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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.

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.

Reconciling carbon export and flux attenuation pathways

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.

Quantifying coupled ice-ocean-microbial interactions

The marginal ice zone along the West Antarctic Peninsula is an important region with potentially strong feedback between sea ice, ocean physics, biogeochemistry, and air-sea CO2 fluxes. This project aims to investigate the influence of winds and ocean subsurface heat on seasonal sea-ice dynamics, microbial system processes, and their impacts on net community production and air-sea CO2 fluxes in the region, using the KPP-Ecosystem-Ice model.

Predicting ecosystem functions using microbial traits 

This project aims to develop a trait-based, 1-D data-assimilation model to link individual cell-based and ecosystem scales in the dynamics of heterotrophic marine bacteria. The 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, which is a 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 ecosystem functions using these bacterial traits.

Quantifying microbial controls on carbon and biogeochemical cycling 

This project aims to develop and evaluate a 1-D numerical marine biogeochemical model for the West Antarctic Peninsula region. The model is equipped with a built-in data assimilation scheme via a variational adjoint method (i.e., a data assimilation model). This project is the first to apply a mechanistic model to the coastal West Antarctic Peninsula region that simulates the time-evolving dynamics of C, N, and P driven by ecological and biogeochemical processes. The model is utilized to investigate the microbial, ecosystem, and biogeochemical responses to climate change and variability along the West Antarctic Peninsula, but is also highly versatile and can thus be applied to other regions.