The multi-scale dynamics of oceanic processes and the complex propagation of acoustic waves are fundamental challenges in marine sciences and operations. Recent computing advances enable such multiresolution ocean and acoustic modeling, but a fully integrated system for sustained coupled predictions and Bayesian data assimilation remains needed. In this study, we integrate the MSEAS Primitive Equation (PE) ocean modeling system and the MSEAS acoustic Parabolic Equation (ParEq) solver, enabling real-time coupled ocean and acoustic predictions. Realistic applications in Massachusetts Bay, the Norwegian Sea, the western Mediterranean Sea, and the New York Bight are used to demonstrate capabilities and validate predictions in diverse shallow and deep-water environments. Results provide the foundation for an end-to-end system for coupled ocean-acoustic probabilistic modeling, Bayesian inversion, and learning.

In recent years, the Gulf of Mexico Loop Current System has received increased attention. Its dynamics and the warm water it transports from the Caribbean influence the local weather and ecosystems. The high velocities of the Loop Current and the eddies it sheds can disrupt important industries. Accurate forecasting of the Loop Current system is challenging, in part because of the lack of data over long enough periods of time, which leads to considerable uncertainty. In this work, we describe and apply our MIT Multidisciplinary Simulation, Estimation, and Assimilation Systems (MSEAS) and Error Subspace Statistical Estimation (ESSE) ensemble forecasting methodology and software to estimate such uncertainty and to inform data collection in a quantitative manner. The ensemble forecasts allow for mitigating risks and optimizing data collection. We demonstrate that our probabilistic system has qualitative skill for over a month. We show that uncertainty grows along and around the Loop Current and its eddies, and transfers to depth from the shelf and slope. Using information theory, we find that our probabilistic hindcasts can have predictive capabilities for one to three months, with a slower loss of predictability in the quieter Loop Current states. Through the use of correlation and mutual information fields, we optimize future sampling by predicting the impacts and information content of observations. We find that the most informative data are those that either directly sample dynamically relevant areas or sample coastal modes that are correlated with these areas. Subsurface data are shown to have more impact on forecasts of one month or longer.

Graduate student Anantha Narayanan Suresh Babu has received the M.A. Abkowitz travel award to attend the SIAM Conference on Mathematical & Computational Issues in the Geosciences in Bergen, Norway. The conference will take place from June 19-22, 2023. The Award was created to commemorate Professor Martin A. Abkowitz. The purpose of this fund, set up through donations by family and friends, was to establish an endowed fellowship to support travel by MIT researchers and faculty to attend international symposia and to foster cooperation between nations in ocean engineering. Congratulations Anantha!

The ocean is vast, complex, and increasingly threatened by human activities. There is an urgent need to find complementary ways to gather information and promote the comprehensive understanding and management of the ocean. The global network of subsea cables provides an opportunity to support a holistic ocean observation system. Data gathered from this system can be employed to anticipate and provide warning about hazardous events. Large-scale and widespread ocean monitoring may also enable the oversight and tracing of global phenomena that have local impacts.

The Knowledge and Data from the Deep to Space (K2D) project aims to develop the critical components that will enable the large-scale coupling of autonomous underwater vehicles (AUVs) and subsea cables for global ocean environmental monitoring and multi-hazard warning. Funded by the Fundação para a Ciência e Tecnologia/Massachusetts Institute of Technology Portugal Program and involving teams from Portugal and the United States, the project started in 2021 with a global budget of 1.4 M€ and an estimated duration of three years. Sustained ocean observation systems are scarce, especially those that focus on or near the seafloor. The combination of subsea cables and marine robotics is promising not only because it allows access to remote locations and provides an extensive network (deep sea, open ocean), but also because it combines a large set of capabilities in a highly resource-efficient way, unmatched by any other ocean observation approach. These assets may initiate the first global ocean “nervous system” in the near future.

As part of the MSEAS trip to Woods Hole in August 2022, a visit to Nobska Light was in order!