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Towards Bayesian Ocean Physical-Biogeochemical-Acidification Prediction and Learning Systems for Massachusetts Bay

Haley, Jr., P.J., A. Gupta, C. Mirabito, and P. F. J. Lermusiaux, 2020. Towards Bayesian Ocean Physical-Biogeochemical-Acidification Prediction and Learning Systems for Massachusetts Bay. In: OCEANS '20 IEEE/MTS, 5-30 October 2020, sub-judice.

Monitoring, quantifying, and predicting the three-dimensional and time-dependent ocean acidification processes, from the atmospheric exchanges and river discharges to the ocean interior, and over days to decades, remains a fascinating observational, theoretical, and modeling challenge. This challenge is the long-term driver of our “Bayesian Intelligent Ocean Modeling and Acidification Prediction Systems” (BIOMAPS) research. Ocean acidification (OA), or the progressive decrease in pH of seawater, is caused primarily by the excess atmospheric CO2 and is linked to climate change (Orr et al., 2005; Doney et al., 2009; Mathis et al., 2015). Its chemical perturbations are expected to be larger in coastal regions than on global average (Feely et al., 2008; Gledhill et al., 2015). In the Gulf of Maine and Massachusetts Bay regions, the shellfish growth and reproduction are affected by coastal acidification, with negative impacts on crustaceans (lobsters, crabs) and both wild and farmed mollusks (scallops, oysters, clams, mussels), hence also on major industries and employment sources (Talmage and Gobler, 2010). Improving the monitoring, modeling, and forecasting of regional OA is urgent.

The overarching goal of our research is to develop and demonstrate principled Bayesian intelligent ocean modeling and acidification prediction systems that discriminate among and infer new OA models, rigorously learning from data-model misfits and accounting for uncertainties, so as better monitor, predict, and characterize OA over time scales of days to months in the Massachusetts Bay and Stellwagen Bank regions.