EGU General Assembly 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Quasi-geostrophic coupled model under location uncertainty

Long Li1, Etienne Mémin1, Bertrand Chapron2, and Noé Lahaye1
Long Li et al.
  • 1INRIA Rennes Bretagne Atlantique, FLUMINANCE, Rennes, France (
  • 2Ifremer, Laboratoire Spatial et Interfaces Air-Mer, Plouzané, France

In this work, we aim to describe atmosphere-ocean coupling through a physically-based stochastic formulation. We adopt the framework of modelling under Location Uncertainty (LU) [Bauer2020a], which is based on a temporal-scale separation and a stochastic transport principle. One important characteristic of such random model is that it conserves the total energy of the resolved flow. This representation has been successfully tested for ocean-only models, such as the barotropic quasi-geostrophic (QG) model [Bauer2020b], the multi-layered QG model [Li2021], as well as the rotating shallow-water model [Brecht2021]. Here, we consider the ocean-atmosphere coupled QG model [Hogg2003]. The LU scheme has been tested for coarse-grid simulations, in which the spatial structure of ocean uncertainty is calibrated from eddy-resolving simulation data while the atmosphere component is parameterized from the ongoing simulation. In other words, the ocean dynamics has a data-driven stochastic component whereas the large-scale atmosphere dynamics is fully parameterized. Two major benefits of the resulting random model are provided on the coarse mesh: it enables us to reproduce the ocean eastward jet and its adjacent recirculation zones; it improves the prediction of intrinsic variability for both ocean and atmosphere components. These capabilities of the proposed stochastic coupled QG model are demonstrated through several statistical criteria and an energy transfers analysis.


  • [Bauer2020a] W. Bauer, P. Chandramouli, B. Chapron, L. Li, and E. Mémin. Deciphering the role of small-scale inhomogeneity on geophysical flow structuration: a stochastic approach. Journal of Physical Oceanography, 50(4):983-1003, 2020.
  • [Bauer2020b] W. Bauer, P. Chandramouli, L. Li, and E. Mémin. Stochastic representation of mesoscale eddy effects in coarse-resolution barotropic models. Ocean Modelling, 151:101646, 2020.
  • [Li2021] Li, L., 2021. Stochastic modelling and numerical simulation of ocean dynamics. PhD Thesis. Université Rennes 1.
  • [Brecht2021] Rüdiger Brecht, Long Li, Werner Bauer and Etienne Mémin. Rotating Shallow Water Flow Under Location Uncertainty With a Structure-Preserving Discretization. Journal of Advances in Modeling Earth Systems, 13, 2021MS002492.
  • [Hogg2003] A.M. Hogg, W.K. Dewar, P.D. Killworth, J.R. Blundell. A quasi-geostrophic coupled model (Q-GCM). Monthly Weather Review, 131:2261-2278, 2003.


How to cite: Li, L., Mémin, E., Chapron, B., and Lahaye, N.: Quasi-geostrophic coupled model under location uncertainty, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5287,, 2022.

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