Publications listed on Google Scholar, see CV for full list, '*' indicates student advised
Review articles:
- Sonnewald, M., Brajard, J., Duben, P., Lguensat, R. and Balaji, V.
Bridging theory,
simulation, and observations of the global ocean using Machine Learning,
2021, Environmental Research Letters
- Irrgang, C., Boers, N., Sonnewald, M., Elizabeth A. Barnes, Christopher Kadow,
Staneva, J., and Saynisch-Wagner, J.
Towards neural Earth system modelling by integrating artificial intelligence in Earth system science,
2021, Nature Machine Intelligence
- Bronner, U., Sonnewald, M. and Wisbeck, M., 2023, Marine modelling as the key to sustainable use and protection of
the marine environment. 2023, invited contribution to the centennial edition of The International Hydrographic Review.
Peer reviewed publications:
- Yik, W.*,Sonnewald, M., Clare, M.*, Lguensat, R., Southern Ocean Dynamics Under Climate Change: New Knowledge Through Physics-Guided Machine Learning. 2023, NeurIPS Climate Change AI workshop..
- Sonnewald, M., Reeve, K., and Lguensat, R., The Southern Ocean supergyre: a unifying dynamical framework identified by machine learning. 2023, Nature Communications Earth & Environment.
- Jones, D., JSonnewald, M., Rosso, I., Zhou, S., and Boehme, L., Unsupervised classification identifies coherent thermohaline structures in the Weddell Gyre. 2023, Ocean Science.
- Clare, M.*, Sonnewald, M., Lguensat, R., Deshayes, J. and Balaji, V., Explainable Artificial Intelligence for Bayesian Neural Networks: Towards trustworthy predictions of ocean dynamics. 2022, Journal of Advances in Modeling Earth Systems.
- Kaiser, B., Saenz, J.A., Sonnewad, M. and Livescu, D., Objective discovery of dominant dynamical processes with machine learning. 2022, Engineering Applications of Artificial Intelligence.
- Krasting, J., M. De Palma, M., Sonnewald, M., J. Dunne, J., and John, J.,
Regional Sensitivity Patterns of Arctic Ocean Acidification Revealed With
Machine Learning. 2022, Nature Communications Earth & Environment.
- Sonnewald, M., and Lguensat, R. Revealing
the impact of global warming on climate modes using transparent machine learning. 2021, Journal of Advances in Modeling Earth Systems
- Sonnewald, M., Lguensat, R., Radhakrishnan, A., Sayibou*, Z., Wittenberg, A.T. and Balaji, V.
Revealing the impact of global
heating on North Atlantic circulation using transparent machine learning. 2021, International
Conference on Machine Learning: Spotlight paper at ClimateChangeAI Workshop.
- 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''
Featured on MIT News, Artificial Intelligence Research
- Sonnewald, M., Wunsch, C. and Heimbach, P., 2018. Linear predictability: A sea surface height case study, 2018, Journal of Climate
- 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
Grey literature:
- 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
- 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
Selected publications in review and revision:
- Sonnewald, M., A hierarchical ensemble manifold methodology for new knowledge on spatial data: An application to ocean physics. In review, JAMES.
- Khatri. H., Griffies, S.M., Storer, B.A., Buzzicotti, M., Aluie, H., Sonnewald, M., Dussin, R. and Shao, A., A scale-dependent analysis of the barotropic vorticity budget in a global ocean simulation. JAMES.
- Navarra, G.G*, Sonnewald, M., Deng, Y., Liguori, G. and Di Lorenzo, E. Using Deep Learning to forecast marine fishery indicators in the North Pacific. In review, Global Change Biology.
- Bingham, R. and Sonnewald, M. Stable Atlantic overturning circulation revealed by a dynamically-proximate reconstruction. In revision, Geophysical
Research Letters.
- Sonnewald, M. , Hirschi, J.J.-M., Nurser, A.G., Firing, Y., Coward, A. and Hyder, P. Increasing ocean model resolution reveals impact of tuning eddy permitting models. In revision. Journal of Advances in Modeling Earth Systems