Research
Reconciling carbon export and flux attenuation mechanisms in the mesopelagic zone at the Bermuda Atlantic Time-series Study site
Photosynthesis produces approximately 100 gigatons of organic carbon per year in the surface ocean, but only a small fraction of this carbon settles into the mesopelagic zone due to extensive respiration and remineralization by heterotrophic bacteria and zooplankton. Studies have demonstrated significant imbalances between carbon supply and demand in the mesopelagic layer, particularly in cases where particle flux attenuation is up to two orders of magnitude lower than heterotrophic metabolism. This suggests that particle export alone is insufficient to meet the carbon demand of mesopelagic biota and that additional, unaccounted-for sources of organic carbon must exist. Our NSF Chemical Oceanography project aims to reassess the mesopelagic carbon budget at the Bermuda Atlantic Time-series Study site by developing a high-resolution model capable of resolving various carbon export and attenuation pathways. These include diel vertical migration of zooplankton (active flux), mixing of dissolved organic carbon, particle sinking, and attenuation, all integrated within a hybrid modeling framework.
Bridging the "Bacteria Gap" in Earth System Models: Multi-scale modeling of marine microbial metabolism
Marine heterotrophic bacteria are the hidden architects of ocean biogeochemistry, processing vast quantities of carbon through complex metabolic networks that determine whether it is respired back to the upper ocean-atmosphere or sequestered in the deep sea. However, the absence of mechanistic bacterial metabolism in current Earth System Models severely limits our ability to predict how these processes will respond to and influence environmental fluctuations. Our NSF Center for Chemical Currencies of a Microbial Planet (C-CoMP) project aims to bridge this critical "bacteria gap" by developing a multi-scale modeling framework that integrates metabolic translators with ocean biogeochemical models. By leveraging Artificial Intelligence/Machine Learning (AI/ML), we develop computational tools that translate microbial metabolism, modeled through genome-scale Flux Balance Analysis, into ecosystem-scale processes within coupled biogeochemical models. This AI-driven approach enables effective scale transitions, allowing for a more mechanistic representation of microbial functions in gloal ocean biogeochemical models.
Quantifying the risks and efficacy of ocean alkalinity enhancement in the Northeast United States shelf and slope region
Ocean Alkalinity Enhancement (OAE) is a process that alters seawater carbonate chemistry by adding alkaline materials. Our Locking Ocean Carbon in the Northeast Shelf and Slope (LOC-NESS) project focuses on understanding the potential effects of OAE through a combination of fieldwork, laboratory experiments, and modeling. We assess how regional ocean conditions interact with OAE, conduct controlled laboratory experiments to evaluate biological responses and engineered safety, and perform small-scale, highly monitored field trials to study alkalinity enhancement in real-world conditions. To expand on field trial insights, we use an advanced coupled physical-biogeochemical ocean model to explore broader regional effects. We also engage with communities interested in understanding the potential impacts of OAE on local waters.
Predicting ecosystem and biogeochemical functions from microbial traits through AI/ML and their impacts on biological carbon pump
Funded by NASA and the NSF Office of Polar Programs, we develop and employ a trait-based, 1-D variational data assimilation biogeochemical model to bridge individual cell-based processes with ecosystem-scale dynamics, focusing on heterotrophic marine bacteria. This model integrates observational data to refine microbial functional representations and improve predictive capabilities. We assimilate 16S rRNA gene amplicon sequencing and flow cytometry data to parameterize the physiological traits, biogeochemical roles, and trophic interactions of high nucleic acid and low nucleic acid bacterial groups distinctly. By capturing these microbial-scale processes, the model simulates the cycling of carbon, nitrogen, and phosphorus within the bacterial community. Specifically, it incorporates a Self-Organizing Map-derived dimension-reduction approach to characterize bacterial community structure, linking genomic and functional traits to biogeochemical fluxes. The model is then utilized to examine how microbial traits influence key ecosystem functions, such as primary production, carbon export flux, and bacterial carbon demand. Through these trait-based insights, we assess the role of heterotrophic bacteria in driving the biological pump and regulating air-sea carbon fluxes, enhancing our understanding of microbial contributions to ocean biogeochemistry.