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Comment on gmd-2021-195
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This is a peer-review comment on a study presenting parameterisations for wind-driven turbulent mixing in ocean surface mixed layers for use in Lagrangian particle tracking models. The comment engages with the methodology for modelling vertical transport of particles — including microplastics — in the ocean.
Abstract. Turbulent mixing is a vital component of vertical particulate transport, but ocean global circulation models (OGCMs) generally have low-resolution representations of near-surface mixing. Furthermore, turbulence data are often not provided in OGCM model output. We present 1D parametrizations of wind-driven turbulent mixing in the ocean surface mixed layer that are designed to be easily included in 3D Lagrangian model experiments. Stochastic transport is computed by Markov-0 or Markov-1 models, and we discuss the advantages and disadvantages of two vertical profiles for the vertical diffusion coefficient Kz. All vertical diffusion profiles and stochastic transport models lead to stable concentration profiles for buoyant particles, which for particles with rise velocities of 0.03 and 0.003âmâsâ1 agree relatively well with concentration profiles from field measurements of microplastics when Langmuir-circulation-driven turbulence is accounted for. Markov-0 models provide good model performance for integration time steps of Îtâ30âs and can be readily applied when studying the behavior of buoyant particulates in the ocean. Markov-1 models do not consistently improve model performance relative to Markov-0 models and require an additional parameter that is poorly constrained.
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Comment on gmd-2021-195
AI summary Read the abstract
This is a reviewer comment on a modeling study about turbulent mixing and vertical transport of particles in ocean circulation models, with no original research content.
Comment on gmd-2021-195
AI summary Read the abstract
This is a reviewer comment on a modeling study about turbulent mixing and vertical particle transport in ocean circulation models, with no original research content.
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AI summary Read the abstract
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