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Read about what snow brings to our world and its animals.

The best way to cure the winter blues is to go outside and build a snowman! In this book, beginning readers join a happy family on a trip through the snow to find some winter fun. Rolling snowballs is just the start of things for their snowman friend. Early readers are taken through the step-by-step process with colorful photographs and accessible text, watching as a pile of snow transforms into a friendly winter character complete with its own eyes, hat, and even a carrot for a nose!

Snow transport (wind drifting and avalanches) can concentrate a large amount of water into a relatively small area, in contrast to precipitation, which is spatially smoother. I develop a framework to constrain the minimum effective seasonal transport necessary to explain observed snowpack patterns. In the Wind River Range, Wyoming, extensive deep snow (4–6 m snow water equivalent, >0.01 km2) is the result of long‐distance transport, with about half of the seasonal accumulation originating >1 km upwind. Cirque glaciers on the downwind margins of alpine plateaus can accumulate snow from contributing source areas exceeding 2–3 km2. Interbasin snow transport augments local snowfall by at least 22% in a glaciated first‐order stream catchment (2 km2), with the upwind “snowshed” doubling the effective catchment area. Snow imported across topographic divides is equivalent to 7% of annual streamflow in a 125 km2 watershed. Kilometer‐scale snow transport is an underappreciated driver of mountain snowpack heterogeneity.

In this illustrated poem in honor of the victims of the 2012 shooting in Newtown, Connecticut, falling snowflakes celebrate the uniqueness of life, its precious, simple moments, and the strength of memory.

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 area Precipitation scaling based on a single satellite snow depth image significantly improves the simulated gravity signal of the snowpack Accurate spatial distribution of snow depth is found to be key to simulate snow mass (SWE) evolution in complex alpine terrain

A simple introduction to the characteristics of snow leopards.

Assessing past distributions, variability and trends in the mountain snow cover and its first-order drivers, temperature and precipitation, is key for a wide range of studies and applications. In this study, we compare the results of various modeling systems (global and regional reanalyses ERA5, ERA5-Land, ERA5-Crocus, CERRA-Land, UERRA MESCAN-SURFEX and MTMSI and regional climate model simulations CNRM-ALADIN and CNRM-AROME driven by the global reanalysis ERA-Interim) against observational references (in situ, gridded observational datasets and satellite observations) across the European Alps from 1950 to 2020. The comparisons are performed in terms of monthly and seasonal snow cover variables (snow depth and snow cover duration) and their main atmospherical drivers (near-surface temperature and precipitation). We assess multi-annual averages of regional and subregional mean values, their interannual variations, and trends over various timescales, mainly for the winter period (from November through April). ERA5, ERA5-Crocus, MESCAN-SURFEX, CERRA-Land and MTMSI offer a satisfying description of the monthly snow evolution. However, a spatial comparison against satellite observation indicates that all datasets overestimate the snow cover duration, especially the melt-out date. CNRM-AROME and CNRM-ALADIN simulations and ERA5-Land exhibit an overestimation of the snow accumulation during winter, increasing with elevations. The analysis of the interannual variability and trends indicates that modeling snow cover dynamics remains complex across multiple scales and that none of the models evaluated here fully succeed to reproduce this compared to observational reference datasets. Indeed, while most of the evaluated model outputs perform well at representing the interannual to multi-decadal winter temperature and precipitation variability, they often fail to address the variability in the snow depth and snow cover duration. We discuss several artifacts potentially responsible for incorrect long-term climate trends in several reanalysis products (ERA5 and MESCAN-SURFEX), which we attribute primarily to the heterogeneities of the observation datasets assimilated. Nevertheless, many of the considered datasets in this study exhibit past trends in line with the current state of knowledge. Based on these datasets, over the last 50 years (1968–2017) at a regional scale, the European Alps have experienced a winter warming of 0.3 to 0.4 ∘C per decade, stronger at lower elevations, and a small reduction in winter precipitation, homogeneous with elevation. The decline in the winter snow depth and snow cover duration ranges from −7 % to −15 % per decade and from −5 to −7 d per decade, respectively, both showing a larger decrease at low and intermediate elevations. Overall, we show that no modeling strategy outperforms all others within our sample and that upstream choices (horizontal resolution, heterogeneity of the observations used for data assimilation in reanalyses, coupling between surface and atmosphere, level of complexity, configuration of the snow scheme, etc.) have great consequences on the quality of the datasets and their potential use. Despite their limitations, in many cases they can be used to characterize the main features of the mountain snow cover for a range of applications.

\"A look at snow leopards, [including] their habitats, physical characteristics such as their retractable claws, behaviors, relationships with humans, and ... ability to survive changing climates in the world today\"-- Provided by publisher.