Multiscale modeling of coastal, shelf, and global ocean dynamics

Abstract

In contemporary ocean science, modeling systems that integrate understanding of complex multiscale phenomena and utilize efficient numerics are paramount. Many of today's fundamental ocean science questions involve multiple scales and multiple dynamics. A new generation of modeling systems would allow to study such questions quantitatively by being less restrictive dynamically and more efficient numerically than more traditional systems.Such multiscale ocean modeling is the theme of this topical collection. Two large international workshops were organized on this theme, one in Cambridge, USA (IMUM2010), and one in Bremerhaven, Germany (IMUM2011). Contributions from the scientific community were encouraged on all aspects of multiscale ocean modeling from ocean science and dynamics to the development of new computational methods and systems. Building on previous meetings (e.g., Deleersnijder and Lermusiaux 2008;Deleersnijder et al. 2010), the workshop discussions and the final contributions to the topical collection are summarized next.The scientific application domains discussed and presented ranged from estuaries to the global ocean, including coastal regions and shelf seas. Multi-resolution modeling of physical, biological, chemical, and sea ice processes as well as air-sea interactions were described. The multiscale dynamics considered involved hydrostatic, non-hydrostatic, turbulent, and sea surface processes.Computational results and discussions emphasized multiresolution simulations using unstructured and structured meshes, aiming to widen the range of resolved scales in space and time. They included finite volume and finite element spatial discretizations, high-order schemes, preconditioners, solver issues, grid generation, adaptive modeling, data assimilation, coupling with atmospheric or biogeochemical models, and distributed computing. The advantages of using unstructured meshes and related approaches in particular multi-grid embedding, nesting systems, wavelets, and other multiscale decompositions were discussed. Techniques for the study of multi-resolution results, visualization, optimization, model evaluations, and uncertainty quantification were also examined.

Discovering multiscale and self-similar structure with data-driven wavelets

“…M any important processes are multiscale in nature, meaning that they exhibit structure at multiple scales of time and/or space. In nature, a prominent example is the dynamics of oceans and associated interactions with the atmosphere, which govern the planet's weather and climate systems (1); much effort is expended in capturing and understanding effects at multiple scales of time and space (2). In engineering, a prominent example is networks, specifically social media networks.…”

Hybridizable discontinuous Galerkin projection methods for Navier–Stokes and Boussinesq equations

“…Two potential improvements are to calculate these bounds based on the upwind direction, or based on points sampled between nodes. Finally, our implementation can be further optimized and parallelized to improve efficiency [38], and allow higher resolution required for more realistic and multiscale applications [17,34].…”

The Fluid Mechanics of Deep-Sea Mining

“…In contrast to collector plumes, buoyant turbulent jets and plumes in stratified environments are ubiquitous in geophysical fluid dynamics and as such have been extensively studied (Morton et al 1956, List 1982, with the role of sedimentation in particle-laden plumes having been further investigated in the context of volcanic ash clouds and hydrothermal vents (Sparks 1986, Ernst et al 1996reviewed in Woods 2010). The evolution of a midwater return plume immediately after discharge from a pipe and in the following buoyancy-driven phase is therefore well-described by existing jet and buoyant plume models (Morton et al 1956), which have been advanced for application by accounting for factors such as sedimentation or the presence of a background crossflow (Ernst et al 1996, Lee & Chu 2003, Devenish et al 2010). While such models adequately capture the discharge and buoyancy-driven phases of midwater return plumes, which set the initial condition for the more challenging passive-transport phase, the key physics and scaling of the former are presented in the following.…”

“…Underresolving the vertical gradients of concentration leads to numerical diffusion that far exceeds the vertical turbulent diffusion of the benthic boundary layer, which plays a crucial role in interacting with sediment settling and in setting the vertical extent of the plume (Ouillon et al 2022b). Aside from the challenges of plume transport modeling, accurately predicting mesocale and submesoscale features in regional models remains highly nontrivial, and models are still being actively developed (Lermusiaux et al 2013).…”