Articles | Volume 15, issue 5
https://doi.org/10.5194/gmd-15-1995-2022
https://doi.org/10.5194/gmd-15-1995-2022
Model description paper
 | 
09 Mar 2022
Model description paper |  | 09 Mar 2022

Empirical Lagrangian parametrization for wind-driven mixing of buoyant particles at the ocean surface

Victor Onink, Erik van Sebille, and Charlotte Laufkötter

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Cited articles

Berloff, P. S. and McWilliams, J. C.: Material transport in oceanic gyres. Part III: Randomized stochastic models, J. Phys. Oceanogr., 33, 1416–1445, 2003. a, b, c
Boufadel, M., Liu, R., Zhao, L., Lu, Y., Özgökmen, T., Nedwed, T., and Lee, K.: Transport of oil droplets in the upper ocean: impact of the eddy diffusivity, J. Geophys. Res.-Oceans, 125, e2019JC015727, https://doi.org/10.1029/2019JC015727, 2020. a, b, c
Brickman, D. and Smith, P.: Lagrangian stochastic modeling in coastal oceanography, J. Atmos. Ocean. Tech., 19, 83–99, 2002. a, b, c, d, e
Brignac, K. C., Jung, M. R., King, C., Royer, S.-J., Blickley, L., Lamson, M. R., Potemra, J. T., and Lynch, J. M.: Marine debris polymers on main Hawaiian Island beaches, sea surface, and seafloor, Environ. Sci. Technol., 53, 12218–12226, 2019. a
Brunner, K., Kukulka, T., Proskurowski, G., and Law, K. L.: Passive buoyant tracers in the ocean surface boundary layer: 2. Observations and simulations of microplastic marine debris, J. Geophys. Res.-Oceans, 120, 7559–7573, 2015. a, b, c, d
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Short summary
Turbulent mixing is a vital process in 3D modeling of particle transport in the ocean. However, since turbulence occurs on very short spatial scales and timescales, large-scale ocean models generally have highly simplified turbulence representations. We have developed parametrizations for the vertical turbulent transport of buoyant particles that can be easily applied in a large-scale particle tracking model. The predicted vertical concentration profiles match microplastic observations well.