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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. 

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 global seasonal cycle of energy in Earth’s climate system is quantified using observations and reanalyses. After removing long-term trends, net energy entering and exiting the climate system at the top of the atmosphere (TOA) should agree with the sum of energy entering and exiting the ocean, atmosphere, land, and ice over the course of an average year. Achieving such a balanced budget with observations has been challenging. Disagreements have been attributed previously to sparse observations in the high-latitude oceans. However, limiting the local vertical integration of new global ocean heat content estimates to the depth to which seasonal heat energy is stored, rather than integrating to 2000 m everywhere as done previously, allows closure of the global seasonal energy budget within statistical uncertainties. The seasonal cycle of energy storage is largest in the ocean, peaking in April because ocean area is largest in the Southern Hemisphere and the ocean’s thermal inertia causes a lag with respect to the austral summer solstice. Seasonal cycles in energy storage in the atmosphere and land are smaller, but peak in July and September, respectively, because there is more land in the Northern Hemisphere, and the land has more thermal inertia than the atmosphere. Global seasonal energy storage by ice is small, so the atmosphere and land partially offset ocean energy storage in the global integral, with their sum matching time-integrated net global TOA energy fluxes over the seasonal cycle within uncertainties, and both peaking in April. 

Abstract Ocean‐to‐ice heat flux (OHF) is important in regulating the variability of sea ice mass balance. Using surface drifting buoy observations, we show that during winter in the Arctic Ocean's Beaufort Gyre region, OHF increased from 0.76 ± 0.05 W/m²over 2006–2012 to 1.63 ± 0.08 W/m²over 2013–2018. We find that this is a result of thinner and less‐compact sea ice that promotes enhanced winter ice growth, stronger ocean vertical convection, and subsurface heat entrainment. In contrast, Ekman upwelling declined over the study period, suggesting it had a secondary contribution to OHF changes. The enhanced ice growth creates a cooler, saltier, and deeper ocean surface mixed layer. In addition, the enhanced vertical temperature gradient near the mixed layer base in later years favors stronger entrainment of subsurface heat. OHF and its increase during 2006–2018 were not geographically uniform, with hot spots found in an upwelling region where ice was most seasonally variable. 

Abstract The Arctic Ocean’s Wandel Sea is the easternmost sector of the Last Ice Area, where thick, old sea ice is expected to endure longer than elsewhere. Nevertheless, in August 2020 the area experienced record-low sea ice concentration. Here we use satellite data and sea ice model experiments to determine what caused this record sea ice minimum. In our simulations there was a multi-year sea-ice thinning trend due to climate change. Natural climate variability expressed as wind-forced ice advection and subsequent melt added to this trend. In spring 2020, the Wandel Sea had a mixture of both thin and—unusual for recent years—thick ice, but this thick ice was not sufficiently widespread to prevent the summer sea ice concentration minimum. With continued thinning, more frequent low summer sea ice events are expected. We suggest that the Last Ice Area, an important refuge for ice-dependent species, is less resilient to warming than previously thought. 

### Access All files can be accessed and downloaded from the directory via: [http://arcticdata.io/data/10.18739/A2MW28G9D](http://arcticdata.io/data/10.18739/A2MW28G9D). ### Overview Storm surge extremes are intensifying across Arctic coastlines, yet limited observational records hamper detailed spatial and temporal characterization of these events. To address that, this data is a 45-year hydrodynamic hindcast of storm-driven water levels across Northern and Western Alaska. We utilize ADCIRC+SWAN to simulate interactions between the ocean, land, sea ice, and atmosphere, focusing on the period from 1979 to 2024 for Western to Northern Alaska coasts. Data from the European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA5), including sea ice concentration and atmospheric forcing were utilized to support these simulations, which investigate annual conditions in the Alaskan Arctic. The Processed_DATA dataset contains extracted parameters for communities located in western to northern Alaska. For other areas in the state not included here, please refer to the Raw_DATA file. ### Goal The goal of this study's data is to attribute long-term changes in Arctic storm surge extremes to evolving physical drivers—primarily the transition from sea-ice-dominated to wind-driven surge regimes. Furthermore, to fill in the gap in observed water levels and wave conditions throughout Alaska. ### Methods This study’s hindcast model framework is to evaluate the storm driven water levels from 1979 to 2024. The framework integrates a coupled hydrodynamic–wave model driven by time-varying boundary inputs representing atmospheric, oceanic, tidal, and sea ice conditions. We used the coupled Advanced CIRCulation and Simulating WAves Nearshore model (ADCIRC+SWAN) to simulate water levels and wave conditions. 

### 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/) 

The High-resolution (6 kilometer (km)) Ice-Ocean Modeling and Assimilation System (HIOMAS) is used to simulate the evolution of sea ice for the Arctic Ocean and adjacent areas, including the Barents Sea, Norwegian Sea, Greenland Sea, Baffin Bay, and waters along Northwest Passage, over the period 2010 to 2069. The hindcast and future forcing over the period is from one of the Coupled Model Intercomparison Project Phase 6 (CMIP6) models, the CNRM-CM6-1-HR global climate model (GCM) run at the National Center for Meteorological Research, Météo-France and CNRS Laboratory (CNRM). Monthly mean sea ice thickness (meters (m)) is provided over 2010 to 2069 in NETCDF file format, with model grid information such as latitudes and longitudes of model grid cells included. I have archived future projection of monthly mean sea ice thickness files over 2010 to 2069 in https://pscfiles.apl.uw.edu/zhang/HIOMAS_6km/. The files are in netcdf format, which are created by running HIOMAS using the future projection forcing of the CNRM-CM6-1-HR GCM run conducted at the National Center for Meteorological Research, Météo-France and CNRS Laboratory (CNRM). Martin interpolated the forcing onto the new, expanded HIOMAS grid. There is a readme.txt file.