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

Abstract

Better quantitative understanding and accurate data-assimilative predictions of the three-dimensional and time-dependent ocean acidification (OA) processes in coastal regions is urgently needed for the protection and sustainable utilization of ocean resources. In this paper, we extend and showcase the use of our MIT-MSEAS systems for high-resolution coupled physical-biogeochemical-acidification simulations and Bayesian learning of OA models in Massachusetts Bay, starting with simple empirical and equilibrium OA models. Simulations are shown to have reasonable skill when compared to available in situ and remote data. The impacts of wind forcing, internal tides, and solitary waves on water transports and mixing, and OA fields, are explored. Strong wind events are shown to reset circulations and the OA state in the Bay. Internal tides increase vertical mixing of waters in the shallow regions. Solitary waves propagating off Stellwagen Bank coupled with lateral turbulent mixing provide a pathway for exchange of surface and deep waters. Both of these effects are shown to impact biological activity and OA. A mechanism for the creation of multiple subsurface chlorophyll maxima is presented, involving wind-induced upwelling, internal tides, and advection of near surface fields. Due to the measurement sparsity and limited understanding of complex OA processes, the state variables and parameterizations of OA models are very uncertain. We thus present a proof-of-concept study to simultaneously learn and estimate the OA state variables and model parameterizations from sparse observations using our novel dynamics-based Bayesian learning framework for high-dimensional and multi-disciplinary estimation. Results are found to be encouraging for more realistic OA model learning.