Snow is a critically important and rapidly changing feature of the Arctic. However, snow-cover and snowpack conditions change through time pose challenges for measuring and prediction of snow. Plausible scenarios of how Arctic snow cover will respond to changing Arctic climate are important for impact assessments and adaptation strategies. Although much progress has been made in understanding and predicting snow-cover changes and their multiple consequences, many uncertainties remain. In this paper, we review advances in snow monitoring and modelling, and the impact of snow changes on ecosystems and society in Arctic regions. Interdisciplinary activities are required to resolve the current limitations on measuring and modelling snow characteristics through the cold season and at different spatial scales to assure human well-being, economic stability, and improve the ability to predict manage and adapt to natural hazards in the Arctic region.Electronic supplementary materialThe online version of this article (doi:10.1007/s13280-016-0770-0) contains supplementary material, which is available to authorized users.

A Lagrangian snow‐evolution model (SnowModel‐LG) was used to produce daily, pan‐Arctic, snow‐on‐sea‐ice, snow property distributions on a 25 × 25‐km grid, from 1 August 1980 through 31 July 2018 (38 years). The model was forced with NASA's Modern Era Retrospective‐Analysis for Research and Applications‐Version 2 (MERRA‐2) and European Centre for Medium‐Range Weather Forecasts (ECMWF) ReAnalysis‐5th Generation (ERA5) atmospheric reanalyses, and National Snow and Ice Data Center (NSIDC) sea ice parcel concentration and trajectory data sets (approximately 61,000, 14 × 14‐km parcels). The simulations performed full surface and internal energy and mass balances within a multilayer snowpack evolution system. Processes and features accounted for included rainfall, snowfall, sublimation from static‐surfaces and blowing‐snow, snow melt, snow density evolution, snow temperature profiles, energy and mass transfers within the snowpack, superimposed ice, and ice dynamics. The simulations produced horizontal snow spatial structures that likely exist in the natural system but have not been revealed in previous studies spanning these spatial and temporal domains. Blowing‐snow sublimation made a significant contribution to the snowpack mass budget. The superimposed ice layer was minimal and decreased over the last four decades. Snow carryover to the next accumulation season was minimal and sensitive to the melt‐season atmospheric forcing (e.g., the average summer melt period was 3 weeks or 50% longer with ERA5 forcing than MERRA‐2 forcing). Observed ice dynamics controlled the ice parcel age (in days), and ice age exerted a first‐order control on snow property evolution.

Snow covers Arctic and boreal regions (ABRs) for approximately 9 months of the year, thus snowscapes dominate the form and function of tundra and boreal ecosystems. In recent decades, Arctic warming has changed the snowcover's spatial extent and distribution, as well as its seasonal timing and duration, while also altering the physical characteristics of the snowpack. Understanding the little studied effects of changing snowscapes on its wildlife communities is critical. The goal of this paper is to demonstrate the urgent need for, and suggest an approach for developing, an improved suite of temporally evolving, spatially distributed snow products to help understand how dynamics in snowscape properties impact wildlife, with a specific focus on Alaska and northwestern Canada. Via consideration of existing knowledge of wildlife-snow interactions, currently available snow products for focus region, and results of three case studies, we conclude that improving snow science in the ABR will be best achieved by focusing efforts on developing data-model fusion approaches to produce fitfor-purpose snow products that include, but are not limited to, wildlife ecology. The relative wealth of coordinated in situ measurements, airborne and satellite remote sensing data, and modeling tools being collected and developed as part of NASA's Arctic Boreal Vulnerability Experiment and SnowEx campaigns, for example, provide a data rich environment for developing and testing new remote sensing algorithms and retrievals of snowscape properties.

Microalgae colonizing the underside of sea ice in spring are a key component of the Arctic foodweb as they drive early primary production and transport of carbon from the atmosphere to the ocean interior. Onset of the spring bloom of ice algae is typically limited by the availability of light, and the current consensus is that a few tens‐of‐centimeters of snow is enough to prevent sufficient solar radiation to reach underneath the sea ice. We challenge this consensus, and investigated the onset and the light requirement of an ice algae spring bloom, and the importance of snow optical properties for light penetration. Colonization by ice algae began in May under >1 m of first‐year sea ice with ∼1 m thick snow cover on top, in NE Greenland. The initial growth of ice algae began at extremely low irradiance (<0.17 μmol photons m−2 s−1) and was documented as an increase in Chlorophyll a concentration, an increase in algal cell number, and a viable photosynthetic activity. Snow thickness changed little during May (from 110 to 91 cm), however the snow temperature increased steadily, as observed from automated high‐frequency temperature profiles. We propose that changes in snow optical properties, caused by temperature‐driven snow metamorphosis, was the primary driver for allowing sufficient light to penetrate through the thick snow and initiate algae growth below the sea ice. This was supported by radiative‐transfer modeling of light attenuation. Implications are an earlier productivity by ice algae in Arctic sea ice than recognized previously.

Animal abundance is a key measure in conservation and management and tightly linked to population demographics. Demographic data from remote regions, however, are often scarce. Here, we present long-term (1996-2013) demographics on the muskox Ovibos moschatus population at Zackenberg in northeast Greenland. We examine both the inter-and intra-annual patterns in demographic parameters and relate these to environmental conditions. The sex and age composition of muskox groups changed during the study period, and changes were particularly evident in the increasing versus the decreasing phase of muskox abundance. The seasonal pattern of muskox density and group size was a parallel increase from late winter to autumn, which peaked at high densities (approximately seven individuals per km 2 ) in the autumn. The composition of muskox groups also changed between seasons. Across years, the muskox population dynamics was mainly driven by spring snow cover (an indicator of winter conditions), which primarily impacted the calf and yearling recruitments. This relationship, however, appeared to have a temporary decoupling, which may be attributable to pathogens. Our study provides rare insight into the longterm demographics of a remote ungulate population in relation to drivers of change and thus aids the development of adequate monitoring and management plans for muskoxen in a changing Arctic.

Climate-induced changes in vegetation phenology at northern latitudes are still poorly understood. Continued monitoring and research are therefore needed to improve the understanding of abiotic drivers. Here we used 14 years of time lapse imagery and climate data from high-Arctic Northeast Greenland to assess the seasonal response of a dwarf shrub heath, grassland, and fen, to inter-annual variation in snow-cover, soil moisture, and air and soil temperatures. A late snow melt and start of growing season is counterbalanced by a fast greenup and a tendency to higher peak greenness values. Snow water equivalents and soil moisture explained up to 77 % of growing season duration and senescence phase, highlighting that water availability is a prominent driver in the heath site, rather than temperatures. We found a significant advance in the start of spring by 10 days and in the end of fall by 11 days, resulting in an unchanged growing season length. Vegetation greenness, derived from the imagery, was correlated to primary productivity, showing that the imagery holds valuable information on vegetation productivity.Electronic supplementary materialThe online version of this article (doi:10.1007/s13280-016-0864-8) contains supplementary material, which is available to authorized users.

In this study, we quantified the spatiotemporal variability and trends in observations of multiple snow characteristics in High Arctic Zackenberg in Northeast Greenland through 18 years. Annual premelt snow-depth observations collected in 2005-2014 along an elevation gradient showed significant differences in snow depth between vegetation types. The seasonal snow cover was characterized by strong interannual variability in the Zackenberg region. Particularly the timing of snow-cover onset and melt, and the annual maximum accumulation, varied up to an order of magnitude between years. Hence, apart from the snow-cover fraction registered annually on 10 June, which exhibits a significant trend of-2.3% per year over the 18-year period, we found little evidence of significant trends in the observed snow-cover characteristics. Moreover, SnowModel results for the Zackenberg region confirmed that the pronounced interannual variability in snow precipitations has persisted in this High Arctic setting since 1979 and may have masked potential temporal trends. In exception, a significant difference in interannual variability of snow-cover onset timing was observed through the period 1997-2014, which in the recent period since 2006 was 7.3 times more variable. We owe enormous gratitude to all GeoBasis field assistants, who have collected the snow observations in the Zackenberg Valley during the period 1997-2014. We wish to thank the logistics team at

Tundra dominates two‐thirds of the unglaciated, terrestrial Arctic. Although this region has experienced rapid and widespread changes in vegetation phenology and productivity over the last several decades, the specific climatic drivers responsible for this change remain poorly understood. Here we quantified the effect of winter snowpack and early spring temperature conditions on growing season vegetation phenology (timing of the start, peak, and end of the growing season) and productivity of the dominant tundra vegetation communities of Arctic Alaska. We used daily remotely sensed normalized difference vegetation index (NDVI), and daily snowpack and temperature variables produced by SnowModel and MicroMet, coupled physically based snow and meteorological modeling tools, to (1) determine the most important snowpack and thermal controls on tundra vegetation phenology and productivity and (2) describe the direction of these relationships within each vegetation community. Our results show that soil temperature under the snowpack, snowmelt timing, and air temperature following snowmelt are the most important drivers of growing season timing and productivity among Arctic vegetation communities. Air temperature after snowmelt was the most important control on timing of season start and end, with warmer conditions contributing to earlier phenology in all vegetation communities. In contrast, the controls on the timing of peak season and productivity also included snowmelt timing and soil temperature under the snowpack, dictated in part by the snow insulating capacity. The results of this novel analysis suggest that while future warming effects on phenology may be consistent across communities of the tundra biome, warming may result in divergent, community‐specific productivity responses if coupled with reduced snow insulating capacity lowers winter soil temperature and potential nutrient cycling in the soil.