ICON https://www.icon-model.org
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ICON is probably the largest model development project within Germany. It has started more than 10 years ago as joint project between the Max-Planck Institute for Meteorology, German Weather Service DWD, the German Climate Computing Centre with the goal to develop a new model for Numerical Weather Prediction and Climate Science. It involves now national and international partners. ICON is a central element of Germany's national climate modelling strategy and it is used in national and International projects.
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Numerics for Atmosphere & Ocean
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The success of ICON-O is due to its underlying numerical method. I develop structure-preserving numerics for atmosphere and ocean dynamics.
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Cryosphere
Together with my former postdoc C. Mehlmann I have developed a new discretization for the sea-ice dynamics. Future work will include also ice-shelf modeling, through the project “Terra-DT” about a “digital Twin of Earth system for Cryosphere, Land surface and related interactions”.
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Mathematical Analysis
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I keep a strong connection to the mathematical analysis of fluid dynamical equations. This inspired much of my work on numerical methods. The problems I am interested in are classical questions of well-posedness and regularity properties for equations of geophysical fluid dynamics.
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Turbulence Modelling and Computational Convex Integration
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Convex integration is a constructive method that was introduced by C. De Ellis and L. Szekelyhidi in the analysis of hydrodynamic equations. The application of convex integration culminated in the completion of the proof of Onsager’s conjecture. Convex integration is a promising tool that opens new direction in turbulence and in fundamental question on hydrodynamic equations such as uniqueness and regularity. In a collaboration with L. Szekelyhidi and R. Klein we aim to formulate and implement a computational version of convex integration and use it in cutting-edge turbulence simulations.
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Machine Learning
ML is an exciting new instrument in the modeller toolbox. Its application for climate modelling is much more challenging than for weather forecasting and a hot research topic. In the project ExaOcean https://www.fz-juelich.de/en/ias/jsc/projects/exaocean we aim to integrate ML-techniques into the numerics of ICON-O to improve the models performance and scalability on Exascale Computers. This project is funded by the German Federal Ministry of Education and Research.
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Computational Ocean Turbulence
​ICON-O is one of the fastest ocean models worldwide. This is the result of its numerical properties, of a careful code design and of advanced optimization techniques. This performance allows us to approach unpre-cedented spatial resolutions. In an attempt to push the envelope we designed an experiment where ICON-O’s horizontal resolution ap-proaches locally 500m and where we have subkilometer resolution over large parts of the North Atlantic. With this experimental configuration we could represent submesoscale dynamics, a class of phenomena with a spatial extend of 100m-10 km spatially and a lifetime of days to weeks that is supposed to play an important role in the ocean’s route to dissipation. With our experiment we are analysing and quantifying the oceanic dissipation mechanism
Coastal Dynamics and Marine Biogeochemistry
As part of the The Hamburg Excelencecluster "Climate, Climatic Change, and Society" ("CLICCS" https://www.cliccs.uni-hamburg.de/) we modelled the physical and biogeochemical dynamics of the global land-ocean transition zone and its role in the Earth system using a coastal telescope configuration of the ICON-O and the integrated marine biogeochemistry model HAMOCC in global configuration. See also the EOS-highlight: https://eos.org/editor-highlights /substantial-advance-towards-a-global-coastal-carbon-model. In order to handle the enormous computational costs that arise by including biogeochemistry models we implemented the concept of "concurrency" into ICON-O that allows us to run the marine biogeochemistry at high resolution.
