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Complex Modeling and Extreme Computing

Our goal is to understand the dynamics of the ocean and its role in the climate by combining numerical methods, physical considerations, mathematical analysis and numerical experiments. The Complex Modeling and Extreme Computing Group

  • developes state-of-the-art circulation models of the ocean,
  • carries out High-Performance Computations,
  • develops sophisticated diagnostics to analyse the simulated circulation.

The theme of the group is located at the interface between Applied Mathematics, Scientific Computing, and Physical Oceanography and centering around the ocean model ICON-O.

Cutting-Edge Numerics

We develope structure-preserving numerical schemes that embed a physical concept into a numerical model. These methods, also known as "mimetic" or "phyiscs capturing method" intend to improve the quality of the simulations by formulating discrete equivalents of essential properties of continuum physics. This idea has been filled with life in the design and realization of the ocean general circulation model ICON-O.

Innovative Parametrizations

Several important processes in the ocean remain unresolved. These processes are represented in ocean models by parametrizations or Subgrid-Scale Models. For the parametrization of diapycnal mixing in the ocean, we develop and implement novel parameterizations describe energetically consistent energy pathways from surface and interior waves to small-scale turbulence. High-resolution ICON-O simulations guide us how to even better connect different energy reservoirs and to further improve the fidelity of the model.

 

Energy conversion by diapycnal mixing in a global ocean simulation of 40km resolution. The parametrization of internal waves and turbulent results in more realistic patterns of diapycnal mixing which is enhanced along topographic features.

Creative Experimental Configurations

To expand the range of resolved scales we push for the possible computational limits with high-resolution simulations in ocean-stand-alone and coupled configuratins. A glance at new dynamical regimes that are beyond the actual possible resolution of global ocean models can be taken by using a *telescoping model configuration* that locally increases the resolution and allows to investigate for example submesoscale dynamics in a realsitic scenario.

Telescoping global ICON-O simulation with a resolution that increases locally to less 600m over wide parts of the North Atlantic. Such simulations allow to simulate submesoscale dynamics like those small eddies visible in the lower right inlet. Submesoscale eddies are important for air-sea heat and gas exchanges.

Novel diagnostics

The increased spatial resolution in many of our simulations and the focus on small-scale processes goes along with a substantial increase of data which needs to be analyzed. We explore different complementary attempts to make such diagnostics possible even for our largest data sets. In this regard, we explore an increased usage of online diagnostics in particular for transport and energy exchanges.

 

Overturning across isopycnal layers in an ICON-O simulation. Left: overturning within density classes. Right: overturning remappged to depth space for more intuitive visualization. Deriving the isopycnal transport at model run time gives a more accurate picture of the pathways of water masses and the meridional overturning function, because isopycnals, which the water masses follow, change in time.

Group members and publications

  • Leimann, I., Epke, M., Dräger‐Dietel, J., Griesel, A., Walter, M., Linardakis, L., Korn, P. & Brüggemann, N. (2026). Diagnosing Kinetic Energy Scaling Using Lagrangian and Eulerian Metrics in Different Dynamical Regimes of the North Atlantic. Journal of Geophysical Research: Oceans, 131(6), e2025JC023666. doi:10.1029/2025JC023666 [publisher-version]
  • Epke, M., Linardakis, L., Korn, P. & Brüggemann, N. (2026). Overturning of Mixed Layer Eddies in a Submesoscale-Resolving Simulation of the North Atlantic. Journal of Physical Oceanography, 56(6), 1437-1468. doi:10.1175/JPO-D-25-0015.1
  • Kumar, A., Brüggemann, N. & Marotzke, J. (2026). Ocean eddies lower the global-mean, maximum intensity of tropical cyclones in a one-year global, coupled simulation. Geophysical Research Letters, 53(2). doi:10.1029/2025GL116072 [publisher-version][supplementary-material]
  • Nielsen, D., Chegini, F., Serra, N., Kumar, A., Brüggemann, N., Hohenegger, C. & Ilyina, T. (2025). Resolved tropical cyclones trigger CO2 uptake and phytoplankton bloom in an Earth system model simulation. Proceedings of the National Academy of Sciences of the United States of America, 122(50), e2506103122. doi:10.1073/pnas.2506103122 [publisher-version]
  • Müller, W., Lorenz, S., Pham, T., Schneidereit, A., Brokopf, R., Brovkin, V., Brüggemann, N., Chegini, F., Domenget, D., Fröhlich, K., Früh, B., Gayler, V., Haak, H., Hagemann, S., Hanke, M., Ilyina, T., Jungclaus, J., Köhler, M., Korn, P., Kornblueh, L., Kroll, C., Krüger, J., Castro-Morales, K., Niemeier, U., Pohlmann, H., Polkova, I., Potthast, R., Riddick, T., Schlund, M., Stacke, T., Wirth, R. & Marotzke, J. (2025). The ICON-based Earth system model for climate predictions and projections (ICON XPP v1.0). Geoscientific Model Development, 18, 9385-9415. doi:10.5194/gmd-18-9385-2025 [publisher-version]
  • Klocke, D., Frauen, C., Engels, J., Alexeev, D., Redler, R., Schnur, R., Haak, H., Kornblueh, L., Brüggemann, N., Chegini, F., Römmer, M., Hoffmann, L., Griessbach, S., Bode, M., Coles, J., Gila, M., Sawyer, W., Calotoiu, A., Budanaz, Y., Mazumder, P., Copik, M., Weber, B., Herten, A., Bockelmann, H., Hoefler, T., Hohenegger, C. & Stevens, B. (2025). Computing the full Earth system at 1km resolution. In SC '25: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis doi:10.1145/3712285.3771789 [any-fulltext]
  • Segura, H., Pedruzo-Bagazgoitia, X., Weiss, P., Müller, S., Rackow, T., Lee, J., Dolores-Tesillos, E., Benedict, I., Aengenheyster, M., Aguridan, R., Arduini, G., Baker, A., Bao, J., Bastin, S., Baulenas, E., Becker, T., Beyer, S., Bockelmann, H., Brüggemann, N., Brunner, L., Cheedela, S., Das, S., Denissen, J., Dragaud, I., Dziekan, P., Ekblom, M., Engels, J., Esch, M., Forbes, R., Frauen, C., Freischem, L., García-Maroto, D., Geier, P., Gierz, P., González-Cervera, Á., Grayson, K., Griffith, M., Gutjahr, O., Haak, H., Hadade, I., Haslehner, K., ul Hasson, S., Hegewald, J., Kluft, L., Koldunov, A., Koldunov, N., Kölling, T., Koseki, S., Kosukhin, S., Kousal, J., Kuma, P., Kumar, A., Li, R., Maury, N., Meindl, M., Milinski, S., Mogensen, K., Niraula, B., Nowak, J., Praturi, D., Proske, U., Putrasahan, D., Redler, R., Santuy, D., Sármány, D., Schnur, R., Scholz, P., Sidorenko, D., Spät, D., Sützl, B., Takasuka, D., Tompkins, A., Uribe, A., Valentini, M., Veerman, M., Voigt, A., Warnau, S., Wachsmann, F., Wacławczyk, M., Wedi, N., Wieners, K.-H., Wille, J., Winkler, M., Wu, Y., Ziemen, F., Zimmermann, J., Bender, F.-M., Bojovic, D., Bony, S., Bordoni, S., Brehmer, P., Dengler, M., Dutra, E., Faye, S., Fischer, E., van Heerwaarden, C., Hohenegger, C., Järvinen, H., Jochum, M., Jung, T., Jungclaus, J., Keenlyside, N., Klocke, D., Konow, H., Klose, M., Malinowski, S., Martius, O., Mauritsen, T., Mellado, J., Mieslinger, T., Mohino, E., Pawłowska, H., Peters-Von Gehlen, K., Sarré, A., Sobhani, P., Stier, P., Tuppi, L., Vidale, P., Sandu, I. & Stevens, B. (2025). nextGEMS: entering the era of kilometer-scale Earth system modeling. Geoscientific Model Development, 18(20), 7735-7761. doi:10.5194/gmd-18-7735-2025 [publisher-version]
  • Müller, W., Früh, B., Korn, P., Potthast, R., Baehr, J., Bettems, J.-M., Bölöni, G., Brienen, S., Fröhlich, K., Helmert, J., Jungclaus, J., Köhler, M., Lorenz, S., Schneidereit, A., Schnur, R., Schulz, J.-P., Schlemmer, L., Sgoff, C., Pham, T., Pohlmann, H., Vogel, B., Vogel, H., Wirth, R., Zaehle, S., Zängl, G., Stevens, B. & Marotzke, J. (2025). ICON: toward vertically integrated model configurations for numerical weather prediction, climate predictions, and projections. Bulletin of the American Meteorological Society, 106(6), E1017-E1031. doi:10.1175/BAMS-D-24-0042.1 [publisher-version]
  • Witte, M., Lapolli, F., Freese, P., Götschel, S., Ruprecht, D., Korn, P. & Kadow, C. (2025). Dynamic deep learning based super-resolution for the shallow water equations. Machine Learning: Science and Technology, 6: 015060. doi:10.1088/2632-2153/ada19f [publisher-version]
  • Bastin, S., Koldunov, A., Schütte, F., Gutjahr, O., Mrozowska, M., Fischer, T., Shevchenko, R., Kumar, A., Koldunov, N., Haak, H., Brüggemann, N., Hummels, R., Specht, M., Jungclaus, J., Danilov, S., Dengler, M. & Jochum, M. (2025). Sensitivity of the tropical Atlantic to vertical mixing in two ocean models (ICON-O v2.6.6 and FESOM v2.5). Geoscientific Model Development, 18(4), 1189-1220. doi:10.5194/gmd-18-1189-2025 [publisher-version]
  • Korn, P. & Titi, E. (2024). Global well-posedness of the primitive equations of large-scale ocean dynamics with the Gent-McWilliams-Redi Eddy Parametrization Model. SIAM journal on mathematical analysis, 56(6), 8011-8036. doi:10.1137/23M1564316 [publisher-version]
  • Brüggemann, N., Losch, M., Scholz, P., Pollmann, F., Danilov, S., Gutjahr, O., Jungclaus, J., Koldunov, N., Korn, P., Olbers, D. & Eden, C. (2024). Parameterized internal wave mixing in three ocean general circulation models. Journal of Advances in Modeling Earth Systems, 16: e2023MS003768. doi:10.1029/2023MS003768 [research-data][publisher-version]
  • Ssebandeke, J., von Storch, J. & Brüggemann, N. (2024). Sensitivity of the global Lorenz energy cycle. Ocean Dynamics, available online.. doi:10.1007/s10236-023-01568-6 [publisher-version][supplementary-material]
  • Lapolli, F., da Silva Peixoto, P. & Korn, P. (2024). Accuracy and stability analysis of horizontal discretizations used in unstructured grid ocean models. Ocean Modelling, 189: 102335. doi:10.1016/j.ocemod.2024.102335 [publisher-version]
  • Sharma, K. & Korn, P. (2023). Numerical simulation of an idealized coupled ocean-atmosphere climate model. In Iske, A. & Rung, T. (Eds.), Modeling, Simulation and Optimization of Fluid Dynamic Applications (pp.113-130). Cham: Springer Nature . doi:10.1007/978-3-031-45158-4_7
  • von Storch, J.-S., Hertwig, E., Lüschow, V., Brüggemann, N., Haak, H., Korn, P. & Singh, V. (2023). Open-ocean tides simulated by ICON-O. Geoscientific Model Development, 16, 5179-5196. doi:10.5194/gmd-16-5179-2023 [supplementary-material][publisher-version][any-fulltext]
  • Crisan, D., Holm, D. & Korn, P. (2023). An implementation of Hasselmann’s paradigm for stochastic climate modelling based on stochastic Lie transport. Nonlinearity, 36, 4862-4903. doi:10.1088/1361-6544/ace1ce [publisher-version]
  • Hohenegger, C., Korn, P., Linardakis, L., Redler, R., Schnur, R., Adamidis, P., Bao, J., Bastin, S., Behravesh, M., Bergemann, M., Biercamp, J., Bockelmann, H., Brokopf, R., Brüggemann, N., Casaroli, L., Chegini, F., Datseris, G., Esch, M., George, G., Giorgetta, M., Gutjahr, O., Haak, H., Hanke, M., Ilyina, T., Jahns, T., Jungclaus, J., Kern, M., Klocke, D., Kluft, L., Kölling, T., Kornblueh, L., Kosukhin, S., Kroll, C., Lee, J., Mauritsen, T., Mehlmann, C., Mieslinger, T., Naumann, A., Paccini, L., Peinado, A., Praturi, D., Putrasahan, D., Rast, S., Riddick, T., Roeber, N., Schmidt, H., Schulzweida, U., Schütte, F., Segura, H., Shevchenko, R., Singh, V., Specht, M., Stephan, C., von Storch, J., Vogel, R., Wengel, C., Winkler, M., Ziemen, F., Marotzke, J. & Stevens, B. (2023). ICON-Sapphire: simulating the components of the Earth System and their interactions at kilometer and subkilometer scales. Geoscientific Model Development, 16, 779-811. doi:10.5194/gmd-16-779-2023 [publisher-version]
  • Linardakis, L., Stemmler, I., Hanke, M., Ramme, L., Chegini, F., Ilyina, T. & Korn, P. (2022). Improving scalability of Earth System Models through coarse-grained component concurrency - a case study with the ICON v2.6.5 modelling system. Geoscientific Model Development, 15, 9157-9176. doi:10.5194/gmd-15-9157-2022 [publisher-version]
  • Korn, P., Brüggemann, N., Jungclaus, J., Lorenz, S., Gutjahr, O., Haak, H., Linardakis, L., Mehlmann, C., Mikolajewicz, U., Notz, D., Putrasahan, D., Singh, V., von Storch, J.-S., Zhu, X. & Marotzke, J. (2022). ICON-O: The Ocean Component of the ICON Earth System Model - Global simulation characteristics and local telescoping capability. Journal of Advances in Modeling Earth Systems, 14: e2021MS002952. doi:10.1029/2021MS002952 [publisher-version]
  • Mathis, M., Logemann, K., Maerz, J., Lacroix, F., Hagemann, S., Chegini, F., Ramme, L., Ilyina, T., Korn, P. & Schrum, C. (2022). Seamless integration of the coastal ocean in global marine carbon cycle modeling. Journal of Advances in Modeling Earth Systems, 14: e2021MS002789. doi:10.1029/2021MS002789 [publisher-version]
  • Jungclaus, J., Lorenz, S., Schmidt, H., Brovkin, V., Brüggemann, N., Chegini, F., Crueger, T., de Vrese, P., Gayler, V., Giorgetta, M., Gutjahr, O., Haak, H., Hagemann, S., Hanke, M., Ilyina, T., Korn, P., Kröger, J., Linardakis, L., Mehlmann, C., Mikolajewicz, U., Müller, W., Nabel, J., Notz, D., Pohlmann, H., Putrasahan, D., Raddatz, T., Ramme, L., Redler, R., Reick, C., Riddick, T., Sam, T., Schneck, R., Schnur, R., Schupfner, M., von Storch, J.-S., Wachsmann, F., Wieners, K.-H., Ziemen, F., Stevens, B., Marotzke, J. & Claussen, M. (2022). The ICON Earth System Model Version 1.0. Journal of Advances in Modeling Earth Systems, 14: e2021MS002813. doi:10.1029/2021MS002813 [publisher-version]
  • Kumar, A., Brüggemann, N., Smith, R. & Marotzke, J. (2022). Response of a tropical cyclone to a subsurface ocean eddy and the role of boundary layer dynamics. Quarterly Journal of the Royal Meteorological Society, 148, 378-402. doi:10.1002/qj.4210 [publisher-version]

Contact

Dr. Nils Brüggemann

Group leader
Phone: +49 (0)40 41173-153
nils.brueggemann@we dont want spammpimet.mpg.de

Dr. Peter Korn

Group leader
Phone: +49 (0)40 41173-470
peter.korn@we dont want spammpimet.mpg.de

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