Maike Sonnewald

Developing pathways between theoretical, observational and computational oceanography

Associate Research Scholar at Princeton University and GFDL

Modern oceanography is interdisciplinary: the field is becoming data rich from observations and models, creating a need for new tools. I am a physical oceanographer using computer science/dynamical systems tools to explore decadal ocean dynamics. Passionate about bringing together different branches of oceanography, my goal is to discover the underlying principles that govern ocean dynamics from small to global scales. My work connects to observational efforts and model parameterizations, and I also work on ocean acidification and ecology in collaborative efforts. I focus on the global ocean, using scalable methods, with a special interest in the Southern Ocean and the North Atlantic.

I currently focus on understanding how small scale dynamics impact global features like heat transport in simulations allowing mesoscale turbulence. This work continues the development of the SAGE (Systematic AGgregated Eco-province) method, combining statistical tools, unsupervised machine learning and graphs, designed to work with non-linear data ubiquitous in oceanography and beyond. Overall, my research areas include:

Discovery of coherent regions in ocean dynamics, ecological and acidification
Vorticity dynamics
Predictability of Sea Level
Small scale interactions with bathymetry
Machine learning applications
Application of dynamical systems theory

My work using unsupervised machine learning to discover ocean dynamical regimes was featured on: MIT News, Artificial Intelligence Research, physics.org, and ECN magazine.

Publications

Sonnewald, M., Dutkiewitz, S., Hill, C. and Forget, G. Elucidating Ecological Complexity: Unsupervised Learning determines global marine eco-provinces, 2020, Science Advances.
Le Bras, I., Sonnewald, M., Toole, J.M. A Barotropic Vorticity Budget for the Subtropical North Atlantic Based on Observations , 2019, Journal of Physical Oceanography
Sonnewald, M., Wunsch, C. and Heimbach, P. Unsupervised Learning Reveals Geography of Global Ocean Dynamical Regions, 2019, Journal of Earth and Space Science ed. ”Geoscience paper of the future”
Sonnewald, M., Wunsch, C. and Heimbach, P., 2018. Linear predictability: A sea surface height case study, 2018, Journal of Climate
The ECCO Consortium. A Twenty-Year Dynamical Oceanic Climatology: 1994-2013. Part 1: Active Scalar Fields: Temperature, Salinity, Dynamic Topography, Mixed-Layer Depth, Bottom Pressure, 2017
The ECCO Consortium. A Twenty-Year Dynamical Oceanic Climatology: 1994-2013. Part 2: Velocities and Property Transports, 2017
Gille, S., Abernathey, R., Chereskin, T., Cornuelle, B., Heimbach, P., Mazloff, M., Menemenlis, D., Rocha, C., Soares, S., Sonnewald. M., Villas Boas, B., Wang, J. Open Code Policy for NASA Space Science: A perspective from NASA-supported ocean modeling and ocean data analysis, 2018, NASA White Paper
Bulczak, A.I., Bacon, S., Naveira Garabato, A.C., Ridout, A., Sonnewald, M., and Laxon, S.W. Seasonal Variability of Sea Surface Height in the Coastal Waters and Deep Basins of the Nordic Seas, 2014, Geophysical Research Letters
Sonnewald, M., Hirschi, J.J.-M., Marsh, R., McDonagh, E.L. and King, B.A. Atlantic meridional ocean heat transport at 26N: impact on subtropical ocean heat content variability, 2013, Ocean Science