Award: OCE-1658436

The purpose of this research was to build a computer model which predicts the concentration of iron (Fe) in the global ocean. Iron is an important nutrient for living things, for example humans must acquire iron from the food we eat in order to stay healthy. Similarly, the phytoplankton (algae) which grow in the oceans require Fe in order to grow, and so in order to live they must extract this iron from seawater. However, it is not well understood how iron gets into seawater, for example it is difficult to quantify the amount which comes from dust deposited in the surface ocean, from seafloor sediments, and from hydrothermal vents. Therefore, with this project we built computer models of Fe in the ocean. Our computer models included different inputs of Fe into the ocean, Fe cycling within the ocean (for example uptake by phytoplankton), and Fe circulation within the ocean. In particular, for this project we built Ocean Circulation Inverse Matrix (OCIM) models of Fe in the oceans. These models use linear algebra techniques so that they can be solved in just a few seconds on a laptop computer (compared to other models which can take weeks to run on supercomputing clusters). Because our models ran so quickly, we were able to test many thousands of different Fe models, in order to see which ones yielded predicted Fe concentrations which were similar to the concentrations of Fe which other scientists had previously measured. By using our special OCIM modeling techniques, we were able to create new models of Fe in the ocean which match observations much better than any previous models. This helps us to better understand how Fe gets into the oceans and what sorts of processes move Fe around in the ocean, thereby giving us valueable information about the nutrition of iron for the phytoplankton that live in the oceans. Last Modified: 10/29/2021 Submitted by: Seth G John