Nitrate Sources, Supply, and Phytoplankton Growth in the Great Australian Bight: An Eulerian‐Lagrangian Modeling Approach

Abstract

The Great Australian Bight (GAB), a coastal sea bordered by the Pacific, Southern, and Indian Oceans, sustains one of the largest fisheries in Australia but the geographical origin of nutrients that maintain its productivity is not fully known. We use 12 years of modeled data from a coupled hydrodynamic and biogeochemical model and an Eulerian‐Lagrangian approach to quantify nitrate supply to the GAB and the region between the GAB and the Subantarctic Australian Front (GAB‐SAFn), identify phytoplankton growth within the GAB, and ascertain the source of nitrate that fuels it. We find that nitrate concentrations have a decorrelation timescale of ∼60 days; since most of the water from surrounding oceans takes longer than 60 days to reach the GAB, 23% and 75% of nitrate used by phytoplankton to grow are sourced within the GAB and from the GAB‐SAFn, respectively. Thus, most of the nitrate is recycled locally. Although nitrate concentrations and fluxes into the GAB are greater below 100 m than above, 79% of the nitrate fueling phytoplankton growth is sourced from above 100 m. Our findings suggest that topographical uplift and stratification erosion are key mechanisms delivering nutrients from below the nutricline into the euphotic zone and triggering large phytoplankton growth. We find annual and semiannual periodicities in phytoplankton growth, peaking in the austral spring and autumn when the mixed layer deepens leading to a subsurface maximum of phytoplankton growth. This study highlights the importance of examining phytoplankton growth at depth and the utility of Lagrangian approaches.

Larval behaviour, dispersal and population connectivity in the deep sea

“…A direct comparison of individual modelled Lagrangian particle tracks with real-world drifter tracks is impossible -uncertainties in the initialization of the model and missing model dynamics mean that the simulated and real flow fields are never the same and Lagrangian particles in each will quickly diverge. However, when large numbers of virtual particles are released, as we do here, ensembles of particle tracks have proved adept at reproducing distributions of drifting buoys, marine litter, and nutrient concentrations [35][36][37][38][39] . The current generation of global-and basin-scale models at least partially resolve mesoscale eddies, and shows some success in Figure 1.…”

Shelf Transport Pathways Adjacent to the East Australian Current Reveal Sources of Productivity for Coastal Reefs

“…Lagrangian particle tracking driven by velocity outputs from a hydrodynamic ocean circulation model is a useful tool with which to investigate the transport pathways of water and particulates in space and time (e.g., Roughan et al, 2003Roughan et al, , 2011Cowen et al, 2006;Cetina-Heredia et al, 2019). Passive particles can be used to represent zooplankton (Roughan et al, 2005a;Cetina-Heredia et al, 2019b;Norrie et al, 2020), nutrients (e.g., Cetina-Heredia et al, 2018), kelp (e.g Coleman et al, 2011Coleman et al, , 2013, watermasses (Roughan et al, 2003;Cetina-Heredia et al, 2014), oil (Paris et al, 2012) or other tracers in order to identify typical dispersal pathways (van Sebille et al, 2018) under present and future scenarios (e.g., Cetina-Heredia et al, 2015).…”

Speeding up Python-based Lagrangian Fluid-Flow Particle Simulations via Dynamic Collection Data Structures

“…Our physics-motivated prime application scenario is the tracking of (virtual) particles in oceanic flows. These virtual particles can for example represent plastic [15], plankton [18], nutrients [5] or fish [23]. The tracing is done by Lagrangian advection, where particles follow a given background (Eulerian) advective flow field [9] and whose trajectories are numerically integrated over time as given in eq.…”