Geophysical Fluid Dynamics

photo of the ocean model FESOM
Group leader
Photo of Sergey Danilov
Numerical Modeling
Specific themes and goals

The main research goal of our group is to improve our understanding and prediction of the ocean and atmospheric circulation as part of the climate system. We use complex climate models to simulate the oceans as well as advanced diagnostic methods to analyze the data from simulations and observations. 

  • Ocean and climate models: Our research tools range from idealized to comprehensive state-of-the art ocean and climate models, partly developed at the Alfred Wegener Institute in Bremerhaven. We run and continuously improve these models using numerical methods and classical mathematical and physical analysis. 
  • Biases and errors: We diagnose the models’ biases and strive to reduce these systematic errors as well as understand how they come about and how they are related to physical properties of complex dynamical systems. 
  • Energy transfers: A specific focus of our group is understanding the kinetic energy contained in the motion of the world’s oceans. In this context, we also investigate the interaction and energy exchanges between the atmosphere and the oceans at the air-sea interface and at various spatial and temporal scales
Highlights and impact
  • We developed and implemented a numerical scheme that improves the representation of organized ocean turbulence — so-called mesoscale ocean eddies — in ocean simulations. Eddies carry heat, salt, and other tracers and are therefore an important ingredient in the dynamics. Their seamless simulation requires very fine computational grids, which is prohibitively expensive in climate studies. The new scheme helps to re-energise eddies in locations where the grid is insufficiently fine and leads to large improvements of ocean models especially in very turbulent regions such as western boundary currents, such as the Gulf Stream, and the Southern Ocean. 
  • We developed new diagnostic tools to separate different types of motion in the ocean, namely balanced and unbalanced motions, which allows a better understanding of the general dynamics and interpretation of observations. These tools ultimately help to improve the representation of these motions in ocean and climate models. 
  • We created new diagnostics to analyze the scaling of turbulent motions, which is how motions of different spatial extents contribute to the overall energy content in the flow. 
  • We developed a new coupled Earth-climate model by coupling the OpenIFS model of the atmosphere and the FESOM ocean model. This model will be used for climate projections and to contribute to future Intergovernmental Panel on Climate Change reports. Its distinctive feature is good numerical efficiency, which allows researchers to resolve the circulation of the atmosphere and ocean with a high level of detail.
Group composition & projects/funding

The group consists of the three principal investigators, one Postdoc, and five doctoral candidates. Funding sources include the DFG (Deutsche Forschungsgemeinschaft, German Research Foundation) via the collaborative SFB (Sonderforschungsbereich, Collaborative Research Centre) TRR181 project, and the Helmholtz School for Marine Data Science (MarDATA) in collaboration with the Alfred Wegener Institute in Bremerhaven and Prof. Zaspel (Constructor University, Computer Science).

Selected publications
  • Streffing, J., D. Sidorenko, T. Semmler, L. Zampieri, P. Scholz, M. AndrésMartínez, N. Koldunov, T. Rackow, J. Kjellsson, H. Goessling, M. Athanase, Q. Wang, J. Hegewald, D. V. Sein, L. Mu, U. Fladrich, D. Barbi, P. Gierz, S. Danilov, S. Juricke, G. Lohmann, and T. Jung, 2022: AWI-CM3 coupled climate model: Description and evaluation experiments for a prototype post-CMIP6 model. Geosci. Model Dev., 15, 6399–6427.
  • Strommen, K., S. Juricke, and F. Cooper, 2022: Improved teleconnection between Arctic sea ice and the North Atlantic Oscillation through stochastic process representation. Weather Clim. Dynam., 3, 951–975.
  • Franzke, C. L. E., F. Gugole, and S. Juricke, 2022: Systematic multi-scale decomposition of ocean variability using machine learning. Chaos, 32, 073122.
  • Juricke, S., S. Danilov, N. Koldunov, M. Oliver, D. V. Sein, D. Sidorenko, and Q. Wang, 2020: A kinematic kinetic energy backscatter parametrization: From implementation to global ocean simulations. Journal of Advances in Modeling Earth Systems, 12, e2020MS002175.
  • Juricke, S., S. Danilov, N. V. Koldunov, M. Oliver, and D. Sidorenko, 2020: Ocean kinetic energy backscatter parametrization on unstructured grids: Impact on global eddy-permitting simulations. Journal of Advances in Modeling Earth Systems, 12, e2019MS001855. https://doi. org/10.1029/2019MS001855