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Publication Date
1 July 2022

Typological Representation of the Offshore Oceanographic Environment along the Alaskan North Slope

Subtitle
Reducing the number of offshore oceanographic boundary conditions needed to characterize coastal processes.
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Science

The goal of the present study is to develop a typological representation of the offshore environment along the Alaskan North Slope consisting of the boundary conditions required for high-resolution nearshore models. Depending upon the methodology employed, up to order 104 – 105 simulations may be required to represent the environment; an offshore typology is a reduced number of parameters (order 10) that are prototypical of the full set of parameters needed for nearshore coastal analyses. Inherent in the development of this typology are scaling mechanisms to achieve location-dependent properties to enable community scale analyses. This approach uses statistically derived representations of an entire year. To achieve this goal, we modify the k-means clustering approach to obtain location-independent wave energy parameters (significant wave height and peak period). We then evaluate distributions of other coupled parameters (peak wave direction, water level, wind direction, wind speed, along with regional salinity and temperature values) to account for the full set of needed boundary conditions for nearshore models.

Impact

Despite evidence of some of the fastest changes in oceanographic conditions with the disappearance of Arctic sea ice and a corresponding increase in open water season and fetch lengths, a robust and comprehensive assessment of the incident Arctic wave climate is lacking with high-fidelity studies only focusing on extreme conditions or, more commonly, Arctic researchers searching for simplifications due to the computational expense and level of expertise required in developing nearshore oceanographic conditions. This present study develops location-independent typologies based on decades of site-specific data to reduce the number of boundary conditions needed to assess nearshore oceanographic environments.  These computationally efficient simulations of the nearshore oceanographic environments in turn enable the characterization of coastal processes (e.g., erosion, flooding, or sediment transport) impacting the region.

Summary

Erosion and flooding impacts on Arctic coastal environments are intensifying with nearshore oceanographic conditions acting as a key environmental driver. A robust and comprehensive assessment of the nearshore oceanographic conditions requires knowledge of the following boundary conditions: incident wave energy, water level, incident wind energy, ocean temperature and salinity, bathymetry, and shoreline orientation. The number of offshore oceanographic boundary conditions can be large, requiring significant computational investment to reproduce nearshore conditions. This present study develops location-independent typologies to reduce the number of boundary conditions needed to assess nearshore oceanographic environments in both a Historical (2007-2019) and Future (2020-2040) timespan along the Alaskan North Slope. We used WAVEWATCH III® and Delft3D-FLOW model output from six oceanographic sites located along a constant ~50 m bathymetric line spanning the Chukchi to Beaufort Seas. K-means clustering was applied to the energy-weighted joint-probability distribution of significant wave height (Hs) and peak period (Tp). Distributions of wave and wind direction, wind speed, and water level associated with location-independent centroids were assigned single values to describe a reduced order, typological rendition of offshore oceanographic conditions. Reanalysis data (e.g., ASRv2, ERA5, and GOFS) grounded the historical simulations while projected conditions were obtained from downscaled GFDL-CM3 forced under RCP8.5 conditions. Location dependence for each site is established through the occurrence of joint-probability distribution in the form of unique scaling factors representing the fraction of time that the typology would occupy over a representative year. As anticipated, these typologies show increasingly energetic ocean conditions in the future. They also enable computationally efficient simulation of the nearshore oceanographic environment along the North Slope of Alaska for better characterization of coastal processes (e.g., erosion, flooding, or sediment transport).

Point of Contact
Diana Bull
Institution(s)
Sandia National Laboratories (SNL)
Funding Program Area(s)
Publication