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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.

A simple introduction to the characteristics of snow leopards.

Snowpack distribution in Arctic and alpine landscapes often occurs in repeating, year‐to‐year patterns due to local topographic, weather, and vegetation characteristics. Previous studies have suggested that with years of observational data, these snow distribution patterns can be statistically integrated into a snow process modeling workflow. Recent advances in snow hydrology and machine learning (ML) have increased our ability to predict snowpack distribution using in‐situ observations, remote sensing data sets, and simple landscape characteristics that can be easily obtained for most environments. Here, we propose a hybrid approach to couple a ML snow distribution pattern (MLSDP) map with a physics‐based, snow process model. We trained a random forest ML algorithm on tens of thousands of snow survey observations from a subarctic study area on the Seward Peninsula, Alaska, collected during peak snow water equivalent (SWE). We validated hybrid model outputs using in‐situ snow depth and SWE observations, as well as a light detection and ranging data set and a distributed temperature profiling sensor data set. When the hybrid results were compared with the physics‐based method, the hybrid method more accurately depicted the spatial patterns of the snowpack, areas of drifting snow, and years when no in‐situ observations were used in the random forest ML training data set. The hybrid method also showed improvements in root mean squared error at 61% of locations where time‐series estimations of snow depth were observed. These results can be applied to any physics‐based model to improve the snow distribution patterning to reflect observed conditions in high latitude and high elevation cold region environments. Plain Language Summary Snowpacks in cold regions often occur in repeated, year‐to‐year patterns, regardless of the amount of snow that has fallen during the winter. This is due to complex landscape and climate interactions, like the shape and angles of the topography, the direction of the prevailing winter winds, and vegetation cover. Previous studies have used snow pattern maps with models to create more accurate estimations of the snowpack. This study uses machine learning (ML) to create the snow pattern maps, along with previously determined methods for integrating those patterns into estimations of snow depth and snow water content. We spent multiple winter fieldwork campaigns taking measurements of the snowpack, including point‐based snow depth and snow water content measurements, and plane‐based measurements of snow depth over large areas. These measurements were used to compare with our simulated snowpack characteristics. The hybrid approach, one that used ML plus physics‐based models, produced better spatial representations of the snowpack than results when ML was not used. Additionally, the hybrid approach generated improvements to modeled snow drift regions in the study area. These results show that ML and physics‐based models can be used together to create better spatial representations of snowpack characteristics in cold regions. Key Points Machine learning, coupled with physics‐based process models, can predict snow patterns with lower error than the process model alone When machine learning and physics‐based process models were used together, drifted snow areas were improved in the model outputs Improvements in root mean squared error were found at 61% of snow depth observation sites throughout the year

\"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.

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.