SnoTATOS: a low-cost, autonomous system for distributed snow depth measurements on sea ice

Abstract. Snow is a critical component of the Arctic sea ice system. With its low thermal conductivity and high albedo, snow moderates energy transfer between the atmosphere and ocean during both winter and summer, thereby playing a significant role in determining the magnitude, timing, and variability in sea ice growth and melt. The depth of snow on Arctic sea ice is highly variable in space and time, and accurate measurements of snow depth and variability are central to improving our basic understanding, model representation, and remote sensing observations of the Arctic system. Our ability to collect those measurements has hitherto been limited by the high cost and large size of existing autonomous snow measurement systems. We designed a new system called SnoTATOS (the Snow Thickness and Temperature Observation System) to address this gap. SnoTATOS is a radio-networked, distributed snow depth observation system that is 95 % less expensive and 93 % lighter than existing systems. In this paper, we describe the technical specifications of the system and present results from a case study deployment of four SnoTATOS networks (each with 10 observing nodes) in the Lincoln Sea between April 2024 and February 2025. The study demonstrates the utility of SnoTATOS in collecting distributed, in situ snow depth, accumulation, and surface melt data. While initial snow depth varied by up to 42 % within each network, a comparison of mean initial snow depth between networks showed a maximum difference of only 26 %. Similarly, whereas surface melt varied within each network by up to 38 %, mean surface melt varied between networks by only up to 9 %. This indicates that floe-scale measurements made using SnoTATOS provide valuable snow depth variability information and therefore more representative data for regional intercomparisons than existing single-station systems. We conclude by recommending further research to determine the optimal number and arrangement of autonomous stations needed to capture the variability in snow depth on Arctic sea ice.