Extreme Low Light Requirement for Algae Growth Underneath Sea Ice:A Case Study From Station Nord, NE Greenland

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

Changing Arctic snow cover: A review of recent developments and assessment of future needs for observations, modelling, and impacts

Open Access Journal (SHERPA RoMEO Green) DOI: 10.1007/s13280-016-0770-0Snow 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

Integrating snow science and wildlife ecology in Arctic-boreal North America

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\u27s 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 fit-for-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\u27s 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

Snow and vegetation seasonality influence seasonal trends of leaf nitrogen and biomass in Arctic tundra

Abstract Climate change, including both increasing temperatures and changing snow regimes, is progressing rapidly in the Arctic, leading to changes in plant phenology and in the seasonal patterns of plant properties, such as tissue nitrogen (N) content and community aboveground biomass. However, significant knowledge gaps remain over how these seasonal patterns vary among Arctic plant functional groups (i.e., shrubs, grasses, and forbs) and across large geographical areas. We used three years of in situ field vegetation sampling from an 80,000‐km2 area in Arctic Alaska, remotely sensed vegetation data (daily normalized difference vegetation index [NDVI]), and modeled output of snow‐free date to determine and model the seasonal trends and primary controls on leaf percent nitrogen and biomass (in grams per square meter) among Arctic vegetation functional groups. We determined relative vegetation phenology stage at a 500‐m spatial scale resolution, defined as the number of days between the date of the seasonal maximum NDVI and the vegetation field sampling date, and relative snow phenology stage (90‐m spatial scale) was determined as the number of days between the date of snow‐free ground and the sampling date. Models including relative phenology stage were particularly important for explaining seasonal variability of %N in shrubs, graminoids, and forbs. Similarly, vegetation and snow phenology stages were also important for modeling seasonal biomass of shrubs and graminoids; however, for all functional groups, the models explained only a small amount of seasonal variability in biomass. Relative phenology stage was a stronger predictor of %N and biomass than geographic position, indicating that localized controls on phenology, acting at spatial scales of 500 m and smaller, are critical to understanding %N and biomass

Transitions in high-Arctic vegetation growth patterns and ecosystem productivity tracked with automated cameras from 2000 to 2013

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 material The online version of this article (doi:10.1007/s13280-016-0864-8) contains supplementary material, which is available to authorized users

A Lagrangian Snow‐Evolution System for Sea‐Ice Applications (SnowModel‐LG): Part I – Model Description

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