Ocean in the Earth System: Waves by Christian Klepp

Numerical Model Development and Data Assimilation

Mission: Climate science relies essentially on the use of numerical models to gain insight into the Earth system. The mission of our group is to contribute to this effort by developing a state-of-the-art global ocean circulation model. Data assimilation methods are developed to merge the ocean model and real-world observations in order to derive an optimal estimate of the present ocean state. This can then be used as a starting point for predictive simulations. In our research we combine numerical methods, physical reasoning and rigorous mathematical analysis.

 

To simulate the ocean circulation within a computer model is a complex multiscale problem. It requires understanding of the physical principles of the ocean circulation, the mathematical formulation of these principles and their numerical realization. Global ocean-simulation models are influenced by a whole range of uncertainties, due to mathematical approximations, physical parametrizations and incomplete knowledge of forcing fields and initial/ boundary conditions. Our strategy to deal with this fundamental problem is twofold: First, we build a flexible and extendable modeling framework that allows a continuing advancement of our numerical ocean model, for example by novel numerical techniques. Second, we respond to the existence of inevitable model errors by developing tools to supplement our model calculation with error estimates.

 

Real-world simulations are accomplished by connecting the ocean model to a variational data assimilation scheme (4D-Var). The trend towards eddy- resolving simulations requires us to address the data assimilation problem in a turbulent regime. For coupled atmosphere-ocean models the data assimilation algorithm must be extended to multicomponent systems.

Research Activities

  • ICON Ocean Model

Development of the ocean component of the ICON model (see also http://www.icon.enes.org)

 

  • Data Assimilation

Development of an adjoint model for the ICON ocean model for application in variational data assimilation

 

  • Turbulence Modeling

Subgrid-scale closure via non-dissipative regularizations of the dynamical equations (see also metstroem.mi.fu-berlin.de)

 

  • Error Estimation
  • Estimation of numerical errors
  • Uncertainty in ocean state estimation

Group Members

Peter Korn (Group leader)

Stephan Lorenz

Ralf Müller

Vadym Aizinger

Andrey Vlasenko

Peter Düben

Florian Rauser (until 1/2011)