SBT type accounts for more than 60% of the heavy snowfall events in Hunan. In terms of the SBT type and the stepped trough type, the water vapor from the high-latitude inland and low-latitude sea surface accounts for a comparable proportion, each accounting for nearly 50%. For the SBT type, the proportion of the water vapor from warm-humid airflows is slightly higher than that from cold-humid airflows. However, in terms of the stepped trough type, the water vapor transported by cold-humid airflows from the north contributes more than that by warm-humid airflows. For the BHC type, the specific humidity and the water vapor from the high-latitude inland contribute 70% of heavy snowfall processes. In addition, the contribution of the two southwesterly water vapor channels to heavy snowfall processes is small. The water vapor sources differ remarkably for different heavy snowfall types, but all of them are dominated by the water vapor transport in the middle and lower troposphere, which is the main reason why the formation of snowfall areas under different weather types is obviously different.

Atmospheric black carbon in a key location of the Chilean Central Andes: Identifying patterns, sources, and potential impacts

Black carbon (BC) has been identified as one of the primary contributors to climate change, primarily due to its exceptional capacity to absorb solar radiation. This leads to a reduction in the albedo of ice and snow surfaces, consequently accelerating their melting. In this work, measurements of atmospheric BC are presented, carried out from October 2016 to December 2019 in Portillo, Chilean Central Andes, in the NUNATAK-1 laboratory refuge (3000 m.a.s.l). This site is close to the Chile-Argentina border and is situated along a heavily trafficked road connecting the two countries. Owing to weather conditions, this route operates on a seasonal schedule, remaining open 24 h during warmer months, while in winter, traffic is restricted to the hours between 8 a.m. and 8 p.m. Access to vehicles is prohibited during periods of heavy snowfall. BC concentrations were continuously monitored by a Multi-Angle Absorption Photometer (MAAP). The variability and temporal cycles of BC levels were investigated in relation to local vehicular activity and meteorological parameters, through multiple linear regression analysis. Results show that vehicular traffic variations led to a systematic pattern in the atmospheric BC measured, with a preponderant role of meteorological factors. During the summer months, BC levels reached their peak (mean of 0.77 μg m−3), with maximum values recorded during the nighttime and early morning hours. In line with this finding, summer counted the highest number of vehicles circulating in the area. In contrast, the winter months exhibited lower average BC atmospheric concentrations compared to summer months (0.35 μg m−3), characterized by a distinct hourly profile. In a complementary way, forward trajectories using the HYSPLIT model were performed for various meteorological conditions. The findings indicated that emissions from the study area had the potential to extend to neighboring glaciers. This impact on the basins' hydrology, vital and fragile components of the Andean region, magnifies climate change effects in these cryospheric zones. Notably, the latest IPCC report highlights limited research on aerosol effects on mountain snow and glaciers. This study also presents the first estimates of high-altitude BC source assessments in Chile and South America. These insights could enhance global atmospheric models in mountainous regions, enhancing their accuracy and utility.

A contrastive analysis on the causes of two regional snowstorm processes influenced by the southern branch trough in Hunan in early 2022

AbstractIn early 2022, there were four low‐temperature weather processes with rain and snow in Hunan Province, China. Two processes occurred on January 28–29 (referred to as the “0128” process) and February 6–7 (referred to as the “0206” process), and they have overlapping areas of heavy snowfall and high intensity of short‐term snowfall. Multi‐source observation data and the National Centers for Environmental Prediction (NCEP) reanalysis data are used to analyze the characteristics of circulation background and mesoscale. In addition, the causes of heavy snowfall processes under the influence of the southern branch trough are discussed based on the dual‐polarization radar products at Changsha station. The results show that two processes are characterized by the rapid phase transformation of rain and snow, concentrated snowfall periods, and heavy snowfall at night. The short‐term snowfall intensity of the “0206” process is greater than that of the “0128” process. The high‐latitude blocking high of the “0206” process is stronger than that of the “0128” process, and the water vapor transport of the southerly jet in low levels in the “0206” process is also stronger. The organized development of cold cloud clusters from the meso‐β scale to the meso‐α scale indicates that the snowfall intensifies, and the maximum blackbody temperature gradient corresponds well to the center of heavy snowfall. The propagation that is similar to the train effect is an important reason for the heavy snowfall process. The vertical variation of the ZH and the bright band of dual‐polarization parameters can determine the phase transformation between rain and snow. When the ZH and ZDR bright bands are 1–3 km away from the ground, the phase state is rain if the ZH near the ground is greater than 0 dBZ and the CC is close to 1; the phase state is the rain‐snow mixed phase if the CC is less than 0.95. When the bottom of the ZH bright band decreases, the CC/ZDR bright band disappears, the near‐surface CC is greater than 0.99 and the ZDR is less than 1 dB, the rain turns to snow. Compared with the “0128” process, the characteristics of the bright ring during the rainfall period of the “0206” process are more obvious, the precipitation intensity judged from the larger ZH and KDP is larger, and the phase transformation is faster due to more significant cooling effect caused by precipitation.

Estimation of Spatial Snowpack Properties in a Snow-Avalanche Release Area: An Extreme Case on Mt. Nodanishoji, Japan, in 2021

An extreme dry-slab snow avalanche occurred on January 10, 2021, at Mt. Nodanishoji, Gifu, Japan, during a heavy snowfall. The avalanche ran down a horizontal distance of approximately 2,800 m and damaged trees and infrastructures. This was estimated to be the second largest recorded avalanche in Japan. However, physical snowpack properties and their vertical profiles and spatial distribution, which caused the avalanche, were not addressed in the release area immediately following the avalanche, mainly due to unsafe and lousy weather conditions. Based on a snow depth distribution observed by an unmanned aerial vehicle and a numerical snowpack simulation in the avalanche release area, the spatial distributions of the mechanical snowpack stability and slab mass and their temporal evolution were estimated in this study. The procedure was validated by comparing the calculation results with the observed snowpit and spatial snow depth data. The results indicated that two heavy snowfall events, approximately 3 and 10 days before the avalanche onset, generated two different weak layers made of precipitation particles and associated slabs above the weak layers. The older weak layer was only generated on the northward slope due to its low temperature, whereas the newer layer was predominant over the avalanche release area. The procedure employed in this study is expected to be applied to other avalanche cases in the future.

Projection of snowfall extremes in the French Alps as a function of elevation and global warming level

Abstract. Following the projected increase in extreme precipitation, an increase in extreme snowfall may be expected in cold regions, e.g., for high latitudes or at high elevations. By contrast, in low- to medium-elevation areas, the probability of experiencing rainfall instead of snowfall is generally projected to increase due to warming conditions. Yet, in mountainous areas, despite the likely existence of these contrasted trends according to elevation, changes in extreme snowfall with warming remain poorly quantified. This paper assesses projected changes in heavy and extreme snowfall, i.e., in mean annual maxima and 100-year return levels, in the French Alps as a function of elevation and global warming level. We apply a recent methodology, based on the analysis of annual maxima with non-stationary extreme value models, to an ensemble of 20 adjusted general circulation model–regional climate model (GCM–RCM) pairs from the EURO-CORDEX experiment under the Representative Concentration Pathway 8.5 (RCP8.5) scenario. For each of the 23 massifs of the French Alps, maxima in the hydrological sense (1 August to 31 July) are provided from 1951 to 2100 and every 300 m of elevations between 900 and 3600 m. Results rely on relative or absolute changes computed with respect to current climate conditions (corresponding here to +1 ∘C global warming level) at the massif scale and averaged over all massifs. Overall, daily mean annual maxima of snowfall are projected to decrease below 3000 m and increase above 3600 m, while 100-year return levels are projected to decrease below 2400 m and increase above 3300 m. At elevations in between, values are on average projected to increase until +3 ∘C of global warming and then decrease. At +4 ∘C, average relative changes in mean annual maxima and 100-year return levels, respectively, vary from −26 % and −15 % at 900 m to +3 % and +8 % at 3600 m. Finally, for each global warming level between +1.5 and +4 ∘C, we compute the elevation threshold that separates contrasted trends, i.e., where the average relative change equals zero. This elevation threshold is shown to be lower for higher return periods, and it is projected to rise from 3000 m at +1.5 ∘C to 3350 m at +4 ∘C for mean annual maxima and from 2600 to 3000 m for 100-year return levels. These results have implications for the management of risks related to extreme snowfall.

Ensemble-based analysis of heavy rainfall–snowfall associated with mesoscale precipitation bands within an extratropical cyclone over northeastern China

This paper investigates the key synoptic-scale factors that affected the forecasting of mesoscale rainfall and snowfall and their associated uncertainties in a heavy rain–snow event in northeastern China on 18–20 November 2020, using ensemble-based sensitivity analysis based on global ensemble forecasts from the European Centre for Medium-Range Weather Forecasts. The heavy precipitation event was attributed to an extratropical cyclone and experienced two stages, with the snowfall stage having a better precipitation forecast skill than the rainfall stage. The mesoscale rainfall and snowfall were caused by a mesoscale rainband over Liaoning Province and two mesoscale snowbands over Heilongjiang Province, respectively, and they showed some differences with respect to their forecast skill and related key synoptic-scale factors contributing to the precipitation centers. The precipitation amount in the two different stages was correlated significantly with the midlevel trough and sensitive to the location and intensity of the low-level vortex (surface cyclone), and particularly the low-level jets and the associated water vapor transport. However, some differences were confirmed in the two different stages: the weaker midlevel trough and accompanying weaker low-level temperature trough in the rainfall stage were related to increased precipitation because the midlevel trough was far away from the control area, while the stronger midlevel trough and accompanying stronger low-level temperature trough were associated with increased precipitation in the snowfall stage. In addition to the synoptic-scale low-level jet (SLLJ), the precipitation in the rainfall stage was also affected by a boundary layer jet (BLJ) over the ocean, while only SLLJs were present in the snowfall stage. The uncertainty of the precipitation forecast was derived mainly from the uncertainty in the strength and location of the SLLJs and BLJ. Notably, the intensity of northeasterly winds west of the low-level vortex may affect the predictability of heavy snowfall.

A Qualitative Slope Stability Assessment of Hill Slopes Using Multiple Integrated Approaches Along Bafliaz – Poshiana (Mughal) Road, Jammu and Kashmir

Abstract Transport, communication and socio-economic activities in the hilly areas are completely dependent on the road network. The road network connecting Bafliaz to Shopian town (known as Mughal Road) plays a vital role in the transportation and other activities of the region. It connects the Kashmir Valley with Jammu city and is strategically very important to the country. Almost the entire road length of Mughal Road is vulnerable to landslide incidences and the events of occurrence/recurrence of landslides indicate that the probability of landslide/slope failure is higher along the road cut slopes during the event of antecedent and heavy rainfall and snowfall events. In order to assess the stability of hill slopes, it becomes of utmost importance to understand the failure mechanism along these slopes. In this regard, the slope stability assessment has been carried out by using multiple integrated approaches like the Markland test of kinematic analysis, Geological Strength Index (GSI), Rock mass classification (RMR), and Slope Mass Rating (SMR) at different locations. The type of failure and direction of potential failures for individual slopes have been identified. The obtained results of RMR vary from 23 to 38 after adjustment rating (poor rock mass), SMR from 0 to 54 (very bad to fair rock mass), CSMR from 0 to 53 (very bad to fair rock mass) and the GSI values range from 35 to 45 (poor to fair rock mass). Markland’s test of Kinematic check reveals that planar and wedge failures are the most common phenomenon along the Mughal Road sector.

Microphysical Characteristics of Snowfall on the Southeastern Tibetan Plateau

AbstractSnowfall on the Tibetan Plateau (TP) is crucially important to the hydrological cycle and glacier acceleration over the TP and surroundings. Climate and weather models are challenged by biases and uncertainties in representing microphysical characteristics of snowfall over TP. In order to bridge the gaps in our understanding of microphysical characteristics of snowfall on the southeastern TP (SETP), an integrated and systematic measurement is conducted at the South‐East Tibetan Plateau Station during the two winters of 2022–2023. It is the first time to conduct a long‐term measurement of microphysical characteristics of snowfall on the SETP. The major objective of this campaign was to collect cloud microphysical properties including, fall velocity, aspect ratio, and particle size distribution (PSD). The results show that the particle number concentration is less than 103 mm−1·m−3, which is one to two orders of magnitude smaller than that reported in low‐altitudes in eastern China. The shape parameter μ is mainly in the range 0–4, and the slope parameter λ is mainly in the range 3–7 mm−1, exceeding the values for eastern China. In heavy snowfall processes (snowfall rate (SR) exceeding 2 mm/hr), the collision‐coalescence process between snowfall particles is enhanced, and the number concentration of small particles decreases with SR, whereas that of large particles increases. A negative correlation was observed between the aspect ratio and the diameter of snowfall particles. The microphysical characteristics of snowfall will be used to improve the microphysical scheme and evaluate the representation of precipitation in the climate model.

Unclear relationships in interference competition among three sympatric medium-sized carnivores in a heavy snowy environment

Abstract Interference competition, a category of interspecific competition, is a critical aspect of understanding the dynamics of carnivore communities, as it can result in a decline in the health and reduced opportunity for resource acquisition of one or both competing species. Heavy snowfall environments during winter are expected to exacerbate interference competition due to decreased food resources and limited foraging opportunities caused by snow cover. However, knowledge on interference competition in such environments is limited. Using camera traps with baits, we evaluated the interference competition among three sympatric medium-sized carnivores, red foxes, raccoon dogs, and Japanese martens, within a heavy snowfall region of northeastern Japan. As in previous studies, we hypothesized that body weight would serve as a determining factor for superiority in winter snow environments. Diel activity patterns using the visitation time to baits did not reveal any clear differences. Nevertheless, the daytime occurrence frequency of Japanese martens during the snowfall season was higher than that of the other two species, indicating inferiority to the other species. The analysis of time intervals between species visiting baits revealed no clear superiority among the target species. These findings suggest that the interspecific relationships among medium-sized carnivores in winter snowy environments are complex and not easily determined.

Байгалийн гамшигт үзэгдэл болох зудын үеийн боловсрол-судалгааны үр дүнгээс

Mongolia is one of the countries with the harshest climate in the world, with a short warm season lasting only two months and a long winter with temperatures below 35-40°C. Due to the severe winter and spring climate, heavy snowfall in the provinces and soums, the workload of herding families increases dramatically, and herders' children are engaged in animal husbandry because of everyday busy schedule. When herders experience severe weather conditions and heavy snowfall, the number of livestock reduce drastically, their family livelihood decreases, which negatively affect their psychological state, pay less attention to their children's schooling. In this article we analyzed the legal regulations on disasters and emergencies, we aimed to clarify secondary schools’ preparedness to the risks such as, drastic drop in attendance of students during natural disasters, failure of the heating system of school buildings and dormitories, cold classrooms, shortage of drinking water, etc.

Watershed resilience to climate change: From hillslopes to rivers and coasts

Climate is changing rapidly, resulting in more frequent and extreme weather events, such as megadroughts or flash droughts, intense rainfall events causing floods and landslides, heavy snowfalls, and windstorms. With a rising global population and increased levels of urbanization, the socio-economic consequences of these changes to Earth's surface processes are severe and have a more significant impact than in the past. A critical need is to improve understanding of physical processes at the watershed scale by integrating modern technologies. This will help define change hotspots and detect cascade effects from hillslopes through rivers to coastal areas. In this watershed approach to resilience management, adaptative strategies need to be re-balanced in terms of structural and non-structural measures. On the one hand, sustainable structural solutions may be the only choice for some densely populated urban areas. On the other hand, Nature-Based Solutions (NBS) may offer an unparalleled approach to combining sustainability and resilience while preserving ecosystems. The lack of a sustainable response to climate change will have only one result: land abandonment with related migration and conflicts.

Evaluation of the snow loads on the snow galleries on the Iron Ore Line in Northern Sweden

AbstractThe Iron Ore Line is a railway in northern Sweden and Norway, crucial for iron ore transportation. Snow galleries safeguard this route, ensuring uninterrupted traffic for efficient operations. A snow gallery is a protective structure that shields roads and railways from heavy snowfall and avalanches. Reported damages to these structures caused by excessive snow loads have prompted an investigation to prevent further issues. This paper presents an analysis of the evolution of snow load calculations over time, though a comparison of Swedish design standards. The design snow loads required by current standards have increased up to 60% from the initial regulations, depending on the location of the structure. Besides, climate change is predicted to further increase snow loads due to more frequent winter precipitation, higher snow density, and extreme snowfall events. This analysis is part of a broader research project involving numerical simulations, live monitoring, and a digital twin monitoring system of some snow galleries in the Iron Ore Line.

Characteristics of Lake-Effect Precipitation over the Black River Valley and Western Adirondack Mountains

Abstract Potential factors affecting the inland penetration and orographic modulation of lake-effect precipitation east of Lake Ontario include the environmental (lake, land, and atmospheric) conditions, mode of the lake-effect system, and orographic processes associated with flow across the downstream Tug Hill Plateau (herein Tug Hill), Black River valley, and Adirondack Mountains (herein Adirondacks). In this study we use data from the KTYX WSR-88D, ERA5 reanalysis, New York State Mesonet, and Ontario Winter Lake-effect Systems (OWLeS) field campaign to examine how these factors influence lake-effect characteristics with emphasis on the region downstream of Tug Hill. During an eight-cool-season (16 November–15 April) study period (2012/13–2019/20), total radar-estimated precipitation during lake-effect periods increased gradually from Lake Ontario to upper Tug Hill and decreased abruptly where the Tug Hill escarpment drops into the Black River valley. The axis of maximum precipitation shifted poleward across the northern Black River valley and into the northwestern Adirondacks. In the western Adirondacks, the heaviest lake-effect snowfall periods featured strong, near-zonal boundary layer flow, a deep boundary layer, and a single precipitation band aligned along the long-lake axis. Airborne profiling radar observations collected during OWLeS IOP10 revealed precipitation enhancement over Tug Hill, spillover and shadowing in the Black River valley where a resonant lee wave was present, and precipitation invigoration over the western Adirondacks. These results illustrate the orographic modulation of inland-penetrating lake-effect systems downstream of Lake Ontario and the factors favoring heavy snowfall over the western Adirondacks. Significance Statement Inland penetrating lake-effect storms east of Lake Ontario affect remote rural communities, enable a regional winter-sports economy, and contribute to a snowpack that contributes to runoff and flooding during thaws and rain-on-snow events. In this study we illustrate how the region’s three major geographic features—Tug Hill, the Black River valley, and the western Adirondacks—affect the characteristics of lake-effect precipitation, describe the factors contributing to heavy snowfall over the western Adirondacks, and provide an examples of terrain effects in a lake-effect storm observed with a specially instrumented research aircraft.

Simulations of a Heavy Snowfall Event in Xinjiang via the WRF Model Coupled with Different Land Surface Parameterization Schemes

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.

Frozen city: Analysing the disruption and resilience of urban activities during a heavy snowfall event using Google Popular Times

Understanding the impact of climate change on cities is fundamental to address the increasing occurrence of extreme weather events. This research aims to raise awareness and emphasise the need and potential of proactive measures to mitigate the adverse effects of climate change. To do so, this article conducts a case study for a huge snowfall that occurred in the city of Madrid (Spain) in January 2021, blocking the city for several days. The analysis is based on geolocated big data sourced from Google Popular Times (GPT), which captures the occupancy of establishments throughout the city over the entire study period. An exploratory spatial-temporal analysis has been conducted to examine the impact of the snowfall on the daily activities of the city, taking into consideration the demographic characteristics. The findings reveal a distinction in the impact of the snowfall on activities. Essential activities experience less impact compared to leisure activities. Furthermore, at the socio-economic level, the impact on low-income neighbourhoods is observed to be less affected than on high-income neighbourhoods. The implications of this research contribute to the body of knowledge on climate change resilience and adaptation, providing valuable insights for urban management strategies and informing future research in this field.

Spatial and temporal variability in snow density across the Northern Hemisphere

As a fundamental physical property of snowpack, snow density is used to describe many essential features of snowpack behaviour. However, the variability in snow density across the Northern Hemisphere (NH) is largely unknown. Here, we investigate snow density variability in conjunction with snow classes and geographic elements in the NH based on 6,954 snow sites from 1909 to 2019. Precipitation, air temperature, and snowfall based on meteorological sites, as well as the aridity index (AI) and wind speed from reanalysis data, are also applied to describe the effect of climate on snow density. The results present that the long-term mean snow density is 246 ± 70 kg/m3 considering all in-situ measurement sites. Considerable spatial heterogeneity in snow density exists with contrasting snow densities among differing snow classes. The values range from 198 ± 79 kg/m3 for ephemeral snow to 363 ± 63 kg/m3 for maritime snow. For the seasonal evolution of snow density, the different snow classes share a general characteristic with the overall NH, a slight decrease from October to September, followed by a sustained increase. Moreover, the densification rate in the snow stable period varies over a much smaller range than that during the snowmelt period. Furthermore, the longitudinal trends in the variability of snow density are more pronounced compared to altitudinal and latitudinal trends. High snow densities are typically associated with adequate precipitation, warm air temperature, large aridity index, a long snow season, and heavy snowfall for different snow classes. The results will deepen the understanding of the snow density distribution at hemispherical scale, and provide basic data for the remote sensing of snow water equivalent and parameterization of snow models.