Reply on AC1

Abstract

<strong class="journal-contentHeaderColor">Abstract.</strong> A common task in oceanography is to model the vertical movement of particles such as microplastics, nanoparticles, mineral particles, gas bubbles, oil droplets, fish eggs, plankton, or algae. In some cases, the distribution of vertical rise or settling velocities of the particles in question can span a wide range, covering several orders of magnitude, often due to a broad particle size distribution or differences in density. This requires numerical methods that are able to adequately resolve a wide and possibly multi-modal velocity distribution. Lagrangian particle methods are commonly used for these applications. strength of such methods is that each particle can have its own rise or settling speed, which makes it easy to achieve a good representation of a continuous distribution of speeds. An alternative approach is to use Eulerian methods, where the partial differential equations describing the transport problem are solved directly with numerical methods. In Eulerian methods, different rise or settling speeds must be represented as discrete classes, and in practice only a limited number of classes can be included. Here, we consider three different examples of applications for a water-column model: positively buoyant fish eggs, a mixture of positively and negatively buoyant microplastics, and positively buoyant oil droplets being entrained by waves. For each of the three cases we formulate a model for the vertical transport, based on the advection-diffusion equation with suitable boundary conditions and in one case a reaction term. We give a detailed description of an Eulerian and a Lagrangian implementation of these models, and we demonstrate that they give equivalent results for selected example cases. We also pay special attention to the convergence of the model results with increasing number of classes in the Eulerian scheme, and the number of particles in the Lagrangian scheme. For the Lagrangian scheme, we see the 1/&radic;<em>N_p</em> convergence as expected for a Monte Carlo method, while for the Eulerian implementation, we see a second order (1/<em>N²_k</em>) convergence with the number of classes.