Modelling carbon overconsumption and the formation of extracellular particulate organic carbon

During phytoplankton growth a fraction of dissolved inorganic carbon (DIC) assimilated by phytoplankton is exuded in the form of dissolved organic carbon (DOC), which can be transformed into extracellular particulate organic carbon (POC). A major fraction of extracellular POC is associated with carbon of transparent exopolymer particles (TEP; carbon content = TEPC) that form from dissolved polysaccharides (PCHO). The exudation of PCHO is linked to an excessive uptake of DIC that is not directly quantifiable from utilisation of dissolved inorganic nitrogen (DIN), called carbon overconsumption. Given these conditions, the concept of assuming a constant stoichiometric carbon-to-nitrogen (C:N) ratio for estimating new production of POC from DIN uptake becomes inappropriate. Here, a model of carbon overconsumption is analysed, combining phytoplankton growth with TEPC formation. The model describes two modes of carbon overconsumption. The first mode is associated with DOC exudation during phytoplankton biomass accumulation. The second mode is decoupled from algal growth, but leads to a continuous rise in POC while particulate organic nitrogen (PON) remains constant. While including PCHO coagulation, the model goes beyond a purely physiological explanation of building up carbon rich particulate organic matter (POM). The model is validated against observations from a mesocosm study. Maximum likelihood estimates of model parameters, such as nitrogen- and carbon loss rates of phytoplankton, are determined. The optimisation yields results with higher rates for carbon exudation than for the loss of organic nitrogen. It also suggests that the PCHO fraction of exuded DOC was 63 +/- 20% during the mesocosm experiment. Optimal estimates are obtained for coagulation kernels for PCHO transformation into TEPC. Model state estimates are consistent with observations, where 30% of the POC increase was attributed to TEPC formation. The proposed model is of low complexity and is applicable for large-scale biogeochemical simulations.