Articles | Volume 9, issue 4
Geosci. Model Dev., 9, 1455–1476, 2016
Geosci. Model Dev., 9, 1455–1476, 2016

Model description paper 19 Apr 2016

Model description paper | 19 Apr 2016

A stochastic, Lagrangian model of sinking biogenic aggregates in the ocean (SLAMS 1.0): model formulation, validation and sensitivity

Tinna Jokulsdottir and David Archer Tinna Jokulsdottir and David Archer
  • Department of the Geophysical Sciences, University of Chicago, Chicago, IL 60637, USA

Abstract. We present a new mechanistic model, stochastic, Lagrangian aggregate model of sinking particles (SLAMS) for the biological pump in the ocean, which tracks the evolution of individual particles as they aggregate, disaggregate, sink, and are altered by chemical and biological processes. SLAMS considers the impacts of ballasting by mineral phases, binding of aggregates by transparent exopolymer particles (TEP), zooplankton grazing and the fractal geometry (porosity) of the aggregates. Parameterizations for age-dependent organic carbon (orgC) degradation kinetics, and disaggregation driven by zooplankton grazing and TEP degradation, are motivated by observed particle fluxes and size spectra throughout the water column. The model is able to explain observed variations in orgC export efficiency and rain ratio from the euphotic zone and to the sea floor as driven by sea surface temperature and the primary production rate and seasonality of primary production. The model provides a new mechanistic framework with which to predict future changes on the flux attenuation of orgC in response to climate change forcing.

Short summary
To better understand what controls the flux of organic and inorganic material down the water column we developed a numerical model that simulates coagulation, settling and bio-chemical transformation of particles in the ocean. To simulate the many types of material the particles constitute, we took a Lagrangian approach. Our results suggest the flux is most sensitive to environmental change in polar regions. We found that zooplankton are the biggest unknown when predicting the flux.