My research interests and achievements since moving to America have mainly involved numerical models and theory. My goal is to develop tools and methods that can be used also to facilitate observational oceanography. My collaborative efforts have led to my work in biological oceanography, and projected ocean acidification. Below are descriptions of selected current projects and achievements.

Open Source Software and its adoption by the oceanographic community is something I am very invested in. I highlight some of the efforts below.

Selected projects

Inferring in depth dynamics from surface fields in CMIP

Climate models are key to understanding and preparing for future change, but a revolution in analysis tools is necessary due to the scale and complexity of the ensembles. The `tracking global heating with ocean regimes' (THOR) method reveals changes in the in-depth drivers in climate model data. Needing only the depth, sea surface height and wind stress, THOR applies an explainable multilayer perceptron trained on data labeled by an interpretable unsupervised ML method. Applied to a climate model, THOR reveals areas for example of deepwater formation, upwelling and the approximate location key currents like the Gulf Stream. THOR could accelerate in-depth climate model analysis with concrete dynamical intercomparisons, helping lower uncertainties. Implications include:

  • Unique in depth dynamical are identified
  • THOR is targeted at CMIP models using limited data
  • Regions key to heat transport can be monitired
  • Applicable across models

Collaborators: Redouane Lguensat, Aparna Radhakrishnan, and Zouberou Sayibou

  • Sonnewald, M., and Lguensat, R. Revealing the Impact of Global Heating on North Atlantic Circulation Using Transparent Machine Learning, 2021, JAMES
  • Sonnewald, M., Lguensat, R., Radhakrishnan, A., and Sayibou Z. Revealing the Impact of Climate Modes Using Transparent Machine Learning, 2021, ICML

  • Unlocking the impact of mesoscale turbulence on the subpolar North Atlantic

    Important for the global heat transport, the subpolar North Atlantic is a place where important dynamics are difficult to observe and to model. Building on the SAGE method, using also methods from topology and unsupervised machine learning, I am uncovering regions of coherent dynamics that can be used to infer mechanisms important for understanding the circulation. Interactions of currents with canyons and regions associated with export of water from the shelf into the deep are topics I am investigating. Implications include:

    • Unique regimes are identified
    • SAGE is scaled to larger data volumes
    • The shelf and deep currents stand out
    • Canyons are of particular importance

    Collaborators: V. Balaji, Alistair Adcroft, Aparna Radhakrishnan and Mathieu Le Corre

    Decoding ecological complexity using the SAGE method

    Global ecology effects our climate and the ocean food chain. Defining glogal regions or provinces help monitor its health, but is difficult due to the overwhelmingly complex ecosystem data. I developed the SAGE (Systematic AGgregated Eco-province) method combining statistical tools, unsupervised machine learning and graphs to determine global provinces in the Darwin ecosystem model. SAGE delivers AEPs (Aggregated Eco-Provinces) for global or regional use, and you can set the 'aggregation' level to fit your needs using the tool here. Implications include:

    • Unique ecological provinces are identified
    • Similar species assemblages are found with very different biomass
    • Regions with similar biomass can have radically different ecology
    • Our framework is both global and nested, making it appropriate for regional and global studies

    Collaborators: Steph Dutkiewitz, Chris Hill, Gael Forget

  • Sonnewald, M., Dutkiewitz, S., Hill, C. and Forget, G. Elucidating Ecological Complexity: Unsupervised Learning determines global marine eco-provinces, 2020, Science Advances

  • Using machine learning to discover global dynamical regions

    Applying an unsupervised learning algorithm the the depth integrated ECCO data, the ocean partitioned itself neatly into globally coherent dynamical regimes in terms of the barotropic vorticity. This means that the way the terms of the barotropic vorticity equation are balanced fall into a subset of configurations. For the decedal ocean, this is remarkably robust. Implications include:

    • Global coherency fitting with the state of the art interpretation
    • Tracing regions that can act as barriers for biology
    • Assessing parameterizations fro the Gulf Stream path or the Antarctic Circumpolar Circulation
    • Ideas of the coupling between the overturning and gyre circulation

    Collaborators: Carl Wunsch, Patrick Heimbach

  • 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''

  • Presenting a benchmark for Sea Surface Height predictability

    Sea Surface Height is a critical quantity for vast numbers of people. Living by the coast means that small changes can accumulate, and understanding how the Sea Surface Height varies is cruicial. To check if predictions are skillful, a benchmark is nessesairy to compare against. Developed using linear methods, this suggests that using more complicated methods is merited. Key points include:

    • Sea Surface Height predictability is reasonable using linear statistical models
    • Predictability is related to planetary waves close to the equator, and more to do with advective signals further polewards
    • Complicated models can likely make progress!

    Collaborators: Carl Wunsch, Patrick Heimbach

  • Sonnewald, M., Wunsch, C. and Heimbach, P., 2018. Linear predictability: A sea surface height case study, 2018, Journal of Climate
  • Barotropic Vorticity dynamics

    Vorticity is a useful tool for understanding ocean dynamics. To assess what term does what and where and how things are dissipated requires a close look at what is going on. Using the closure in the MITgcm/ECCO, we can:

    • Determine relative contribution magnitudes
    • Assess impact of small-scale terms
    • Determine the impact of frameworks e.g. depth integrated or depth averaged

    Collaborators: Yvonne Firing, Joel J.-M. Hirschi A. George Nurser

    Open Source software initiatives

    Adopting Open Source means that oceanography can lean on innovations across a wide range of fields and industry. Open development processes lead to better software in terms of readability, ability to modify and fix bugs as well as for teaching purposes.