Annual Snow Research Articles

The influence of regional climatic changes on the flood regime in European Russia in the 21st Century

Possible changes in the average annual maximum snow reserves and flood runoff in European Russia (ER), on the basis of global climate modelling data was estimated. The data on precipitation and temperature from 5 AOGCMs (atmospheric and ocean general circulation models) of the CMIP5 project, based on the best reproduction of the current climate were used. The multidirectional changes in the maximum snow reserves and flood runoff are expected in ER, although there is a tendency in the southern territories towards a decrease in these characteristics; this intensifies near the end of the 21st Century and when using data from the ‘hard’ scenario of greenhouse gas emissions.

Deep Learning on Airborne Radar Echograms for Tracing Snow Accumulation Layers of the Greenland Ice Sheet

Climate change is extensively affecting ice sheets resulting in accelerating mass loss in recent decades. Assessment of this reduction and its causes is required to project future ice mass loss. Annual snow accumulation is an important component of the surface mass balance of ice sheets. While in situ snow accumulation measurements are temporally and spatially limited due to their high cost, airborne radar sounders can achieve ice sheet wide coverage by capturing and tracking annual snow layers in the radar images or echograms. In this paper, we use deep learning to uniquely identify the position of each annual snow layer in the Snow Radar echograms taken across different regions over the Greenland ice sheet. We train with more than 15,000 images generated from radar echograms and estimate the thickness of each snow layer within a mean absolute error of 0.54 to 7.28 pixels, depending on dataset. A highly precise snow layer thickness can help improve weather models and, thus, support glaciological studies. Such a well-trained deep learning model can be used with ever-growing datasets to aid in the accurate assessment of snow accumulation on the dynamically changing ice sheets.

GlobSnow v3.0 Northern Hemisphere snow water equivalent dataset

We describe the Northern Hemisphere terrestrial snow water equivalent (SWE) time series covering 1979–2018, containing daily, monthly and monthly bias-corrected SWE estimates. The GlobSnow v3.0 SWE dataset combines satellite-based passive microwave radiometer data (Nimbus-7 SMMR, DMSP SSM/I and DMSP SSMIS) with ground based synoptic snow depth observations using bayesian data assimilation, incorporating the HUT Snow Emission model. The original GlobSnow SWE retrieval methodology has been further developed and is presented in its current form in this publication. The described GlobSnow v3.0 monthly bias-corrected dataset was applied to provide continental scale estimates on the annual maximum snow mass and its trend during the period 1980 to 2018.

Future snow projections in a small basin of the Western Himalaya

Snow is a crucial component of the hydrological cycle in the Western Himalaya. Water from snowmelt is used in various sectors in downstream regions, thus playing a critical role in securing the livelihoods of millions of people. In this study, we investigated the future evolution of snow cover and snowmelt in the Panjshir catchment of Afghanistan, a sub-basin of the Indus, in the Western Himalaya. We applied a three-step approach to select a few global climate model (GCM) simulations from CMIP5 climate datasets for RCP4.5 and RCP8.5, which showed reasonable performance with ERA5-Land dataset for the chosen historical period (1981–2010). The selected model simulations were then segregated into two groups: those projecting a cold-wet climate and those projecting a warm-dry climate by the end of the 21st century (2071–2100). These GCMs were downscaled to a higher resolution using empirical statistical downscaling. To simulate the snow processes, we used the distributed cryospheric-hydrological J2000 model. The results indicate that the model captures well the snow cover dynamics for the historical period when compared with the daily MODIS-derived snow cover. The J2000 model was then forced by climate projections from the selected GCMs to quantify future changes in snow cover area, snow storage and snowmelt. While a 10–18% reduction in annual snow cover area is projected in the cold-wet models, a 22–36% reduction is projected in the warm-dry models. Similarly, the snow cover area is projected to decrease in all elevation bands under climate change. At the seasonal scale, across all models and scenarios, the snow cover in the autumn and spring seasons are projected to reduce by as much as 25%, with an increase in winter and spring snowmelt and a decrease in summer snowmelt. The projected changes in the seasonal availability of snowmelt-driven water resources are likely to have direct implications for water-dependent sectors in the region and call for a better understanding of water usage and future adaptation practices.

Historical evolution and future trend of Northern Hemisphere snow cover in CMIP5 and CMIP6 models

Historical snow cover over the Northern Hemisphere was examined in the satellite-based NOAA-CDR data for the period of 1970–2019. Observed annual snow cover fraction (SNF) has reduced over most areas by up to 2%/decade, while annual snow cover area (SCA) has reduced by 2 × 105 km2/decade. However, SCA in the October–December season has increased by about 5 × 105 km2/decade. CMIP5 and CMIP6 historical experiments were validated against the NOAA-CDR data. Snow cover was generally well simulated in both CMIPs, with CMIP6 models performing better. The biases in SCA reduction were larger and smaller during summer and winter, respectively. The observed increase of October–November–December SCA in the 2000s was not reproduced. Climate projections of future snow cover were evaluated in CMIP6. SNF is projected to decrease in the next 80 years, under all four scenarios evaluated (SSP126, SSP245, SSP370 and SSP585). The higher the greenhouse emissions in the shared socio-economic pathways, the faster the reduction. Under the SSP585 scenario, the rate of SCA reduction is projected to exceed −1.2 × 106 km2/decade. By 2081–2100, annual (January–March) SCA is projected to decrease by more than 30% (20%). Under the SSP126 scenario, annual (January–March) SCA is projected to only reduce by about 10% (5%) relative 1995–2014 values. The reduction of Greenhouse gas emissions is critical to controlling the loss of snow cover; future snow cover only stabilizes under the SSP126 scenario, but continue to decrease under the other three scenarios.

Variation of Snow Mass in a Regional Climate Model Downscaling Simulation Covering the Tianshan Mountains, Central Asia

AbstractMountain snow is a fundamental freshwater supply in the arid regions. Climate warming alters the timing of snowmelt and shortens the snow cover duration, which greatly influences the regional climate and water management. However, a reliable estimation of snow mass in the Tianshan Mountains (TS) is still unclear due to the scarcity of extensive continuous surface observations and a complex spatial heterogeneity. Therefore, a long‐time snow simulation from 1982 to 2018 was performed in WRF/Noah‐MP to quantify snow mass in the TS, forced by ERA5 reanalysis data and real‐time updated leaf area index and green vegetation fraction. Meanwhile, March snow mass (close to the annual peak snow mass), snow cover fraction (SCF), and their associated trends were investigated in the TS. The results indicated a good accuracy of the simulated snow water equivalent (root mean square error [RMSE]: 7.82 mm/day) with a slight overestimation (2.84 mm/day). Compared with ERA5 data set, the RMSE and mean bias of the daily snow depth from WRF/Noah‐MP downscaling were significantly reduced by 95.74% and 93.02%, respectively. The climatological March snow mass measured 97.85 (±16.60) Gt in the TS and exhibited a negligible tendency during the study period. The total precipitation during the cold season controlled the variations of March snow mass. The increased precipitation in the high‐altitude regions contributed to an extensive snow mass, which could offset the loss in the TS lowland. In contrast, rapidly rising air temperature caused a significant reduction of March SCF, particularly in the Southern TS.

Linking Sub‐Tropical Evaporation and Extreme Precipitation Over East Antarctica: An Atmospheric River Case Study

AbstractWe investigate an intense snowfall event between 15 and 18 February 2011 over the East Antarctic coastal region which contributed to roughly 24% of the annual snow accumulation. The event was previously associated with an atmospheric river, and here we use both Eulerian and Lagrangian analysis to gain an understanding of the processes contributing to the atmospheric river signature. The planetary‐scale configuration during the event consisted of a persistent blocking situation resulting in a sustained meridional flow from the sub‐tropics to the Antarctic ice sheet between 20 and 50°E. Within this configuration, synoptic‐scale cyclogenesis contributed to slantwise ascent of moisture loaded air parcels toward Antarctica. Landfall of this cyclone’s warm sector coincided with the onset of Antarctic precipitation. Subsequently, a secondary cyclone developed along a pre‐existing baroclinic zone. The rapid intensification and propagation speed of this mesoscale cyclone alongside the warm, moist air mass resulted in strong moisture flux convergence ahead of the cyclone, providing additional poleward moisture transport. The poleward progression of warm moist air and a corresponding decrease of sea‐surface temperatures implied downward surface sensible and latent heat fluxes throughout the region of intense poleward moisture, roughly between 40 and 60°S. Hence, moisture uptake via surface evaporation was suppressed between the sub‐tropics and the polar continent, favoring long‐range transport. Identification of the surface moisture uptake region by tracing changes in moisture in air parcels confirmed the limited uptake of moisture during the poleward transport in this case study, with the primary moisture source for Antarctic precipitation located in the sub‐tropics.

Snow Depth and Air Temperature Seasonality on Sea Ice Derived From Snow Buoy Measurements

Snow depth on sea ice is an essential state variable of the polar climate system and yet one of the least known and most difficult to characterize parameters of the Arctic and Antarctic sea ice systems. Here, we present a new type of autonomous platform to measure snow depth, air temperature, and barometric pressure on drifting Arctic and Antarctic sea ice. “Snow Buoys” are designed to withstand the harshest environmental conditions and to deliver high and consistent data quality with minimal impact on the surface. Our current dataset consists of 79 time series (47 Arctic, 32 Antarctic) since 2013, many of which cover entire seasonal cycles and with individual observation periods of up to 3 years. In addition to a detailed introduction of the platform itself, we describe the processing of the publicly available (near real time) data and discuss limitations. First scientific results reveal characteristic regional differences in the annual cycle of snow depth: in the Weddell Sea, annual net snow accumulation ranged from 0.2 to 0.9 m (mean 0.34 m) with some regions accumulating snow in all months. On Arctic sea ice, the seasonal cycle was more pronounced, showing accumulation from synoptic events mostly between August and April and maxima in autumn. Strongest ablation was observed in June and July, and consistently the entire snow cover melted during summer. Arctic air temperature measurements revealed several above-freezing temperature events in winter that likely impacted snow stratigraphy and thus preconditioned the subsequent spring snow cover. The ongoing Snow Buoy program will be the basis of many future studies and is expected to significantly advance our understanding of snow on sea ice, also providing invaluable in situ validation data for numerical simulations and remote sensing techniques.

Glacio-Nival Regime Creates Complex Relationships between Discharge and Climatic Trends of Zackenberg River, Greenland (1996–2019)

Arctic environments experience rapid climatic changes as air temperatures are rising and precipitation is increasing. Rivers are key elements in these regions since they drain vast land areas and thereby reflect various climatic signals. Zackenberg River in northeast Greenland provides a unique opportunity to study climatic influences on discharge, as the river is not connected to the Greenland ice sheet. The study aims to explain discharge patterns between 1996 and 2019 and analyse the discharge for correlations to variations in air temperature and both solid and liquid precipitation. The results reveal no trend in the annual discharge. A lengthening of the discharge period is characterised by a later freeze-up and extreme discharge peaks are observed almost yearly between 2005 and 2017. A positive correlation exists between the length of the discharge period and the Thawing Degree Days (r=0.52,p<0.01), and between the total annual discharge and the annual maximum snow depth (r=0.48,p=0.02). Thereby, snowmelt provides the main source of discharge in the first part of the runoff season. However, the influence of precipitation on discharge could not be fully identified, because of uncertainties in the data and possible delays in the hydrological system. This calls for further studies on the relationship between discharge and precipitation. The discharge patterns are also influenced by meltwater from the A.P. Olsen ice cap and an adjacent glacier-dammed lake which releases outburst floods. Hence, this mixed hydrological regime causes different relationships between the discharge and climatic trends when compared to most Arctic rivers.

Spatial features of the chemical composition of Thala Hills surface snow, East Antarctica

Despite the great interest in the study of the chemical composition of the surface snow cover in Antarctica, the knowledge of the Enderby Land area remains extremely limited. In the Vecherny Oasis, where the construction of the Belarusian Antarctic Research Station has been carried out since 2015, the study of the chemical composition of the surface snow began in 2012 in preparation for the Comprehensive Environmental Evaluation. Its continuation is due to the need to assess the consequences of the construction and operation of the station in accordance with the requirements of the Antarctic Treaty Protocol on Environmental Protection.Snow samples were taken from 2012 to 2019 during the seasonal Belarusian Antarctic expeditions. Sampling was carried out from the surface horizons, which characterize the annual snow fallout. Chemical analytical studies were performed using standard methods. A total of 144 samples of snow water were analyzed.The aim of the study is to characterize the chemical composition of the surface snow of the Vecherny Oasis (and of the Thala Hills as a whole) to identify the areas of anthropogenic impact and trends in its change.The data on the main ions content in the surface snow, the value of pH and electrical conductivity, as well as the variabilities of the main indicators are presented. It is shown that the snow water of the Vecherny Oasis is very low-mineralized, with the sum of ions in the range of 1,04–57,3 mg/l (average — 7,4 mg/l), the values of electrical conductivity — 2,7–85,1 µS/cm (10,7 µS/cm). The snow water in most cases is characterized as slightly acidic. The chemical composition of the snow water and its mineralization is determined mainly by the content of chlorides and sodium ions. The high variability of the indicators of snow water hydrochemical composition within the areas of former and current human activities, as well as the increased content of sulfate ions, is considered to be indicative of anthropogenic impact.

Canadian In Situ Snow Cover Trends for 1955–2017 Including an Assessment of the Impact of Automation

ABSTRACT Snow cover trends for Canada over the 1955–2017 period for the daily snow depth–observing network of Environment and Climate Change Canada (ECCC) are presented based on an updated quality-controlled historical daily in situ snow depth dataset. The period since approximately 1995 is characterized by a rapid decline in manual observations (loss of over 800 manual observing sites between 1995 and 2017) and an increasing number of automated stations equipped with sonic snow depth sensors. In 2017 these accounted for approximately 45% of the network and more than 80% of the snow depth–observing network north of latitude 55°N. Automated stations are characterized by more frequent missing and anomalous data than manual ruler observations, particularly at Arctic sites. A comparison of closely located automated sonic and manual ruler observations showed similar numbers of days with snow cover but the sonic sensors detected significantly lower snow depths. For time series analysis of annual snow cover variables, the systematic difference between ruler and sonic snow depth can be removed using a common 2003–2016 reference period to compute snow cover anomalies. The updated trend results are broadly similar to previously published assessments showing long-term decreases in annual snow cover duration (SCD) and snow depth over most of Canada, with the largest decreases observed in spring snow cover and seasonal maximum snow depth (SDmax). Significant declines in SCD and SDmax of −1.7 (±1.1) days decade-1 and −1.8 cm (±0.8) cm decade−1 were observed in the Canada–averaged series over the 1955–2017 period. These trends mainly reflect snow cover conditions over southern Canada where the observing network is concentrated and where there are significant negative correlations between snow cover and winter air temperature. Declining numbers of stations reporting snow depth, issues with sonic sensor data quality, and systematic differences between ruler and sonic sensor measurements are major challenges for continued climate monitoring with the current ECCC snow depth–observing network.

Snow-Induced PV Loss Modeling Using Production-Data Inferred PV System Models

Snow-induced photovoltaic (PV)-energy losses (snow losses) in snowy and cold locations vary up to 100% monthly and 34% annually, according to literature. Levels that illustrate the need for snow loss estimation using validated models. However, to our knowledge, all these models build on limited numbers of sites and winter seasons, and with limited climate diversity. To overcome this limitation in underlying statistics, we investigate the estimation of snow losses using a PV system’s yield data together with freely available gridded weather datasets. To develop and illustrate this approach, 263 sites in northern Sweden are studied over multiple winters. Firstly, snow-free production is approximated by identifying snow-free days and using corresponding data to infer tilt and azimuth angles and a snow-free performance model incorporating shading effects, etc. This performance model approximates snow-free monthly yields with an average hourly standard deviation of 6.9%, indicating decent agreement. Secondly, snow losses are calculated as the difference between measured and modeled yield, showing annual snow losses up to 20% and means of 1.5–6.2% for winters with data for at least 89 sites. Thirdly, two existing snow loss estimation models are compared to our calculated snow losses, with the best match showing a correlation of 0.73 and less than 1% bias for annual snow losses. Based on these results, we argue that our approach enables studying snow losses for high numbers of PV systems and winter seasons using existing datasets.

Behind the scenes of streamflow model performance

Abstract. Streamflow is often the only variable used to evaluate hydrological models. In a previous international comparison study, eight research groups followed an identical protocol to calibrate 12 hydrological models using observed streamflow of catchments within the Meuse basin. In the current study, we quantify the differences in five states and fluxes of these 12 process-based models with similar streamflow performance, in a systematic and comprehensive way. Next, we assess model behavior plausibility by ranking the models for a set of criteria using streamflow and remote-sensing data of evaporation, snow cover, soil moisture and total storage anomalies. We found substantial dissimilarities between models for annual interception and seasonal evaporation rates, the annual number of days with water stored as snow, the mean annual maximum snow storage and the size of the root-zone storage capacity. These differences in internal process representation imply that these models cannot all simultaneously be close to reality. Modeled annual evaporation rates are consistent with Global Land Evaporation Amsterdam Model (GLEAM) estimates. However, there is a large uncertainty in modeled and remote-sensing annual interception. Substantial differences are also found between Moderate Resolution Imaging Spectroradiometer (MODIS) and modeled number of days with snow storage. Models with relatively small root-zone storage capacities and without root water uptake reduction under dry conditions tend to have an empty root-zone storage for several days each summer, while this is not suggested by remote-sensing data of evaporation, soil moisture and vegetation indices. On the other hand, models with relatively large root-zone storage capacities tend to overestimate very dry total storage anomalies of the Gravity Recovery and Climate Experiment (GRACE). None of the models is systematically consistent with the information available from all different (remote-sensing) data sources. Yet we did not reject models given the uncertainties in these data sources and their changing relevance for the system under investigation.

Influence of the height of Antarctic ice sheet on its climate

The influence of the height of the Antarctic Ice Sheet (AIS) on its climate is investigated through numerical modeling, using NCAR-CESM-CAM5, which is found to be one of the best performing models over the region. The investigation reveals that on the reduction of its height, the troposphere over Antarctica will become warmer and wetter, annual precipitation and snow accumulation would enhance, and there will be a net enhancement in southward energy transport towards the Antarctic from the lower latitudes. The response will be opposite if the height of the AIS increases. The effect is found to be driven by the radiation imbalance developed from the changes in the outgoing longwave radiation at the top of the atmosphere due to perturbations in the ice sheet's elevation. An increase (decrease) in precipitation will occur on reduction (enhancement) of the AIS, which will try to stabilize its height, but this will be accompanied by an increase (decrease) in the surface temperature, which would have an opposite effect. A significant influence of the AIS elevation was also felt on the surface winds, stationary waves, and the eddy-driven jet around the continent, highlighting the impact that changes in the height of AIS have on the surrounding atmospheric circulation.

Revisiting climatic features in the Alaskan Arctic using newly collected data

Climatic features in the Alaskan Arctic have typically been analyzed using data from the limited National Weather Service stations. However, the increasing availability of in situ data in this area allows a more comprehensive understanding of recent changes. This study used newly collected data from 41 stations to investigate climatic features and recent changes in the Alaskan Arctic from the mid-1940s to 2018. We found that the mean annual air temperature (MAAT) ranged from –11.0 to –6.4 ∘C, annual amplitude of air temperature (AAAT) ranged from 16 to 22 ∘C, annual precipitation ranged from 85 to 300 mm, and annual mean snow depth ranged from 13.5 to 34.5 cm during 2007–2012. Compared with data since the late 1980s, MAAT increased by $\sim $ 2 ∘C near the coastline whereas AAAT did not significantly change. Changes in annual precipitation were complex among stations but showed a considerable increase in precipitation, snowfall, and snow depth during the cold months. The number of snow cover days declined, whereas the number of snowfall days increased at both Barrow and Kuparuk. This increase in snowfall events may be attributed to the declining sea ice concentration, which may enhance hydrological cycles. The observed bulk density of fresh snow was around 40–80 kg/m3 and declined from the mid-1980s to the late-1990s, then increased until the end of the study period. This expanded in situ dataset provides a more comprehensive understanding of climatic conditions in the Alaskan Arctic and confirms rapid changes during recent decades. This study may also serve to validate and benchmark high-resolution climate models.

Snow on Arctic Sea Ice in a Warming Climate as Simulated in CESM.

Earth system models are valuable tools for understanding how the Arctic snow‐ice system and the feedbacks therein may respond to a warming climate. In this analysis, we investigate snow on Arctic sea ice to better understand how snow conditions may change under different forcing scenarios. First, we use in situ, airborne, and satellite observations to assess the realism of the Community Earth System Model (CESM) in simulating snow on Arctic sea ice. CESM versions one and two are evaluated, with V1 being the Large Ensemble experiment (CESM1‐LE) and V2 being configured with low‐ and high‐top atmospheric components. The assessment shows CESM2 underestimates snow depth and produces overly uniform snow distributions, whereas CESM1‐LE produces a highly variable, excessively‐thick snow cover. Observations indicate that snow in CESM2 accumulates too slowly in autumn, too quickly in winter‐spring, and melts too soon and rapidly in late spring. The 1950–2050 trends in annual mean snow depths are markedly smaller in CESM2 (−0.8 cm decade−1) than in CESM1‐LE (−3.6 cm decade−1) due to CESM2 having less snow overall. A perennial, thick sea‐ice cover, cool summers, and excessive summer snowfall facilitate a thicker, longer‐lasting snow cover in CESM1‐LE. Under the SSP5‐8.5 forcing scenario, CESM2 shows that, compared to present‐day, snow on Arctic sea ice will: (1) undergo enhanced, earlier spring melt, (2) accumulate less in summer‐autumn, (3) sublimate more, and (4) facilitate marginally more snow‐ice formation. CESM2 also reveals that summers with snow‐free ice can occur ∼30–60 years before an ice‐free central Arctic, which may promote faster sea‐ice melt.

Changes in the Skuta Glacier (southeastern Alps) assessed using non-metric images

The Skuta Glacier in the Kamnik–Savinja Alps (in northern Slovenia) is one of the two remaining glaciers in Slovenia. It is located in a cirque oriented toward the northwest, which shields it from sunlight for most of the year. The glacier lies at an average elevation of 2070m. In recent years, its average area has measured around 1.5 hectares. Monitoring of the glacier has been performed since 1946. In 1962, regular photographing of the glacier with various cameras started from various non-fixed standpoints. Using the single image interactive orientation acquisition method, in which a single photograph is compared with the projection of a modern digital terrain model, seventeen photographs covering the period from 1970 to 2015 were used to acquire the 3D-perimeters of the glacier. The data shows that the elevation of glacier’s upper edge decreased by approximately 40m in the last half-century. Changes in the glacier’s area and average upper edge elevation were compared with average annual temperature and maximum seasonal snow cover depth.

The influence of atmospheric circulation patterns during large snowfall events in New Zealand's Southern Alps

AbstractLarge snowfall events contribute significantly to total annual snow accumulation across the maritime Southern Alps. However, the knowledge about atmospheric circulation patterns associated with large snowfall events over the New Zealand Southern Alps is very limited. Daily snow observation data from three automatic weather stations and ERA‐Interim reanalysis data were used to investigate the relationship between atmospheric forcing and large snowfall events across the Southern Alps. To do so, analysis of composite anomaly maps during large snowfall events were carried out to identify the common features of the days with heavy snow accumulation. Large snowfall across the Southern Alps are mainly associated with strong negative anomalies of sea level pressure (SLP) located over the southwest of New Zealand's South Island. These conditions are concurrent with negative anomalies of geopotential heights at 500 (Z500) located in the centre of low‐pressure systems. However, over New Zealand, days leading to large snowfall events experience positive anomalies of Z500 showing a relatively warm environment during such events in the maritime Southern Alps. Positive anomalies of low‐tropospheric temperatures (850 and 1,000 hPa) over the Tasman Sea and across the Southern Alps, strong values of integrated vapour transport (IVT) as well as high frequency of local synoptic patterns associated with troughing regimes (~78%) during large snowfall events provide more evidence of the important contribution of warm air flows.

Snow cover duration trends observed at sites and predicted by multiple models

Abstract. The 30-year simulations of seasonal snow cover in 22 physically based models driven with bias-corrected meteorological reanalyses are examined at four sites with long records of snow observations. Annual snow cover durations differ widely between models, but interannual variations are strongly correlated because of the common driving data. No significant trends are observed in starting dates for seasonal snow cover, but there are significant trends towards snow cover ending earlier at two of the sites in observations and most of the models. A simplified model with just two parameters controlling solar radiation and sensible heat contributions to snowmelt spans the ranges of snow cover durations and trends. This model predicts that sites where snow persists beyond annual peaks in solar radiation and air temperature will experience rapid decreases in snow cover duration with warming as snow begins to melt earlier and at times of year with more energy available for melting.

Events Due to Snowblower Use Seen in US Emergency Departments From 2003 Through 2018.

ObjectiveTo comprehensively analyze emergency department (ED) visits associated with snowblower use in the United States.MethodsData on National Electronic Injury Surveillance System ED visits due to snow blowers from 2003 through 2018 were analyzed by age, sex, diagnosis, anatomic location of the injury, and year, month, or weekday. The mechanism of injury and alcohol use were noted. Statistical analyses were performed, accounting for the weighted, stratified nature of the data.ResultsThere were an estimated 91,451 patients with an average age of 51 years; 91.2% were male. Amputation, fracture, or laceration accounted for 43,524 (47.6%) of the ED visits. The mechanism of injury was placing the hand into the chute (44.5%), a fall/slip (13.3%), medical events (6.1%), and miscellaneous (33.8%). Most (68.9%) occurred at home. Alcohol was rarely involved (0.4%). There were 648 deaths; 647 were due to cardiac events. The five major injury diagnoses were fracture (25.9%), laceration (20.2%), strain/sprain (15.0%), amputation (11.2%), and contusion/abrasion (10.2%); 99.8% of the amputations involved fingers. The incidence of ED snowblower visits was 1.845 per 100,000 US population with no change over time. There was a general correlation between the number of visits and the annual snow cover.ConclusionsAmple opportunity for injury prevention exists, as there was no change in the incidence over time. Cardiac events accounted for essentially all of the deaths.