Impact of particle aggregation on vertical fluxes of organic matter
Introduction
Particle fluxes from the ocean’s surface layers to its bottom are important means of carbon export and therefore have a crucial role in the global regulation of atmospheric CO2. Many particles sinking into deep layers do so in the form of aggregates, which are produced by the coagulation of smaller particles. Coagulation does not only enhance the removal of material from euphotic zone, but it can also control the maximum phytoplankton concentration in the ocean (Jackson and Kiørboe, 2008). Among eastern boundary upwelling systems, the NW African Upwelling retains elevated concentrations of aggregates due to high biological productivity, is distinguished by their enhanced export and belongs to the most productive ecosystems of the world’s ocean.
The efficiency of aggregate formation is regulated by factors like particle concentration, stickiness and collision rate. Although settling rates generally increase with aggregate size (Alldredge and Gotschalk, 1988, Alldredge and Gotschalk, 1989), large size does not always guarantee fast sinking (Kiørboe et al., 1998) and sinking characteristics of aggregates depend on their composition, shape and porosity. The processes that can reduce the aggregate size are remineralisation, zooplankton feeding (Stemmann et al., 2004), fragmentation by zooplankton (Dilling and Alldredge, 2000) and disaggregation due to physical shear, which is caused by turbulence. The processes that aggregates go through during their sedimentation determine their vertical flux and distribution of elements they carry in the water column.
A number of models have been developed for a numerical definition of aggregation processes in the ocean with different levels of complexity. According to how collision between particles is described, Jackson (2005) classifies these models into two groups as those having rectilinear and curvilinear coagulation kernels. Rectilinear approximation belongs to the early formulations of coagulation, which assume that particles do not influence water motion and that chemical attraction and repulsion between particles can be ignored. Curvilinear kernels, on the contrary, take into account the effect of the particles on the surrounding fluid and on each other, i.e. van der Waals forces, and predict significantly less collision between particles of dissimilar sizes (Jackson and Burd, 1998). Rectilinear kernels appear, therefore, to generate a much stronger coagulation effect and vertical flux (Jackson, 2001).
Despite being the dominant process in controlling vertical carbon flux from biological production (Jackson et al., 2005) there has been almost no implementation of aggregation models on 3-D oceanic-biogeochemical models. Gehlen et al. (2006) presented a first attempt by employing Kriest’s (2002) model to extend the simple parameterisation of aggregation used in the global biogeochemical model of Aumont and Bopp (2006) into its detritus pool. Dadou et al. (2001) developed a 1-D biogeochemical model to reproduce organic matter fluxes in the NW African Upwelling zone and parameterised aggregation as a second-order process. Herein we use a biogeochemical model in a regional setup of the NW African Upwelling System and consider both phytoplankton and detritus as the constituents of aggregates. The model response to changes in porosity, maximum aggregate size and stickiness is evaluated. The purpose is not to detect which aggregate composition (dense or porous) represents the particle behaviour in the ocean, but rather to find out which set of parameters like aggregate density, i.e. settling velocity, and remineralisation rate give the best estimate of deep fluxes with the same or similar surface productivity pattern. We then compare the simulated particle number concentrations and the particle size spectra with those as measured by the camera profiles. The predicted organic carbon fluxes are also compared to the recordings of sediment traps located off Cape Blanc as well as to the fluxes produced by the original biogeochemical model. By bringing the findings from the field measurements like particle camera and settling chamber together with modelling work this paper tries to shed a little light upon the complexities surrounding the particle transformations in the water column.
Section snippets
Sediment trap, particle camera and settling chamber measurements
We refer to Fischer and Karakaş (2009), for the sediment trap characteristics and analysis of the collection. The particle camera (ParCa) system we used was a modified version of the system described in Ratmeyer and Wefer (1996). It consisted of a NIKON Coolpix digital camera with a 3.34 megapixel resolution. A strobe mounted perpendicular to the camera illuminated the water. Each image recorded the particles within a volume of 12.4 l. A SeaBird 36 telemetry unit and a SeaBird 19 CTD (equipped
Comparison of surface chlorophyll fields to the satellite data
The validation of the generated physical fields by the model has been discussed in Karakaş et al., 2006, Marchesiello and Estrade, 2007. In Fig. 2, we illustrate mean surface chlorophyll distribution from SeaWiFS and model experiments for the year 2002. The general pattern of chlorophyll distribution is similar in all simulations to the satellite data and there is very little discrepancy. This is not surprising because by setting the remineralisation rate proportional to the sinking velocity
Summary and conclusions
Various model experiments were carried out by applying an aggregation model into the phytoplankton and detritus compartments of a biogeochemical model. The model was based on a continuous size spectrum of aggregates. The simulations covered maximum aggregate sizes ranging between 0.4 and 5 mm. Both porous and dense aggregates were considered with reduced stickiness in order to yield limited collision between particles. One experiment also involved a disaggregation term to reduce the size of
Acknowledgements
We thank anonymous referees for their constructive comments that helped to improve the manuscript significantly. The numerical experiments were partly carried out on IBM pSeries 690 Supercomputer of Norddeutscher Verbund für Hoch- und Höchstleistungsrechnen (HLRN). This research was funded by the German Research Foundation (DFG) – Centre for Marine Environmental Sciences (MARUM).
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