Snowfall Events Research Articles

Primary Modes of Northern Hemisphere Snowfall Particle Size Distributions

Abstract A comprehensive understanding of snowfall microphysics is crucial for enhancing the accuracy of remote sensing snowfall retrievals. However, variations in regional and seasonal snow particle size distributions (PSDs) contribute substantial uncertainty. Here, we examine snowfall PSDs from across the Northern Hemisphere, applying Principal Component Analysis (PCA) to disdrometer observations with the aim of identifying dominant modes of variability across varying regional climates. The PCA revealed three Empirical Orthogonal Functions (EOFs) that account for a combined 95% of the variability across the dataset, which are attributed to latent linear embeddings of snowfall intensity (EOF1), snowfall character (EOF2) and snowfall regime (EOF3). Examining point clusters with the highest combined EOF values reveals six distinct modes of variability (i.e., Principal Component [PC] groups) with unique PSD traits. These groups are then correlated with environmental factors using data from collocated vertically pointing radar, surface meteorology, and reanalysis to assist in assigning physical attributes. The first and second PC groups, linked to EOF1’s intensity embedding, are described by their PSD intercepts, snowfall rates, and reflectivity and Doppler velocity values, representing low and high intensity snowfall modes, respectively. The third and fourth PC groups, associated with EOF2’s character embedding, are defined by temperature, fall speed, and density, indicative of cold, fluffy snowfall and warm, dense snowfall, respectively. The fifth and sixth PC groups, related to EOF3’s regime embedding, are distinguished by their PSD slope, snowfall rate, and reflectivity profiles, signifying shallow, weak convective systems with small particles, and deep, stratiform snowfall events with large aggregates, respectively.

An extreme precipitation event over Dronning Maud Land, East Antarctica - A case study of an atmospheric river event using the Polar WRF Model

Extreme precipitation events (EPEs) are crucial in Antarctica, impacting the Antarctic ice sheet's surface mass balance and stability. Comprehensive case studies are essential for better understanding these events and the underlying processes driving them. Here, we investigate an extreme snowfall event in Dronning Maud Land (DML), East Antarctica on November 8 and 9, 2015. This event contributed approximately 22 % of the annual accumulation in less than two days and exhibited high spatial variability in precipitation distribution. We employed a high-resolution atmospheric model specifically optimized for the polar regions (Polar WRF) and ERA5 reanalysis data to analyze the event in detail. Our findings highlight the importance of a blocking high-pressure ridge of record strength that effectively blocked and diverted a strong extratropical cyclone into DML, ultimately leading to the heavy snowfall event. The sudden deepening of the cyclone was initiated by a ‘jet streak’ in the upper atmosphere that steered the system southeastwards towards the Antarctic coast. Notably, we observed an anomalously high poleward moisture transport in the form of a strong atmospheric river on November 7, 2015. This atmospheric river originated in the South Atlantic Ocean and tracked poleward from the 30°S-40°S latitude band. Vertical cross-sections of the model outputs indicate that most of the precipitation was concentrated in regions with steep orography along the path of the atmospheric river. This interaction between the atmospheric river and the steep terrain led to the uplift of maritime air, resulting in heavy snowfall. This study highlights the significance of extreme upper and lower atmospheric conditions in driving intense moisture transport towards coastal DML. The interaction between the atmospheric river and the steep orography contributed to heavy snowfall, underscoring the importance of considering orographic influences in understanding EPEs in Antarctica.

Impact of the combined assimilation of GPM/IMGER precipitation and Himawari-8/AHI water vapor radiance on snowfall forecasts using WRF model and 4Dvar system

Impact of the combined assimilation of GPM/IMGER precipitation and Himawari-8/AHI water vapor radiance on snowfall forecasts using WRF model and 4Dvar system

Stakeholder perceptions, challenges, and sustainable solutions for achieving safely managed sanitation in Kazakhstan: a case study from a cold region

ABSTRACT In this paper, we present survey results from 30 sanitation stakeholders across the public and private sectors, professional associations, non-governmental organisations (NGOs), and households in the two coldest cities of Kazakhstan. The objectives were to identify the perceived sanitation challenges, document innovative, and traditional solutions employed by the stakeholders to manage sanitation challenges during the cold season, and to propose initiatives and policy interventions to alleviate pressing sanitation issues. Commonly encountered problems included improper installation of septic tanks (e.g. above the frost line) by non-professionals and the difficulty of households to access latrines during heavy snowfall events. Ninety-three percent of respondents expressed that foul odours and sewage overflows were their primary concerns, with only 7% reporting health impacts as a primary concern. There were significant differences in the viewpoints of the respondents regarding the extent of sanitation infrastructure maintenance or upgrades needed in the two cities, suggesting regional differences across the country rather than a uniform national situation. In the context of Kazakhstan, local NGOs were identified as playing a key role in engaging with households to co-devise affordable and sustainable sanitation solutions that are suitable for cold regions, which can then be communicated to other sanitation stakeholders, including the public sector.

Extreme snowfall variations in the Southeastern Tibetan Plateau under warming climate

Snowfall is a critical component of the Earth system and an important indicator and amplifier of climate change. Climate warming is reducing the seasonal snowpack globally, which could have catastrophic consequences for the regions in high dependence on snow for water recharge. However, the climate influences on extreme snowfall events, which significantly impact humans, are still poorly understood. This study uses the Tibetan Plateau snowpack observation dataset combined with NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6) climate data to investigate the spatial and temporal characteristics of snowfall, and to explore its response mechanism to climate change and future change trends across the Lower Yarlung Zangbo River (LYZR) in Southeastern Tibetan Plateau (SETP). Annual snowfall decreased at a rate of 1.05 mm per decade (P < 0.05) during the historical period (1960–2014). Relative to 1960–2014, annual snowfall would decline of ∼38 % and ∼ 73 % by the end of the 21st century under the SSP245 and SSP585 scenarios, respectively. At the same time, a general decrease in extreme snowfall averaged the LYZR is expected for historical and future periods, but it showed notable spatial variations. The regional increase in extreme snowfall is mainly distributed along the valley of Brahmaputra-Yarlung Zangbo. In addition, precipitation accounted for 66.5 % of the snowfall variations during the historical period. Meanwhile, according to future warming, temperature would dominate snowfall variations, contributing 56.66 % to 72.92 % during 2015–2100. The projected response of mean and extreme snowfall to climate change indicates that it is a double-edged sword. To scientifically address the risks and challenges posed by snowfall changes in alpine regions, it is imperative to limit future climate warming.

Detecting snowfall events over the Arctic using optical and microwave satellite measurements

Detecting snowfall events over the Arctic using optical and microwave satellite measurements

The Effects of Summer Snowfall on Arctic Sea Ice Radiative Forcing

AbstractSnow is the most reflective natural surface on Earth. Since fresh snow on bare sea ice increases the surface albedo, the impact of summer snow accumulation can have a negative radiative forcing effect, which would inhibit sea ice surface melt and potentially slow sea‐ice loss. However, it is not well known how often, where, and when summer snowfall events occur on Arctic sea ice. In this study, we used in situ and model snow depth data paired with surface albedo and atmospheric conditions from satellite retrievals to characterize summer snow accumulation on Arctic sea ice from 2003 to 2017. We found that, across the Arctic, ∼2 snow accumulation events occurred on initially snow‐free conditions each year. The average snow depth and albedo increases were ∼2 cm and 0.08, respectively. 16.5% of the snow accumulation events were optically thick (&gt;3 cm deep) and lasted 2.9 days longer than the average snow accumulation event (3.4 days). Based on a simple, multiple scattering radiative transfer model, we estimated a −0.086 ± 0.020 W m−2 change in the annual average top‐of‐the‐atmosphere radiative forcing for summer snowfall events in 2003–2017. The following work provides new information on the frequency, distribution, and duration of observed snow accumulation events over Arctic sea ice in summer. Such results may be particularly useful in understanding the impacts of ephemeral summer weather on surface albedo and their propagating effects on the radiative forcing over Arctic sea ice, as well as assessing climate model simulations of summer atmosphere‐ice processes.

A snowfall climatology of the Ohio River Valley, USA

Snowfall in the Ohio River Valley, USA, presents a relatively unique challenge due to the large gradient of event frequency and magnitude, and subsequent levels of preparation within local communities. Even relatively small magnitude events can cause widespread impacts due to available infrastructure. Here we present a climatology of snowfall conditions and events over a 74-year period using a network of daily observational stations across the region. Snowfall totals and event frequencies both exhibit a southwest to northeast gradient of increasing snowfall, where the majority of snowfall (> 80%) occurs during the core winter months of December through February. There is a clear influence of Lake Erie on snowfall conditions in the northeast corner of the domain, where snowfall frequency, totals, and trends are substantially higher within the lake belt relative to areas further inland. Over time, snowfall significantly increased downwind of Lake Erie by as much as 42%, while significant decreases of over 55% occurred in central Tennessee and eastern Ohio. Intra-seasonally, snowfall totals trended significantly less during November and March for much of the domain, suggesting a compression of the snowfall season to more core winter months. Trends in snowfall frequency were apparent for many sub-regions, however evidence here suggests the trends in snowfall totals were primarily driven by trends in snowfall magnitude per event.

A dynamic snow depth retrieval model based on time-series clustering optimization for GPS-IR

Due to the influence of environmental factors (i.e., terrain and surface coverage) around the GPS receivers, the snow depth retrieval results obtained by the existing global positioning system interferometric reflection (GPS-IR) method show significant variability. The resulting loss of reliability and accuracy limits the broad application of this technology. Therefore, this paper proposes a dynamic snow depth retrieval model based on time-series clustering optimization for GPS-IR to fully leverage multi-source satellite observation data for automatic and high-precision snow depth retrieval. The model employs Dynamic Time Warping distance measurement combined with the K-Medoids clustering algorithm to categorize frequency sequences obtained from various satellite trajectories, facilitating effective integration of multi-constellation data and acquisition of optimal datasets. Additionally, Long Short-Term Memory networks are integrated to capture and process the long-term dependencies in snow depth data, enhancing the model’s adaptability in handling time-series data. Validated against SNOTEL measured data and standard machine learning algorithms (such as BP Neural Networks, RBF, and SVM), the model’s retrieval capability is confirmed. For P351 and AB39 sites, the correlation coefficients for L1 band data retrieval were both 0.996, with RMSEs of 0.051 and 0.018 m, respectively. The experiment results show that the proposed model demonstrates superior precision and robustness in snow depth retrieval compared to the previous method. Then, we analyze the accuracy loss caused by sudden snowfall events. The proposed model and methodology offer new insights into the in-depth study of snow depth monitoring.

Extraordinary 2021 snowstorm in Spain reveals critical threshold response to anthropogenic climate change

Attribution of extreme weather events to anthropogenic climate change (ACC) has become an increasingly important line of research in recent years. However, the potential influence of ACC on heavy snowstorms remains largely unexplored. Here we focus on studying the exceptional January 2021 snowfall event in Spain, known as Filomena. First, using observational data and flow analogs, we show that the characteristic synoptic pattern leading to the episode has not significantly changed in frequency over the past decades. Based on this, we assume a fixed dynamical pattern and focus on studying the influence of ACC on the thermodynamics of the event using an atmospheric model and a storyline attribution approach. Our simulations indicate that in northern highlands, ACC intensified snowfall by up to +40% compared with pre-industrial conditions, while in nearby southern lowlands ACC weakened snowfall by up to –80%. This characteristic shift from weakening to intensification is well defined by a critical threshold in temperature. Furthermore, we show that if Filomena were to occur at the end of the 21st century, this contrasting response to ACC would be enhanced. Altogether, our findings highlight the large but uneven impact of global warming on extreme snowstorm events.

Multi-year evaluation of CloudSat, ERA5, and IMERG global snowfall products

Snowfall products derived from satellite-based radar observations, reanalysis products and phase-classified precipitation products are the three primary sources of information on surface snowfall. This study compares snowfall rate products from CloudSat, ERA5 and IMERG using the Triple Collocation (TC) method on a global scale covering almost the entire period of CloudSat cloud-profiling radar (CPR) 2C products from 2006 to 2019, especially considering the precipitation phase classification field of IMERG (probability of Liquid Precipitation, pLP). The main conclusions include but are not limited to the following: (1) The pLP threshold significantly impacts the ability of IMERG to identify snowfall from precipitation, with an optimal pLP threshold of 25 percent determined by matching IMERG with CloudSat and ERA5 snowfall events globally. (2) CloudSat presents the highest performance and stability on a global scale, with average TC-estimated CC and RMSE of 0.472 and 1.465, respectively. (3) The TC-estimated CC of IMERG exhibit significant spatiotemporal heterogeneities, but IMERG product with the optimal pLP threshold show comparable performance with CloudSat, with an average TC-estimated RMSE of ∼ 1.533. (4) ERA5 performed worse than the other two products, except in some high-latitude and high-altitude areas. This study provides novel insights into the performances of snowfall rate products from different sources at the global scales and provides a foundation for improving satellite- and model-based snowfall rate retrievals.

Classifying moisture sources associated with snowfall in the mountains of Lesotho

An average of eight snowfall events occur each year in the eastern Lesotho Highlands. These snowfall events are typically associated with cut-off low (CoLs) systems and mid-latitude cyclones. However, the moisture sources of the snowfall are unclassified and unclear. The Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model, an air mass back trajectory model, has been used to evaluate moisture source waters locally in southern Africa and internationally in China and Europe. This study uses HYSPLIT to determine the source moisture of snow in Lesotho. A list of all 82 snowfall events in Lesotho spanning 2017 to 2022 was compiled using the Snow Report SA Instagram page, including the date and location of snowfall. A 72-hour back trajectory for each snowfall event was initiated for both Afriski and the whole of Lesotho. This amounts to models of moisture source trajectories for 28 and 82 snowfall days, respectively. These air mass pathways are classified according to their frequency per snowfall event, per month in the snow season, per year and for the full period. From this, associated moisture source regions and dominant air mass trajectories were identified. This study reports that the air mass trajectories associated with Afriski and Lesotho as a whole are very similar. The most common pathway of air mass trajectories transporting snow-bearing moisture to Lesotho was an inland trajectory from the northern regions of southern Africa. This pathway makes up 16.6% of all trajectories reported and is associated with the Angola Low, the Congo Air Boundary and the St. Helena High Pressure.

Snowpack variations and their hazardous effects under climate warming in the central Tianshan Mountains

Climate change alters snowpack evolution, which in turn influences the likelihood of snow avalanches and flood risks. The lack of systemic observational data on key snow characteristics in high mountains remains a scientific challenge in terms of systematically elucidating the dynamic chain of variations in climate–snowpack–snow disasters. This restricts our understanding and poses challenges in the prediction of snow-related disaster risks. As such, this study analysed the variations of temperature and snowfall and the physical characteristics of snowpacks based on ground-based observations from the Kunse River Valley situated in the Tianshan Mountains from 1967 to 2021. The results reveal that the temperature increased significantly by 0.32 °C per decade (p < 0.01) during the snow season, along with more extreme snowfall events. The snow-cover duration was observed to have been shortened by 4.77 d per decade (p < 0.01) from 1967 to 2021, which is characterised by later snow-cover onset and earlier snowmelt. Concurrently, average and maximum snow depths increased along with an increase in peak snow water equivalent, thus indicating a higher frequency of extremely scarce or abundant snow years. The low snowpack temperature gradient and earlier snowmelt dates in spring lead to earlier occurrences of snowmelt floods and wet avalanches. As the risks of these events increase, they pose greater threats to farmlands, road transportation, water–electricity infrastructure and several other human activities. Therefore, these insights are critical for providing vital information that can deepen our understanding of the impact of climate change on snowpack characteristics and improve management strategies for snow-related disaster prevention and mitigation.

Applying coal char to cattle pens for sustainable agriculture in the semiarid US High Plains

AbstractApplying high carbon (C) additive to cattle pens and land application of the resultant manure mix offers a potential strategy for optimizing manure and soil management while mitigating environmental concerns. An experiment was conducted in western Nebraska from 2019 to 2022 to evaluate the effect of adding coal char (∼290 g C kg−1 by wt.) on feedlot manure's properties and stability and the interacting effect of manure‐char on crop yields in a corn (Zea mays L.)–dry bean (Phaseolus vulgaris L.)–corn rotation. Treatments in the crop field included manure from pens with or without char (each at 34 and 68 Mg ha−1; low and high rate), urea at 100% recommended nitrogen (N) rate with or without 45 Mg char ha−1, and a control. Applying char to pens kept them drier following snowfall events. The high surface area and cation exchange capacity of char improved soil and manure nutrient retention. The 100% urea‐N plus char treatment had a greater corn yield than the low‐rate char–manure mix or high‐rate manure in 2020. In 2021, there was a trend for higher bean yields with the high char–manure rate treatment than the control. In 2022, all the fertilized treatments had greater grain yields than the control. A one‐time high‐rate char–manure mix or manure application could replace 314 kg N ha−1 and 90 kg P2O5 ha−1 over 2 years without any yield penalty. This study underscores the synergy between char and manure or chemical fertilizers to improve nutrient balance and supply, ultimately enhancing crop production.

Diverse Characteristics of Extreme Orographic Snowfall Events in Little Cottonwood Canyon, Utah

Abstract Heavy orographic snowfall can disrupt transportation and threaten lives and property in mountainous regions but benefits water resources, winter sports, and tourism. Little Cottonwood Canyon (LCC) in northern Utah’s Wasatch Range is one of the snowiest locations in the interior western United States and frequently observes orographic snowfall extremes with threats to transportation, structures, and public safety due to storm-related avalanche hazards. Using manual new-snow and liquid precipitation equivalent (LPE) observations, ERA5 reanalyses, and operational radar data, this paper examines the characteristics of cool-season (October–April) 12-h snowfall extremes in upper LCC. The 12-h extremes, defined based on either 95th percentile new snow or LPE, occur for a wide range of crest-level flow directions. The distribution of LPE extremes is bimodal with maxima for south-southwest or north-northwest flow, whereas new-snow extremes occur most frequently during west-northwest flow, which features colder storms with higher snow-to-liquid ratios. Both snowfall and LPE extremes are produced by diverse synoptic patterns, including inland-penetrating or decaying atmospheric rivers from the south through northwest that avoid the southern high Sierra Nevada, frontal systems, post-cold-frontal northwesterly flow, south-southwesterly cold-core flow, and closed low pressure systems. Although often associated with heavy precipitation in other mountainous regions, the linkages between local integrated water vapor transport (IVT) and orographic precipitation extremes in LCC are relatively weak, and during post-cold-frontal northwesterly flow, highly localized and intense snowfall can occur despite low IVT. These results illustrate the remarkable diversity of storm characteristics producing orographic snowfall extremes at this interior continental mountain location. Significance Statement Little Cottonwood Canyon in northern Utah’s central Wasatch Range frequently experiences extreme snowfall events that pose threats to lives and property. In this study, we illustrate the large diversity of storm characteristics that produce this extreme snowfall. Meteorologists commonly use the amount of water vapor transport in the atmosphere to predict heavy mountain precipitation, but that metric has limited utility in Little Cottonwood Canyon where heavy snowfall can occur with lower values of such transport. Our results can aid weather forecasting in the central Wasatch Range and have implications for understanding precipitation processes in mountain ranges throughout the world.

Snow chemical characteristics and meteorological controlling factors from three snowfalls of Gande in the Tibetan plateau

Water-soluble ions (Cl−, SO42−, NO3−, Ca2+, Mg2+, Na+, K+ and NH4+) and trace elements (Pb, Cd, Cr and As) in snow samples were analyzed from three different snowfall events (Ⅰ, Ⅱ, Ⅲ) in December 2019 at Gande Meteorological Station, located in the southeastern part of the Tibetan Plateau region. From event Ⅰ to Ⅲ, the total concentration of water-soluble ions presented an increasing trend of 0.92, 1.43 and 3.01 mg L−1, respectively. The dominant cation was Ca2+ for all the events, while the dominate anion was SO42−, SO42− and Cl− in the event Ⅰ, Ⅱ and Ⅲ, respectively. Meteorological field and backward trajectory cluster analysis presented three different large-scale predominant wind directions of northwest wind, southwest wind and west wind for each event, revealing different transport processes and sources for snow components. The total concentration of water-soluble ions was mainly related to PM10 as the in-situ PM10 observation showed a similar increasing trend. The dominant trace element was different Pb, Cr and Pb in each event. The mixed source of crustal and industrial inputs was the key factor in three snowfalls, and its contribution was consistent with the concentration of dominant trace element. Herein, high fractions of Pb and Ca2+ were appeared in the event Ⅰ and Ⅲ, but largest proportion to Cr was enhanced in the event Ⅱ.

The influence of Urmia Lake desiccation on an extreme snowfall event: A case study using the WRF-Lake model

Urmia Lake in the northwest Iran has undergone significant desiccation in recent decades, shrinking to a much smaller size than its original. This study implements the WRF-Lake coupled modeling system to examine the impacts of the observed physical changes over the Lake during a heavy snowfall event occurred February 1–2, 2017. Modifications are introduced to represent the shrunken, salt-encrusted lake surface and surrounding arid environment through updating land use, soil characteristics, lake size and depth, and albedo data. Experiments incorporating lake-atmosphere coupling and land surface refinements (Lake_CTL, Lake_LULC, Lake_AL) outperform simulations without a lake (WRF_noLake) in reproducing observed precipitation patterns at stations surrounding the Urmia Lake. All simulations underestimate the extreme 290 mm snowfall recorded at the Urmia station; however, the Lake_LULC and Lake_AL improve snowfall representation at other stations around the lake. Regarding snow cover validation, Lake_LULC and Lake_AL exhibit a higher correlation with satellite observations and show a significant improvement in snow detection near Urmia Lake. They have a Probability of Detection (POD) of 0.94 and a Critical Success Index (CSI) of 0.91, compared to a POD of 0.79–0.80 and a CSIs of ∼0.77 for the default lake model (Lake_CTL) and the WRF_noLake experiments, respectively. Additionally, adjusting the initial lake surface temperature to match observations substantially reduces cold biases in near-surface air temperatures over Urmia Lake. The lack of lake temperature updating in WRF-Lake poses ongoing challenges in the model, though. In summary, this study demonstrates that refining the representations of desiccated lakes and their surroundings in high-resolution coupled models would improve simulations of meteorological processes influenced by lake-atmosphere interactions.

Improvement of Albedo and Snow-Cover Simulation during Snow Events over the Tibetan Plateau

Abstract The snow albedo is a vital component of land–atmosphere coupling models. It plays a critical role in regulating land surface energy exchange by controlling incoming solar radiation absorbed by the land surface and influencing the timing and rate of snowmelt. Accurate snow albedo simulation is essential to obtain surface energy balance and snow-cover estimates. Here, the simulation of albedo and snow cover using the Weather Research and Forecasting Model and an improved snow albedo scheme is verified against satellite-retrieved products during and immediately following eight snowfall events over the Tibetan Plateau. The improved model successfully characterizes the spatial pattern and inverted U-shaped temporal pattern of albedo over the entire Tibetan Plateau. This is attributed to the local optimization of snow-age parameters and explicit consideration of snow depth in the improved scheme. Compared with the previous model, the model proposed herein greatly decreases the overestimated albedo (by 0.13–0.27), yielding a bias range of ±0.08, mean relative bias decrease of 70%, and significant increase in the spatial correlation coefficient of 0.03–0.39 (mean: 0.13). The significant improvements of albedo estimates appear in deep snow-covered regions, largely attributed to parameter optimization related to snow albedo decay, while less improvements appear over the shallow snow-covered regions. Accurate reproduction of the spatiotemporal variation in albedo alleviated snow-cover overestimation by small amounts. For snow-cover estimates, the improved model consistently decreases the false-alarm rate by 0.03, and increases the overall accuracy and equitable threat score by 0.04 and 0.03, respectively. Moreover, the improved scheme shows an equivalent improvement of albedo estimates at both 1- and 5-km grid spacing over the eastern Tibetan Plateau; this is also true for snow-cover estimates. Significance Statement Snow albedo schemes in widely used numerical weather prediction models show notable shortcomings in complex mountainous regions, hindering accurate surface energy balance and snow-cover prediction. The purpose of this study is to better understand the role of snow albedo on snow-cover estimates and reveal the application potential of an improved snow albedo scheme across the Tibetan Plateau. This is important because snow albedo influences the timing and rate of snowmelt, and in turn snow-cover estimates, through regulating the surface energy budget. Our results highlight the strong application potential of our improved scheme in reducing snow simulation errors, confirm the importance of snow depth on snow albedo, and provide a new perspective for improving the accuracy of snow forecast over the topographically high Tibetan Plateau.

Decreasing trends of mean and extreme snowfall in High Mountain Asia

Intense warming profoundly alters precipitation phase patterns and intensity in High Mountain Asia (HMA). While snowfall climatology and precipitation extremes have been studied, there is a lack of understanding of snowfall extremes within HMA. Here, we investigate the spatial and temporal variability of non-extreme and extreme snowfall in hydrological years 1979–2020 using multi-source meteorological data, compare weather systems during extreme and non-extreme snowfall events, and identify key circulation factors that influence fluctuations in mean annual snowfall and extreme snowfall. The snowfall amount (−0.13 d/mm), days (−0.56 d/a), and fraction (−0.0012) were significantly reduced in HMA, with a shorter snowfall season (−0.52 d/a). Some extreme snowfall metrics (maximum 1-day snowfall and maximum 3-day snowfall) were insensitive to climate change, whereas the maximum consecutive snowfall days (−0.007 d/a), snowfall amount (−0.0023 mm/a), heavy snowfall days (S95pD; 0.0087 d/a), and extremely heavy snowfall days (S99pD; −0.1019 d/a) showed significant decreases. Synthetic analyses show that extreme snowfall events were more likely to occur within a narrow temperature range (−5 °C to 3 °C) with higher relative humidity and precipitation compared to non-extreme events. A stepwise regression method was used to determine that the fluctuation in the average annual snowfall was closely related to the Atlantic Multidecadal Oscillation, whereas the variation in extreme snowfall was mainly influenced by the Southern Oscillation Index. Our research provides a reference for assessing the potential impacts of climate change on a regional scale for risk management and disaster adaptation.

A novel framework to investigate wind-driven snow redistribution over an Alpine glacier: combination of high-resolution terrestrial laser scans and large-eddy simulations

Abstract. Wind-driven snow redistribution affects the glacier mass balance by eroding or depositing mass from or to different parts of the glacier’s surface. High-resolution observations are used to test the ability of large-eddy simulations as a tool for distributed mass balance modeling. We present a case study of observed and simulated snow redistribution over Hintereisferner glacier (Ötztal Alps, Austria) between 6 and 9 February 2021. Observations consist of three high-resolution digital elevation models (Δx=1 m) derived from terrestrial laser scans taken shortly before, directly after, and 15 h after snowfall. The scans are complemented by datasets from three on-site weather stations. After the snowfall event, we observed a snowpack decrease of 0.08 m on average over the glacier. The decrease in the snow depth can be attributed to post-snowfall compaction and the wind-driven redistribution of snow. Simulations were performed with the Weather Research and Forecasting (WRF) model at Δx=48 m with a newly implemented snow drift module. The spatial patterns of the simulated snow redistribution agree well with the observed generalized patterns. Snow redistribution contributed −0.026 m to the surface elevation decrease over the glacier surface on 8 February, resulting in a mass loss of −3.9 kg m−2, which is on the same order of magnitude as the observations. With the single case study we cannot yet extrapolate the impact of post-snowfall events on the seasonal glacier mass balance, but the study shows that the snow drift module in WRF is a powerful tool to improve knowledge on wind-driven snow redistribution patterns over glaciers.