Abstract

Iron is a key micronutrient for marine biogeochemistry, limiting growth and nitrogen fixation in over a third of the ocean. However, the impacts of iron-binding ligands and iron source processes on dissolved iron distributions is not well known. The goal of this dissertation is to better understand the cycling of iron-binding ligands and the controls on their distributions and how that impacts dissolved iron. This dissertation also seeks to understand how individual iron sources influence iron distributions and biogeochemistry. To accomplish this, a new prognostic ligand tracer and iron-ligand speciation chemistry are incorporated into the Community Earth System Model. The CESM is now able to simulate realistic distributions of iron-binding ligands. The results show that with relatively few ligand sources and sinks the model was able to match observations of ligands, and that inclusion of a dynamic ligand tracer improves simulation of dissolved iron. To better understand the influence iron sources have on dissolved iron concentrations and biogeochemistry, sensitivity experiments for each source are conducted with the CESM. The results show that atmospheric dust and sedimentary iron inputs have the largest impact on dissolved iron concentrations and biogeochemistry. Hydrothermal vent inputs are important for deep ocean iron, and their inclusion in global biogeochemical ocean models would allow for more realistic iron simulation. This dissertation work also reevaluates the parameters governing the temperature influence on community phytoplankton growth rates. A dataset of in situ community phytoplankton growth rates was compiled and parameter values for the Q10 and Arrhenius models were optimized for use in global biogeochemical and ecosystem models. The results show and optimized Q10 value of 1.47 and an activation energy of 0.277 eV. Both the Q10 and Arrhenius models do equally well for estimating the temperature influence on community phytoplankton growth rates against the dataset. Evaluation of global biogeochemical and ecosystem models against our dataset will allow for further constraints on phytoplankton ecology and associated biogeochemitry.