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
Smolentseva, M., & Danilov, S. (2020). Comparison of several high-order advection schemes for vertex-based triangular discretization. Ocean Dyn., 70(4), 463-479, https://doi.org/10.1007/s10236-019-01337-4 .
Franzke, C. L., Barbosa, S., Blender, R., Fredriksen, H. B., Laepple, T., Lambert, F., ... & Vannitsem, S. (2020). The Structure of Climate Variability Across Scales. Rev. Geophys., e2019RG000657, https://doi.org/10.1029/2019RG000657 .
Yang, L., Franzke, C. L., & Fu, Z. (2020). Power‐law behaviour of hourly precipitation intensity and dry spell duration over the United States. I. J. Clim., 40(4), 2429-2444, https://doi.org/10.1002/joc.6343.
Yang, L., Franzke, C. L., & Fu, Z. (2020). Evaluation of the Ability of Regional Climate Models and a Statistical Model to Represent the Spatial Characteristics of Extreme Precipitation. Int. J. Clim., doi: https://doi.org/10.1002/joc.6602.
Akramov, I. & Oliver, M. (2020). On the existence of solutions to a bi-planar Monge–Ampère equation. Acta Math. Sci. 40, 379–388, doi: https://doi.org/10.1007/s10473-020-0206-6.
Yousefi, K., Veron, F., & Buckley, M.P. (2020). Momentum flux measurements in the airflow over wind-generated surface waves. J. Fluid Mech. 73, doi: https://doi.org/10.1016/j.euromechflu.2018.04.00.
Kutsenko, A. (2020). An entire function connected with the approximation of the golden ratio. Am. Math. Monthly 127(9), doi: https://doi.org/10.1080/00029890.2020.1801079.
Huang, Y., Fu, Z., & Franzke, C.L.E. (2020). Detecting causality from time series in a machine learning framework. Chaos: An Interdisciplinary Journal of Nonlinear Science 30(6), doi: https://doi.org/10.1063/5.0007670.
Gugole, F. & Franzke, C.L.E. (2020). Spatial Covariance Modeling for Stochastic Subgrid-Scale Parameterizations Using Dynamic Mode Decomposition. J. Adv. Model Earth Sy. 12(8), e2020MS002115, doi: https://doi.org/10.1029/2020MS002115.
Chegini, F., Klingbeil, K., Burchard, H., Winter, C. et al. (2020). Processes of Stratification and Destratification During An Extreme River Discharge Event in the German Bight ROFI. J. Geophys. Res.- Oceans 125(8), doi: https://doi.org/10.1029/2019JC015987.