Transformations and deep intrusions of particles and plankton in the global oceans: which particles sink deeper and why

Abstract

Sinking marine particles transport carbon from the ocean’s surface to the deep ocean, thereby contributing to atmospheric carbon dioxide modulation and benthic food supply. Many studies have shown that particle size is not a good predictor of particle sinking speed or behavior. Thus, the overarching question of this dissertation: why do certain particles sink faster or deeper than others, and is there a way to predict what depth a particle will reach in the ocean? Multiple facets of the ocean’s biological carbon pump are investigated using a combination of sediment traps, in situ particle imaging, and machine learning technology. In the Gulf of Alaska, we find aggregates contributed 61% to total carbon flux, suggesting that aggregation processes, not zooplankton repackaging, played a dominant role in carbon export. The role of the physical environment on the biological carbon pump was investigated in the Southern Ocean. Fluffy aggregates and grazers were most common at the surface during a phytoplankton bloom, whereas 1-3 months after a bloom, grazers are in the mesopelagic and feces and dense aggregates are in high abundance in the bathypelagic. These results shed light on how frontal structures in the Southern Ocean influence patterns of particle export and remineralization in the mesopelagic with implications for how this influences global biogeochemical cycles. Finally, the effect of biogeochemical province and carbonate saturation state was investigated in the tropical and subtropical North Atlantic and Pacific. We find that plankton distribution and marine particle morphology in the Atlantic Ocean are more strongly impacted by aragonite and calcite saturation state, despite much shallower saturation horizons in the Pacific. This research can help better predict how the strength of carbon storage in the ocean may change with climate change, which is critical for climate modelers to predict the effects of climate change more accurately.