Typological representation of the offshore oceanographic environment along the Alaskan North Slope. (15th July 2022) - Eymold, William K.; Flanary, Christopher; Erikson, Li; Nederhoff, Kees; Chartrand, Christopher C.; Jones, Craig; Kasper, Jeremy; Bull, Diana L.
Abstract: Erosion and flooding impacts to Arctic coastal environments are intensifying with nearshore oceanographic conditions acting as a key environmental driver. Robust and comprehensive assessment of the nearshore oceanographic conditions require 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 a 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 Flexible Mesh 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 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). Highlights: K-means clustering oceanographic data reveals a subset of Arctic Ocean sea states associated with the North Slope of Alaska. We associate wave, wind, water level, temperature, and salinity data with clustered wave heights and period centroids. Sea states determined for the Historical (2007-2019) time span are expected to be more energetic in the Future (2020-2040). Our technique can be applied in diverse ocean settings around the world and to simplify site-specific ocean conditions. Results can be used in coastal hazard analysis (e.g., flooding/erosion) of villages located along the North Slope of Alaska.