Iron is one of the most commonly studied trace metals as it exerts a significant influence on ocean productivity, carbon sequestration as well as modulating atmospheric CO2 concentrations. As iron is such a vital nutrient for biogeochemical processes it is often included as a variable in ocean biogeochemical models. In representing the iron cycle, biogeochemical models must parameterise the major processes of uptake by phytoplankton, remineralisation and scavenging. However, there is no generally accepted set of equations to represent iron dynamics and thus a variety of different parameterisations are employed across the modelling community. The thesis work focussed on the inorganic iron parameterisations with an emphasis on the scavenging formalisms which are employed in current biogeochemical models. Using an open-source numerical model (Biogeochemical Flux Model, BFM) as a background model, a more advanced inorganic iron parameterisations that simulates free iron scavenging and ligands linked to dissolved organic carbon (DOC) (from the open-source model PISCES) was included and compared to assess the implications on iron cycling and plankton community structure. The parameterisations were compared by running box models (0D) in four different regions: Southern Ocean, Equatorial Pacific, North Atlantic gyre and North-east Pacific, representing different types of iron dynamics. The free scavenging model (FePISCES) resulted in dissolved iron concentrations being two to three times greater than with the standard formulation (FeBFM), which used a simpler formalism for scavenging. Consequently, the elevated iron concentrations in FePISCES resulted in altered community compositions for phytoplankton which impacted the seasonal cycle of macronutrients and chlorophyll concentrations. Furthermore, the prognostic appreciation of ligand dynamics in FePISCES lead to a decoupling of dissolved iron from its organic species with the DOC content for a region being indirectly implicated in driving the iron system by affecting the scavenging regime. Therefore, using a different set of iron parameterisations will alter the biogeochemical behaviour of a model. The results suggest that the testing of parameterisations should be initially done within 0D models in order to assess any non-linear behaviours and ultimately embedded in 3D models to study how they interact with physics.

In this thesis I explore the effect of parameter uncertainty in ocean biogeochemical models on the calculation of carbon uptake by the ocean. The ocean currently absorbs around a quarter of the annual anthropogenic CO2 emissions to the atmosphere [Scholes et al., 2009], slowing the increase in radiative forcing associated with the increasing atmospheric CO2 concentration. Ocean biogeochemical models have been developed to study the role of the ocean ecosystem in this process. Such models consist of a greatly simplified representation of the hugely complex ocean ecosystem. This simplification requires extensive parameterisation of the biological processes that convert inorganic carbon to and from organic carbon in the ocean. The HadOCC ocean biogeochemical model is a Nutrient-Phytoplankton-Zooplankton-Detritus (NPZD) model that is used to represent the role of the ocean ecosystem in the global carbon cycle in the HadCM3 and FAMOUS GCMs. HadOCC uses twenty parameters to control the processes of biological growth, mortality, grazing and detrital sinking that control the uptake and cycling of carbon in the ocean ecosystem. These parameters represent highly complex and in some cases incompletely understood biological processes, and as a result are uncertain in value. A sensitivity analysis is performed to identify the HadOCC parameters that due to uncertainty in value have the greatest possible effect on the exchange of CO2 between the atmosphere and the ocean—the air-sea CO2 flux. These are found to be the parameters that control phytoplankton growth in the well lit surface ocean, the formation of carbonate by marine organisms and the sinking of biological detritus. The uncertainty in these parameters is found to cause changes to the air-sea CO2 flux calculated by the FAMOUS GCM. The initial effect of these changes is equivalent to the order of the error of current estimates of the net annual carbon uptake by the ocean (2.2 ± 0.3 Pg C y−1 [Gruber et al., 2009], 2.2 ± 0.5 Pg C y−1 [Denman et al., 2007]). This indicates that while the effect of ocean biogeochemical parameter uncertainty is non-negligible, it is within the bounds of the uncertainty of the total (inorganic and organic) ocean carbon system, and is considerably less than the uncertainty in the carbon uptake of the terrestrial biosphere [Houghton, 2007]. However, as the ocean plays a crucial role in the global carbon cycle and the regulation of the Earth’s climate, further understanding and better modelling of the role of the ocean ecosystem in the global carbon cycle and its reaction to anthropogenic climate forcing remains important.