Abstract
AbstractSnow is a key driver for biotic processes in Arctic ecosystems. Yet, quantifying relationships between snow metrics and biological components is challenging due to lack of temporally and spatially distributed observations at ecologically relevant scales and resolutions. In this study, we quantified relationships between snow, air temperature, and vegetation greenness (using annual maximum normalized difference vegetation index [MaxNDVI] and its timing [MaxNDVI_DOY]) from ground‐based and remote‐sensing observations, in combination with physically based models, across a heterogeneous landscape in a high‐Arctic, northeast Greenland region. Across the 98‐km distance from the Greenland Ice Sheet (GrIS) to the coast, we quantified significant inland–coast gradients of air temperature, winter precipitation (using pre‐melt snow‐water‐equivalent [SWE]), and snowmelt timing (using snow‐free day of year [SnowFree_DOY]). Near the coast, the mean annual air temperature was 4.5°C lower, the mean SWE was 0.3 m greater, and the mean SnowFree_DOY was 37 d later, than near the GrIS. The regional continentality gradient was eight times stronger than the south‐to‐north air–temperature gradient along the Greenland east coast. Across this strong gradient, the mean vegetation greening‐up period (SnowFree_DOY‐MaxNDVI_DOY) varied spatially by 24–57 d. We quantified significant non‐linear relationships between the vegetation characteristics of MaxNDVI and MaxNDVI_DOY, and SWE, SnowFree_DOY, and growing degree‐days‐sums during greening‐up (Greening_GDD) across the 16‐yr study period (2000–2015). These demonstrated that the snow metrics, both SWE and SnowFree_DOY, were more important drivers of MaxNDVI and MaxNDVI_DOY than Greening_GDD within this seasonally snow‐covered region. The methodologies that provided temporally and spatially distributed snow, air temperature, and vegetation greenness data are applicable to any snow‐ and vegetation‐covered area on Earth.
Highlights
Seasonal snow is a key driver of climate change effects, and it controls a range of Arctic ecosystem processes (Jones 1999, Post et al 2009, Brooks et al 2011, Callaghan et al 2011, Bokhorst et al 2016)
We found clear temporal linkages between the two spatially distributed and temporally evolving, but completely independent, data sets we developed and analyzed: SnowModel outputs and Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) (Fig. 3)
An example of the spatial and daily temporal evolution of the SnowModel snow depth and the MODIS NDVI values across the region in year 2003 is provided in Video S1
Summary
Seasonal snow is a key driver of climate change effects, and it controls a range of Arctic ecosystem processes (Jones 1999, Post et al 2009, Brooks et al 2011, Callaghan et al 2011, Bokhorst et al 2016). The snow cover acts as an efficient insulator during winter with its low thermal conductivity (Goodrich 1982, Sturm et al 1997) and high thermal resistance (Liston et al 2002). This insulating effect keeps soil thermal conditions relatively stable during snow-covered periods (Taras et al 2002, Zhang 2005) and protects the vegetation from frost damages (Bokhorst et al 2011). Precipitation accumulated in the snowpack during winter is released as meltwater in spring (Jones 1999) and provides moisture for vegetation growth at the start and during the growing season (Elberling et al 2008, Ellebjerg et al 2008)
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