Simulating Lagrangian subgrid-scale dispersion on neutral surfaces in the ocean
Reijnders, D.; Deleersnijder, E.; van Sebille, E. (2022). Simulating Lagrangian subgrid-scale dispersion on neutral surfaces in the ocean. J. Adv. Model. Earth Syst. 14(2): e2021MS002850. https://dx.doi.org/10.1029/2021MS002850
In: Journal of Advances in Modeling Earth Systems. American Geophysical Union: Washington. e-ISSN 1942-2466, more
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Lagrangian; dispersion; diffusion; neutral surfaces; subgrid-scale; Markov models |
| Authors | | Top |
- Reijnders, D.
- Deleersnijder, E., more
- van Sebille, E.
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| Abstract |
To capture the effects of mesoscale turbulent eddies, coarse-resolution Eulerian ocean models resort to tracer diffusion parameterizations. Likewise, the effect of eddy dispersion needs to be parameterized when computing Lagrangian pathways using coarse flow fields. Dispersion in Lagrangian simulations is traditionally parameterized by random walks, equivalent to diffusion in Eulerian models. Beyond random walks, there is a hierarchy of stochastic parameterizations, where stochastic perturbations are added to Lagrangian particle velocities, accelerations, or hyper-accelerations. These parameterizations are referred to as the first, second and third order “Markov models” (Markov-N), respectively. Most previous studies investigate these parameterizations in two-dimensional setups, often restricted to the ocean surface. On the other hand, the few studies that investigated Lagrangian dispersion parameterizations in three dimensions, where dispersion is largely restricted to neutrally buoyant surfaces, have focused only on random walk (Markov-0) dispersion. Here, we present a three-dimensional isoneutral formulation of the Markov-1 model. We also implement an anisotropic, shear-dependent formulation of random walk dispersion, originally formulated as a Eulerian diffusion parameterization. Random walk dispersion and Markov-1 are compared using an idealized setup as well as more realistic coarse and coarsened (50 km) ocean model output. While random walk dispersion and Markov-1 produce similar particle distributions over time when using our ocean model output, Markov-1 yields Lagrangian trajectories that better resemble trajectories from eddy-resolving simulations. Markov-1 also yields a smaller spurious dianeutral flux. |
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