Abstract: Coming out of WWII, our knowledge of the physics and dynamics of the atmosphere together with the advent of digital computing ushered in the present era of numerical weather prediction. Today, we find ourselves at a similar juncture. Decades of observations from the Earth Observing System, related understanding of Earth System Science and Earth as a coupled system, combined with the prospect of exascale computing, have placed us on the cusp of a new era of Earth System Prediction.

At the other end of the temporal spectrum far away from day to day weather prediction, climate change projections from decades to centuries have served as the basis for policy decisions on how best to respond to ever increasing levels of greenhouse gases. In between weather prediction and climate change projections is a spectral gap of subseasonal to decadal in which numerous infrastructure, investment, and policy decisions are made. Society requires expanded prediction capabilities and future environmental products beyond the bounds of weather prediction for areas such as coastal oceans, marine and terrestrial ecosystems, agriculture, air and water quality, regional CO₂ and other chemical constituents, and environmental health parameters.

Development of an environmental prediction capability will require incorporation of additional components of the Earth System beyond the physical climate system such as biological properties of terrestrial and ocean ecosystems and an assessment of the limits to their predictability. The core elements and expertise needed in this regard include atmospheric general circulation models, ocean circulation models, land surface models, interactive vegetation models, marine ecosystem models, atmospheric chemistry models, global carbon cycle models, assimilation techniques for atmosphere-ocean-land, population dynamics, crop models, infectious disease models and modules to name a few. The challenge now is to bring these core elements together within a common infrastructure and with a central focus on subseasonal to decadal prediction of the Earth System in the broadest sense. Furthermore, the prospect of Earth System prediction has unique policy relevance at both the national and international levels with respect to agriculture, hydrology, ocean resources, energy, transportation, commerce, health, and global security.

Biography: Antonio J. Busalacchi became president of UCAR in August 2016. An expert in Earth’s climate system and ocean-atmosphere interactions, he helps guide NCAR’s world-leading research into the Earth system sciences and its support of the research community through supercomputing, observing instruments, and community models.

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.

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.

Cost-effective seafloor mapping at high resolution is yet to be attained. A possible solution consists of using a mobile, wide-aperture, sparse array with subarrays distributed across multiple autonomous surface vessels. Such wide-area mapping with multiple dynamic sources and receivers require accurate modeling and processing systems for imaging the seabed. In this paper, we focus on computational schemes and challenges for such high-resolution acoustic imaging or migration. Starting from the imaging condition from the adjoint-state method, we derive a closed-form expression for Gaussian beam migration in stratified media. We employ this technique on simulated data and on real data collected with our novel acoustic array over shipwrecks in the Boston Harbor. We compare Gaussian beam migration with diffraction stack and Kirchhoff migration, and we find that Gaussian beam migration produces the clearest images with the fewest artifacts.