Reconciling carbon export and flux attenuation pathways in the mesopelagic zone
Photosynthesis produces ~100 gigatons of organic carbon per year in the surface ocean, but only a small fraction of this settles into the mesopelagic zone because of strong respiration (remineralization) by heterotrophic bacteria and zooplankton. Studies have demonstrated significant imbalances between carbon supply and demand in the mesopelagic layer, in particular particle flux attenuation that 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 to the mesopelagic ocean exist. This project aims to reassess the current carbon budget in the mesopelagic zone by developing a multi-scale modeling framework that combines a 1-D data-assimilation model and a 3-D coupled physical-biogeochemical model.
LOC-NESS Project: Quantifying the risks and efficacy of ocean alkalinity enhancement
Meeting the Paris Agreement goal of limiting global warming to below 2°C compared to the pre-industrial levels requires a massive climate mitigation effort by removing a few gigatons of CO2 per year. Slow progress in emissions reduction has made it difficult to reach this target without deploying additional methods of removing CO2 either naturally or artificially. This project aims to assess the risks and efficacy of ocean alkalinity enhancement, which is an ocean-based CO2 removal approach that naturally enhances the ocean’s ability to sequester atmospheric CO2 by adding alkaline materials or altering seawater carbonate chemistry. As a research tool, we are developing a hybrid modeling framework that brings together machine learning techniques, a 1-D data-assimilation model, and a 3-D coupled physical-biogeochemical model (ROMS) for the Northeast United States Shelf region.
Predicting ecosystem and biogeochemical functions from microbial traits through Artificial Intelligence/Machine Learning
We develop and employ a trait-based, 1-D variational data assimilation biogeochemical (1D-VAR-BGC) model to link individual cell-based and ecosystem scales in the dynamics of heterotrophic marine bacteria. For example, our model assimilates 16S rRNA gene amplicon and flow cytometry data through the Palmer Long-Term Ecological Research program and differently parameterizes the physiology, biogeochemistry, and trophic dynamics of high nucleic acid and low nucleic acid bacterial groups. The model simulates microbially-mediated C, N, and P stocks and flows of a distinct bacterial mode, i.e., a Self-Organizing Map-derived, dimension-reduction product of the bacterial community structure associated with its specific genomic and functional traits. The model is then utilized to examine the predictability of key ecosystem functions (e.g., primary production, C export flux, bacterial C demand) using these traits.
Projecting the response of marine heterotrophic bacteria to climate change: Coupled Model Intercomparison Project Phase 6
Marine heterotrophic bacteria respire a large fraction of organic carbon into CO2 and regenerate nutrients, playing an important biogeochemical role in the ocean carbon cycle. This project aims to examine the global and regional trends in bacterial carbon biomass and rates under future emission scenarios over the 21st century (2015-2100), using a 3-D Earth System Model with an explicit bacterial treatment (CMCC-ESM2) as part of the Coupled Model Intercomparison Project Phase 6 (CMIP6). This project provides a first critical step toward better quantification and projection of the ocean's microbial feedback on global climate.