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Register a new userParcels v0.9: prototyping a Lagrangian Ocean Analysis framework for the petascale age
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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.
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Dynamical systems theory approach has been successfully used in physical oceanography for the last two decades to study mixing and transport of water masses in the ocean. The basic theoretical ideas have been borrowed from the phenomenon of chaotic advection in fluids, an analogue of dynamical Hamiltonian chaos in mechanics. The starting point for analysis is a velocity field obtained by this or that way. Being motivated by successful applications of that approach to simplified analytic models of geophysical fluid flows, researchers now work with satellite-derived velocity fields and outputs of sophisticated numerical models of ocean circulation. This review article gives an introduction to some of the basic concepts and methods used to study chaotic mixing and transport in the ocean and a brief overview of recent results with some practical applications of Lagrangian tools to monitor spreading of Fukushima-derived radionuclides in the ocean.
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.
A new framework is proposed for the evaluation of stochastic subgrid-scale parameterizations in the context of MAOOAM, a coupled ocean-atmosphere model of intermediate complexity. Two physically-based parameterizations are investigated, the first one based on the singular perturbation of Markov operator, also known as homogenization. The second one is a recently proposed parameterization based on the Ruelles response theory. The two parameterization are implemented in a rigorous way, assuming however that the unresolved scale relevant statistics are Gaussian. They are extensively tested for a low-order version known to exhibit low-frequency variability, and some preliminary results are obtained for an intermediate-order version. Several different configurations of the resolved-unresolved scale separations are then considered. Both parameterizations show remarkable performances in correcting the impact of model errors, being even able to change the modality of the probability distributions. Their respective limitations are also discussed.
Sedimentation of particles in the ocean leads to inhomogeneous horizontal distributions at depth, even if the release process is homogeneous. We study this phenomenon considering a horizontal sheet of sinking particles immersed in an oceanic flow, and determine how the particles are distributed when they sediment on the seabed (or are collected at a given depth). The study is performed from a Lagrangian viewpoint attending to the properties of the oceanic flow and the physical characteristics (size and density) of typical biogenic sinking particles. Two main processes determine the distribution, the stretching of the sheet caused by the flow and its projection on the surface where particles accumulate. These mechanisms are checked, besides an analysis of their relative importance to produce inhomogeneities, with numerical experiments in the Benguela region. Faster (heavier or larger) sinking particles distribute more homogeneously than slower ones.
Dissolved manganese (Mn) is a biologically essential element, and its oxidised form is involved in the removal of trace elements from ocean waters. Recently, a large number of highly accurate Mn measurements have been obtained in the Atlantic, Indian and Arctic Oceans as part of the GEOTRACES programme. The goal of this study is to combine these new observations with state-of-the-art modelling to give new insights into the main sources and redistribution of Mn throughout the ocean. To this end, we simulate the distribution of dissolved Mn using a global-scale circulation model. This first model includes simple parameterisations to account, realistically, for the sources, processes and sinks of Mn in the ocean. Whereas oxidation and (photo)reduction, as well as aggregation and settling are parameterised in the model, biological uptake is not yet taken into account by the model. Our model reproduces observations accurately and provides the following insights: - The high surface concentrations of manganese are caused by the combination of photoreduction and sources to the upper ocean. The most important sources are dust, then sediments, and, more locally, rivers. - Results show that surface Mn in the Atlantic Ocean moves downwards into the North Atlantic Deep Water, but because of strong removal rates the Mn does not propagate southwards. - There is a mostly homogeneous background concentration of dissolved Mn of about 0.10 to 0.15 nM throughout most of the deep ocean. The model reproduces this by means of a threshold on manganese oxides of 25 pM, suggesting that a minimal concentration of Mn is needed before aggregation and removal become efficient. - The observed sharp hydrothermal signals are produced by assuming both a high source and a strong removal of Mn near hydrothermal vents.
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