Abstract: The New England Seamount Chain in the North Atlantic presents a combination of complex bathymetry and highly dynamic currents due to their location near the Gulf Stream. The Task Force Ocean Biological Soundscapes team have sampled simultaneous ambient noise, biological oceanography, and bio-physical oceanographic sections around the Kelvin Seamount to better understand the impacts of both the seamount bathymetry and Gulf Stream features on the structure of pelagic biology in the water column and physical oceanographic properties. During an October 2023 field campaign on the R/V Langseth, Tropical Storm Phillippe interrupted a research cruise such that oceanographic sections were collected before and after the storm, and water column as well as bottom mounted ambient noise data were recorded before, during, and after the storm. While the storm and periodic ship traffic affect ambient noise levels as historically described by Piggott and Wenz, oceanographic mixing and bio-physical interactions are more complex with Gulf Stream front and eddy dynamics appearing to be more significant drivers than potential mixing associated with the tropical storm passage.

Abstract: The mass loss from the Greenland and Antarctic ice sheets is a major contribution to global sea level rise. To generate accurate projections of ice sheet mass loss, it’s crucial to model the dynamics and evolution of ice sheets, while also considering the uncertainties present in observational data and computational models. In this presentation, we discuss state-of-the-art methods for calibrating Greenland and Antarctic ice sheet models by inverting for high-dimensional model parameters. This involves the use of large-scale PDE (Partial Differential Equation)-constrained optimization techniques and the application of Bayesian inference to efficiently approximate the posterior distribution of the parameters we infer. We then turn our attention to the Humboldt glacier in Greenland and model how uncertainties in the basal friction parameter influence the glacier’s mass loss. We present recent work employing multi-fidelity methods to reduce the computational cost of estimating the mean and variance of glacier mass-change. Our results show that the multi-fidelity approach leads to over an order of magnitude speed-up compared to the traditional Monte Carlo method for uncertainty propagation.

Biography: Dr. Mauro Perego is a computational scientist at the Center for Computing Research, Sandia National Laboratories. Mauro achieved his PhD in mathematical engineering at the Polytechnic University of Milan, Italy. His work spans several aspects of scientific computing, including the discretization and solution of nonlinear partial differential equations, numerical optimization, uncertainty quantification, and scientific machine learning. His current research is in large part applied to ice sheet modeling, with the ultimate goal of providing reliable projections of sea-level rise.

Abstract: Lagrangian tracers are drifters or floaters that follow a parcel of fluid’s movement. These Lagrangian trajectories are widely used as observations, combined with dynamical or statistical models, to recover the underlying flow field. This is known as Lagrangian data assimilation. In the first part of this talk, I will discuss the general methodology for Lagrangian data assimilation. In addition to the ensemble data assimilation, I will present a mathematical framework that allows analytically solvable Lagrangian data assimilation solutions. I will also show a multiscale data assimilation method combining Lagrangian trajectories with the induced Eulerian measurements. In the second part of the talk, I will discuss a few topics focusing on the uncertainty resulting from the solution of Lagrangian data assimilation. They include quantifying the information gain in the state estimation as a number of tracers, eddy identification in the presence of uncertainty, and optimal design of the locations to deploy additional tracers for uncertainty reduction.

Biography: Nan Chen is an Assistant Professor at the Department of Mathematics, University of Wisconsin-Madison. He is also a faculty affiliate of the Institute for Foundations of Data Science. Dr. Chen received his Ph.D. from the Courant Institute of Mathematical Sciences and the Center of Atmosphere and Ocean Science, New York University (NYU), in 2016. He worked as a postdoc research associate at NYU for two years before joining UW-Madison. Dr. Chen’s research interests lie in applied mathematics, geophysics, complex dynamical systems, stochastic methods, numerical algorithms, and general data science. He is also active in developing dynamical and stochastic models and using these models to analyze and predict real-world phenomena related to atmosphere-ocean science, climate, and other complex systems with the help of real observational data.  He is a member of the U.S. CLIVAR Working Group on ENSO Conceptual Models. He has received several awards, including the Kurt O. Friedrichs Prize for an outstanding dissertation in mathematics and the Young Investigator Award from the Office of Naval Research.

Abstract: We begin with a motivation for the challenges faced in weather and climate modeling and then describe why we need special time-integration methods in order to evolve the governing equations forward in time. A quick review of element-based Galerkin (EBG) methods that we use in our models will be given followed by a description of the contravariant form of the discretization that then simplifies the application of horizontally explicit vertically implicit (HEVI) time-integrators regardless of whether we are solving regional or global models. This talk is motivated by my group and collaborators’ research in building operational weather prediction models as well as advancing the field for application in climate, space weather, and ocean dynamics. A list of publications on these topics can be found at: https://frankgiraldo.wixsite.com/mysite/publications

Biography: Francis Giraldo is a distinguished professor in the Department of Applied Mathematics at the Naval Postgraduate School in Monterey, California. He is in the Scientific Computing group and mostly teaches and performs research in this area. For example, he teaches Numerical Linear Algebra, Numerical Analysis, Galerkin Methods, and Scientific Computing. He is also an Adjunct Professor of Applied Mathematics at the University of California at Santa Cruz. His research area is in numerical methods for partial differential equations (PDEs). Although he mainly works on nonlinear systems of hyperbolic equations, he also works on elliptic and parabolic PDEs.