Satellite and modelling based snow season time series for Svalbard: Intercomparisons and assessment of accuracy (SATMODSNOW 2)

• Main Authors: Vickers, Hannah, Malnes, Eirik, Karlsen, Stein Rune, Saloranta, Tuomo, Killie, Mari Anne, van der Pelt, Ward, Notarnicola, Claudia, Stendardi, Laura
• Format: Report
• Language: English
• Published: Svalbard Integrated Arctic Earth Observing System 2024
• Subjects: Snow; Remote sensing; Modelling; Snow cover; Snow water equivalent; Climate change; Svalbard
• Online Access: https://doi.org/10.5281/zenodo.10257427

This is chapter 4 of the State of Environmental Science in Svalbard (SESS) report 2023. Climate change is taking place at a much faster pace in the Arctic and polar regions compared to the global average. Across the Norwegian archipelago of Svalbard, a warming climate is impacting where and when there is snow cover, which in turn has consequences for the physical environment, terrestrial and marine ecosystems. Remote sensing observations and snow models represent valuable tools for large scale monitoring of snow cover and provide historical data spanning several decades. These approaches provide complementary data that can contribute to filling important gaps in both datasets. However, we must first understand the how and how much the datasets differ. Only then can we use these complementary datasets to develop accurate, complete and consistent snow cover time series for Svalbard. The research in this update chapter builds on the SESS report 2020 chapter SATMODSNOW by utilising additional new years of snow cover data from remote sensing and models to examine inter-sensor and inter-model differences. Our results highlight some systematic differences in the temporal characteristics of snow cover onset and disappearance between models and remote sensing, as well as the significance of cloud cover masks and retrieval algorithms on the snow cover fraction derived from identical remote sensing datasets.