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This article provides a detailed analysis of the Crocus-ERA5 snow product covering the Northern Hemisphere from 1950 to 2022. It assesses the product's performance in terms of snow depth and extent compared to in situ observations and satellite data. Compared to its predecessor, Crocus-ERA-Interim, Crocus-ERA5 benefits from improved spatial resolution and better atmospheric data assimilation, resulting in more accurate snowpack estimates, especially in spring in Eurasia. The findings show a good match with observations, though biases remain, particularly in boreal forest areas and some Arctic regions, where the model tends to overestimate spring melt. The production of this snow dataset is motivated by its use by the continental cryosphere community, and in particular by the collaboration between the French National Center for Meteorological Research (CNRM) and Environment and Climate Change Canada (ECCC), which has been involved in Arctic snow cover monitoring as part of the 'Terrestrial Snow' section of the Arctic Report Card since 2017. The Crocus-ERA5 product is freely available on a daily basis and at 0.25° resolution over the 1950-07-01 to 2023-06-30 period (Decharme et al., 2024, https://doi.org/10.5281/zenodo.14513248). [ABSTRACT FROM AUTHOR]

In situ observations of snow water equivalent (SWE) are critical for climate applications and resource management yet there is no global database of in situ SWE observations. We present a dataset the Northern Hemisphere in situ snow water equivalent dataset (NorSWE) consisting of over 11.5 million SWE observations from more than 10 thousand different locations across the Northern Hemisphere spanning the modern satellite era (1979–2021). NorSWE builds on an existing framework applied to Canadian data (CanSWE; Vionnet et al., 2021) and includes SWE observations from manual snow courses covering Canada, the United States, Finland and Russia. Snow pillows, automated passive gamma radiation sensors, and airborne passive gamma SWE estimates provide additional coverage over North America. In addition to SWE, snow depth (SD) and derived bulk snow density are included when available. A consistent quality control is applied to all records and the final dataset delivered as a single NetCDF file that is publicly available at https://doi.org/10.5281/zenodo.14503592 (Mortimer and Vionnet, 2024). [ABSTRACT FROM AUTHOR]

Sea ice thickness is an essential variable to understand and forecast the dynamic ice cover and can be estimated by satellite altimetry. Nevertheless, it is affected by uncertainties especially from snow depth, a key parameter to derive it from ice freeboard. We developed a snow depth product based on the differences between CryoSat-2 SAR Ku and IceSat-2 laser altimeters which have different snow penetration capabilities. We compared this product to other existing snow depth products. This comparison indicates correlations all above 0.37 and reaching 0.91 relative to airborne snow radar. We estimate the snow depth uncertainties of our product to be 0.06 m on average. Combining two frequencies to simultaneously measure ice and total freeboard reduces the uncertainties in ice thickness by around a factor of two compared with a single-frequency solution combined with a snow depth climatology. The lack of in situ measurements is a limiting factor to assess the advantages of coincident CryoSat-2 and ICESat-2 acquisitions. Validation data are of major importance for upcoming missions. [ABSTRACT FROM AUTHOR]

Snow cover plays a key role in determining the albedo and thus the shortwave radiation balance of a surface. The effect of snow on albedo is modulated by land use: tree canopies break the uniform snow layer and lower the albedo as compared to an open ground. This results in a higher fraction of shortwave radiation being absorbed in forests. At seasonally snow-covered high latitudes, this lowering of the albedo has been suggested to offset some or all of the climate cooling effect of the carbon stored by forests. We used long-term in situ measurements to study the albedo and shortwave radiation balance of two pairs of sites, each consisting of an open peatland and a forest. One pair is located in northern and one in southern Finland in the boreal zone. We found that both forest sites had a low, constant albedo during the snow-free period. In contrast, both peatland sites had a higher snow-free albedo, with a clear seasonal cycle. The albedo was found to depend on the diffuse fraction of the incoming radiation: during the snow-covered period, higher diffuse fraction was associated with lower albedo, while during the snow-free period it was associated with higher albedo. The thinning of the southern forest site, resulting in a significant reduction of the leaf area index, increased the albedo especially in the snow-covered period. During the snow-covered period, the peatland sites again had higher albedo than the forest sites. The transition between the high and low albedo upon snow accumulation and especially snowmelt was more abrupt at the peatland sites. In the northern pair, the forest site absorbed on average 0.47 GJ m−2 more (around 23 % more) energy from net shortwave radiation than the peatland site annually, whereas in the southern pair, the forest site absorbed on average 0.37 GJ m−2 more (around 14 % more) than the peatland site. The difference in the annual absorbed energy between the peatland site and the forest site was greater in the northern pair due to longer snow-cover duration. This was partially offset by the greater difference in snow-free albedos and higher solar radiation at the southern site pair. The annual difference in the absorbed shortwave radiation between the forest site and the peatland site varied considerably between the years (from 0.37 to 0.61 GJ m−2 for the northern pair and from 0.20 to 0.53 GJ m−2 for the southern pair). The annual variation was mainly controlled by the snow-cover duration in the spring at the peatland sites. These findings have implications for the future climate, as snow cover continues to evolve under global warming. [ABSTRACT FROM AUTHOR]

The rapid warming of the Arctic, driven by glacial and sea ice melt, poses significant challenges to Earth's climate, ecosystems, and economy. Recent evidence indicates that the snow-darkening effect (SDE), caused by black carbon (BC) deposition, plays a crucial role in accelerated warming. However, high-resolution simulations assessing the impacts from the properties of snowpack and land–atmosphere interactions on the changes in the surface energy balance of the Arctic caused by BC remain scarce. This study integrates the Snow, Ice, and Aerosol Radiative (SNICAR) model with a polar-optimized version of the Weather Research and Forecasting model (Polar-WRF) to evaluate the impacts of snow melting and land–atmosphere interaction processes on the SDE due to BC deposition. The simulation results indicate that BC deposition can directly affect the surface energy balance by decreasing snow albedo and its corresponding radiative forcing (RF). On average, BC deposition at 50 ngg-1 causes a daily average RF of 1.6 Wm-2 in offline simulations (without surface feedbacks) and 1.4 Wm-2 in online simulations (with surface feedback). The reduction in snow albedo induced by BC is strongly dependent on snow depth, with a significant linear relationship observed when snow depth is shallow. In regions with deep snowpack, such as Greenland, BC deposition leads to a 25 %–41 % greater SDE impact and a 19 %–40 % increase in snowmelt compared to in areas with shallow snow. Snowmelt and land–atmosphere interactions play significant roles in assessing changes in the surface energy balance caused by BC deposition based on a comparison of results from offline and online coupled simulations via Polar-WRF and the community Noah land surface model (LSM) with multiple parameterization options (Noah-MP) and SNICAR. Offline simulations tend to overestimate SDE impacts by more than 50 % because crucial surface feedback processes are excluded. This study underscores the importance of incorporating detailed physical processes in high-resolution models to improve our understanding of the role of the SDE in Arctic climate change. [ABSTRACT FROM AUTHOR]

The lack of accurate information on the spatiotemporal variations of snow water equivalent (SWE) in mountain catchments remains a key problem in snow hydrology and water resources management. This is partly because there is no sensor to measure SWE beyond local scale. At Mt. Zugspitze, Germany, a superconducting gravimeter senses the gravity effect of the seasonal snow, reflecting the temporal evolution of SWE in a few kilometers scale radius. We used this new observation to evaluate two configurations of the Alpine3D distributed snow model. In the default run, the model was forced with meteorological station data. In the second run, we applied precipitation correction based on an 8 m resolution snow depth image derived from satellite observations (Pléiades). The snow depth image strongly improved the simulation of the snowpack gravity effect during the melt season. This result suggests that satellite observations can enhance SWE analyses in mountains with limited infrastructure. Plain Language Summary: This study addresses the challenge of accurately computing the amount of water stored in snow (known as snow water equivalent or SWE) in mountainous areas, which is important for managing water resources. Typically, there are no tools that can measure SWE across large areas in complex high alpine surroundings, only at specific points. However, at Mt. Zugspitze at the border of Germany and Austria, a special device called a superconducting gravimeter can detect changes in gravity caused by the snow, providing a way to estimate SWE over large areas. We used data from this gravimeter to test two versions of a snow model called Alpine3D. In the first version, the model relied only on weather station data. In the second version, we improved the model by using satellite images to adjust the amount and spatial distribution of precipitation (snowfall) in the model. The results showed that the model gets more accurate by using satellite data to predict SWE changes during the melting season. This finding suggests that satellite images could be a useful tool for analyzing SWE in mountainous regions with limited infrastructure. Key Points: Evaluation of a distributed physically based snow model using superconducting gravimetry in a high alpine areaPrecipitation scaling based on a single satellite snow depth image significantly improves the simulated gravity signal of the snowpackAccurate spatial distribution of snow depth is found to be key to simulate snow mass (SWE) evolution in complex alpine terrain [ABSTRACT FROM AUTHOR]

High spatial resolution snow depth (SD) is crucial for hydrological, ecological, and disaster research. However, passive microwave SD product (10/25 km) is increasingly insufficient to meet contemporary requirements due to its coarse spatial resolution, particularly in heterogeneous alpine areas. In this study, we develop a superior SD downscaling algorithm based on the FT-Transformer (Feature Tokenizer + Transformer) model, termed FTSD. This algorithm fuses the latest calibrated enhanced resolution brightness temperature (CETB) (3.125/6.25 km) with daily cloud-free optical snow data (500 m), including snow cover fraction (SCF) and snow cover days (SCD). Developed and evaluated using 42,692 ground measurements across China from 2000 to 2020, FTSD demonstrated notable improvements in accuracy and spatial resolution of SD retrieval. Specifically, the RMSE of temporal and spatiotemporal independent validation for FTSD is 7.64 cm and 9.74 cm, respectively, indicating reliable generalizability and stability. Compared with the long-term series of SD in China (25 km, RMSE = 10.77 cm), FTSD (500 m, RMSE = 7.67 cm) provides superior accuracy, especially improved by 48% for deep snow (> 40 cm). Moreover, with the higher spatial resolution, FTSD effectively captures the SD's spatial heterogeneity in the mountainous regions of China. When compared with downscaling algorithms utilizing the raw TB data and the traditional random forest model, the CETB data and FT-Transformer model optimize the RMSE by 10.08% and 4.84%, respectively, which demonstrates the superiority of FTSD regarding data sources and regression methods. Collectively, these results demonstrate that the innovative FTSD algorithm exhibits reliable performance for SD downscaling and has the potential to provide a robust data foundation for meteorological and environmental research. [ABSTRACT FROM AUTHOR]

In this study, the characteristics of tropical cyclones (TCs) over the Bay of Bengal (BoB) that affect snowfall on the Tibetan Plateau (TP) and spatiotemporal distribution of snowfall related to BoB TCs are statistically analysed by using multi‐sources data from 1981 to 2020, with partitioning TC‐influenced snowfall by tracking cloud clusters. The results show that 141 TCs formed during the 40‐year period of 1981–2020, of which about 35% (50 TCs) impacted snowfall at 83% of meteorological stations on the TP during their northward or westward movement, and the average distance between the TC centre and the snowfall stations is 1277 km. The proportion of snowfall‐related TC frequency shows a significantly decreasing trend with a predominant cycle of 10a. The TC‐influenced snowfall frequency (SF), precipitation amount (PA) on a snowfall day and snow depth (SD) during 1981–2020 all show a non‐significant weak decreasing trend, while TC‐influenced snowfall is significantly increased in the eastern and southern edges of Xizang, western Sichuan and the southern margin of Qinghai. PA and SD in December account for more than 75% and 55% of the monthly total, respectively. The spatial pattern of PA could be objectively categorised into west‐type (24%) and southeast‐type (76%). The moisture transported by the BoB TC and a southerly jet stream formed between the trough and the western Pacific subtropical high (WPSH), the convergence of cold air and warm–moist airstream over the TP and the change in position of the south Asian high in the upper troposphere are significant factors causing the different spatial distribution. The results can provide reference for TC‐related snowfall, SD prediction and disaster assessment on the TP. [ABSTRACT FROM AUTHOR]

The meridional displacements of the East Asian westerly jet (EAJ) exhibit remarkable interannual variability characteristics, significantly modulating the weather and climate over East Asia. Our study aims to refine the thermal‐driven (angular momentum transport) and dynamically driven (atmospheric eddy activities) processes that dominate the interannual meridional motion of the wintertime EAJ. We employ the recently proposed three‐pattern decomposition of global atmospheric circulation (3P‐DGAC) theory to decompose the zonal momentum equation for the purpose of diagnosing the zonal momentum budget of the EAJ from three‐dimensional (3D) horizontal, meridional and zonal circulation perspectives. Results indicate that, in addition to the widely recognised driving mechanisms of angular momentum transported by thermal direct meridional Hadley circulation and horizontal eddy momentum flux convergence, the previously neglected role of local zonal circulation in transporting angular momentum significantly impacts the meridional shift of EAJ. The uneven accumulation of angular momentum transported by asymmetric zonal circulations emerges on the northern and southern sides of EAJ axis, causing an abnormal meridional displacement of EAJ. Notably, the local zonal circulation rising from the Tibetan Plateau and descending across the North Pacific Ocean transported angular momentum to the core and downstream regions of EAJ, thereby significantly accelerating zonal winds and pushing the EAJ northward. Further investigation into the potential mechanisms suggests that anomalous snow depth on the southern Tibetan Plateau is the main factor causing abnormal local zonal circulation. Our study addresses the deficiency in traditional two‐dimensional (2D) circulation decomposition methods that neglect the significant role of zonal circulation in the thermal‐driven process of EAJ, complements the understanding of 3D angular momentum transport mechanisms and reveals potential nonlinear synergies of 3D circulations related to the interannual meridional displacement of EAJ. [ABSTRACT FROM AUTHOR]

Climate change is resulting in significant transformations in mountain areas all over the world, causing the melting of glacier ice, reduction in snow accumulation, and permafrost loss. Changes in the mountain cryosphere are not only modifying flora and fauna distributions but also affecting the stability of slopes in those regions. For all these reasons, and because of the risks these phenomena pose to the population, the dentification of dangerous areas is a crucial step in the development of risk reduction strategies. While several methods and examples exist that cover the assessment and computation of single sub-components, there is still a lack of application of risk assessment due to glacier melting over large areas in which the final result can be directly employed in the design of risk mitigation policies at regional and municipal levels. This research is focused on landslides and gravitational movements on slopes resulting from rapid glacier melting phenomena in the Valle d'Aosta region in Italy, with the aim of providing a tool that can support spatial planning in response to climate change in Alpine environments. Through the conceptualization and development of a GIS-based and multi-criteria approach, risk is then estimated by defining hazard indices that consider different aspects, combining the experience acquired from studies carried out in various disciplinary fields, to obtain a framework at the regional level. This first assessment is then deepened for the Lys River Valley, where the mapping of hazardous areas was implemented, obtaining a classification of buildings according to their hazard score to estimate the potential damage and total risk relating to possible slope instability events due to ice melt at the local scale. [ABSTRACT FROM AUTHOR]

ABSTRACT Summer snow plays an essential role in Arctic hydrology and in maintaining mass and energy balance of sea ice. However, there are great challenges in retrieving long‐term summer snow depths over Arctic sea ice. Here, we proposed a combined novel five‐variable long short‐term memory (hereafter CN5VLSTM) model based on brightness temperature data to yield warm‐season snow depth estimates. Then, year‐round snow depth estimates were obtained for the first time. The CN5VLSTM model and five additional snow depth methods were assessed during the warm season based on the ice mass balance buoy (IMB), Alfred Wegener Institute (AWI) snow buoy (AWI‐SB) and Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC) snow buoy (MOSAiC‐SB). According to the three buoy products, the accuracy of the CN5VLSTM‐derived snow depth was highest among the five snow depth estimates with RMSEs of 10.2, 16.4, and 10.1 cm, respectively. Except for in May, the Arctic snow depth showed mainly a downward trend in warm months, and a significant downward trend was found in the Central Arctic. Excluding the Barents Sea, Kara Sea and Canadian Archipelago, the average year‐round snow depth decreased in the other subregions, and a significant negative trend was observed in the East Siberian and Chukchi Seas. Snowfall was an important factor that was related to the changes in snow depth in the East Siberian and Chukchi Seas. This study can provide new insights into the evolution characteristics of summer snow depth. [ABSTRACT FROM AUTHOR]

Snow depth plays an important role in the seasonal climatic and hydrological cycles of alpine regions. Previous studies have shown predominantly decreasing trends in average seasonal snow depth across the European Alps. Additionally, prior work has shown bivariate statistical relationships between average seasonal snow depth and mean air temperature or precipitation. Building upon existing research, our study uses observational records of in situ station data across Austria and Switzerland to better quantify the sensitivity of historical changes in seasonal snow depth through a multivariate framework that depends on elevation, mean temperature, and precipitation. These historical sensitivities, which are obtained over the 1901–1902 to 1970–1971 period, are then used to estimate snow depths over the more recent period of 1971–1972 to 2020–2021. We find that the year-to-year estimates of snow depths, which are derived from an empirical–statistical model (SnowSens), that rely solely on the historical sensitivities are nearly as skillful as the operational SNOWGRID-CL model used by the weather service at GeoSphere Austria. Furthermore, observed long-term changes over the last 50 years are in better agreement with SnowSens than with SNOWGRID-CL. These results indicate that historical sensitivities between snow depth, temperature, and precipitation are quite robust over decadal-length scales of time, and they can be used effectively to translate expected long-term changes in temperature and precipitation into changes in seasonal snow depth. [ABSTRACT FROM AUTHOR]

Seasonal snow cover has important climatic and ecological implications for the ice-free regions of coastal Greenland. Here we present, for the first time, a dataset of quality-controlled snow depth measurements from nine locations in coastal Greenland with varying periods between 1997 and 2021. Using a simple modelling approach (Δ snow), we estimate snow water equivalent values solely based on the daily time series of snow depth. Snow pit measurements from two locations enable us to evaluate the Δ snow model. As there are very little in situ data available for Greenland, we then test the performance of the regional atmospheric climate model (RACMO2.3p2, 5.5 km spatial resolution) and reanalysis product (CARRA, 2.5 km spatial resolution) at the nine locations with snow observations. Using the combined information from all three data sources, we study spatio-temporal characteristics of the seasonal snow cover in coastal Greenland using the example of six ecologically relevant snow indicators (maximum snow water equivalent, melt onset, melt duration, snow cover duration, snow cover onset, and snow cover end date). In particular, we evaluate the ability of RACMO2.3p2 and CARRA to simulate these snow indicators at the nine different locations, perform a time series analysis of the indicators, and assess their spatial variability. The different locations have considerable spatial and temporal variability in snow cover characteristics, and seasonal maximum snow water equivalent (amount of liquid water stored in the snowpack) values range from less than 50 kg m−2 to greater than 600 kg m−2. The correlation coefficients between maximum snow water equivalent output from Δ snow and CARRA and RACMO are 0.73 and 0.48, respectively. Correlation coefficients are highest for maximum snow water equivalent and snow cover duration, and model and reanalysis output underestimate snow cover onset. We find little evidence of statistically significant (p<0.05) trends at varied periods between 1997 and 2021 except for the earlier onset of snowmelt in Zackenberg (-8 d per decade, p=0.02 , based upon RACMO output). While we stress the need for context-specific validation, this study suggests that in most cases snow depth or snow water equivalent output from CARRA can describe spatial and temporal characteristics of seasonal snow cover, particularly changes in melt onset and snow cover end date. [ABSTRACT FROM AUTHOR]

Distributed energy and mass balance snowpack models at sub-kilometric scale have emerged as a tool for snow-hydrological forecasting over large areas. However, their development and evaluation often rely on a handful of well-observed sites on flat terrain with limited topographic representativeness. Validation of such models over large scales in rugged terrain is therefore necessary. Remote sensing of wet snow has always been motivated by its potential utility in snow hydrology. However, its concrete potential to enhance physically based operational snowpack models in real time remains unproven. Wet-snow maps could potentially help refine the temporal accuracy of simulated snowmelt onset, while the information content of remotely sensed snow cover fraction (SCF) pertains predominantly to the ablation season. In this work, wet-snow maps derived from Sentinel-1 and SCF retrieval from Sentinel-2 are compared against model results from a fully distributed energy balance snow model (FSM2oshd). The comparative analysis spans the winter seasons from 2017 to 2021, focusing on the geographic region of Switzerland. We use the concept of wet-snow line (WSL) to compare Sentinel-1 wet-snow maps with simulations. We show that while the match of the model with flat-field snow depth observation is excellent, the WSL reveals a delayed snowmelt in the southern aspects. Amending the albedo parametrization within FSM2oshd allowed for the achievement of earlier melt in such aspects preferentially, thereby reducing WSL biases. Biases with respect to Sentinel-2 snow-line (SL) observations were also substantially reduced. These results suggest that wet-snow maps contain valuable real-time information for snowpack models, complementing flat-field snow depth observations well, particularly in complex terrain and at higher elevations. The persisting correlation between wet-snow-line and snow-line biases provides insights into refined development, tuning, and data assimilation methodologies for operational snow-hydrological modelling. [ABSTRACT FROM AUTHOR]

Sophisticated snowpack models such as Crocus and SNOWPACK struggle to properly simulate profiles of density and specific surface area (SSA) within Arctic snowpacks due to underestimation of wind-induced compaction, misrepresentation of basal vegetation influencing compaction and metamorphism, and omission of water vapour flux transport. To improve the simulation of profiles of density and SSA, parameterisations of snow physical processes that consider the effect of high wind speeds, the presence of basal vegetation, and alternate thermal conductivity formulations were implemented into an ensemble version of the Soil, Vegetation, and Snow version 2 (SVS2-Crocus) land surface model, creating Arctic SVS2-Crocus. The ensemble versions of the default and Arctic SVS2-Crocus were driven with in situ meteorological data and evaluated using measurements of snowpack properties (snow water equivalent, SWE; depth; density; and SSA) at Trail Valley Creek (TVC), Northwest Territories, Canada, over 32 years (1991–2023). Results show that both the default and Arctic SVS2-Crocus can simulate the correct magnitude of SWE (root-mean-square error, RMSE, for both ensembles – 55 kg m−2) and snow depth (default RMSE – 0.22 m; Arctic RMSE – 0.18 m) at TVC in comparison to measurements. Wind-induced compaction within Arctic SVS2-Crocus effectively compacts the surface layers of the snowpack, increasing the density, and reducing the RMSE by 41 % (176 kg m−3 to 103 kg m−3). Parameterisations of basal vegetation are less effective in reducing compaction of basal snow layers (default RMSE – 67 kg m−3; Arctic RMSE – 65 kg m−3), reaffirming the need to consider water vapour flux transport for simulation of low-density basal layers. The top 100 ensemble members of Arctic SVS2-Crocus produced lower continuous ranked probability scores (CRPS) than the default SVS2-Crocus when simulating snow density profiles. The top-performing members of the Arctic SVS2-Crocus ensemble featured modifications that raise wind speeds to increase compaction in snow surface layers and to prevent snowdrift and increase viscosity in basal layers. Selecting these process representations in Arctic SVS2-Crocus will improve simulation of snow density profiles, which is crucial for many applications. [ABSTRACT FROM AUTHOR]

Geodetic surface mass balance calculations rely on satellite radar altimeters such as CryoSat-2 to understand elevation and volumetric changes of the Greenland Ice Sheet (GrIS). However, the impact of varying GrIS shallow subsurface stratigraphic conditions on level 2 CryoSat-2 elevation products is poorly understood. We investigate the reliability of the Offset Center Of Gravity (OCOG) and University College London Land-Ice (ULI) elevation retracking algorithms through the analysis of (and comparison with) level 1B waveform-derived leading-edge width (LeW). We generate a 2010 to 2021 LeW time series using temporal clustering and Bayesian model averaging, and we compare them with level 2 OCOG and ULI elevation time series. We perform this workflow at Summit Station, North Greenland Eemian Ice Drilling (NEEM) Camp, and Raven Camp, chosen to represent the upper and lower bounds of the dry-snow zone and percolation zone. We note that melting event, snowpack recovery, and potentially anomalous snow accumulation and high-speed wind signatures are evident in Summit Station's LeW time series. We find that level 1B LeW has a significant inverse relationship with the ULI level 2 elevations at NEEM Camp and Summit Station and likely with the entire dry-snow zone. The ULI retracked level 2 elevations at Raven Camp (and likely the entire percolation zone) have no clear elevation bias associated with significant melt events. The OCOG retracked level 2 elevations showed no significant association with LeW at any site. Future work is needed to understand the impacts of GrIS high-speed wind events and snow accumulation on elevation products. [ABSTRACT FROM AUTHOR]

Ultra-Thin Bonded Wearing Course (UTBWC) surfacing was developed as a preventative maintenance option to extend pavement life. Despite the successful applications of UTBWC in various states, feedback indicates that severe icing and snow accumulation occur during winter storms. Similar concerns have been identified by other states. Therefore, the objective of this study was to investigate how prevalent the problem is, to assess the winter performance of UTBWC compared to adjacent dense graded hot mix asphalt (HMA) surfaces and to determine whether any issue is dependent on temperature, snowfall, mixture properties or traffic using geospatial and statistical analysis. Survey responses from districts indicated that more than half of the sections experienced moderate snow accumulation and ice formation issues. The geospatial analysis showed that such issues may not be dependent only on temperature and/or snow accumulation. Statistical analysis on mix design and quality assurance (QA) indicated that mixture air voids play an important role. While this study considered all UTBWC sections in the state, it represents an initial assessment, and thus it is recommended that additional data over time should be used for further validating the results and analysis. Finally, recommendations for improving winter maintenance operations were provided. [ABSTRACT FROM AUTHOR]

The equilibrium line altitude (ELA) is an immediate indicator of the mass balance of glaciers. The evolution of the ELA of four major glaciers in the Monte Rosa (European Alps) massif was investigated in this study. We used Sentinel-2 satellite images to derive the end-of-summer snowline altitude (SLA) as an approximation of ELA considering the fluctuations in the snowline at the end of the ablation season (from August to mid-October in 2016--2023). SLA was estimated as a percentile of the histogram of the DEM of each glacier with the applied snow-mask, based on the normalised difference snow index (NDSI). ELA was determined as the maximum snowline elevation reached in the season. We found the mean ELA for the studied period as 3560, 3230, 3430 and 3570 m above the sea level for Gornergletscher, Belvedere Glacier, Grenzgletscher, and Lys Glacier respectively. These differences are likely due to the variation in slope orientation and amount of snow accumulation. An increase in ELA was found for all the glaciers in the studied period amounting to 22.7, 8.3, 33.1 and 27.0 m/y respectively. The pattern of temporal behaviour was similar for all the glaciers, although we expected a different behaviour of the Belvedere Glacier, which is characterized by various local effects, such as frequent avalanching and rough topography. [ABSTRACT FROM AUTHOR]

High-speed trains operating in snowy conditions during winter encounter complex variations in wind speed and direction, which can significantly affect snow accumulation on bogies and impact train safety. This paper employs sliding mesh method, discrete phase model, and MATLAB to simulate snow accumulation by generating a random point lattice on the ground. Chinese hat gust model and uniformly rotating crosswind model are designed to investigate the influence of crosswind speed and direction on snow accumulation on high-speed train bogies. The results indicate that as crosswind speed increases from 5 to 15 m/s, the snow mass on the bogies changes as follows: bogie 1 experiences slight fluctuations, bogies 2–4 gradually decrease, bogie 5 increases, and bogie 6 initially increases and then decreases. In comparison with the steady wind, directionally rotating crosswinds result in a 29.8%, 12.8%, 10.5%, 7.5%, 26.6%, and 26.7% increase in the number of snow particle impacts per bogie, respectively. The mass of snow accumulation per bogie is increased by factors of 1.56, 1.17, 0.32, 1.15, 0.48, and 0.67, respectively. The Chinese hat gust model does not exhibit unique effects compared to steady wind models. Crosswinds cause uneven distribution of snow particles on bogies, with snow accumulation being more pronounced on leeward side, particularly on the frame and cavity. Uniformly rotating crosswinds change friction wind speed on the surfaces beneath the train and in the bogie region, leading to a significant increase in both the number of snow particle impacts and the mass of snow accumulation on the bogies. [ABSTRACT FROM AUTHOR]

Snow water equivalent (SWE), as one of the land initial or boundary conditions, plays a crucial role in global or regional energy and water balance, thereby exerting a considerable impact on seasonal and subseasonal-scale predictions owing to its enduring persistence over 1 to 2 months. Despite its importance, most SWE initialization remains challenging due to its reliance on simple approaches based on spatially limited observations. Therefore, this study developed an advanced SWE data assimilation framework with satellite remote sensing data utilizing the local ensemble transform Kalman filter (LETKF) and the Joint UK Land Environment Simulator (JULES) land model. This approach constitutes an objective method that optimally combines two previously unattempted incomplete data sources: the satellite SWE retrieval from the Advanced Microwave Scanning Radiometer 2 (AMSR2) and dynamically balanced SWE from the JULES land surface model. In this framework, an algorithm is additionally considered to determine the assimilation process based on the presence or absence of snow cover from the Interactive Multisensor Snow and Ice Mapping System (IMS) satellite, renowned for its superior reliability. The baseline model simulation from JULES without satellite data assimilation shows better performance in high-latitude regions with heavy snow accumulation but is relatively inferior in the transition regions with less snow and high spatial and temporal variation. Contrastingly, the AMSR2 satellite data exhibit better performance in the transition regions but poorer performance in the high latitudes, presumably due to the limitation of the satellite data in the penetrating depth. The data assimilation (DA) demonstrates the positive impacts by reducing uncertainty in the JULES model simulations in most areas, particularly in the midlatitude transition regions. In the transition regions, the model background errors from the ensemble runs are significantly larger than the observation errors, emphasizing great uncertainty in the model simulations. The results of this study highlight the beneficial impact of data assimilation by effectively combining land surface model and satellite-derived data according to their relative uncertainty, thereby controlling not only transitional regions but also the regions with heavy snow accumulation that are difficult to detect by satellite. [ABSTRACT FROM AUTHOR]

Sea ice thickness is an important component of the Arctic environment, bearing crucial significance in investigations pertaining to global climate and environmental changes. This study employs data from the HaiYang-2B satellite altimeter (HY-2B ALT) for the estimation of Arctic Sea ice thickness from November 2021 to April 2022. The HY-2B penetration coefficient is calculated for the first time to correct the freeboard in areas with sea ice concentration greater than 90%. The estimation accuracy is improved by enhancing the data on sea ice density, seawater density, snow depth, and snow density. The research analyzed the effects of snow depth and penetration coefficient on sea ice thickness results. The results of sea ice type classification were compared with OSI-SAF ice products, and the sea ice thickness estimation results were compared with four satellite ice thickness products (CryoSat-2 and SMOS (CS-SMOS), Centre for Polar Observation and Modelling Data (CPOM), CryoSat-2 (CS-2), and Pan-Arctic Ice-Ocean Modeling and Assimilation System (PIOMAS)) as well as two validation ice thickness data sets (Operation IceBridge (OIB) and ICEBird). The accuracy of sea ice classification exceeds 92%, which is in good agreement with ice type product data. The RMSD of sea ice thickness estimation is 0.56 m for CS-SMOS, 0.68 m for CPOM, 0.47 m for CS-2, 0.69 m for PIOMAS, and 0.79 m for validation data. [ABSTRACT FROM AUTHOR]

Snow depth (SD) is a crucial property of snow, its spatial and temporal variation is important for global change, snowmelt runoff simulation, disaster prediction, and freshwater storage estimation. Polarimetric Synthetic Aperture Radar (PolSAR) can precisely describe the backscattering of the target and emerge as an effective tool for SD retrieval. The backscattering component of dry snow is mainly composed of volume scattering from the snowpack and surface scattering from the snow-ground interface. However, the existing method for retrieving SD using PolSAR data has the problems of over-reliance on in-situ data and ignoring surface scattering from the snow-ground interface. We proposed a novel SD retrieval method for PolSAR data by fully considering the primary backscattering components of snow and through multi-parameter estimation to solve the snow backscattering model. Firstly, a snow backscattering model was formed by combining the small permittivity volume scattering model and the Michigan semi-empirical surface scattering model to simulate the different scattering components of snow, and the corresponding backscattering coefficients were extracted using the Yamaguchi decomposition. Then, the snow permittivity was calculated through generalized volume parameters and the extinction coefficient was further estimated through modeling. Finally, the snow backscattering model was solved by these parameters to retrieve SD. The proposed method was validated by Ku-band UAV SAR data acquired in Altay, Xinjiang, and the accuracy was evaluated by in-situ data. The correlation coefficient, root mean square error, and mean absolute error are 0.80, 4.49 cm, and 3.95 cm, respectively. Meanwhile, the uncertainties generated by different SD, model parameters estimation, solution method, and underlying surface are analyzed to enhance the generality of the proposed method. [ABSTRACT FROM AUTHOR]

In this study, the performance of two empirical and physical snow models at the Zarrineh Obatou synoptic station during the period 1989-2022 was evaluated. To calibrate these models, the Generalized Likelihood Uncertainty Estimation (GLUE) method was employed by selecting 6 parameters and generating 8000 random vectors from the uncertainty domain of these parameters. The models were then run based on these parameters over the period 1989- 2022, using RMSE, MBE, and Nash-Sutcliffe coefficient to identify the best simulations (1% of total simulations). This procedure was also used for model validation, with the models being calibrated based on odd years and validating on even years. The results indicated approperiate performance of both models in simulating snow depth at the Zarrineh station, with the physical model demonstrating better overall performance compared to the empirical model. The results also showed that the best performance of both models occurred during the simulation of moderate snow depths, with both models tending to overestimate light snow and underestimate heavy snow. Sensitivity analysis of models indicated that snow melting processes are key processes in both models. Given the limited measured data on snow in Iran and the necessity of using snow models for various purposes such as estimating past snow and projection of future snow in response to climate change, the overall results of this study suggest that the studied models in this research have a good potential for simulating various snow-related variables in Iran and employing them (especially the physical model) is strongly recommended. [ABSTRACT FROM AUTHOR]

The snow cover in the Tibetan Plateau (TP) responds keenly to global climate and hydrological shifts, with snow albedo variation serving as a pivotal indicator of these changes. In this study, we explored snow albedo changes over the period (2001–2022) in the TP combined with the high-resolution near-surface meteorological forcing datasets (2001–2022). The study utilized Ding's method to separate precipitation patterns, and then employed path analysis to evaluate the vertical response of snow albedo to air temperature, rainfall, and snowfall across four periods. The findings are as follows: (1) Snow albedo in area above 4000 m ranged from 0.4 to 0.7, while below 4000 m, snow albedo was primarily below 0.4. Snow albedo was generally higher in the northern TP. (2) During the snow accumulation period (October to December), snow albedo showed a decreasing trend in most areas of the TP. Conversely, snow albedo exhibited overall increasing trends during the snow stable period (January to February), snowmelt period (March to May), and snowless period (June to September). Especially in the central TP, snow albedo showed significant decrease during the snow accumulation period, and it increased significantly in the other periods. (3) Air temperature, rainfall, and snowfall influenced directly and predominantly snow albedo changes in the TP. Especially, air temperature and snowfall were the primary driving factors in most areas. (4) During different periods, air temperature was the main factor driving changes in snow albedo below 5000 m, but snowfall had a stronger influence above 5000 m. Except during the snow accumulation period, the impact of rainfall on snow albedo decreased with increasing altitude. During the snowless period, rainfall affected snow albedo obviously, but snowfall remained the dominant factor in areas above 6500 m. These results provide new insights on climate-driven changes in the snow albedo over the TP. [ABSTRACT FROM AUTHOR]

Snow plays a crucial role in the water balance of mountainous regions by affecting the timing and magnitude of runoff and, thus, water availability and flood hazards. However, estimating snow water equivalent (SWE) in mountainous regions is challenging due to its substantial spatial variability, the lack of accurate distributed measurements, and the uncertainties of snow models. Model uncertainties are primarily bound to uncertainties in the meteorological forcings. This study proposes an assimilation scheme to identify and correct spatiotemporal error patterns in the meteorological forcing data. Using a particle filter, we assimilated in situ snow depth observations from 444 stations across Switzerland into an ensemble simulated by the multi‐layer, physics‐based snow model FSM2OSHD. The ensemble is created by applying traceable, fixed perturbations to the energy input and the amount and phase of precipitation. This allows us to identify and correct errors in the meteorological forcing data for each station site and each 3‐day assimilation window. Leveraging spatial correlation in these errors, we distribute the corrections across the entire model domain using a weighted three‐dimensional spatial interpolation method. The refined meteorological data then serve as forcing for improved model runs, allowing unobserved grid points to benefit from the point assimilation. A leave‐one‐station‐out cross‐validation shows marked improvements in root‐mean‐squared error and bias for estimates of snow depth and SWE over the entire elevation range and multiple winter seasons. The proposed scheme is a promising step in developing comprehensive data assimilation solutions for large‐scale, fully distributed, near real‐time snow modeling applications, taking into account operational constraints and practical considerations. Key Points: Assimilation of point snow observations improves fully distributed snow depth and snow water equivalent (SWE) estimates of a physics‐based, multi‐layer snow modelThe particle filter‐based approach proves effective in identifying spatiotemporal error patterns in the meteorological forcing dataThe distribution of forcing corrections to unobserved locations shows reliable reductions of RMSE and bias in a leave‐one‐out validation [ABSTRACT FROM AUTHOR]

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Snow conditions are changing rapidly across our planet, which has important implications for wildlife managers. In Alaska, USA, the later arrival of snow is challenging wildlife managers' ability to conduct aerial fall (autumn) moose (Alces alces) surveys. Complete snow cover is required to reliably detect and count moose using visual observation from an aircraft. With inadequate snow to help generate high‐quality moose survey data, it is difficult for managers to determine if they are effectively meeting population goals and optimizing hunting opportunities. We quantified past relationships and projected future trends between snow conditions and moose survey success across 7 different moose management areas in Alaska using 32 years (1987–2019) of moose survey data and modeled snow data. We found that modeled mean snow depth was 15 cm (SD = 11) when moose surveys were initiated, and snow depths were greater in years when surveys were completed compared to years when surveys were canceled. Further, we found that mean snow depth toward the beginning of the survey season (1 November) was the best predictor of whether a survey was completed in any given year. Based on modeled conditions, the trend in mean snow depth on 1 November declined from 1980 to 2020 in 5 out of 7 survey areas. These findings, coupled with future projections, indicated that by 2055, the delayed onset of adequate snow accumulation in the fall will prevent the completion of moose surveys over roughly 60% of Alaska's managed moose areas at this time of the year. Our findings can be used by wildlife managers to guide decisions related to the future reliability of aerial fall moose surveys and help to identify timelines for development of alternate measurement and monitoring methods. [ABSTRACT FROM AUTHOR]

In this study, the unsteady Reynolds-averaged Navier–Stokes algorithm coupled with the Discrete Phase Model (DPM) was used to study the accumulation of snow in the bogie region of a high-speed train under crosswind conditions. Moreover, the impact of active blowing schemes on the airflow around the bogie and the dynamics and deposition of snow particles were also assessed. According to the results: in the crosswind environment, active blowing changes the flow field in the bogie area, reducing the flow of air coming from the windward side and bottom of the bogie. The trajectory of snow particles carried by crosswinds is modified due to the reduced airflow into the bogie region. With no active blowing, snow accumulation is mainly concentrated in the bogie cavity, frame, and primary suspension; while it is reduced by nearly an order of magnitude as soon as blowing is enabled. Blowing speeds need to be distributed appropriately in order to achieve the best possible snow protection. Continuously increasing the blowing speed on one side does not improve the amount of snow in the bogie region. The optimal condition for snow prevention of the entire train is achieved with a windward side blowing speed of 4 m/s and a leeward side blowing speed of 6 m/s, resulting in a snow reduction rate of 95.6%. Moreover, higher blowing speeds on the leeward side are beneficial for mitigating snow accumulation in the bogie region. [ABSTRACT FROM AUTHOR]

Seismic lines are 1.5 to 10 m wide linear corridors associated with petroleum exploration that fragment the landscape of northern Alberta. After clearing, seismic lines frequently exhibit low seedling recruitment and growth rates, due in part to the altered hydrologic conditions on the line. This research quantifies how seismic lines alter soil hydrophysical properties, hydrologic properties and snow accumulation patterns at two sites south of Fort McMurray, Alberta. During 2021 and 2022, water table levels were measured weekly during the summer months and were paired with measurements of soil properties (bulk density, saturated hydraulic conductivity, porosity and specific yield) on seismic lines and in the adjacent matrix. Bulk density increased on seismic lines by 19% and 113% in lowland and upland ecosystems, respectively. Saturated hydraulic conductivity decreased on seismic lines by one order of magnitude for both upland and lowland ecosystems. Water table variability was increased on seismic lines, but depth to water table was related to external factors such as topographic position. Snow measurements in March 2022 indicated that snow water equivalent was greater on seismic lines that were oriented in the East–West (EW) direction and on lines that were widest compared to SWE within the adjacent matrix. Our results provide information to researchers and industry on the alteration of important ecosystem factors on seismic lines that may limit line recovery trajectories. This information will help to guide restoration planning decisions through an enhanced knowledge of limiting factors for vegetation growth on seismic lines. [ABSTRACT FROM AUTHOR]

In this paper, we estimate the vulnerability of railway infrastructure, switches, signals, tracks, and catenaries to different weather conditions, temperature, precipitation, snow depth, and wind speed across the entire Swedish railway network between 2006-2020. Using a method to quantify the fault rate we establish thresholds that can be useful for identifying areas of concern for operations. Results reveal that high or low temperatures have a noticeable impact on the fault rates for switches, tracks, and catenaries. High levels of precipitation are associated with higher fault rates across tracks and catenaries. Snow depth has an influence on fault rates for switches and tracks, and high wind speeds are associated with higher fault rates for tracks and catenaries. Finally, signals were found to be the most resilient asset. When comparing two dominant climate zones, notable differences were only found for track asset vulnerability. [ABSTRACT FROM AUTHOR]

Liquid-water oceans likely underlie the ice shells of Europa and Enceladus, but ocean properties are challenging to measure due to the overlying ice. Here, we consider gravity-driven flow of the ice shells of icy satellites and relate this to ocean freeze and melt rates. We employ a first-principles approach applicable to conductive ice shells in a Cartesian geometry. We derive a scaling law under which ocean freeze/melt rates can be estimated from shell-thickness measurements. Under a steady-state assumption, ocean freeze/melt rates can be inferred from measurements of ice thickness, given a basal viscosity. Depending on a characteristic thickness scale and basal viscosity, characteristic freeze/melt rates range from around O(10 |$^{-1}$|⁠) to O(10 |$^{-5}$|⁠) mm/yr. Our scaling is validated with ice-penetrating radar measurements of ice thickness and modelled snow accumulation for Roosevelt Island, Antarctica. Our model, coupled with observations of shell thickness, could help estimate the magnitudes of ocean freeze/melt rates on icy satellites. [ABSTRACT FROM AUTHOR]

This study evaluates the accuracy of the U.S. interactive multisensor snow and ice mapping system (IMS) 1 km product with recall, precision, false alarm rate, and overall accuracy by utilizing ground-observed snow depth data from meteorological stations in China over six snow seasons spanning from 2015 to 2020. Furthermore, it examines the correlation between recall and snow depth, as well as the correlation between overall accuracy and snow depth. The results reveal the following: The precision typically peaks during the middle of the snow season, predominantly exceeding 80% but rarely reaching 100%, with minimal spatial variation. The precision generally ranges from 75% to 80%, exhibiting slight spatial variations during this period. The overestimation rate remains below 5%, seldom exceeding 10%. The overall accuracy mostly exceeds 95%, showing no significant spatial variations. Across both China and its five regions, the recall demonstrates a significant logarithmic correlation with snow depth, while the overall accuracy exhibits an almost horizontal linear correlation with snow depth once it exceeds 3 cm. [ABSTRACT FROM AUTHOR]

The cryosphere in mountain regions is rapidly transforming due to climate change, yet the impact of these changes on snow avalanche activity remains uncertain. Here, we use a snow cover model driven by downscaled climate projections to evaluate future alterations in dry- and wet-snow avalanche occurrences throughout the 21st century in the Swiss Alps. We assess avalanche activity by employing machine learning models trained with observed records of avalanches. Our findings indicate an overall decline in the occurrence of dry-snow avalanches during the months of December to May that is partially compensated for by an increase in wet-snow avalanche activity. Depending on elevation and the emission scenario considered, we anticipate a net reduction in the total avalanche activity ranging from under 10 % to as much as 60 % by the end of the century compared to 45–75 avalanche days per year at the beginning of the century. Projections further reveal a shift of wet-snow avalanche activity to earlier winter months. Analysis of changes in prominent snow grain types offers a coherent explanation of projected changes beyond a mere decrease in snow depth and snow cover duration. Overall, our study quantifies for the first time the significant influence of climate change on snow avalanche activity in the Swiss Alps and may serve as a benchmark for further mountain regions with similar avalanche climates. [ABSTRACT FROM AUTHOR]

Snow depth retrievals from spaceborne C-band synthetic aperture radar (SAR) backscatter have the potential to fill an important gap in the remote monitoring of seasonal snow. Sentinel-1 (S1) SAR data have been used previously in an empirical algorithm to generate snow depth products with near-global coverage, subweekly temporal resolution and spatial resolutions on the order of hundreds of meters to 1 km. However, there has been no published independent validation of this algorithm. In this work we develop the first open-source software package that implements this Sentinel-1 snow depth retrieval algorithm as described in the original papers and evaluate the snow depth retrievals against nine high-resolution lidar snow depth acquisitions collected during the winters of 2019–2020 and 2020–2021 at six study sites across the western United States as part of the NASA SnowEx mission. Across all sites, we find agreement between the Sentinel-1 snow depth retrievals and the lidar snow depth measurements to be considerably lower than requirements placed for remotely sensed observations of snow depth, with a mean root mean square error (RMSE) of 0.92 m and a mean Pearson correlation coefficient r of 0.46. Algorithm performance improves slightly in deeper snowpacks and at higher elevations. We further investigate the underlying Sentinel-1 data for a snow signal through an exploratory analysis of the cross- to co-backscatter ratio (σVH/σVV ; i.e., cross ratio) relative to lidar snow depths. We find the cross ratio increases through the time series for snow depths over ∼ 1.5 m but that the cross ratio decreases for snow depths less than ∼ 1.5 m. We attribute poor algorithm performance to (a) the variable amount of apparent snow depth signal in the S1 cross ratio and (b) an algorithm structure that does not adequately convert S1 backscatter signal to snow depth. Our findings provide an open-source framework for future investigations, along with insight into the applicability of C-band SAR for snow depth retrievals and directions for future C-band snow depth retrieval algorithm development. C-band SAR has the potential to address gaps in radar monitoring of deep snowpacks; however, more research into retrieval algorithms is necessary to better understand the physical mechanisms and uncertainties of C-band volume-scattering-based retrievals. [ABSTRACT FROM AUTHOR]

Seasonal snowpack is an important predictor of the water resources available in the following spring and early-summer melt season. Total basin snow water equivalent (SWE) estimation usually requires a form of statistical analysis that is implicitly built upon the Gaussian framework. However, it is important to characterize the non-Gaussian properties of snow distribution for accurate large-scale SWE estimation based on remotely sensed or sparse ground-based observations. This study quantified non-Gaussianity using sample negentropy; the Kullback–Leibler divergence from the Gaussian distribution for field-observed snow depth data from the North Slope, Alaska; and three representative SWE distributions in the western USA from the Airborne Snow Observatory (ASO). Snowdrifts around lakeshore cliffs and deep gullies can bring moderate non-Gaussianity in the open, lowland tundra of North Slope, Alaska, while the ASO dataset suggests that subalpine forests may effectively suppress the non-Gaussianity of snow distribution. Thus, non-Gaussianity is found in areas with partial snow cover and wind-induced snowdrifts around topographic breaks on slopes and on other steep terrain features. The snowpacks may be considered weakly Gaussian in coastal regions with open tundra in Alaska and alpine and subalpine terrains in the western USA if the land is completely covered by snow. The wind-induced snowdrift effect can potentially be partitioned from the observed snow spatial distribution guided by its Gaussianity. [ABSTRACT FROM AUTHOR]

Determining the distribution and dynamics of water on land at any given moment poses a significant challenge due to the constraints of observation. Consequently, as advancements in land surface models (LSMs) have been made, numerical simulation has emerged as an increasingly accurate and effective method for hydrological research. Nonetheless, systems that represent multiple land surface parameters in a near‐real‐time manner are scarce. In this study, we present an innovative land surface and river simulation system, termed Today's Earth (TE), which generates state and flux values for the near‐surface environment with multiple outputs in near‐real‐time. There are currently three versions of TE, distinguished by the forcing data utilized: JRA‐55 version, employing the Japanese 55‐year Reanalysis (JRA‐55, from 1958 to the present); GSMaP version, utilizing, the Global Satellite Mapping of Precipitation (GSMaP, from 2001 to the present), and MODIS version, utilizing the Moderate Resolution Imaging Spectroradiometer (MODIS, from 2003 to the present). These long‐term forcing data set allow for outputs of the JRA‐55 version from 1958, the GSMaP version from 2001, and the MODIS version from 2003. Aiming to provide water and energy values on a global scale in real‐time, the TE system utilizes the LSM Minimal Advanced Treatments of Surface Interaction and Runoff (MATSIRO) (Takata et al., 2003, https://doi.org/10.1016/s0921‐8181(03)00030‐4; Yamazaki et al., 2011, https://doi.org/10.1029/2010wr009726) at a horizontal resolution of 0.5°, along with the river routing model CaMa‐Flood (Yamazaki et al., 2011, https://doi.org/10.1029/2010wr009726) at a horizontal resolution of 0.25°. Both land surface and river products are available in 3‐hourly, daily, and monthly intervals across all three versions. A notable feature of TE is its ability to release both state and flux parameters in near‐real‐time, offering convenience for various aspects of hydrological research. In addition to presenting the general features of TE‐Global, this study examines the performance of snow depth, soil moisture, and river discharge data in daily intervals from 2003 to 2021, with validation spanning 2003 to 2016. When comparing snow depth results, the correlation coefficient ranged between 0.644 and 0.658, while for soil moisture it ranged between 0.471 and 0.494. These findings suggest that the LSM yields comparable results when utilizing JRA‐55, MODIS, or GSMaP. Interestingly, river output from the three products exhibited distinct characteristics varying from GSMaP to JRA‐55 and MODIS. For river discharge, the correlation coefficient ranged from 0.494 to 0.519, the root mean square error ranged from 3,730 m3/s to 6,330 m3/s, and the mean absolute error ranged from 3,000 m3/s to 5,160 m3/s among the different forcing versions. The overall bias in river discharge from GSMaP was 1,570 m3/s, in contrast to −589 m3/s for JRA‐55 and −200 m3/s for MODIS. These metrics demonstrate that the TE system is capable of generating practical land surface and river products, highlighting differences arising from the use of various types of forcing data. This comprehensive system would be valuable for monitoring water‐related movements, predicting disasters, and contributing to sophisticated water resource management. Regarding its application, the TE system has been included in the World Meteorological Organization as a Global Hydrological Modelling System. All TE‐Global products can be freely accessed through File Transfer Protocol. Plain Language Summary: Understanding the distribution and movement of water on land in real‐time is difficult due to observation limitations. However, recent advancements in land surface models (LSMs) have allowed numerical simulations to become more accurate and effective for hydrological research. This study introduces a new system named Today's Earth (TE), which provides near‐real‐time data on the state of the Earth's surface and water movement. The TE system uses three different versions, using JRA55, GSMaP, and MODIS to generate outputs from the past to the present. It combines two models, LSM Minimal Advanced Treatments of Surface Interaction and Runoff, and CaMa‐Flood, for land surface and river simulation, respectively. The TE system offers state and flux values at different time intervals and has been tested for its performance in estimating snow depth, soil moisture, and river discharge data. The results show that the TE system produces comparable outcomes across different data sources. The river discharge, however, varied among the different versions. Overall, the TE system is valuable for monitoring water‐related movements, predicting disasters, and managing water resources effectively. It is recognized by the World Meteorological Organization as a Global Hydrological Modelling System, and the TE‐Global products are freely accessible through FTP. Key Points: We present an innovative land surface and river simulation system, Today's Earth (TE)TE releases state and flux parameters in near‐real‐time, offering convenience for various aspects of hydrological researchThe TE system can generate practical land surface and river products, highlighting differences arising from various types of forcing data [ABSTRACT FROM AUTHOR]

The 2023-2024 snow season was generally underperforming, with long snowless stretches and few notable events across the United States. Some regions, like western mountain areas, saw exceptions with significant snowfall. The season was chronicled month-by-month in Weatherwise, highlighting noteworthy snow events and totals. Overall, snow cover extent was below normal for most of the season, with only a few exceptions in certain regions. [Extracted from the article]

Current numerical research on snow accumulation on bogies often simplifies train models as 1.5 cars or 3 cars, and there is less research on long marshalling trains with real bogies. In this article, based on the Euler–Lagrange method, the airflow structure and snow accumulation characteristics in the bogie area of an 8-car high-speed train (HST) are studied by numerical simulation. The results show that the airflow velocity and snow distribution concentration inside the bogie cabin vary along the flow direction. The downstream bogie cabin has a lower airflow speed, spatial snow particle concentration, and components snow accumulation. Because of the bogie cabin cover structure, the main airflow direction above the first and second bogie cabin is different, resulting in higher snow concentration and range on the windward side of the first bogie. There is a significant difference in snow accumulation characteristics between powered and nonpowered bogies. The powered bogie gearbox is directly affected by the airflow carrying snow particles, leading to severe snow accumulation on the windward side. This study provides a research foundation and engineering guidance for the snow protection strategy of HST bogies and is important for improving the comfort and safety of HSTs on snow-covered rails. [ABSTRACT FROM AUTHOR]

In North America, most ungulate species exhibit life‐history traits typical of long‐lived, iteroparous species wherein young males tend to prioritize essential life functions including body growth and maintenance that constrains allocation of resources to horn, antler, and pronghorn growth. As a result, males of most ungulate species require several years of growth before reaching asymptotic body size and thereafter, peak weapon size is attained. Unique among ungulate species in North America, pronghorn possess a suite of life‐history traits resulting in a precocious (i.e., unusually early development) pace of life relative to other North American ungulates. We tested the hypothesis that the fast pace of life of pronghorn extends to precocious development of large horns, and evaluated how horn size was affected by environmental conditions during the year they were grown and the potential for cohort effects associated with environmental signatures during the year of birth. We evaluated the influence of age and the environment on horn size of pronghorn using data collected from 1,789 male pronghorn harvested from 2019 to 2022 in Wyoming, USA. Pronghorn attained 95% of their peak horn size by 3.5 years old. Climatic conditions influenced horn growth through cohort effects and year of growth pathways. Snow depth during the year of birth positively influenced horn size, whereas the effects of environmental conditions during the year of horn growth were dependent on age. For young animals, snow depth and moderate drought positively influenced horn size during the year of horn growth, but the effect was negligible for prime aged and old animals. The precocious nature of pronghorn extended to their horn growth characteristics, resulting in early attainment of a large proportion of their peak horn size. The unique ecology of pronghorn and rapid attainment of size early in life can allow for greater flexibility to balance hunter opportunity and production of large‐horned males for pronghorn as compared with other ungulates. [ABSTRACT FROM AUTHOR]

Peter I Island is situated in the Bellingshausen Sea, a region that has experienced considerable climate change in recent decades. Warming sea surface temperatures and reduced sea ice cover have been accompanied by warming surface air temperature, increased snowfall, and accelerated mass loss over the adjacent ice sheet. Here we present data from the first firn core drilled on Peter I Island, spanning the period 2001–2017 CE. The stable water isotope data capture regional changes in surface air temperature and precipitation (snow accumulation) at the site, which are highly correlated with the surrounding Amundsen–Bellingshausen seas and the adjacent Antarctic Peninsula (r > 0.6, p < 0.05). The firn core data, together with the unique in situ data from an automatic weather station, confirm the high skill of the ERA5 reanalysis in capturing daily mean temperature and inter-annual precipitation variability, even over a small sub-Antarctic island. This study demonstrates the suitability of Peter I Island for future deep-ice-core drilling, with the potential to provide a valuable archive to explore ice–ocean–atmosphere interactions over decadal to centennial timescales for this dynamic region. [ABSTRACT FROM AUTHOR]

Seasonal snow is an important water source and contributor to river discharge in mountainous regions. Therefore the amount of snow and its distribution are necessary inputs for hydrological modeling. Recent research has shown the potential of the Sentinel‐1 radar satellite to map snow depth (SD) at sub‐kilometer resolution in mountainous regions. In this study we assimilate these new SD retrievals into the Noah‐Multiparameterization land surface model using an ensemble Kalman filter for the western European Alps. The land surface model was coupled to the Hydrological Modeling and Analysis Platform (HyMAP), a global flow routing scheme that provides simulations of routed river discharge. The performance with different precipitation forcing inputs, namely MERRA‐2 (with and without gauge based correction) and ERA5, was compared based on in situ precipitation and SD stations, with ERA5 leading to the best SD performance. The Sentinel‐1 based data assimilation (DA) results show small but systematic improvements for SD estimates, with the mean absolute error reducing from 36.4 cm for the open loop (OL) to 35.6 cm for the DA across all stations and timesteps, improving 318 out of 516 in situ sites. The DA updates in SD also result in enhanced snow water equivalent and discharge simulations. The median temporal correlation between discharge simulations and measurements increases from 0.73 to 0.78 for the DA. This study demonstrates the utility of the Sentinel‐1 SD retrievals to improve not only the representation of snow in mountain ranges, but also the snow melt contribution to river discharge, and hydrological modeling in general. Key Points: The assimilation of Sentinel‐1 snow depth (SD) retrievals improves NoahMP SD, snow water equivalent and streamflow simulationsDifferent reanalysis products lead to different biases in SD, with higher SD biases when gauge‐based precipitation is usedSentinel‐1 based SD estimates can be of considerable value for hydrological modeling in mountainous regions [ABSTRACT FROM AUTHOR]

This research analyses the snow depth distribution in canopy gaps across two plots in Central Pyrenees, to improve understanding of snow–forest and topography interactions. Snow depth maps, forest structure–canopy gap (FSCG) characteristics and topographic variables were generated by applying Structure from Motion algorithms (SfM) to images acquired from Unmanned Aerial Vehicles (UAVs). Six flights were conducted under different snowpack conditions in 2021, 2022 and 2023. Firstly, the snow depth database was analysed in terms of the ratio between the radius of the canopy gap and the maximum height of the surrounding trees (r/h), in order to classify the gaps as small‐size, medium‐size, large‐size, or open areas at both sites independently. Then Kendall's correlation coefficients between the snow depth, FSCG and topographic variables were computed and a Random Forest (RF) model for each survey was implemented, to determine the influence of these variables in explaining snow depth patterns. The results demonstrate the consistency of the UAV SfM photogrammetry approach for measuring snowpack dynamics at fine scale in canopy gaps and open areas. At the northeast exposed Site 1, the larger the r/h observed, the greater was the snow depth obtained. This pattern was not evident at the southwest exposed Site 2, which presented high variability related to the survey dates and categories, highlighting the relevance of topography for determining optimum snow accumulation in forested areas. Slope systematically exhibited a negative and significant correlation with snow depth and was consistently the highest‐ranked variable for explaining snow distribution at both sites according to the RF models. Distance to the Canopy Edge also presented high influence, especially at Site 1. The findings suggest differences in the main drivers throughout each site and surveys of the topographic and FSCG variables are needed to understand snow depth distribution over heterogeneous mountain forest domains. [ABSTRACT FROM AUTHOR]

Wildfire activity in the western United States (WUS) is increasingly impacting water supply, and land surface models (LSMs) that do not explicitly account for fire disturbances can have critical uncertainties in burned areas. This study quantified responses from the Weather Research and Forecasting Hydrological modelling system (WRF‐Hydro) to a suite of fire‐related perturbations to hydrologic soil and runoff parameters, vegetation area, land cover classifications and associated vegetation properties, and snow albedo across the heavily burned Feather River Basin in California. These experiments were used to quantify the impacts of fire‐related perturbations in model simulations under the observed meteorological conditions during the 2000–2022 water years and determine whether applying these fire‐related perturbations enhanced post‐fire model accuracy across the 11–12 post‐fire months evaluated herein. The most comprehensive fire‐aware simulation consistently modelled enhanced annual catchment streamflow (by 8%–37%), subsurface flow (by 72%–116%), and soil moisture (by 4%–9%), relative to the baseline simulation which neglected fire impacts. Simulated fire‐enhanced streamflow was predominately attributable to fire‐induced vegetation area reductions that reduced transpiration. Simulated streamflow enhancements occurred throughout the water year, excluding early‐summer (e.g., May–June) when the baseline simulation modelled relatively more snowmelt and streamflow because fire perturbations caused earlier model snow depletion. Vegetation area reductions favoured increased model ground snow accumulation and enhanced snow ablation while imposed snow albedo darkening enhanced ablation, ultimately resulting in similar peak SWE and earlier snow disappearance (on average by 8‐days) from the most comprehensive fire‐aware simulation relative to the baseline simulation. The baseline simulation had large degradations in streamflow accuracy following major fire events that were likely partially attributable to neglecting fire disturbances. Applying fire‐related perturbations reduced post‐fire streamflow anomaly biases across the three study catchments. However, remaining large post‐fire streamflow uncertainties in the fire‐perturbed simulation underscores the importance of additional observationally constrained fire‐disturbance model developments. [ABSTRACT FROM AUTHOR]

The snow disasters in the Ili region of Xinjiang, China, have led to significant economic and social impacts, mainly through reduced agricultural production and ecosystem damage. To quantify and evaluate the impact of snow disasters in this region, this study analyzed 95 snow disaster events that occurred between 1990 and 2020. A disaster loss index was calculated using selected indicators, dimensionless processing, and weight assignment. The severity of snow disasters was categorized into four levels (mild, moderate, severe, and extreme) using the percentile method. The key findings are as follows: (1) Snow disasters in the Ili region exhibited a bimodal distribution, with peak occurrences in November and January-February, aligning with the region's seasonal climatic characteristics. Since 2014, the frequency of snow disasters has notably decreased. (2) Mild snow disasters were the most frequent, accounting for 49.4% of events, while moderate, severe, and extreme snow disasters represented 23.2%, 24.2%, and 5.0%, respectively. (3) The most severe disaster losses were observed in Nilka County, followed by Xinyuan County and Yining County, whereas Zhaosu County was the least affected. (4) Key meteorological factors influencing snow disasters, including cumulative snowfall, maximum snow depth, minimum temperature, and snowfall duration, displayed regional variations. Areas with high snow disaster incidence had higher mean values of maximum snow depth compared to other regions. This study provides a scientific foundation for risk assessment and management of snow disasters in the Ili region. [ABSTRACT FROM AUTHOR]

Both Arctic and Antarctic sea ice are affected by climate change. While Arctic sea ice has been declining for several decades, Antarctic sea ice extent slowly increased until 2015, followed by a sharp drop in 2016. Quantifying sea ice changes is essential to assess their impacts on the ocean, atmosphere, ecosystems and Arctic communities. In this study, we combine sea ice thickness estimates from four satellite radar altimeters to derive the longest time series of homogeneous sea ice thickness for both hemispheres over 30 years (1994–2023). The record supports the rapid loss of sea ice in the Arctic for each month of the year and the heterogeneous changes in sea ice thickness in the Antarctic. The study confirms that most of the volume variability is due to the thickness variability, which holds true for both hemispheres. The sea ice thickness time series presented here offer new insights for models, in particular the possibility to evaluate sea ice reanalyses and to initialize forecasts, especially in the Antarctic, where the data set presented here has no equivalent in terms of spatial and temporal coverage. Plain Language Summary: Arctic and Antarctic sea ice respond significantly to climate change, as documented by decades of satellite records of sea ice coverage. Arctic sea ice coverage has declined steadily over the past 40 years, while Antarctic sea ice coverage increased until 2016, followed by a rapid decline. In contrast, changes in sea ice thickness, which contribute to changes in sea ice volume, are much less well documented due to the lack of long‐term observations. In this study, we address this gap by producing a 30‐year time series of sea ice thickness (1994–2023) using data from four satellite radar altimeters, representing the longest consistent data set for both polar regions. This was made possible by using machine learning to align the different satellite missions and incorporating an ensemble of snow depth products to cover the entire period. The resulting time series shows a rapid decrease in Arctic sea ice thickness over the study period. In contrast, Antarctic sea ice shows thickness changes with significant spatial variability. This new data set provides critical insights into sea ice dynamics, their causes, and their impacts on climate, ecosystems, and Arctic communities. Key Points: A 30‐year data set from four satellite radar altimeters provides the longest sea ice thickness record for both polar regionsArctic sea ice is significantly thinning for each month of the year, whereas Antarctic undergoes complex regional thickness changesSea ice volume variability can be explained by sea ice thickness variability in both polar hemispheres [ABSTRACT FROM AUTHOR]

Sea ice altimetry currently remains the primary method for estimating sea ice thickness from space, however time-series of sea ice thickness estimates are of limited use without having been quality-controlled against reference measurements. Such reference observations for sea ice thickness validation in the polar regions are sparse and rarely presented in a format matching the satellite-derived products. Here, the first published comprehensive collection of sea ice reference observations including freeboard, thickness, draft and snow depth from sea ice-covered regions in the Northern Hemisphere (NH) and the Southern Hemisphere (SH) is presented. The observations have been collected using airborne sensors, autonomous drifting buoys, moored and submarine-mounted upward-looking sonars, and visual observations. The data package has been prepared to match the spatial (25 km for NH and 50 km for SH) and temporal (monthly) resolutions of conventional satellite altimetry-derived sea ice thickness data products for a direct evaluation of these. This data package, also known as the Climate Change Initiative (CCI) sea ice thickness (SIT) Round Robin Data Package (RRDP) was produced within the ESA CCI sea ice project. The current version of the CCI SIT RRDP covers the polar satellite altimetry era (1993–2021) and is part of ongoing efforts to keep the dataset updated. The CCI SIT RRDP has been collocated to satellite-derived sea ice thickness products from CryoSat-2, Envisat and ERS-1/2 produced within ESA CCI and the Fundamental Data Records for Altimetry (FDR4ALT) project to demonstrate the overlap and inter-comparison between the reference observations and satellite-derived products. Here, the CCI SIT RRDP is introduced along with examples of its use as a validation source for satellite altimetry products, where the averaging, collocation and uncertainty methodology is presented and their advantages and limitations are discussed. [ABSTRACT FROM AUTHOR]

Despite its substantial impacts on human society, winter weather is difficult to predict on subseasonal‐to‐seasonal (S2S) timescales. To improve its predictability at longer timescales, this study examines whether the Madden–Julian Oscillation (MJO) has a significant impact on the frequency of extreme winter weather (i.e., heavy accumulations of snow, ice pellets, and freezing rain) over the contiguous United States. We find an increased frequency of extreme winter weather in the Ohio Valley and Mid‐Atlantic regions when MJO enhanced convection is over Africa and the western Indian Ocean, due to imposed lower 850‐hPa temperatures associated with MJO teleconnections. More frequent subfreezing temperatures lead to an increased likelihood of frozen precipitation compared with liquid, increasing the frequency of extreme winter weather. The MJO also increases the frequency of winter weather in the Central Great Plains region of the United States when MJO enhanced convection is over the eastern Indian Ocean and the Maritime Continent. Rather than through effects on temperature, the likely mechanism of increased winter weather over this region is enhanced synoptic forcing that increases overall precipitation. These effects can be seen up to 15 days in advance, suggesting the utility of using the MJO in S2S forecasting of extreme winter weather. [ABSTRACT FROM AUTHOR]