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Arctic Indigenous coastal communities sustain rich environmental knowledge for subsistence activities, yet their knowledge and observations of changing conditions remain underrepresented in scientific datasets. This dataset documents local Sivuqaq Yupik Indigenous hunters’ observations of environmental conditions and associated subsistence activity based out of Sivuqaq (Gambell), St. Lawrence Island, Alaska. Two community research leads and experienced Sivuqaq Yupik Hunters (Ungott and Kaningok) compiled a series of regular observations over the course of three sea ice seasons from 2023 to 2025. These included quantitative measurements made with a handheld weather meter and visual assessments of specific categories of weather, ice and ocean conditions, and subsistence activities. Specifically, the quantitative observations include wind speed, direction and air temperature, while visually assessed weather conditions include cloud cover, precipitation, and visibility. Reported categories of ice and ocean conditions include current direction, state of waves, Akuzipik ice type terminology, the presence and state of shorefast ice, and the distribution of ice and open water both near the beach and further offshore. Categories related to subsistence activities noted whether any community members were participating in hunting, fishing, or crabbing, and the number of boats in the water. These observations were accompanied by narrative commentary and often photographs to add context to each record. Observations were focused between the months of December and June, in relation to local ice presence, and ice-associated subsistence hunting. The range of data for each observed day enables comparison with other large-scale datasets while remaining grounded in the applied local context of subsistence hunting. This dataset was developed as part of the NSF Arctic Robust Communities – Navigating Adaptation to Variability (ARC-NAV) project. For further information see arcnav.org 

We apply the Canny edge algorithm to imagery from the Utqiaġvik coastal sea ice radar system (CSIRS) to identify regions of open water and sea ice and quantify ice concentration. The radar-derived sea ice concentration (SIC) is compared against the (closest to the radar field of view) 25 km resolution NSIDC Climate Data Record (CDR) and the 1 km merged MODIS-AMSR2 sea ice concentrations within the ∼11 km field of view for the year 2022–2023, when improved image contrast was first implemented. The algorithm was first optimized using sea ice concentration from 14 different images and 10 ice analysts (140 analyses in total) covering a range of ice conditions with landfast ice, drifting ice, and open water. The algorithm is also validated quantitatively against high-resolution MODIS-Terra in the visible range. Results show a correlation coefficient and mean bias error between the optimized algorithm, the CDR and MODIS-AMSR2 daily SIC of 0.18 and 0.54, and ∼−1.0 and 0.7%, respectively, with an averaged inter-analyst error of ±3%. In general, the CDR captures the melt period correctly and overestimates the SIC during the winter and freeze-up period, while the merged MODIS-AMSR2 better captures the punctual break-out events in winter, including those during the freeze-up events (reduction in SIC). Remnant issues with the detection algorithm include the false detection of sea ice in the presence of fog or precipitation (up to 20%), quantified from the summer reconstruction with known open water conditions. The proposed technique allows for the derivation of the SIC from CSIRS data at spatial and temporal scales that coincide with those at which coastal communities members interact with sea ice. Moreover, by measuring the SIC in nearshore waters adjacent to the shoreline, we can quantify the effect of land contamination that detracts from the usefulness of satellite-derived SIC for coastal communities. 

Abstract. Although Arctic marine ecosystems are changing rapidly,year-round monitoring is currently very limited and presents multiplechallenges unique to this region. The Chukchi Ecosystem Observatory (CEO)described here uses new sensor technologies to meet needs for continuous,high-resolution, and year-round observations across all levels of theecosystem in the biologically productive and seasonally ice-covered ChukchiSea off the northwest coast of Alaska. This mooring array records a broadsuite of variables that facilitate observations, yielding betterunderstanding of physical, chemical, and biological couplings, phenologies,and the overall state of this Arctic shelf marine ecosystem. While coldtemperatures and 8 months of sea ice cover present challenging conditions forthe operation of the CEO, this extreme environment also serves as a rigoroustest bed for innovative ecosystem monitoring strategies. Here, we presentdata from the 2015–2016 CEO deployments that provide new perspectives on theseasonal evolution of sea ice, water column structure, and physicalproperties, annual cycles in nitrate, dissolved oxygen, phytoplankton blooms,and export, zooplankton abundance and vertical migration, the occurrence ofArctic cod, and vocalizations of marine mammals such as bearded seals. Theseintegrated ecosystem observations are being combined with ship-basedobservations and modeling to produce a time series that documents biologicalcommunity responses to changing seasonal sea ice and water temperatures whileestablishing a scientific basis for ecosystem management.