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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.
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Assessing uncertainties in sea ice extent climate indicators
The uncertainties in sea ice extent (total area covered by sea ice with concentration>15%) derived from passive microwave sensors are assessed in two ways. Absolute uncertainty (accuracy) is evaluated based on the comparison of the extent between several products. There are clear biases between the extent from the different products that are of the order of 500 000 to 1×106 km2 depending on the season and hemisphere. These biases are due to differences in the algorithm sensitivity to ice edge conditions and the spatial resolution of different sensors. Relative uncertainty is assessed by examining extents from the National Snow and Ice Data Center Sea Ice Index product. The largest source of uncertainty,∼100 000 km2, is between near-real-time and final products due to different input source data and different processing and quality control. For consistent processing, the uncertainty is assessed using different input source data and by varying concentration algorithm parameters. This yields a relative uncertainty of 30 000–70 000 km2. The Arctic minimum extent uncertainty is∼40 000 km2. Uncertainties in comparing with earlier parts of the record may be higher due to sensor transitions. For the first time, this study provides a quantitative estimate of sea ice extent uncertainty.
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- Award ID(s):
- 1748953
- PAR ID:
- 10128750
- Date Published:
- Journal Name:
- Environmental letters
- Volume:
- 14
- ISSN:
- 0013-9300
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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