Scalings for Eddy Buoyancy Fluxes Across Prograde Shelf/Slope Fronts

Abstract

Continental shelves and slopes host a variety of baroclinic ocean currents, which can arise from tidal mixing fronts (

Impact of Parameterized Isopycnal Diffusivity on Shelf‐Ocean Exchanges Under Upwelling‐Favorable Winds: Offline Tracer Simulations Augmented by Artificial Neural Network

“…Y. Wang and Stewart (2020) and Wei et al. (2022) proposed a set of bathymetry‐aware, energetically constrained scalings for eddy buoyancy fluxes across continental slopes, which should be tested together with our proposed Redi closures prognostically in non‐eddying simulations of continental slope flows. A physics‐based parameterization for the cross‐slope eddy momentum transfer (Manucharyan & Isachsen, 2019; Y. Wang & Stewart, 2018) is yet to be developed.…”

“…∬ 𝑑𝑑𝑑𝑑 𝑑𝑑𝑑𝑑 denotes a spatial-mean operator across a selected region. Notice that R 2 differs from the coefficient of determination in linear least-squares regressions, and ranges between -∞ and unity (Wei & Wang, 2021;Wei et al, 2022). A "perfect" prediction for χ corresponds to R 2 tending to unity and Er approaching zero.…”

“…To quantify the predictive skill of a selected Redi variant, we adopt two statistical metrics: the volume‐weighted Nash and Sutcliffe (1970) coefficient of efficiency R 2 (see also McCuen et al., 2006; Ritter & Munoz‐Carpena, 2013) and the volume‐weighted relative error Er , defined as $$$\left\{\begin{array}{l}{R}^{2}=1-\frac{\langle {\left({\chi }^{\text{true}}-{\chi }^{\text{param}}\right)}^{2}\rangle }{\langle {\left({\chi }^{\text{true}}-\langle {\chi }^{\text{true}}\rangle \right)}^{2}\rangle },\quad \hfill \\ Er=\frac{\sqrt{\langle {\left({\chi }^{\text{true}}-{\chi }^{\text{param}}\right)}^{2}\rangle }}{\sqrt{\langle {\left({\chi }^{\text{true}}\right)}^{2}\rangle }},\quad \hfill \end{array}\right.\quad $$$ where χ true denotes the MITgcm‐based “true” tracer content (interpolated onto the MAMEBUS grid), χ param stands for the content of the same tracer predicted by MAMEBUS, and the angle bracket $$\langle \bullet \rangle =\frac{\iint \bullet \ dy\ dz}{\iint dy\ dz}$$ denotes a spatial‐mean operator across a selected region. Notice that R 2 differs from the coefficient of determination in linear least‐squares regressions, and ranges between ‐ ∞ and unity (Wei & Wang, 2021; Wei et al., 2022). A “perfect” prediction for χ corresponds to R 2 tending to unity and Er approaching zero.…”

Heat transport across the Antarctic Slope Front controlled by cross-slope salinity gradients

“…Moreover, the standard GM-Redi eddy parameterization scheme is not enough to reproduce the positive feedback, leading to even worse performance in shoreward heat transport (fig. S2, A and B), although it is possible that sophisticated "slope-aware" eddy parameterization schemes would perform better (58)(59)(60)(61).…”

The Sensitivity of an Idealized Weddell Gyre to Horizontal Resolution

“…The development and testing of eddy parametrizations is a busy area of research; Hewitt et al (2020) reviews the various approaches that could be deployed in eddy-parametrized and eddy-permitting ocean models. Of particular relevance to the Weddell gyre is a recent study by Wei et al (2022) which finds encouraging results for parametrized mesoscale eddy buoyancy fluxes over large scale bathymetry when topographic suppression effects are incorporated.…”