Seasonal Snow Depth Research Articles

Modeling pan-Arctic seasonal and interannual landfast sea ice thickness and snow depth between 1979 and 2021

ABSTRACT Landfast sea ice (LFSI) is sensitive to local climate change, making it an important component of the cryosphere system. In this study, the LFSI around the pan-Arctic domain was simulated from 1979 to 2021 using a well-validated snow and ice thermodynamic model (HIGHTSI) under the framework of the Fast Ice Prediction System (FIPS), forced by the ERA5 reanalysis. The simulation results agree well with the in-situ observations in the Canadian Arctic, with a mean error of −0.06 ± 0.29 m for ice thickness and −0.04 ± 0.12 m for snow depth. A decrease of −2.8 ± 0.4 cm/10a in thickness and −16.2 ± 1.5 km3/a in volume for the Arctic LFSI was modeled during this period. There was significant spatial variability among the different domains, with the fastest decline found in the Vilkitsky Strait. The modeled snow depth shows large interannual and spatial variations, which was confirmed by other modeling results. The spatiotemporal variations in both air temperature and precipitation are the driving factors for the multi-decadal variations in LFSI thickness. The decreasing air temperature during the 2010s aligned with a slower thickness decrease and a slight volume increase for LFSI, which agreed with the pan-Arctic sea ice pattern.

Observed changes in the climate and snow dynamics of the Third Pole

The Third Pole (TP) is the world’s largest highland and has one of the biggest reservoirs of glacier ice mass and snow cover on the Earth. Three major Asian rivers (the Indus, Ganga and Brahmaputra) are nourished by the melting of glaciers and snow in Central Himalaya, which are inevitable for the socioeconomic sustainability and water security of South Asia. Here, we investigate the long-term (1980–2020) changes in snow depth and precipitation in TP, where major precipitation occurs in the form of rainfall in summer, and snowfall in winter and spring. The seasonal mean snow depth is deep (≥1 m) in winter and shallow (≤0.2 m) in summer. The average snowmelt and snow water equivalent are higher in the central and western Himalaya and Karakoram ranges in spring, which are the regions with most glaciers in TP. There is a significant positive trend in total precipitation, about 0.01–0.03 mm d−1 yr−1 in the central and eastern TP during the South Asian Summer Monsoon for the 1980–2020 period. Snowmelt is also increasing (>0.5 × 10−3 mm yr−1) in the western Himalaya during spring, which is consistent with the temperature rise (0.04–0.06 °C yr−1) there. In addition, there is a notable increase in the annual mean glacier melt (here, the water equivalent thickness) in TP (−1 to −5 cm w.e. yr−1), with its highest values in the eastern and central Himalaya (−3 to −5 cm w.e. yr−1), as estimated for the period 2003–2020. On top of these, by the end of the 21st century, the Coupled Model Intercomparison Project Phase 6 (CMIP6) projections show that there would be a significant decrease in snow depth and an increase in temperature of TP in all shared socioeconomic pathways (SSPs). Henceforth, the increasing trend in temperature and melting of snow/glaciers in TP would be a serious threat to the regional climate, water security and livelihood of the people of South Asia.

Implementing robust outlier detection to enhance estimation accuracy of GNSS-IR based seasonal snow depth retrievals

ABSTRACT Monitoring snow depth variations aligned with the seasonal cycle is crucial for studies on climate change and its impacts due to global warming. Especially in terms of snow hydrology, the determination and continuous monitoring of snow depth to determine snow water equivalent is a priority in climate, water science, drought, flood, and inundation prediction studies, particularly in the water cycle. Recently, the so-called Global Navigation Satellite Systems – Interferometric Reflectometry (GNSS-IR) method enables to extract environmental radiometric and geometric characteristics of surface where the signals transmitted from the satellites reflect. The reflected signal follows an extra path than the direct one and causes a major error source for accurate point positioning but a useful tool for sensing the environment. However, outliers remain a challenge in long-term GNSS-IR estimations. In this study, we proposed a median-based robust outlier detection (ROD) approach for identifying outliers in long-term GNSS-IR snow depth estimations. To validate our approach, we analysed 5-year GNSS L1 SNR and L2 SNR data from 1 January 2015 to 31 December 2019 provided by AB33 and AB39 GNSS stations in Alaska, U.S.A. using the GNSS-IR method. We validated GNSS-IR estimations using snow depth measurements from the Coldfoot and Fort Yukon stations in the SNOTEL network. Applying ROD to long-term snow depth estimates increased the highest correlation from 91.58% to 94.93%, and reduced the lowest RMSE from 8.6 cm to 6.7 cm. In addition, the improvement rates calculated to assess the contribution of ROD to the results showed improvements of up to 6.9% in correlation and 26.1% in RMSE. Overall, the results demonstrate that ROD can be effectively used to detect outliers in long-term GNSS-IR snow depth time series.

An Ensemble Learning Model Reveals Accelerated Reductions in Snow Depth Over Arctic Sea Ice Under High‐Emission Scenarios

AbstractThere are significant differences in snow depth predictions among different earth system models, and many models underestimate snow depth, restricting their application. Here, major factors influencing snow depth changes in the Coupled Model Intercomparison Project Phase 6 (CMIP6) were identified and evaluated. Based on satellite‐derived snow depth and CMIP6 data, an ensemble learning model based on multiple deep learning methods (hereafter referred to as the Multi‐DL model) was developed to predict future snow depth. According to satellite observations and two Operation IceBridge products, the Multi‐DL model yielded root mean square errors of 7.48, 6.20, and 6.17 cm. A continuous decrease in snow depth was observed from 2002 to 2100, and the rate of decrease accelerated with increasing emissions. Under the highest emission scenario, the first snow‐free year occurred in 2047, within the same decade as the first ice‐free year (2056). The predicted warm season snow depth was sensitive to sea ice velocity, sea ice concentration (siconc), precipitation, sea surface temperature (tos) and albedo, while the predicted cold season snow depth was sensitive to tos, air temperature, and siconc. The above parameters introduce some snow depth uncertainty. This method provides new ideas for predicting snow depth, and the generated snow depth records can provide data support for formulating Arctic‐related policies.

Impact of climate change on snowpack dynamics in coastal Central-Western Greenland

Snow patterns in ice-free areas of Greenland play important roles in ecosystems. Within a changing climate, a comprehensive understanding of the snow responses to climate change is of interest to anticipate forthcoming dynamics in these areas. In this study, we analyze the future snowpack evolution of a polar maritime Arctic location, Qeqertarsuaq (Disko Island, Central-Western Greenland). A physically-based snow model (FSM2) is validated and forced with CMIP6 projections for SSP2–4.5 and SSP5–8.5 greenhouse gasses emission scenarios, using two models: CanESM5 and MIROC6. The future snowpack evolution is assessed through four key seasonal (October to May) snow climate indicators: snow depth, snow days, snowfall fraction and ablation rate. Comparison against the observed air temperature for the reference climate period demonstrates superior accuracies for MIROC6 SSP2.4–5, with anomalies at 19 %, compared to CanESM5 SSP5.8–5 (25 %) and CanESM5 SSP2.4–5 (78 %). In terms of precipitation, CanESM5 SSP2.4–5 and SSP2.4–5 exhibit smaller anomalies against the observed data (5 %) in contrast to MIROC6 SSP2.4–5 (15 %) and MIROC6 SSP2.8–5 (17 %). Results demonstrate distinct snowpack responses to climate change depending on the model and emission scenario. For CanESM5, seasonal snow depth anomalies with respect to the reference period range from – 38 % (SSP2–4.5, 2040–2050 period) to – 74 % (SSP5–8.5, 2090–2100 period). MIROC6 projects lower snowpack reductions, with a decrease ranging from – 38 % (SSP2–4.5, 2040–2050 period) to – 57 % (SSP5–8.5, 2090–2100 period). Similar reductions are anticipated for snowfall and snow days. Changes in the snowpack evolution are primarily driven by positive trends in downwelling longwave radiation and air temperature. The projected increase in precipitation by the mid to late 21st century will lead to more frequent rain-on-snow events, intensifying snowpack melting. These findings help enhance the comprehension of future snow dynamics in the ice-free zones of Greenland, as well as the associated hydrological and ecological changes.

Six Consecutive Seasons of High‐Resolution Mountain Snow Depth Maps From Satellite Stereo Imagery

AbstractFine‐scale seasonal snow depth observations can improve estimates of snow water equivalent at critical times of year. Airborne lidar is the current gold standard for snow depth measurement, but it involves high costs and relatively limited coverage. Using very‐high‐resolution satellite stereo images from WorldView‐2, WorldView‐3, and Pléiades‐HR 1A/1B, we produced a six‐year time series (2017–2022) of spatially continuous digital elevation models for an 874 km2 study area over Grand Mesa, Colorado. We generated high‐resolution stereo snow depth maps that capture intra‐ and interannual variability and span multiple anomalous years (58%–158% of median peak SNOTEL snow depth). Comparisons with near‐contemporaneous airborne lidar snow depth measurements showed good agreement, with median offset of −0.13 m, precision of 0.19 m and accuracy of 0.31 m. Our results suggest that satellite stereo can provide snow depth observations with the spatiotemporal coverage needed to improve operational forecast models and inform adaptive management strategies.

Measuring the spatiotemporal variability in snow depth in subarctic environments using UASs – Part 2: Snow processes and snow–canopy interactions

Abstract. Detailed information on seasonal snow cover and depth is essential to the understanding of snow processes, to operational forecasting, and as input for hydrological models. Recent advances in uncrewed or unmanned aircraft systems (UASs) and structure from motion (SfM) techniques have enabled low-cost monitoring of spatial snow depth distribution in resolutions of up to a few centimeters. Here, we study the spatiotemporal variability in snow depth and interactions between snow and vegetation in different subarctic landscapes consisting of a mosaic of conifer forest, mixed forest, transitional woodland/shrub, and peatland areas. To determine the spatiotemporal variability in snow depth, we used high-resolution (50 cm) snow depth maps generated from repeated UAS–SfM surveys in the winter of 2018/2019 and a snow-free bare-ground survey after snowmelt. Due to poor subcanopy penetration with the UAS–SfM method, tree masks were utilized to remove canopy areas and the area (36 cm) immediately next to the canopy before analysis. Snow depth maps were compared to the in situ snow course and a single-point continuous ultrasonic snow depth measurement. Based on the results, the difference between the UAS–SfM survey median snow depth and single-point measurement increased for all land cover types during the snow season, from +5 cm at the beginning of the accumulation to −16 cm in coniferous forests and −32 cm in peatland during the melt period. This highlights the poor representation of point measurements in selected locations even on the subcatchment scale. The high-resolution snow depth maps agreed well with the snow course measurement, but the spatial extent and resolution of maps were substantially higher. The snow depth range (5th–95th percentiles) within different land cover types increased from 17 to 42 cm in peatlands and from 33 to 49 cm in the coniferous forest from the beginning of the snow accumulation to the melt period. Both the median snow depth and its range were found to increase with canopy density; this increase was greatest in the conifer forest area, followed by mixed forest, transitional woodland/shrub, and open peatlands. Using the high-spatial-resolution data, we found a systematic increase (2–20 cm) and then a decline in snow depth near the canopy with increasing distance (from 1 to 2.5 m) of the peak value through the snow season. This study highlights the applicability of the UAS–SfM in high-resolution monitoring of snow depth in multiple land cover types and snow–vegetation interactions in subarctic and remote areas where field data are not available or where the available data are collected using classic point measurements or snow courses.

The change and variability of snow cover in Kraków in a 100-year observation series

This article presents the results of research on the changes and variability of snow cover in Kraków in the 100-year period 1921/22–2020/21 and in its two sub-periods covering the years of the slow and rapid territorial, urban and industrial development of Kraków (respectively, 1921/22–1960/61 and 1961/62–2020/21). The long-term variability of the number of days with snow cover, the maximum depth of the snow layer, the dates of the beginning and end of snow cover duration in the winter season, the potential snow cover duration and the index of snow cover stability were analysed. The directions of changes in the snow cover in the last 100 winter seasons in Kraków correspond to the global changes in air temperature presented in the latest IPCC reports: until the end of the 1950s there were no significant trends, or only small trends were observed, whereas from the beginning of the 1960s faster changes in the snow cover duration and maximum seasonal snow depth have been visible. In the last 60 years (1961/62–2020/21), the impact of global changes in Kraków has been joined by the impact of territorial, demographic and industrial development of the city, causing significant negative trends in snow cover with relative values of less than −9% ∙ 10 years−1, both in the case of snow cover duration and its maximum depth in the winter season; these changes are statistically significant. Throughout the whole 100-year period (1921/22–2020/21) and in its second part (1961/62–2020/21), a decrease in snow cover stability has also been observed.

Improving snow depth simulations on Arctic Sea ice by assimilating a passive microwave-derived record

The snow depth can modulate sea ice changes and is a necessary input parameter to obtain altimeter-derived sea ice thickness values. First, we determine the optimal model parameters for simulating snow depth based on a two-layer snow depth model. Then, we use the particle filter (PF) approach to assimilate satellite-derived snow depth observations to improve the snow depth simulations. Next, we generate daily snow depth estimates from 2012 to 2020 at a 50-km resolution. With the use of three airborne Operation IceBridge (OIB) datasets (i.e., the NSIDC OIB quick look product, NSIDC OIB L4 product and NOAA OIB product), this work reveals that compared to the original simulated snow depths, the snow depth estimates are improved, with root mean square error (RMSE) decreases of 0.8 cm (12%), 1.3 cm (22%) and 1.1 cm (15%), respectively, corresponding to the three OIB products. With the use of Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC) snow buoy and Snow and Ice Mass Balance Array (SIMBA) buoy data, it can be verified that the application of PF improves snow depth simulations in the Central Arctic. The variations in the monthly and seasonal snow depth estimates retrieved from the proposed method agree well with those in the estimates retrieved from two other existing algorithms. Based on the presented snow depth estimates, we can perform long-term snow depth and sea ice analysis. Snow depth estimates improve the understanding of Arctic environmental change and promote the future development of sea ice models.

Temporal stability of long-term satellite and reanalysis products to monitor snow cover trends

Abstract. Monitoring snow cover to infer climate change impacts is now feasible using Earth observation data together with reanalysis products derived from Earth system models and data assimilation. Temporal stability becomes essential when these products are used to monitor snow cover changes over time. While the temporal stability of satellite products can be altered when multiple sensors are combined and due to the degradation and orbital drifts in each sensor, the stability of reanalysis datasets can be compromised when new observations are assimilated into the model. This study evaluates the stability of some of the longest satellite-based and reanalysis products (ERA5, 1950–2020, ERA5-Land, 1950–2020, and the National Oceanic and Atmospheric Administration Climate Data Record (NOAA CDR), 1966–2020) by using 527 ground stations as reference data (1950–2020). Stability is assessed with the time series of the annual bias in snow depth and snow cover duration of the products at the different stations. Reanalysis datasets face a trade-off between accuracy and stability when assimilating new data to improve their estimations. The assimilation of new observations in ERA5 improved its accuracy significantly during the recent years (2005–2020) but introduced three negative step discontinuities in 1977–1980, 1991–1992, and 2003–2004. By contrast, ERA5-Land is more stable because it does not assimilate snow observations directly, but this leads to worse accuracy despite having a finer spatial resolution. The NOAA CDR showed a positive artificial trend from around 1992 to 2015 during fall and winter that could be related to changes to the availability of satellite data. The magnitude of most of these artificial trends and/or discontinuities is larger than actual snow cover trends and the stability requirements of the Global Climate Observing System (GCOS). The use of these products in seasons and regions where artificial trends and discontinuities appear should be avoided. The study also updates snow trends (1955–2015) over local sites in the Northern Hemisphere (NH), corroborating the retreat of snow cover, driven mainly by an earlier melt and recently by a later snow onset. In warmer regions such as Europe, snow cover decrease is coincident with a decreasing snow depth due to less snowfall, while in drier regions such as Russia, earlier snowmelt occurs despite increased maximum seasonal snow depth.

The benefits of homogenising snow depth series – Impacts on decadal trends and extremes for Switzerland

Abstract. Our current knowledge of spatial and temporal snow depth trends is based almost exclusively on time series of non-homogenised observational data. However, like other long-term series from observations, they are prone to inhomogeneities that can influence and even change trends if not taken into account. In order to assess the relevance of homogenisation for time-series analysis of daily snow depths, we investigated the effects of adjusting inhomogeneities in the extensive network of Swiss snow depth observations for trends and changes in extreme values of commonly used snow indices, such as snow days, seasonal averages or maximum snow depths in the period 1961–2021. Three homogenisation methods were compared for this task: Climatol and HOMER, which apply median-based adjustments, and the quantile-based interpQM. All three were run using the same input data with identical break points. We found that they agree well on trends of seasonal average snow depth, while differences are detectable for seasonal maxima and the corresponding extreme values. Differences between homogenised and non-homogenised series result mainly from the approach for generating reference series. The comparison of homogenised and original values for the 50-year return level of seasonal maximum snow depth showed that the quantile-based method had the smallest number of stations outside the 95 % confidence interval. Using a multiple-criteria approach, e.g. thresholds for series correlation (>0.7) as well as for vertical (<300 m) and horizontal (<100 km) distances, proved to be better suited than using correlation or distances alone. Overall, the homogenisation of snow depth series changed all positive trends for derived series of snow days to either no trend or negative trends and amplifying the negative mean trend, especially for stations >1500 m. The number of stations with a significant negative trend increased between 7 % and 21 % depending on the method, with the strongest changes occurring at high snow depths. The reduction in the 95 % confidence intervals of the absolute maximum snow depth of each station indicates a decrease in variation and an increase in confidence in the results.

Snow depth alteration and its relation with climate variability in China

AbstractVarious climatic factors and their interactions affect China's snow depth alterations. However, a measure of the relative importance of climatic factors regarding the variance in snow depth is lacking. Here, we use a heretofore largest dataset, quality‐controlled daily observations from 1,665 meteorological stations, and statistical analyses to identify proxy drivers and their contributions regarding regional variability and trends in China's snow depth and snow depth‐climate relationships from 1951 to 2019. Results show that seasonal snow depth alterations are substantial in China and caused by various climatic drivers. In winter, observed positive trends in the northwest and northeast of China mainly respond to augmentation in solid precipitation and snow accumulation in fall. For the Tibetan Plateau, the negative snow depth anomaly seems to respond to the rising air temperature below the freezing point along with some secular changes in solid precipitation and shallower fall snowpacks. All these trends in winter snow depth over the northwest, northeast, and Tibetan Plateau of China account approximately for 30% of the total interannual variance in spring snow depth in these regions. In the milder central north and south of China, snow accumulation is not evident. Besides solid precipitation and rising air temperature below the freezing point, atmospheric humidity is also notable for its effect on the interannual variance in winter snow depth in these regions. In spring and fall, however, the negative effect of warming climate on snow depth becomes remarkably more important. It seems that about 13–33% (12–50%) of the total interannual variance in the widespread decrease (insignificant perturbation), except for some weak increases in the northeast region (decreases in the Tibetan Plateau), of China's spring (fall) snow depth are caused by the rising air temperature below the freezing point.

A quantile‐based approach to improve homogenization of snow depth time series

AbstractAustrian observations of snow depth date back to 1895 and are thus among the longest available quantitative snow information from hydrometeorological networks worldwide. It is well known that such long‐term observations are prone to inhomogeneities, which may not only affect climatologies and trends, but derived products used in research or practice. While the reliability of available methods for detecting breaks in snow time series has been shown before and could also be confirmed by our work, we focused on improving the adjustment method. Conventional methods often refer to the median of difference or quotient series (INTERP), whereas our proposed method also uses a quantile‐wise adjustment (InterpQM), which is useful to minimize a bias on the tails of the frequency distribution. We demonstrated the success of the new method by using Swiss parallel snow depth observations. Errors of the analysed indicators could be reduced in 68% of the cases when compared with INTERP. The results were best for large snow depths, being up to 19% better. Overall, InterpQM was better in 75% of validation cases for the daily large, 72% of all observations and 56% of mean seasonal snow depth cases. We describe the performed homogenization procedure in detail, including quality control, gap filling, homogeneity testing, break detection, calculation of and improvements to the adjustment method. Our results show that snow depth time series generally have a lower number of breaks compared with station data of other climate variables. This underlines their high quality, even if measuring snow presents challenges. Using Austrian snow depth series as an example, the effects of the new adjustment method on trends were analysed using the Mann–Kendall and Sen's Slope. Homogenization may have a significant effect on derived trends: Two of the six adjusted series were changed from nonsignificant to significant and one vice versa.

Snow Depth Retrieval With an Autonomous UAV-Mounted Software-Defined Radar

We present results from a field campaign to measure seasonal snow depth at Cameron Pass, Colorado, using a synthetic ultrawideband software-defined radar (SDRadar) implemented in commercially available Universal Software Radio Peripheral (USRP) software-defined radio hardware and flown on a small hexacopter unmanned aerial vehicle (UAV). We coherently synthesize an ultrawideband signal from stepped frequency 50-MHz subpulses across 600–2100-MHz frequency bands using a novel nonuniform nonlinear synthetic wideband waveform reconstruction technique that minimizes sweep time and completely eliminates problematic grating lobes and other processing artifacts traditionally seen in stepped waveform synthesis. We image seasonal snow across two transects: a 400-m open Meadow Transect and a 380-m forested transect. We present a surface detection algorithm that fuses data from LiDAR, global navigation satellite system (GNSS)/global positioning system (GPS), and features in the radargram itself to obtain high precision estimates of both snow and ground surface reflections, and thus total snow depth, represented as two-way travel time. The measurements are validated against independent ground-based ground-penetrating radar measurements with correlations coefficients as high as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\rho = 0.9$ </tex-math></inline-formula> demonstrated. Finally, we compare backscattered radar data collected by the UAV-SDRadar while hovering proximal to a known snow pit with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> measured snow dielectric profiles and demonstrate imaging of snow stratigraphy.

О РЕЖИМАХ СНЕЖНОСТИ ЦЕНТРАЛЬНОЙ АЗИИ В 2001–2021 гг.

The aim of the work is to study the patterns in long-term snow regime of highland pastures of the Inner Tien Shan. Methods: The daily Snow Depth FEWS NET scenes for Central Asia were used for monitoring of snow cover of four test sites. Three test sites were located on the plains of South Kazakhstan near Tien Shan mountains. The primary test site was a highland pasture in Inner Tian Shan mountains. This is the Aksai-Chatyrkul basin, with an area of about 2800 km2, located in the altitude range of 3200-3500 m above sea level, in Kyrgyzstan on the border with China.Time series of the average seasonal snow depth for each test site were formed, which meant the average snow depth from January 1 to March 31. Monitoring for 2001-2021 years was carried out. A first-order autocorrelation was calculated for each time series. Results: The analysis indicated that the snow cover depth of neighboring years are rather poorly related to each other for three test sites located on the plains of Southern Kazakhstan. In contrast to the S outh Kazakhstan plains, the highland Inner Tien Shan pasture was characterized by pronounced persistence ("long memory effect"). Conclusion: The highland Inner Tien Shan pasture are characterized by the presence of pronounced persistence in the long-term dynamics of the average snow depth in January-March. That is, it is more likely that after a many-snow year there will be a many-snow one, and after a low-snow year, respectively, a low-snow one. The probable cause of this phenomenon is the persistence in the long-term dynamics of the seasonal characteristics of the Siberian anticyclone, which affects the ability in the cold period to atmospheric transport of water vapors through the mountain ranges of the Northern and Western Tien Shan to the Inner Tien Shan. This pattern in the long-term dynamics of highland pasture snow depth in Inner Tien Shan can be useful for long-term planning of calendar dates of spring migration of livestock from winter plain pastures to highland summer pastures.

Rain on snow induced urban floods in cold maritime climate: Risk, indicators and trends

AbstractUrban floods pose a societal and economical risk. This study evaluated the risk and hydro‐meteorological conditions that cause pluvial flooding in coastal cities in a cold climate. Twenty years of insurance claims data and up to 97 years of meteorological data were analysed for Reykjavík, Iceland (64.15°N; &lt;100 m above sea level). One third of the city's wastewater collection system is combined, and pipe grades vary from 0.5% to 10%. Results highlight semi‐intensive rain (&lt;7 mm/h; ≤3 year return period) in conjunction with snow and frozen ground as the main cause for urban flood risk in a climate which undergoes frequent snow and frost cycles (avg. 13 and 19 per season, respectively). Floods in winter were more common, more severe and affected a greater number of neighbourhoods than during summer. High runoff volumes together with debris remobilized with high winds challenged the capacity of wastewater systems regardless of their age or type (combined vs. separate). The two key determinants for the number of insurance claims were antecedent frost depth and total precipitation volume per event. Two pluvial regimes were particularly problematic: long duration (13–25 h), late peaking rain on snow (RoS), where snowmelt enhanced the runoff intensity, elongated and connected independent rainfall into a singular, more voluminous (20–76 mm) event; shorter duration (7–9 h), more intensive precipitation that evolved from snow to rain. Closely timed RoS and cooling were believed to trigger frost formation. A positive trend was detected in the average seasonal snow depth and volume of rain and snowmelt during RoS events. More emphasis, therefore, needs to be placed on designing and operating urban drainage infrastructure with regard to RoS co‐acting with frozen ground. Furthermore, more detailed, routine monitoring of snow and soil conditions is important to predict RoS flood events.

Observed snow depth trends in the European Alps: 1971 to 2019

Abstract. The European Alps stretch over a range of climate zones which affect the spatial distribution of snow. Previous analyses of station observations of snow were confined to regional analyses. Here, we present an Alpine-wide analysis of snow depth from six Alpine countries – Austria, France, Germany, Italy, Slovenia, and Switzerland – including altogether more than 2000 stations of which more than 800 were used for the trend assessment. Using a principal component analysis and k-means clustering, we identified five main modes of variability and five regions which match the climatic forcing zones: north and high Alpine, north-east, north-west, south-east, and south and high Alpine. Linear trends of monthly mean snow depth between 1971 and 2019 showed decreases in snow depth for most stations from November to May. The average trend among all stations for seasonal (November to May) mean snow depth was −8.4 % per decade, for seasonal maximum snow depth −5.6 % per decade, and for seasonal snow cover duration −5.6 % per decade. Stronger and more significant trends were observed for periods and elevations where the transition from snow to snow-free occurs, which is consistent with an enhanced albedo feedback. Additionally, regional trends differed substantially at the same elevation, which challenges the notion of generalizing results from one region to another or to the whole Alps. This study presents an analysis of station snow depth series with the most comprehensive spatial coverage in the European Alps to date.

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.

Environment and physiology shape Arctic ungulate population dynamics.

Species conservation in a rapidly changing world requires an improved understanding of how individuals and populations respond to changes in their environment across temporal scales. Increased warming in the Arctic puts this region at particular risk for rapid environmental change, with potentially devastating impacts on resident populations. Here, we make use of a parameterized full life cycle, individual-based energy budget model for wild muskoxen, coupling year-round environmental data with detailed ontogenic metabolic physiology. We show how winter food accessibility, summer food availability, and density dependence drive seasonal dynamics of energy storage and thus life history and population dynamics. Winter forage accessibility defined by snow depth, more than summer forage availability, was the primary determinant of muskox population dynamics through impacts on calf recruitment and longer term carryover effects of maternal investment. Simulations of various seasonal snow depth and plant biomass and quality profiles revealed that timing of and improved/limited winter forage accessibility had marked influence on calf recruitment (±10-80%). Impacts on recruitment were the cumulative result of condition-driven reproductive performance at multiple time points across the reproductive period (ovulation to calf weaning) as a trade-off between survival and reproduction. Seasonal and generational condition effects of snow-rich winters interacted with age structure and density to cause pronounced long-term consequences on population growth and structure, with predicted population recovery times from even moderate disturbances of 10years or more. Our results show how alteration in winter forage accessibility, mediated by snow depth, impacts the dynamics of northern herbivore populations. Further, we present here a mechanistic and state-based model framework to assess future scenarios of environmental change, such as increased or decreased snowfall or plant biomass and quality to impact winter and summer forage availability across the Arctic.

Assessment of Snow Depth over Arctic Sea Ice in CMIP6 Models Using Satellite Data

Snow depth over sea ice is an essential variable for understanding the Arctic energy budget. In this study, we evaluate snow depth over Arctic sea ice during 1993–2014 simulated by 31 models from phase 6 of the Coupled Model Intercomparison Project (CMIP6) against recent satellite retrievals. The CMIP6 models capture some aspects of the observed snow depth climatology and variability. The observed variability lies in the middle of the models’ simulations. All the models show negative trends of snow depth during 1993–2014. However, substantial spatiotemporal discrepancies are identified. Compared to the observation, most models have late seasonal maximum snow depth (by two months), remarkably thinner snow for the seasonal minimum, an incorrect transition from the growth to decay period, and a greatly underestimated interannual variability and thinning trend of snow depth over areas with frequent occurrence of multi-year sea ice. Most models are unable to reproduce the observed snow depth gradient from the Canadian Arctic to the outer areas and the largest thinning rate in the central Arctic. Future projections suggest that snow depth in the Arctic will continue to decrease from 2015 to 2099. Under the SSP5-8.5 scenario, the Arctic will be almost snow-free during the summer and fall and the accumulation of snow starts from January. Further investigation into the possible causes of the issues for the simulated snow depth by some models based on the same family of models suggests that resolution, the inclusion of a high-top atmospheric model, and biogeochemistry processes are important factors for snow depth simulation.