Objectives: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 region.Methodology:We will employ our MSEAS multi-resolution physical-biogeochemical modeling, uncertainty predictions, Lagrangian-Eulerian data fusion and assimilation, path planning, and information theory for adaptive sampling, to better understand, monitor and predict OA processes. We will utilize our new stochastic physics-based Bayesian and deep machine learning methods to rigorously discriminate among a hierarchy of OA models, and discover efficient OA model parameterizations and formulations.We will test our systems in MB and SB, integrating a range of data sets and demonstrating optimal monitoring, forecasting of the most informative data, and risk assessment . We will characterize OA in the region and collaborate.Rationale: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 driver of our BIOMAPS research. In the Gulf of Maine and Massachusetts Bay region, the shellfish growth and reproduction are affected by coastal acidification, with likely negative impacts on crustaceans and both wild and farmed mollusks, hence also on major industries and employment sources. Improving the monitoring, modeling, and forecasting of regional OA is urgent.