Ocean Model Development, Data-driven Parameterizations, and Machine Learning in Ocean Models of the Earth System

Background

Dynamical core formulation, numerical methods, and spatial resolution have all made advances in recent years with ramifications for the global earth system models. Nevertheless, by virtue of the finite resolution imposed by limited computational resources, numerical models still require the parameterization of missing, unresolved, or poorly resolved processes. Without sub-grid parameterizations models develop large biases and/or become inaccurate for prediction. The workshop will therefore have a focus on both numerics and parameterizations, and in the context of both regional and global scale applications.

Parameterizations have traditionally been developed with a blend of empiricism and theory. Recently, machine learning as a phenomenon has been transforming science and the world at large. At the foundation of machine learning is the use of functions that model data and is fundamentally an empirical approach. The alignment of the data-driven aspects of machine learning with the needs of parameterizations has led to growing interest in using machine learning for parameterization development. Beyond the data-driven opportunities for parameterizations, the prominence and computational demands of machine learning represent a potentially disruptive moment in the history of ocean modeling and the course for the future of existing models is unclear. Making use of machine learning in ocean models seems to be an imminent transition so we aim to peer into the future to seek a path forward for the adoption of the new techniques in both parameterization, modeling more broadly, and analysis.

Objectives

This workshop will bring together scientists from the ocean modeling, ocean processes, and machine learning communities to review advances in dynamical cores and parameterizations as well as to explore the opportunities, successes, and challenges of applying data-driven methodologies and machine learning techniques to the development and analysis of numerical ocean models.

The machine learning focus of the workshop includes the following topics:

• Data-driven and machine-learning methodologies for sub-grid scale parameterizations
• Use of machine learning in and besides ocean circulation models
• Opportunities and perspectives on future directions for ocean models

The numerical modeling focus of the workshop will include the following topics:

• Structured- and unstructured-grid models and numerical methods for modeling
• Equations, formulations, and vertical coordinates for modeling coastal to large scales
• Coupling between parameterizations and the resolved fluid dynamics
• Coupling between the ocean and other components of the Earth system
• Large-eddy simulations, non-hydrostatic modeling, and process studies
• Development processes, model evaluation, testing, and test cases for ocean models

Participants

We welcome participation of experts from ocean and climate modeling groups worldwide and scientists from the broader international community who are working on numerical model development and data-driven techniques focused on ocean parameterizations using machine learning and traditional methods.

Attendance is open with a target workshop size of 80 in-person participants. While we strongly encourage attending in-person, virtual participation will be available at a reduced registration rate. Students, postdocs, and early-career researchers are especially encouraged to participate, with reduced registration fees offered.

Format

This 4-day workshop will be hybrid, with both in-person and virtual options. The schedule will include plenary sessions, poster sessions, discussion periods. 

On the day following the workshop, 13 September, NSF-NCAR is hosting an open public event featuring a series of seminars from guest speakers celebrating the accomplishments of Dr. Frank Bryan and the 50th anniversary of the Oceanography Section (OS) of the NSF-NCAR. Workshop participants are welcome to participate.

Scientific Organizing Committee

Gustavo Marques (NSF NCAR)
Alistair Adcroft (Princeton University)
Florian Lemarie (INRIA)
Julie McClean (Scripps/UCSD)
Gokhan Danabasoglu (NSF NCAR)

Program Organizing Committee

Elizabeth Faircloth (NSF NCAR)
Alyssa Johnson (US CLIVAR)
Jessica Martinez (CPAESS)
Mike Patterson (US CLIVAR)

Workshop Sponsors

Diversity, equity, and inclusion are core values of the US CLIVAR Program. These values will be reflected through the planning and execution of the workshop.