Using satellite ocean colour to explore phytoplankton dynamics and size in East Australian waters

University of Technology Sydney. Faculty of Science.The eastern Australian ocean region is strongly influenced by the East Australian Current (EAC). Waters in this region are generally oligotrophic; despite this, nutrient enrichment and phytoplankton blooms occur as a response to physical events such as the seasonal deepening of the mixed layer or the formation of cyclonic eddies. In this PhD project, biogeochemical and optical modelling, ocean color data assimilation, in situ measurements and ship-board experiments were used to investigate phytoplankton dynamics and size structure in offshore eastern Australian waters, information that is necessary to improve estimates of future ocean primary productivity. First, the seasonal phytoplankton dynamics in averaged cyclonic and anticyclonic eddies (CE and ACE, respectively) off eastern Australia were explored through a single and a multi-phytoplankton class biogeochemical model. Seasonal climatologies of surface chlorophyll-a concentration (Chl-a) and mixed layer depth for both CE and ACE were obtained by combining remotely sensed sea surface height, remotely sensed ocean color and in situ profiles from Argo floats. Simulated phytoplankton responses to changes in nutrients and light were compared with a ship-based experiment. The experimental results were consistent with the model result, where the seasonal deepening of the mixed layer during winter produced a rapid increase in large phytoplankton. Although the Chl-a concentration in CE was larger than ACE, the primary production estimates obtained through the assimilation of the ocean colour product within different types of eddies were similar, showing an inconsistency with previously published studies that suggest CE are significantly more productive. To explore the properties and relationship of the satellite ocean colour product and in situ observations, theoretical experiments were performed through a coupled biogeochemical-optical model. Specifically, an optical model was used to calculate the inherent optical properties (IOPs) of seawater from size dependent multi-phytoplankton biogeochemical model simulations and convert them into remote-sensing reflectance (R). Then, R was used to produce a satellite-like estimate of the simulated surface Chl-a concentration through the OC3M algorithm. The information content of simulated in situ and simulated remotely-sensed data sources was investigated through theoretical experiments that suggested the OC3M algorithm underestimates the simulated Chl-a concentration because of the weak relationship between large-sized phytoplankton and R. Finally, this concept was tested with real data collected on a voyage in 2016, to investigate the relationship between the in situ sampled phytoplankton size structure and the corresponding satellite Chl-a product. Ocean colour match-up points confirmed the underestimation of in situ Chl-a concentrations when phytoplankton larger than 10 μm dominated the photosynthetic community. Furthermore, optical model simulations suggested that large phytoplankton cells cause a decrease in both the absorption and backscattering signals, which in turn affect the R and cause the underestimation of Chl-a by the satellite Chl-a product. To understand impacts of contemporary ocean change on regional primary productivity, we rely on biogeochemical models to scale up sparse in situ observations. Although ocean colour provides information at high spatial and temporal resolution, this information has limited accuracy. Results presented in this thesis show that a simultaneous assimilation of in situ and satellite remote sensing can provide additional information about the phytoplankton size structure, crucial data to progress our understanding of processes influencing regional primary productivity and elemental cycling. Therefore, parameter optimization through a combination of the information provided by two distinct observation platforms (in situ and satellite remote sensing) will lead to the development of next-generation biogeochemical models