A Stochastic, Lagrangian Model of Sinking biogenic aggregates in the ocean (SLAMS 1.0): model formulation, validation and sensitivity

Jokulsdottir, T.; Archer, David

doi:10.5194/gmdd-8-5931-2015

Cited by 7 publications

(16 citation statements)

References 119 publications

(113 reference statements)

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“…More recently, models have begun to consider variable particle sinking speeds with respect to depth and small numbers of size classes of particles (Aumont & Bopp, ; Gregg et al, ; Schmittner et al, ), or variation in temperature and oxygen concentrations (Laufkötter et al, ), and aggregation and disaggregation of a few size classes of particles (Gehlen et al, ). Our data suggest that it will be particularly important to consider the effects of particle size, water temperature, and, to a lesser extent, oxygen concentration to constrain particle flux (see also Jokulsdottir & Archer, ). While it is computationally expensive to simulate particle size distribution dynamics as we have done here, models like this one can be used to inform the relationship between particle size and flux to constrain more elaborate global ocean circulation models.…”

Section: Discussionmentioning

confidence: 90%

The Role of Particle Size, Ballast, Temperature, and Oxygen in the Sinking Flux to the Deep Sea

Cram

Weber

Leung

et al. 2018

Global Biogeochemical Cycles

115

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The “transfer efficiency” of organic particles from the surface to depth is a critical determinant of ocean carbon sequestration. Recently, direct observations and geochemical analyses have revealed a systematic geographical pattern of transfer efficiency, which is highest in high latitude regions and lowest in the subtropical gyres. We evaluate the possible causes of this pattern using a mechanistic model of sinking particle dynamics. The model represents the size distribution of particles, the effects of mineral ballast, seawater temperature (which influences both particle settling velocity and microbial metabolic rates), and O2. Parameters are optimized within reasonable ranges to best match the observational constraints. Our model shows that no single factor can explain the observed pattern of transfer efficiency, but the biological effect of temperature on remineralization rate and particle size effects together can reproduce most of the regional variability with both factors contributing to low transfer efficiency in the subtropical gyres and high transfer efficiency in high latitudes. Particle density from mineral ballast has a similar directional effect to temperature and size but plays a substantially smaller role in our optimum solution, due to the opposing patterns of silicate and calcium carbonate ballasting. Oxygen effects modestly improved model fit by depressing remineralization rates and thus increasing transfer efficiency in the Eastern Tropical Pacific. Our model implies that climate‐driven changes to upper ocean temperature and associated changes in surface plankton size distribution would reduce the carbon sequestration efficiency in a warmer ocean.

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Section: Discussionmentioning

confidence: 90%

The Role of Particle Size, Ballast, Temperature, and Oxygen in the Sinking Flux to the Deep Sea

Cram

Weber

Leung

et al. 2018

Global Biogeochemical Cycles

115

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“…Being readily abundant and adhesive, TEP play a pivotal role in particle aggregation processes and carbon export fluxes 16 . Variations in carbon export fluxes influenced by TEP are assumed to be significant for CO 2 sequestration on a global scale 17, 18 . According to a global ocean carbon cycling model, a 5% increase in TEP export between preindustrial and present day times could be responsible for an increase in CO 2 sequestration by 97 Gt C for the time period 1770–2200 18 .…”

Section: Introductionmentioning

confidence: 99%

Inter-annual variability of transparent exopolymer particles in the Arctic Ocean reveals high sensitivity to ecosystem changes

Engel

Piontek

Metfies

et al. 2017

Sci Rep

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Transparent exopolymer particles (TEP) are a class of marine gel particles and important links between surface ocean biology and atmospheric processes. Derived from marine microorganisms, these particles can facilitate the biological pumping of carbon dioxide to the deep sea, or act as cloud condensation and ice nucleation particles in the atmosphere. Yet, environmental controls on TEP abundance in the ocean are poorly known. Here, we investigated some of these controls during the first multiyear time-series on TEP abundance for the Fram Strait, the Atlantic gateway to the Central Arctic Ocean. Data collected at the Long-Term Ecological Research observatory HAUSGARTEN during 2009 to 2014 indicate a strong biological control with highest abundance co-occurring with the prymnesiophyte Phaeocystis pouchetii. Higher occurrence of P. pouchetii in the Arctic Ocean has previously been related to northward advection of warmer Atlantic waters, which is expected to increase in the future. Our study highlights the role of plankton key species in driving climate relevant processes; thus, changes in plankton distribution need to be accounted for when estimating the ocean’s biogeochemical response to global change.

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“…To simulate coagulation of multiple particle sources, we based our model on the Stochastic Lagrangian Aggregate Model for Sinking Particles (SLAMS) developed and described by Jokulsdottir and Archer (). This is a 1‐D model that uses a Monte Carlo approach to simulate the coagulation and disaggregation of marine particles, predicting the evolution of the particle size spectrum with time and depth.…”

Section: Model Developmentmentioning

confidence: 99%

“…In the model, aggregate breakup occurs if either of the following criteria are satisfied: (1) the size of the aggregate is larger than the Kolmogorov length scale

η = {(ν^{3} / ϵ)}^{1 / 4}

, where ν is the fluid kinematic viscosity and ϵ is the energy dissipation rate, and (2) aggregate stickiness <0.02 (Jokulsdottir & Archer, ). The kinematic viscosity of the water is calculated from the dynamic viscosity (

μ = ν ρ_{w}

where ρ w is the density of water) based on seawater properties (section 2.3).…”

Section: Model Developmentmentioning

confidence: 99%

“…Because equation is somewhat ad hoc, and there are no existing models for the stickiness of a heterogenous aggregate, we used two additional formulations for aggregate stickiness to investigate the sensitivity of the model results. In the first case, we assumed that aggregate stickiness depended only on the volume fractions of organic carbon and TEP

S_{a g g} = (A V_{O r g C} + V_{T E P}) / V_{a g g}

where A = 0.1 is a constant that was defined in Jokulsdottir and Archer (). The third model we assumed that the same components contributed to stickiness as in equation , but that each contributed equally to the overall stickiness depending on their volume fraction

S_{a g g} = (V_{O r g C} + V_{T E P} + V_{O i l} + V_{S e d}) / V_{a g g}

…”

Section: Model Developmentmentioning

confidence: 99%

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Numerical Modeling of the Interactions of Oil, Marine Snow, and Riverine Sediments in the Ocean

et al. 2018

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Natural or spilled oil in the ocean can interact with marine snow and sediment from riverine sources and form Marine Oil Snow (MOS) aggregates including aggregates consisting of phytoplankton, detritus, and feces. Such aggregates have a fractal structure and can transport oil from the surface layers to greater depths in the ocean, eventually settling on the seafloor. In recent studies of the Deepwater Horizon and IXTOC‐1 oil spills in the Gulf of Mexico, this process was identified as one of the main mechanisms for transporting oil vertically in the water column. We have adapted a stochastic, one‐dimensional numerical model that uses coagulation theory to simulate MOS formation and sinking in the ocean and predict the time evolution of physical properties and spatial distribution of MOS. Here we present the model development, calibration, and validation with measured MOS field data in the Gulf of Mexico during the Deepwater Horizon spill. We use a sensitivity analysis to identify critical parameters, and suggest future model improvements and areas where further experimental investigation is needed to improve our understanding of MOS formation and sedimentation. The model can be used during response and planning activities associated with oil spills in the marine environments.

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A Stochastic, Lagrangian Model of Sinking biogenic aggregates in the ocean (SLAMS 1.0): model formulation, validation and sensitivity

Cited by 7 publications

References 119 publications

The Role of Particle Size, Ballast, Temperature, and Oxygen in the Sinking Flux to the Deep Sea

The Role of Particle Size, Ballast, Temperature, and Oxygen in the Sinking Flux to the Deep Sea

Inter-annual variability of transparent exopolymer particles in the Arctic Ocean reveals high sensitivity to ecosystem changes

Numerical Modeling of the Interactions of Oil, Marine Snow, and Riverine Sediments in the Ocean

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