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Area M: Mathematics, new concepts and methods

Area M represents the foundation of our project. The scientists in that area work on new mathematical concepts and numerical methods to be tested in the other project areas. Thus, it forms the basis for future work.

Interdisciplinary approach

Applied mathematicians and experts from geo sciences are working together in area M, to foster an exchange with the other research areas and to transfer knowledge between the different disciplines. By working on consistent model formulation, new and consistent parameterisations and numerics for both atmosphere and ocean, the mathematicians can help climate scientists improve their models and thus enhance climate projections.

Specific research questions in Research Area M are:

  • What is a mathematically and physically consistent model formulation for the different dynamical regimes and their interaction?
  • Can we formulate better and physically consistent sub-grid scale parameterisations for the interaction between different dynamical regimes?
  • Can we develop better numerical schemes?

  • Franzke, C.L.E., Gugole, F. & Juricke, S. (2022). Systematic multi-scale decomposition of ocean variability using machine learning. Chaos: An Interdisciplinary Journal of Nonlinear Science 32(7), 073122, doi: https://doi.org/10.1063/5.0090064. 

  • Uchida, T., Danilov, S., Koldunov, N. et al. (2022). Cloud-based framework for inter-comparing submesoscale-permitting realistic ocean models. Geosci. Model Dev. 15, 5829–5856, doi: https://doi.org/10.5194/gmd-15-5829-2022. 

  • Strommen, K., Juricke, S. & Cooper, F. (2022). Improved teleconnection between Arctic sea ice and the North Atlantic Oscillation through stochastic process representation. Weather Clim. Dynam. 3(3), 951–975, doi: https://doi.org/10.5194/wcd-3-951-2022.

  • Feng, Y., Mazzucato, A.L. & Nobili, C. (2023). Enhanced dissipation by circularly symmetric and parallel pipe flows. Physica D: Nonlinear Phenomena 445, 133640, doi: https://doi.org/10.1016/j.physd.2022.133640. 

  • Juricke, S., Bellinghausen, K., Danilov, S., Kutsenko, A. & Oliver, M. (2023). Scale analysis on unstructured grids: Kinetic energy and dissipation power spectra on triangular meshes. J. Adv. Model Earth Sy. 15, e2022MS003280, doi: https://doi.org/10.1029/2022MS003280.

  • Brecht, R. & Bihlo, A. (2023). Computing the Ensemble Spread From Deterministic Weather Predictions Using Conditional Generative Adversarial Networks. Geophys. Res. Lett. 50(2), e2022GL101452, doi: https://doi.org/10.1029/2022GL101452. 

  • Kutsenko, A.A. (2023). A note on exotic integrals. Proc. Amer. Math. Soc. 151, 1697-1703, doi: https://doi.org/10.1090/proc/16279.

  • Kutsenko, A.A. (2023). Approximation of the Number of Descendants in Branching Processes. J. Stat. Phys. 190(68), doi: https://doi.org/10.1007/s10955-023-03079-6.

  • Darbenas, Z., van der Hout, R. & Oliver, M. (2023). Conditional uniqueness of solutions to the Keller–Rubinow model for Liesegang rings in the fast reaction limit. J. Differential Equations 347, 212–245, doi: https://doi.org/10.1016/j.jde.2022.11.038. 

  • Brecht, R., Bakels, L., Bihlo, A. & Stohl, A. (2023). Improving trajectory calculations by FLEXPART 10.4+ using single-image super-resolution. Geosci. Model Dev. 16(8), 2181–2192, doi: https://doi.org/10.5194/gmd-16-2181-2023.