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### Overview This dataset contains simulated significant wave height data generated from the WaveWatch III model run from 2020 up to 2070. It was produced to predict future environmental hazards threatening maritime navigation within the Arctic. Four unique simulations were produced using different Coupled Model Intercomparison Project Phase 6 (CMIP6) climate models' wind and sea ice projections along the shared socioeconomic pathways 5-8.5 (SSP5-8.5) future emissions scenario. The climate models used include: CNRM-CM6-1-HR, EC-Earth3, MPI-ESM1-2-HR, and MRI-ESM2-0. For each climate model, data is organized into yearly files written to NetCDF format. The data is contained on a spatially-varying unstructured triangular mesh which spans from 50° North (N) to 89.9°N and 180° West (W) to 180° East (E). The 'hs' variable presents the significant wave height (highest one thirds of wave heights) to occur for each node during the simulation in 6 hour intervals. ### Access Data files can be accessed via: [https://arcticdata.io/data/10.18739/A2ST7DZ74/](https://arcticdata.io/data/10.18739/A2ST7DZ74/) 

Abstract Declining Arctic sea ice over recent decades has been linked to growth in coastal hazards affecting the Alaskan Arctic. In this study, climate model projections of sea ice are utilized in the simulation of an extratropical cyclone to quantify how future changes in seasonal ice coverage could affect coastal waves caused by this extreme event. All future scenarios and decades show an increase in coastal wave heights, demonstrating how an extended season of open water in the Chukchi and Beaufort Seas could expose Alaskan Arctic shorelines to wave hazards resulting from such a storm event for an additional winter month by 2050 and up to three additional months by 2070 depending on climate pathway. Additionally, for the Beaufort coastal region, future scenarios agree that a coastal wave saturation limit is reached during the sea ice minimum, where historically sea ice would provide a degree of protection throughout the year. 

Abstract The Arctic region is experiencing significant changes due to climate change, and the resulting decline in sea ice concentration and extent is already impacting ocean dynamics and exacerbating coastal hazards in the region. In this context, numerical models play a crucial role in simulating the interactions between the ocean, land, sea ice, and atmosphere, thus supporting scientific studies in the region. This research aims to evaluate how different sea ice products with spatial resolutions varying from 2 to 25 km influence a phase averaged spectral wave model results in the Alaskan Arctic under storm conditions. Four events throughout the Fall to Winter seasons in 2019 were utilized to assess the accuracy of wave simulations generated under the dynamic sea ice conditions found in the Arctic. The selected sea ice products used to parameterize the numerical wave model include the National Snow and Ice Data Center (NSIDC) sea ice concentration, the European Centre for Medium‐Range Weather Forecasts (ECMWF) Re‐Analysis (ERA5), the HYbrid Coordinate Ocean Model‐Community Ice CodE (HYCOM‐CICE) system assimilated with Navy Coupled Ocean Data Assimilation (NCODA), and the High‐resolution Ice‐Ocean Modeling and Assimilation System (HIOMAS). The Simulating WAves Nearshore (SWAN) model's accuracy in simulating waves using these sea ice products was evaluated against Sea State Daily Multisensor L3 satellite observations. Results show wave simulations using ERA5 consistently exhibited high correlation with observations, maintaining an accuracy above 0.83 to the observations across all events. Conversely, HIOMAS demonstrated the weakest performance, particularly during the Winter, with the lowest correlation of 0.40 to the observations. Remarkably, ERA5 surpassed all other products by up to 30% in accuracy during the selected storm events, and even when an ensemble was assessed by combining the selected sea ice products, ERA5's individual performance remained unmatched. Our study provides insights for selecting sea ice products under different sea ice conditions for accurately simulating waves and coastal hazards in high latitudes. 

Rising waters and land subsidence are increasing relative sea levels in western and northern Alaska, forcing communities to relocate or armor in place. To appropriately plan and make equitable decisions, there is a need to forecast the risk of flood exposure in coastal Alaskan communities and to evaluate methods to mitigate that risk. This paper conducts use-inspired science to evaluate the current and future flood exposure of roads in Hooper Bay, Alaska, proposes a unit cost of flood exposure to estimate the cost of flooding, and compares various mitigation efforts including elevating roads and building dikes. Nine historic storms and their associated flood depths were subject to return-period analysis and modeled for several sea level rise scenarios. Based on the simulated road flood exposure (km hours/storm), and the storm-return period, an annual flood exposure (km hours/year) was computed. Then, the unit cost of flood exposure (USD/km hours) was determined as the ratio of the cost of flood mitigation (USD/year) to the annual flood exposure mitigated by the project. The analysis found that the unit cost of flood exposure, in conjunction with flood exposure calculations, does provide an approximate flood risk calculation, though a unitized cost of flood exposure needs to be divided into lump sum costs and materials costs. The analysis also found that dikes may be a more cost-effective alternative than road elevation. The flood risk calculation, based on the unit cost of flood exposure, could be made for all of the communities in a given region to identify those communities that face a high flood risk. Furthermore, if one divides the unit cost of flood exposure by the population, one obtains a cost/benefit ratio that potentially could be used to prioritize flood mitigation work. 

Two prominent arctic coastal erosion mechanisms affect the coastal bluffs along the North Slope of Alaska. These include the niche erosion/block collapse mechanism and the bluff face thaw/slump mechanism. The niche erosion/block collapse erosion mechanism is dominant where there are few coarse sediments in the coastal bluffs, the elevation of the beach below the bluff is low, and there is frequent contact between the sea and the base of the bluff. In contrast, the bluff face thaw/slump mechanism is dominant where significant amounts of coarse sediment are present, the elevation of the beach is high, and contact between the sea and the bluff is infrequent. We show that a single geologic parameter, coarse sediment areal density, is predictive of the dominant erosion mechanism and is somewhat predictive of coastal erosion rates. The coarse sediment areal density is the dry mass (g) of coarse sediment (sand and gravel) per horizontal area (cm 2 ) in the coastal bluff. It accounts for bluff height and the density of coarse material in the bluff. When the areal density exceeds 120 g cm −2 , the bluff face thaw/slump mechanism is dominant. When the areal density is below 80 g cm −2 , niche erosion/block collapse is dominant. Coarse sediment areal density also controls the coastal erosion rate to some extent. For the sites studied and using erosion rates for the 1980–2000 period, when the sediment areal density exceeds 120 g cm −2 , the average erosion rate is low or 0.34 ± 0.92 m/yr. For sediment areal density values less than 80 g cm −2 , the average erosion rate is higher or 2.1 ± 1.5 m/yr.