Using Gliders to Observe Submesoscale Flows

Project Lead: Dhruv Balwada, School of Oceanography, College of the Environment

eScience Liaison: Rob Fatland and Scott Henderson

Since the industrial revolution 25-30% of the human-created carbon and 90% of the excess heat in the earth system has been sequestered into the deep ocean. These tracers (like heat, carbon and oxygen) are transported from the surface into the interior in narrow filaments (sub-mesoscale flows), which then merge and mix together at depth to result in a net increase in amount of tracer at depth. To study the dynamics of these structures we need to make observations that span the depth of the water column, and are collected at scales of a few kilometers and hours. This is possible using gliders, which profile the ocean on a zig-zag path (scattered in space and time) as they move up and down through the water column. The goal of this project was to develop tools to better explore these glider datasets. In particular we developed:

  • A mapping algorithm to map from the scattered space-time observations collected by the glider to a grid, which is easier to visualize and conduct analysis on, and also respects the structural properties of the fields. We used Gaussian Process Regression for this.
  • A visualization dashboard for the glider, which allows for an interactive analysis of the data such as co-locating multiple variables to get a deeper insight into how observed structures might be generated. We used the Holoviz ecosystem in Python for this.

View the project GitHub here.

An example of a temperature section collected by a glider showing the zig-zag sampling, and examples of temperature filaments extending across depth.