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.
Objectives
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?
Publications
Kutsenko, A. A. (2019). A note on sharp spectral estimates for periodic Jacobi matrices. J. Approx. Theory, Vol. 242, p. 58-63, doi: https://doi.org/10.1016/j.jat.2019.03.003.
Gálfi, V. M., Lucarini, V., & Wouters, J. (2019). A large deviation theory-based analysis of heat waves and cold spells in a simplified model of the general circulation of the atmosphere. J. Stat. Mech.-Theory. E., 2019(3), 033404, https://doi.org/10.1088/1742-5468/ab02e8 .
Juricke, S., S. Danilov, A. Kutsenko and M. Oliver (2019). Ocean kinetic energy backscatter parametrizations on unstructured grids: Impact on mesoscale turbulence in a channel.Ocean Model., doi: https://doi.org/10.1016/j.ocemod.2019.03.009.
Lucarini, V., & Bódai, T. (2019). Transitions across Melancholia States in a Climate Model: Reconciling the Deterministic and Stochastic Points of View. Phys. Rev. Lett., 122(15), 158701, doi.org/10.1103/PhysRevLett.122.158701.
Gräwe, U., K. Klingbeil, J. Kelln, and S. Dangendorf (2019). Decomposing mean sea level rise in a semi-enclosed basin, the Baltic Sea. J. Climate, doi: https://doi.org/10.1175/JCLI-D-18-0174.1.
Carlu, M., Ginelli, F., Lucarini, V., & Politi, A. (2019). Lyapunov analysis of multiscale dynamics: the slow bundle of the two-scale Lorenz 96 model. Nonlinear Proc. Geoph., 26, 73-89, https://doi.org/10.5194/npg-26-73-2019 .
Gugole, F., and C.L. Franzke (2019). Numerical Development and Evaluation of an Energy Conserving Conceptual Stochastic Climate Model. Math. Climate Weather Forecasting, 5(1), 45-64, doi: https://doi.org/10.5194/os-15-601-2019.
Lorenz, M., K. Klingbeil, P. MacCready, and H. Burchard (2019). Numerical issues of the Total Exchange Flow (TEF) analysis framework for quantifying estuarine circulation, Ocean Sci., 15, 601-614.
Stähler, S. C., Panning, M. P., Hadziioannou, C., Lorenz, R. D., Vance, S., Klingbeil, K., & Kedar, S. (2019). Seismic signal from waves on Titan's seas. Earth Planet Sc. Lett., 520, 250-259, doi: https://doi.org/10.1016/j.epsl.2019.05.043.
Noethen, F. (2019). Well-separating common complements of a sequence of subspaces of the same codimension in a Hilbert space are generic, arXiv:1906.08514