Lagrangian simulations and interannual variability of anchovy egg and larva dispersal in the Sicily Channel

Palatella, Luigi; Bignami, Fabio; Falcini, Federico; Lacorata, Guglielmo; Lanotte, Alessandra S.; Santoleri, Rosalia

doi:10.1002/2013jc009384

Cited by 38 publications

(62 citation statements)

References 52 publications

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“…(2) in oceanic flows, where the particle v and flow u velocities are expressed in a frame rotating with the Earth angular velocity Ω (Elperin et al, 2002;Biferale et al, 2016;Tanga et al, 1996;Provenzale, 1999;Sapsis and Haller, 2009). Both time derivatives d dt and D Dt have to be corrected following the rule…”

Section: The Mrg Equation In a Rotating Frame And Further Simplicationsmentioning

confidence: 99%

“…In the physical community, the framework to model sinking particles is based on the Maxey-Riley-Gatignol equation for a small spherical particle moving in an ambient flow (Maxey and Riley, 1983;Gatignol, 1983;Michaelides, 1997;Provenzale, 1999;Cartwright et al, 2010), which highlights the importance of mechanisms beyond passive transport and constant sinking velocity, such as the role of finite size, inertia and history dependence. A major outcome of these studies is that inhomogeneities and particle clustering can arise spontaneously even if the fluid velocity field is incompressible and particles do not interact (Squires and Eaton, 1991).…”

Section: Introductionmentioning

confidence: 99%

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Modeling the dynamical sinking of biogenic particles in oceanic flow

Monroy

Hernández-Garcı́a

Rossi

et al. 2017

Nonlin. Processes Geophys.

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Abstract. We study the problem of sinking particles in a realistic oceanic flow, with major energetic structures in the mesoscale, focussing on the range of particle sizes and densities appropriate for marine biogenic particles. Our aim is to evaluate the relevance of theoretical results of finite size particle dynamics in their applications in the oceanographic context. By using a simplified equation of motion of small particles in a mesoscale simulation of the oceanic velocity field, we estimate the influence of physical processes such as the Coriolis force and the inertia of the particles, and we conclude that they represent negligible corrections to the most important terms, which are passive motion with the velocity of the flow, and a constant added vertical velocity due to gravity. Even if within this approximation three-dimensional clustering of particles can not occur, two-dimensional cuts or projections of the evolving three-dimensional density can display inhomogeneities similar to the ones observed in sinking ocean particles.

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Section: The Mrg Equation In a Rotating Frame And Further Simplicationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modeling the dynamical sinking of biogenic particles in oceanic flow

Monroy

Hernández-Garcı́a

Rossi

et al. 2017

Nonlin. Processes Geophys.

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“…More details on the KLM definition and implementation can be found in Palatella et al (2014) and Lacorata et al (2014).…”

Section: Numerical Simulations Of Lagrangian Dispersionmentioning

confidence: 99%

Effects of vertical shear in modelling horizontal oceanic dispersion

et al. 2016

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Abstract. The effect of vertical shear on the horizontal dispersion properties of passive tracer particles on the continental shelf of the South Mediterranean is investigated by means of observation and model data. In situ current measurements reveal that vertical gradients of horizontal velocities in the upper mixing layer decorrelate quite fast ( ∼ 1 day), whereas an eddy-permitting ocean model, such as the Mediterranean Forecasting System, tends to overestimate such decorrelation time because of finite resolution effects. Horizontal dispersion, simulated by the Mediterranean sea Forecasting System, is mostly affected by: (1) unresolved scale motions, and mesoscale motions that are largely smoothed out at scales close to the grid spacing; (2) poorly resolved time variability in the profiles of the horizontal velocities in the upper layer. For the case study we have analysed, we show that a suitable use of deterministic kinematic parametrizations is helpful to implement realistic statistical features of tracer dispersion in two and three dimensions. The approach here suggested provides a functional tool to control the horizontal spreading of small organisms or substance concentrations, and is thus relevant for marine biology, pollutant dispersion as well as oil spill applications.

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“…Mesoscale turbulent pair dispersion is simulated by means of a kinematic Lagrangian model, or KLM, i.e., a deterministic velocity field, analytically defined in terms of spatial derivatives of a given stream function, which gives rise to chaotic Lagrangian trajectories [Lacorata et al, 2008;Palatella et al, 2014]. At this regard, we recall that the lack of motion of the Eulerian structures of the kinematic velocity field, at the origin of the so-called ''sweeping'' problem [Thomson and Devenish, 2005], can be overtaken by computing the components of the kinematic velocity field in the reference frame of the mass center of a particle pair [Lacorata et al, 2008].…”

Section: Subgrid Parameterization: the Kinematic Lagrangian Modelmentioning

confidence: 99%

Lagrangian predictability characteristics of an Ocean Model

Lacorata

Palatella

Santoleri

2014

J. Geophys. Res. Oceans

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The Mediterranean Forecasting System (MFS) Ocean Model, provided by INGV, has been chosen as case study to analyze Lagrangian trajectory predictability by means of a dynamical systems approach. To this regard, numerical trajectories are tested against a large amount of Mediterranean drifter data, used as sample of the actual tracer dynamics across the sea. The separation rate of a trajectory pair is measured by computing the Finite-Scale Lyapunov Exponent (FSLE) of first and second kind. An additional kinematic Lagrangian model (KLM), suitably treated to avoid ''sweeping''-related problems, has been nested into the MFS in order to recover, in a statistical sense, the velocity field contributions to pair particle dispersion, at mesoscale level, smoothed out by finite resolution effects. Some of the results emerging from this work are: (a) drifter pair dispersion displays Richardson's turbulent diffusion inside the [10-100] km range, while numerical simulations of MFS alone (i.e., without subgrid model) indicate exponential separation; (b) adding the subgrid model, model pair dispersion gets very close to observed data, indicating that KLM is effective in filling the energy ''mesoscale gap'' present in MFS velocity fields; (c) there exists a threshold size beyond which pair dispersion becomes weakly sensitive to the difference between model and ''real'' dynamics; (d) the whole methodology here presented can be used to quantify model errors and validate numerical current fields, as far as forecasts of Lagrangian dispersion are concerned.

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Lagrangian simulations and interannual variability of anchovy egg and larva dispersal in the Sicily Channel

Cited by 38 publications

References 52 publications

Modeling the dynamical sinking of biogenic particles in oceanic flow

Modeling the dynamical sinking of biogenic particles in oceanic flow

Effects of vertical shear in modelling horizontal oceanic dispersion

Lagrangian predictability characteristics of an Ocean Model

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