A holistic approach to snow observations and models in Svalbard (Snow 23)

• Main Authors: Malnes, Eirik, Vickers, Hannah, Salzano, Roberto, Killie, Mari Anne, Luks, Bartłomiej, Gallet, Jean-Charles, Rinne, Eero
• Format: Report
• Language: English
• Published: Svalbard Integrated Arctic Earth Observing System 2024
• Subjects: Snow; remote sensing; modelling; snow essential climate variables; digital twins; Svalbard
• Online Access: https://doi.org/10.5281/zenodo.10257050

This is chapter 6 of the State of Environmental Science in Svalbard (SESS) report 2023. The chapter gives joint recommendations from the two updated SESS chapters SATMODSNOW and PASSES in this SESS report and set them into a wider context on snow research in Svalbard where SIOS now develops supersites for snow parameter monitoring in several projects such as Crios, SIOS Snow Pilot and SnowInOpt. The chapter also gives a brief overview over upcoming satellite sensors allowing for measurements of snow depth and snow water equivalents, which previously have been unavailable fromsatellites. The chapter also reviews the snow observations provided in the SIOS Data Management System, and advises improvements. We also highlight the importance of snow models and their ability to assimilate different in situ and earth observations to accurately represent the snow cover. At the end of the chapter, we look forward to new possibilities for a holistic approach where models (from past data and future climate scenarios) and observations are merged in a digital twin based on AI techniques for pattern recognition, allowing for detailed prediction of the future snow cover.