Japan Geoscience Union Meeting 2022

Presentation information

[E] Oral

A (Atmospheric and Hydrospheric Sciences ) » A-CG Complex & General

[A-CG36] Dynamics of Oceanic and Atmospheric Waves, Vortices, and Circulations

Wed. May 25, 2022 10:45 AM - 12:15 PM Exhibition Hall Special Setting (2) (Exhibition Hall 8, Makuhari Messe)

convener:Kunihiro Aoki(Japan Agency for Marine Earth Science and Technology), convener:Shane R Keating(University of New South Wales), Yukiharu Hisaki(University of the Ryukyus), convener:Norihiko Sugimoto(Keio University, Department of Physics), Chairperson:Shane R Keating(University of New South Wales), Norihiko Sugimoto(Keio University, Department of Physics)

10:45 AM - 11:00 AM

[ACG36-07] Predicting internal tides and large amplitude nonlinear internal waves in the ocean

★Invited Papers

*Nicole L Jones1, Matthew Rayson1, Yankun Gong2, Edward Cripps1, Gregory Ivey1 (1.University of Western Australia, 2.South China Sea Institute of Oceanology)

Keywords:internal waves, directional decomposition, internal tides, uncertainty quantification, ocean prediction

Prediction of internal tides and non-linear internal waves is important for numerous practical and ecological applications. For example, accurate prediction of internal tides is a crucial step in interpreting the future Surface Water Ocean Topography (SWOT) high-resolution altimetry mission and hence obtaining the submesoscale variability. Here we will describe our recent advances towards the prediction of internal tides and internal waves. First, we have gained predictive ability of the seasonal variations in the internal tide in the SE Indian Ocean through the development of a new seasonal harmonic model. Second, we have developed methodologies to determine appropriate incoming internal tide boundary conditions for regional ocean circulation models by separating incoming and outgoing internal tides in global products. Third, we have developed a technique for the efficient prediction of nonlinear internal waves via a variable coefficient Kortewg-deVries equation using Monte Carlo methods to propagate uncertainty in the initial conditions.