Articles | Volume 10, issue 3
https://doi.org/10.5194/gmd-10-1091-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Special issue:
https://doi.org/10.5194/gmd-10-1091-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Development of a probabilistic ocean modelling system based on NEMO 3.5: application at eddying resolution
Laurent Bessières
CORRESPONDING AUTHOR
CNRS/CERFACS, CECI UMR 5318, Toulouse, France
Stéphanie Leroux
Univ. Grenoble Alpes, CNRS, IRD, IGE, 38000 Grenoble, France
Jean-Michel Brankart
Univ. Grenoble Alpes, CNRS, IRD, IGE, 38000 Grenoble, France
Jean-Marc Molines
Univ. Grenoble Alpes, CNRS, IRD, IGE, 38000 Grenoble, France
Marie-Pierre Moine
CNRS/CERFACS, CECI UMR 5318, Toulouse, France
Pierre-Antoine Bouttier
Univ. Grenoble Alpes, CNRS, IRD, IGE, 38000 Grenoble, France
Thierry Penduff
Univ. Grenoble Alpes, CNRS, IRD, IGE, 38000 Grenoble, France
Laurent Terray
CNRS/CERFACS, CECI UMR 5318, Toulouse, France
Bernard Barnier
Univ. Grenoble Alpes, CNRS, IRD, IGE, 38000 Grenoble, France
Guillaume Sérazin
CNRS/CERFACS, CECI UMR 5318, Toulouse, France
Univ. Grenoble Alpes, CNRS, IRD, IGE, 38000 Grenoble, France
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- Inverse Cascades of Kinetic Energy as a Source of Intrinsic Variability: A Global OGCM Study G. Sérazin et al. 10.1175/JPO-D-17-0136.1
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- An Objective Method for Probabilistic Forecasting of Multimodal Kuroshio States using Ensemble Simulation and Machine Learning K. Aoki et al. 10.1175/JPO-D-19-0316.1
- Extending an oceanographic variational scheme to allow for affordable hybrid and four-dimensional data assimilation A. Storto et al. 10.1016/j.ocemod.2018.06.005
- Assimilation of chlorophyll data into a stochastic ensemble simulation for the North Atlantic Ocean Y. Santana-Falcón et al. 10.5194/os-16-1297-2020
- Chaotic Variability of Ocean: Heat Content Climate-Relevant Features and Observational Implications T. Penduff et al. 10.5670/oceanog.2018.210
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- Interannual to decadal variability of the Kuroshio extension: analyzing an ensemble of global hindcasts from a dynamical system viewpoint G. Fedele et al. 10.1007/s00382-021-05751-7
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- Imprint of intrinsic ocean variability on decadal trends of regional sea level and ocean heat content using synthetic profiles W. Llovel et al. 10.1088/1748-9326/ac5f93
- Interpreting Reynolds stress from perspective of eddy geometry K. Aoki & Y. Masumoto 10.1016/j.dynatmoce.2021.101223
- Laboratory experiments reveal intrinsic self-sustained oscillations in ocean relevant rotating fluid flows S. Pierini et al. 10.1038/s41598-022-05094-1
- Trends of Coastal Sea Level Between 1993 and 2015: Imprints of Atmospheric Forcing and Oceanic Chaos T. Penduff et al. 10.1007/s10712-019-09571-7
- Dynamical attribution of North Atlantic interdecadal predictability to oceanic and atmospheric turbulence under diagnosed and optimal stochastic forcing D. Stephenson & F. Sévellec 10.1175/JCLI-D-21-0035.1
- Diagnosing the Thickness‐Weighted Averaged Eddy‐Mean Flow Interaction From an Eddying North Atlantic Ensemble: The Eliassen‐Palm Flux T. Uchida et al. 10.1029/2021MS002866
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Latest update: 20 Nov 2024
Short summary
A new, probabilistic version of an ocean modelling system has been implemented in order to simulate the chaotic and the atmospherically forced contributions to the ocean variability. For that purpose, a large ensemble of global hindcasts has been performed. Results illustrate the importance of the oceanic chaos on climate-related oceanic indices, and the relevance of such probabilistic ocean modelling approaches to anticipating the behaviour of the next generation of coupled climate models.
A new, probabilistic version of an ocean modelling system has been implemented in order to...
Special issue