A one-dimensional model of iron speciation and biogeochemistry, coupled with the General Ocean Turbulence Model (GOTM) and a NPZD-type ecosystem model, is applied for the Tropical Eastern North Atlantic Time-series Observatory (TENATSO) site. Aimed at investigating the role of organic complexation and dust particles in Fe 5 speciation and bioavailability, the model is extended in this study by a more complex description of the origin and fate of organic ligands and of particle aggregation and

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... nking. Model results show that the profile of dissolved iron is strongly influenced by the abundance of organic ligands. Modelled processes controlling the source and fate 10 of ligands can well explain the abundance of strong ligands. However, a restoring of total weak ligands towards a constant value is required for reproducing the observed nutrient-like profile of weak ligands, indicating that decay time of weak ligands might be too long for a 1d-model. High dust deposition brings not only considerable input of iron into surface waters but 15 also fine inorganic particles for particle aggregation and Fe scavenging. Simulated profiles of dissolved iron show high sensitivity to re-dissolution of colloidal and particulate iron. The colloidal to soluble iron ratio is underestimated assuming that colloidal iron is mainly composed of inorganic colloids. That strongly argues for introducing organic colloids into the model in future work. 20 25 et al., 1996, 2004). It has been hypothesised that iron could indirectly affect primary 4306 Abstract Introduction Conclusions References Tables Figures Back Close Full Screen / Esc Printer-friendly Version Interactive Discussion production in low nutrient regions by limiting nitrogen-fixation, which transforms atmospheric nitrogen into new biologically available nitrate (Mills et al., 2004.; Falkowski, 1997). The solubility of iron is low under oxic conditions. It exists in seawater in different physical and chemical forms, e.g. inorganic soluble ferric and ferrous iron, organically 5 15 pends on the processes influencing iron speciation and bioavailability. Recent studies enhanced our knowledge on many reactions in Fe speciation and factors influencing them. How these individual processes interact and how ecosystems respond to varying Fe speciation, is still not well known. To provide a better understanding of this complex, several numerical models of iron cycling have been developed: global mod-20 els of iron cycling primarily aimed at reproducing the scavenging removal of iron in the deep ocean (Parekh et al., 2004) or the characteristics of regions under iron limitation (Aumont et al., 2003); process-based models have been refined for coastal waters by Rose and Waite (2003a), and for the upper ocean at the Bermuda Atlantic Time-series Study (BATS) site by Weber et al. (2005, 2007). Weber et al. (2007) coupled a one-25 dimensional model of iron speciation and biogeochemistry with the General Ocean Turbulence Model (GOTM) and a NPZD-type ecosystem model. Our model is based on the model by Weber et al. (2007) and has been extended to include a more complex description of the origin and fate of organic ligands and of particle aggregation and 4307 Abstract Introduction Conclusions References Tables Figures Back Close Full Screen / Esc Printer-friendly Version Interactive Discussion sinking. We use it to simulate the cycling of iron at the Tropical Eastern North Atlantic Time-series Observatory (TENATSO) site (17.4 • N, 24.5 • W), a new time-series station north of the Cape Verde Islands. The TENATSO site is strongly influenced by Saharan dust events and the mixed layer depth there has very low seasonal variability providing ideal conditions for investigation of dust deposition on Fe speciation and bioavailability. 5 This study is focusing on two key questions: 1) the role of dust particles in Fe speciation and removal, and 2) the control of dissolved iron concentration by origin and fate of organic Fe-binding ligands. Model description Our model consists of a physical, chemical, and biological model coupled in a one-10 dimensional vertical water column representing the upper 400 m water depth, e.g. horizontal gradients are assumed to be small and thus neglected. The water column under consideration is divided into 67 vertical layers. Spacing increases non-linearly with depth in such a way that the uppermost layer has a thickness of 1 m and there are 28 layers in the upper 100 m. Another setup for the upper 1000 m in 100 layers has 15 been used to compare model-generated vertical fluxes of carbon with sediment trap data. We integrated the model for a total period of 5 years, from 1 January 1990 to 31 December 1994 using the first 2 years for spin-up. Physical model The physical model is the General Ocean Turbulence Model (GOTM, Umlauf and Bur-20 chard, 2005, www.gotm.net), that provided the vertical mixing and advection for given forcing by wind, heat and freshwater fluxes at the surface. The model configuration is based on the configuration by Weber et al. (2007), and has been adapted for the TENATSO site by forcing the model with fluxes derived from the ECMWF atmospheric reanalysis for the TENATSO site, by choosing a slightly different turbulence parameter-25 4308 Abstract Introduction Conclusions References Tables Figures Back Close Full Screen / Esc Printer-friendly Version Interactive Discussion 25 coagulation between small particles. Parameterisation and choosing of the rates and constants are explained in detail in Sect. 4.4. Dust deposition fluxes simulated by Mahowald et al. (2003) are used for prescribing the surface flux of dust particles. The surface flux of iron is calculated with a constant 4310 Abstract 2001). However, increase of ligands was observed after iron fertilisation indicating that some marine organisms release ligands only to keep iron in solution as much as pos-4317 Abstract 6, 4305-4359Abstract 6, 4305-4359