Optimizing Ocean Acidification Observations for Model Parameterization in the Coupled Slope Water System of the U.S. Northeast Large Marine Ecosystem

Grace Saba, Rutgers University

The U.S. Northeast Shelf Large Marine Ecosystem, supports some of the nation’s most economically valuable coastal fisheries, yet most of this revenue comes from shellfish that are sensitive to ocean acidification (OA). Furthermore, the weakly buffered northern region of this area is expected to have greater susceptibility to OA. Existing OA observations in the NES do not sample at the time, space, and depth scales needed to capture the physical, biological, and chemical processes occurring in this dynamic coastal shelf region. Specific to inorganic carbon and OA, the data available in the region has not been leveraged to conduct a comprehensive regional-scale analysis that would increase the ability to understand and model seasonal-scale, spatial-scale, and subsurface carbonate chemistry dynamics, variability, and drivers in the NES. This project optimizes the NES OA observation network encompassing the Mid-Atlantic and Gulf of Maine regions by adding seasonal deployments of underwater gliders equipped with transformative, newly developed and tested deep ISFET-based pH sensors and additional sensors (measuring temperature, salinity for total alkalinity and aragonite saturation [ΩArag] estimation, oxygen, and chlorophyll), optimizing existing regional sampling to enhance carbonate chemistry measurements in several key locations, and compiling and integrating existing OA assets. The researchers will apply these data to an existing NES ocean ecosystem/biogeochemical (BGC) model that resolves carbonate chemistry and its variability. 


Tuesday, March 3, 2020
MIT Sea Grant Announces Three Newly Funded Projects Studying Ocean Acidification

MIT Sea Grant Announces Three Newly Funded Projects Studying Ocean Acidification

MIT Sea Grant

MIT Sea Grant has selected three research projects for funding from our annual request for proposals. The projects focus on developing new ocean acidification sensor technology and using modeling techniques to consolidate historical data to inform future coastal ocean acidification monitoring.

Friday, February 24, 2017
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