EGU23-15700
https://doi.org/10.5194/egusphere-egu23-15700
EGU General Assembly 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

On the numerical dependence of balance state in geophysical flows

Manita Chouksey1, Carsten Eden2, Gökce Tuba Masur3, and Marcel Oliver4
Manita Chouksey et al.
  • 1Institut für Umweltphysik, Universität Bremen, Bremen, Germany (manita.chouksey@uni-hamburg.de)
  • 2Institut für Meereskunde, Universität Hamburg, Hamburg, Germany
  • 3Institut für Atmosphäre und Umwelt, Goethe Universität Frankfurt, Frankfurt, Germany
  • 4Mathematical Institute for Machine Learning and Data Science, KU Eichstätt–Ingolstadt, Ingolstadt, Germany

Balance flows dictate the evolution and dynamics of geophysical flows, such as the atmosphere and ocean, that are central to the Earth's climate. Here, balance geophysical flows are balanced using two different methods and compared in simulations of the single-layer shallow water model with two different numerical model codes and two different initial conditions over a range of different parameters. Both methods: nonlinear higher order balancing and optimal balance, add to the linear geostrophic mode, the linear wave mode contributions. The resulting approximately balanced states are characterized by very small residual wave emission during time evolution of the flow. Overall, the performance of both methods is comparable. Cross-balancing suggests that both methods find approximately the same balanced states. The results contradict previous studies claiming significant spontaneous wave emission from balanced flow. Further, the results clearly show that the notion of balance in numerical models of geophysical flows is ultimately related to the particular discretization.

How to cite: Chouksey, M., Eden, C., Masur, G. T., and Oliver, M.: On the numerical dependence of balance state in geophysical flows, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-15700, https://doi.org/10.5194/egusphere-egu23-15700, 2023.