A framework for seamless one-way nesting of internal wave-resolving ocean models

JS Rogers, MD Rayson, DS Ko, KB Winters, OB Fringer - Ocean Modelling, 2019 - Elsevier
JS Rogers, MD Rayson, DS Ko, KB Winters, OB Fringer
Ocean Modelling, 2019Elsevier
To resolve nonlinear internal wave motions in regional domains, we propose a one-way
ocean model nesting framework in which a nonhydrostatic model is nested within a regional
model. The nesting scheme produces internal tides that propagate freely into and out of the
nonhydrostatic model domain with no reflections while nudging the low-frequency motions.
We demonstrate the method with idealized test cases and a field-scale simulation of the
South China Sea. The method relies on four parameters, the time scale of low-pass filtering …
Abstract
To resolve nonlinear internal wave motions in regional domains, we propose a one-way ocean model nesting framework in which a nonhydrostatic model is nested within a regional model. The nesting scheme produces internal tides that propagate freely into and out of the nonhydrostatic model domain with no reflections while nudging the low-frequency motions. We demonstrate the method with idealized test cases and a field-scale simulation of the South China Sea. The method relies on four parameters, the time scale of low-pass filtering, the time scale of low frequency forcing, the time scale of sponge damping and the width of the sponge damping layer. We present guidelines for selecting these four parameters based on the background stratification. The internal tides from the large-scale model steepen through nonlinear and nonhydrostatic effects and reasonably match observations at a fraction of the computational cost needed with a single uncoupled model. The model critically relies on the background stratification inherited from the large-scale model, which in this case leads to an underpredicted phase speed and amplitude in the nested model result. This highlights the need for large-scale ocean models to incorporate observational stratification data, especially in the ocean interior.
Elsevier
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