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Array-like collection data structures are widely established in Python's scientific computing-ecosystem for high-performance computations. The structure maps well to regular, gridded lattice structures that are common to computational problems in physics and geosciences. High performance is, however, only guaranteed for static computations with a fixed computational domain. We show that for dynamic computations within an actively changing computational domain, the array-like collections provided by NumPy and its derivatives are a bottleneck for large computations. In response, we describe the integration of naturally-dynamic collection data structures (e.g. double-linked lists) into NumPy simulations and \textit{ctypes}-based C-bindings. Our benchmarks verify and quantify the performance increase attributed to the change of the collection data structure. Our application scenario, a Lagrangian (oceanic) fluid-flow particle simulation within the \textit{Parcels} framework, demonstrates the speed-up yield in a realistic setting and demonstrates the novel capabilities that are facilitated by optimised collection data structures., Comment: submitted for review in SIAM Journal on Scientific Computing on 2021-01-25

We study the vertical dispersion and distribution of negatively buoyant rigid microplastics within a realistic circulation model of the Mediterranean sea. We first propose an equation describing their idealized dynamics. In that framework, we evaluate the importance of some relevant physical effects: inertia, Coriolis force, small-scale turbulence and variable seawater density, and bound the relative error of simplifying the dynamics to a constant sinking velocity added to a large-scale velocity field. We then calculate the amount and vertical distribution of microplastic particles on the water column of the open ocean if their release from the sea surface is continuous at rates compatible with observations in the Mediterranean. The vertical distribution is found to be almost uniform with depth for the majority of our parameter range. Transient distributions from flash releases reveal a non-Gaussian character of the dispersion and various diffusion laws, both normal and anomalous. The origin of these behaviors is explored in terms of horizontal and vertical flow organization.

Field studies have shown that plastic fragments make up the majority of plastic pollution in the oceans in terms of abundance. How quickly environmental plastics fragment is not well understood, however. Here, we study this fragmentation process by considering a model which captures continuous fragmentation of particles over time in a cascading fashion. With this cascading fragmentation model we simulate particle size distributions (PSDs), specifying the abundance or mass of particles for different size classes. The fragmentation model is coupled to an environmental box model, simulating the distributions of plastic particles in the ocean, coastal waters, and on the beach. We compare the modelled PSDs to available observations, and use the results to illustrate the effect of size-selective processes such as vertical mixing in the water column and resuspension of particles from the beach into coastal waters. The model quantifies the role of fragmentation on the marine plastic mass budget: while fragmentation is a major source of (secondary) plastic particles in terms of abundance, it seems to have a minor effect on the total mass of particles larger than 0.1 mm. Future comparison to observed PSD data allow us to understand size-selective plastic transport in the environment, and potentially inform us on plastic longevity.

Any type of non-buoyant material in the ocean is transported horizontally by currents during its sinking journey. This lateral transport can be far from negligible for small sinking velocities. To estimate its magnitude and direction, the material is often modelled as a set of Lagrangian particles advected by current velocities that are obtained from Ocean General Circulation Models (OGCMs). State-of-the-art OGCMs are strongly eddying, similar to the real ocean, providing results with a spatial resolution on the order of 10 km on a daily frequency. While the importance of eddies in OGCMs is well-appreciated in the physical oceanographic community, other marine research communities may not. To demonstrate how much the absence of mesoscale features in low-resolution models influences the Lagrangian particle transport, we simulate the transport of sinking Lagrangian particles using low- and high-resolution global OGCMs, and assess the lateral transport differences resulting from the difference in spatial and temporal model resolution. We find major differences between the transport in the non-eddying OGCM and in the eddying OGCM. Addition of stochastic noise to the particle trajectories in the non-eddying OGCM parameterises the effect of eddies well in some cases. The effect of a coarser temporal resolution (5-daily) is smaller compared to a coarser spatial resolution (0.1$^{\circ}$ versus 1$^{\circ}$ horizontally). We recommend to use sinking Lagrangian particles, representing e.g. marine snow, microplankton or sinking plastic, only with velocity fields from eddying OGCMs, requiring high-resolution models in e.g. paleoceanographic studies. To increase the accessibility of our particle trace simulations, we launch planktondrift.science.uu.nl, an online tool to reconstruct the surface origin of sedimentary particles in a specific location.

As Ocean General Circulation Models (OGCMs) move into the petascale age, where the output from global high-resolution model runs can be of the order of hundreds of terabytes in size, tools to analyse the output of these models will need to scale up too. Lagrangian Ocean Analysis, where virtual particles are tracked through hydrodynamic fields, is an increasingly popular way to analyse OGCM output, by mapping pathways and connectivity of biotic and abiotic particulates. However, the current software stack of Lagrangian Ocean Analysis codes is not dynamic enough to cope with the increasing complexity, scale and need for customisation of use-cases. Furthermore, most community codes are developed for stand-alone use, making it a nontrivial task to integrate virtual particles at runtime of the OGCM. Here, we introduce the new Parcels code, which was designed from the ground up to be sufficiently scalable to cope with petascale computing. We highlight its API design that combines flexibility and customisation with the ability to optimise for HPC workflows, following the paradigm of domain-specific languages. Parcels is primarily written in Python, utilising the wide range of tools available in the scientific Python ecosystem, while generating low-level C-code and using Just-In-Time compilation for performance-critical computation. We show a worked-out example of its API, and validate the accuracy of the code against seven idealised test cases. This version~0.9 of Parcels is focussed on laying out the API, with future work concentrating on optimisation, efficiency and at-runtime coupling with OGCMs., Comment: Submitted to Geoscientific Model Development (GMD)