Details

Autor(en) / Beteiligte

• Scoto, Federico
• Pappaccogli, Gianluca
• Mazzola, Mauro
• Donateo, Antonio
• Salzano, Roberto
• Monzali, Matteo
• de Blasi, Fabrizio
• Larose, Catherine
• Gallet, Jean-Charles
• Decesari, Stefano
• Spolaor, Andrea

Titel

Automated observation of physical snowpack properties in Ny-Ålesund

Ist Teil von

• Frontiers in earth science (Lausanne), 2023-03, Vol.11

Ort / Verlag

Frontiers Media

Erscheinungsjahr

2023

Quelle

EZB Electronic Journals Library

Beschreibungen/Notizen

• The snow season in the Svalbard archipelago generally lasts 6–10 months a year and significantly impacts the regional climate, glaciers mass balance, permafrost thermal regime and ecology. Due to the lack of long-term continuous snowpack physical data, it is still challenging for the numerical snow physics models to simulate multi-layer snowpack evolution, especially for remote Arctic areas. To fill this gap, in November 2020, an automated nivometric station (ANS) was installed ∼1 km Southwest from the settlement of Ny-Ålesund (Spitzbergen, Svalbard), in a flat area over the lowland tundra. It automatically provides continuous snow data, including NIR images of the fractional snow-cover area (fSCA), snow depth (SD), internal snow temperature and liquid water content (LWC) profiles at different depths with a 10 min time resolution. Here we present the first-year record of automatic snow preliminary measurements collected between November 2020 and July 2021 together with weekly manual observations for comparison. The snow season at the ANS site lasted for 225 days with an annual net accumulation of 117 cm (392 mm of water equivalent). The LWC in the snowpack was generally low (<4%) during wintertime, nevertheless, we observed three snow-melting events between November and February 2021 and one in June 2021, connected with positive temperature and rain on snow events (ROS). In view of the foreseen future developments, the ANS is the first automated, comprehensive snowpack monitoring system in Ny-Ålesund measuring key essential climate variables needed to understand the seasonal evolution of the snow cover on land.