Area L: Large-Scale and Balanced Processes

Both the large-scale currents and mesoscale eddies in the ocean are essentially quasi-geostrophically balanced in the horizontal and hydrostatically in the vertical. Area L focuses on those processes in the ocean.

Eddies in the ocean

Mesoscale eddies in the ocean, sometimes referred to as the oceanic equivalent of atmospheric storms, derive their energy from the large-scale flow mainly through baroclinic instability processes. These processes are parameterized in climate models with down-gradient parameterizations using eddy diffusivities. In geostrophic turbulence, eddies then tend to transfer their energy upscale in an inverse cascade. But ultimately, this energy has to be dissipated and it is largely unknown how and where.

Our scientists specifically assess one important pathway to dissipation, the spontaneous emission of gravity waves by the quasi-balanced flows, and analyze drifter and float data to estimate eddy production and Lagrangian mixing from observations, models and theory.

Specific research questions in Area L are:

  • How is the balanced flow dissipated in the ocean, and how important is the route to dissipation via spontaneous wave generation?
  • What are the limits and validities of the eddy diffusion model and how can we quantify and parameterize the effects of mesoscale eddies in an energy-consistent way?
  • Chouksey, M., Eden, C., Masur, G. & Oliver, M. (2023). A comparison of methods to balance geophysical flows. Journal of Fluid Mechanics 971, A2, doi:

  • Shevchenko, R., Hohenegger, C., & Schmitt, M. (2023). Impact of diurnal warm layers on atmospheric convection. J. Geophys. Res. - Atmospheres 128(14), e2022JD038473, doi:

  • Shi, J., Stepanek, C., Sein, D., Streffing, J., & Lohmann, G. (2023). East Asian summer precipitation in AWI-CM3: Comparison with observations and CMIP6 models. International Journal of Climatology, 1– 16, doi:

  • Pithan, F., Athanase, M., Dahlke, S., Sánchez-Benítez, A., Shupe, M. D., Sledd, A., Streffing, J., Svensson, G., & Jung, T. (2023). Nudging allows direct evaluation of coupled climate models with in situ observations: a case study from the MOSAiC expedition. Geosci. Model Dev. 16(7), 1857–1873, doi:

  • Hohenegger, C., Korn, P., Brüggemann, N., Gutjahr, O., Jungclaus, J., Shevchenko, R., von Storch, J.S. et al. (2023). ICON-Sapphire: simulating the components of the Earth system and their interactions at kilometer and subkilometer scales. Geosci. Model Dev. 16, 779–811,

  • Denamiel, C., Vasylkevych, S., Žagar, N., Zemunik, P. & Vilibić, I. (2023). Destructive potential of planetary meteotsunami waves beyond the Hunga Tonga–Hunga Ha’apai volcano eruption. B. Am. Meteorol. Soc. 104(1), E178–E191, doi:

  • Chrysagi, E., Basdurak, N.B., Umlauf, L., Gräwe, U. & Burchard, H. (2022). Thermocline Salinity Minima Due To Wind-Driven Differential Advection. J. Geophys. Res.- Oceans 127(11), doi:

  • Streffing, J., Scholz, P., Koldunov, N., Danilov, S., Juricke, S., Jung, T. et al. (2022). AWI-CM3 coupled climate model: description and evaluation experiments for a prototype post-CMIP6 model. Geosci. Model Dev. 15, 6399–6427, doi:

  • Strommen, K., Juricke, S. & Cooper, F. (2022). Improved teleconnection between Arctic sea ice and the North Atlantic Oscillation through stochastic process representation. Weather Clim. Dynam. 3(3), 951–975, doi:

  • Franzke, C.L.E., Gugole, F. & Juricke, S. (2022). Systematic multi-scale decomposition of ocean variability using machine learning. Chaos: An Interdisciplinary Journal of Nonlinear Science 32(7), 073122, doi: