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Quasi-geostrophic coupled model under location uncertainty
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<p>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,&#160;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.&#160;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&#160;capabilities of the proposed stochastic coupled QG model are&#160;demonstrated&#160;through several&#160;statistical&#160;criteria&#160;and an energy transfers analysis.</p><p>References:</p><ul><li>[Bauer2020a] W. Bauer, P. Chandramouli, B. Chapron, L. Li, and E. M&#233;min. Deciphering the role&#160;of small-scale inhomogeneity on geophysical flow structuration: a stochastic approach. Journal of Physical Oceanography, 50(4):983-1003, 2020.</li>
<li>[Bauer2020b] W. Bauer, P. Chandramouli, L. Li, and E. M&#233;min. Stochastic representation of mesoscale&#160;eddy effects in coarse-resolution barotropic models. Ocean Modelling, 151:101646, 2020.</li>
<li>[Li2021] Li, L., 2021. Stochastic modelling and numerical simulation of ocean dynamics. PhD Thesis. Universit&#233; Rennes 1.</li>
<li>[Brecht2021] R&#252;diger Brecht, Long Li<span>, </span><span>Werner Bauer and<span>&#160;</span>Etienne M&#233;min.&#160;</span>Rotating Shallow Water Flow Under Location Uncertainty With a Structure-Preserving Discretization. Journal of Advances in Modeling Earth Systems, 13, 2021MS002492.</li>
<li>[Hogg2003] A.M. Hogg, W.K. Dewar, P.D. Killworth, J.R. Blundell. A quasi-geostrophic coupled model (Q-GCM). Monthly&#160;Weather Review, 131:2261-2278, 2003.</li>
</ul><p>&#160;</p>
Title: Quasi-geostrophic coupled model under location uncertainty
Description:
<p>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,&#160;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.
&#160;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&#160;capabilities of the proposed stochastic coupled QG model are&#160;demonstrated&#160;through several&#160;statistical&#160;criteria&#160;and an energy transfers analysis.
</p><p>References:</p><ul><li>[Bauer2020a] W.
Bauer, P.
Chandramouli, B.
Chapron, L.
Li, and E.
M&#233;min.
Deciphering the role&#160;of small-scale inhomogeneity on geophysical flow structuration: a stochastic approach.
Journal of Physical Oceanography, 50(4):983-1003, 2020.
</li>
<li>[Bauer2020b] W.
Bauer, P.
Chandramouli, L.
Li, and E.
M&#233;min.
Stochastic representation of mesoscale&#160;eddy effects in coarse-resolution barotropic models.
Ocean Modelling, 151:101646, 2020.
</li>
<li>[Li2021] Li, L.
, 2021.
Stochastic modelling and numerical simulation of ocean dynamics.
PhD Thesis.
Universit&#233; Rennes 1.
</li>
<li>[Brecht2021] R&#252;diger Brecht, Long Li<span>, </span><span>Werner Bauer and<span>&#160;</span>Etienne M&#233;min.
&#160;</span>Rotating Shallow Water Flow Under Location Uncertainty With a Structure-Preserving Discretization.
Journal of Advances in Modeling Earth Systems, 13, 2021MS002492.
</li>
<li>[Hogg2003] A.
M.
Hogg, W.
K.
Dewar, P.
D.
Killworth, J.
R.
Blundell.
A quasi-geostrophic coupled model (Q-GCM).
Monthly&#160;Weather Review, 131:2261-2278, 2003.
</li>
</ul><p>&#160;</p>.
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