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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?

  • Huang, Y., Franzke, C.L.E., Yuan, N. & Fu, Z. (2020). Systematic identification of causal relations in high-dimensional chaotic systems: application to stratosphere-troposphere coupling. Clim. Dyn. 55, 2469–2481. doi: https://doi.org/10.1007/s00382-020-05394-0. 

  • Schulz, K., Klingbeil, K., Morys, C., & Gerkema, T. (2020). The fate of mud nourishment in response to short-term wind forcing. Estuar. Coast 44, doi: https://doi.org/10.1007/s12237-020-00767-4.

  • Nian, D., Franzke, C.L.E., Yuan, N. et al. (2020). Identifying the sources of seasonal predictability based on climate memory analysis and variance decomposition. Clim. Dyn. 55, 3239–3252. doi: https://doi.org/10.1007/s00382-020-05444-7. 

  • Franzke, C.L.E. & Torelló i Sentelles, H. (2020). Risk of extreme high fatalities due to weather and climate hazards and its connection to large-scale climate variability. Climatic Change 162,507–525, doi: https://doi.org/10.1007/s10584-020-02825-z. 

  • Önskog, T., Franzke, C.L.E. & Hannachi, A. (2020). Nonlinear time series models for the North Atlantic Oscillation. Adv. Stat. Clim. Meteorol. Oceanogr. 6(2), 141–157, doi: https://doi.org/10.5194/ascmo-6-141-2020. 

  • Kerimoglu, M., Voynova, Y.G., Klingbeil, K. et al. (2020). Interactive impacts of meteorological and hydrological conditions on the physical and biogeochemical structure of a coastal system. Biogeosciences 17(20), doi: https://doi.org/10.5194/bg-17-5097-2020. 

  • Kutsenko, A. (2020). Isomorphism between one-Dimensional and multidimensional finite difference operators. Commun. Pure. Appl. Ana., doi: 10.3934/cpaa.2020270.

  • Darbenas, Z. & Oliver, M. (2021). Breakdown of Liesegang precipitation bands in a simplified fast reaction limit of the Keller–Rubinow model. Nonlinear Differ. Equ. Appl. 28(4), doi: https://doi.org/10.1007/s00030-020-00663-7. 

  • Juricke, S., Danilov, S., Koldunov, N., Oliver, M.,Sein, D.V.,Sidorenko, D. & Wang, Q. (2020). A Kinematic Kinetic Energy Backscatter Parametrization: From Implementation to Global Ocean Simulations. J. Adv. Model Earth Sy. 12, e2020MS002175. doi: https://doi.org/10.1029/2020MS002175.

  • Osinski, R.D., Enders, K., Klingbeil, K. et al. (2020). Model uncertainties of a storm and their influence on microplastics and sediment transport in the Baltic Sea. Ocean Sci. 16(6), 1491–1507, doi: https://doi.org/10.5194/os-16-1491-2020.