Maike Sonnewald on Modeling Oceanic Currents

When it comes to climate change, the ocean is one of our largest natural heat sinks but we still have a lot left to learn

Carry the Two
Carry the Two
Maike Sonnewald on Modeling Oceanic Currents

Show Notes

Welcome to the first episode of Carry the Two’s collaboration with the American Geophysical Union’s Third Pod from the Sun!

In this episode, we get our feet wet with physical oceanographer, Maike Sonnewald. Maike explains how the ocean currents interplay with our warming atmosphere and what that means for our climate. Using machine learning to build climate models, Maike analyzes how things like greenhouse gases are warming our oceans and changing the pattern of currents.

And don’t forget to listen to Maike’s work through a geophysical lens, over at Third Pod from the Sun!

Check out the AGU’s Third Pod from the Sun with Maike:

Find our transcript here: LINK

Curious to learn more? Check out these additional links:

Hear Maike’s talk for IMSI’s Confronting Global Climate Change:

Upcoming paper from Maike: The Southern Ocean supergyre: a unifying dynamical framework identified by machine learning. In press, Nature Communications Earth & Environment.

A review paper on ML in oceanography: Bridging theory, simulation, and observations of the global ocean using Machine Learning, 2021, Environmental Research Letters

Paper on the North Atlantic: Revealing the impact of global warming on climate modes using transparent machine learning. 2021, Journal of Advances in Modeling Earth Systems

For a math and AI twist on predicting ocean dynamics: Explainable Artificial Intelligence for Bayesian Neural Networks: Towards trustworthy predictions of ocean dynamics. 2022, Journal of Advances in Modeling Earth Systems.

Follow more of IMSI’s work:, (twitter) @IMSI_institute, (mastodon), (instagram)

Follow Maike Sonnewald:

This episode was audio engineered by Tyler Damme. Special thanks to Third Pod’s producer Anupama Chandrasekaran.

Music by Blue Dot Sessions.

The Institute for Mathematical and Statistical Innovation (IMSI) is funded by NSF grant DMS-1929348.