The proposed project aims to further develop, assess and analyse numerical algorithms leading to reduction in spurious diapycnal mixing in ocean circulation models. This goal will be achieved by (i) the design and implementation of vertical mesh motion algorithms that reduce spurious mixing; (ii) use of advective schemes with isopycnal diffusion and special design of limiters; (iii) development and analysis of high-order advection algorithms relying on high-order flux evaluation.
Reducing spurious diapycnal mixing in ocean models
My part of work is studying the newly proposed methods of rotating the diffusive part of the advection schemes into the isoneutral plane.
Hi everyone, my name is Margarita and I’m a PhD student of the subproject M5 “Reducing spurious diapycnal mixing in ocean models.” This project is about development and analysis of algorithms leading to reducing spurious mixing in ocean models.
Particularly my part of work is studying the newly proposed methods of rotating the diffusive part of the advection schemes into the isoneutral plane. I work in AWI under supervision of Sergey Danilov.
By the moment adapted to triangular meshes with vertex-based and cell-based placement of scalar variables algorithm described by Lemarie at al. (2012) was implemented on the sea-ice model FESOM2.0. I started doing reference potential energy (RPE) analysis of the implemented harmonic version of the algorithm. Also biharmonic version is to be implemented soon. RPE analysis will be held for both versions and also on dissorted meshes. This analysis allows to determine spurious mixing depending on advection schemes and meshes type.
The future work includes analysis of spurious mixing with variance decay technique (by Knut Klingbeil et al.), analysis of regular and irregular meshes, carrying of realistic ocean simulations and analysis of spurious mixing under real conditions.
Solving equations faster and more accurately
Designing high-order numerical methods needs a good understanding of the mathematical equations we want to solve.
Hi, I’m Claus and I am a PostDoc in Project M5. The focus of my research in this project is the development and analysis of high-order advection methods and high-oder flux evaluation techniques for ocean models. Our goal is to reduce spurious diapycnal mixing in ocean models and I’m working on the intersection between mathematical analysis and numerical methods to help with that.
High-order numerical methods for flow computations are becoming increasingly more popular in computational engineering, but may be not as widespread in climate and ocean science. So what’s the deal with high and low order?
Classical finite differences and finite volume methods for the discretization of partial differential equations use one piece of information in each cell (say, a function value at the cell-center or an integral average over the cell) and put this information into a discrete version of the PDE. This allows for fast and robust algorithms, but in order to resolve small scale features, we often need very fine grids. On the other hand, the high-order methods we are interested in, use higher degree polynomials or other nonlinear functions in each cell. This extra information allow us resolve more features of the solution, even on coarser girds. In many computational fluid dynamics applications this leads to a smaller overall computational time and we would like to show that this is also true for problems in ocean science.
However, carelessly throwing high-order polynomials at your problem is a sure recipe for failure. If we want to include more features of the analytical solution in our numerical solution, we need a good understanding of the analytical properties and how they can be translated into our numerical scheme. For our particular application, e.g., we want tight control over diffusion properties and need to tune our methods to avoid artificial diffusion without losing stability properties that numerical diffusion brings.
In short, designing high-order numerical methods needs a good understanding of the mathematical equations we want to solve. I’m happy to be involved with learning more about the equations in ocean science, so that we can solve them faster and more accurately.
Klingbeil, K., Debreu, L., Lemarié, F., and Burchard, H. (2018). The numerics of hydrostatic structured-grid coastal ocean models: state of the art and future perspectives. Ocean Modelling, Vol. 125, 80-105.
Frassl, M., B. Boehrer, P. Holtermann, W. Hu, K. Klingbeil, Z. Peng, ... and K. Rinke (2018). Opportunities and Limits of Using Meteorological Reanalysis Data for Simulating Seasonal to Sub-Daily Water Temperature Dynamics in a Large Shallow Lake. Water, 10(5), 594.
Lemmen, C., R. Hofmeister, K. Klingbeil, Nasermoaddeli, M. H., O. Kerimoglu, H. Burchard, F. Kösters, K. W. Wirtz (2018). Modular System for Shelves and Coasts (MOSSCO v1.0) – a flexible and multi-component framework for coupled coastal ocean ecosystem modelling,Geoscientific Model Development, 10.5194/gmd-2017-138 .
Nasermoaddeli, M. H., Lemmen, C., Stigge, G., Kerimoglu, O., Burchard, H., Klingbeil, K., Hofmeister, R., Kreus, M., Wirtz, K. W. & Kösters, F. A (2018). A model study on the large-scale effect of macrofauna on the suspended sediment concentration in a shallow shelf sea Estuarine, Coastal and Shelf Science, Geoscientific Model Development, https://doi.org/10.1016/j.ecss.2017.11.002 .