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.
Model description paper
| 
10 Mar 2017
Model description paper |
| 10 Mar 2017
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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- Statistical Significance of Small Ensembles of Simulations and Detection of the Internal Climate Variability: An Excitable Ocean System Case Study S. Pierini 10.1007/s10955-019-02409-x
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- A means of estimating the intrinsic and atmospherically-forced contributions to sea surface height variability applied to altimetric observations S. Close et al. 10.1016/j.pocean.2020.102314
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- Ocean modelling and altimeter data reveal the possible occurrence of intrinsic low-frequency variability of the Kuroshio Extension V. Gentile et al. 10.1016/j.ocemod.2018.08.006
- 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
- Sea-state contributions to sea-level variability in the European Seas A. Bonaduce et al. 10.1007/s10236-020-01404-1
- Atmospherically Forced and Chaotic Interannual Variability of Regional Sea Level and Its Components Over 1993–2015 A. Carret et al. 10.1029/2020JC017123
- Crossing the chasm: how to develop weather and climate models for next generation computers? B. Lawrence et al. 10.5194/gmd-11-1799-2018
- Uncertainty and scale interactions in ocean ensembles: From seasonal forecasts to multidecadal climate predictions L. Zanna et al. 10.1002/qj.3397
- 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
- The Theory of Parallel Climate Realizations T. Tél et al. 10.1007/s10955-019-02445-7
- 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
- Circumpolar Variations in the Chaotic Nature of Southern Ocean Eddy Dynamics A. Hogg et al. 10.1029/2022JC018440
- Global-scale random bottom pressure fluctuations from oceanic intrinsic variability M. Zhao et al. 10.1126/sciadv.adg0278
- Assessment of a regional physical–biogeochemical stochastic ocean model. Part 1: Ensemble generation V. Vervatis et al. 10.1016/j.ocemod.2021.101781
- Intrinsic and Atmospherically Forced Variability of the AMOC: Insights from a Large-Ensemble Ocean Hindcast S. Leroux et al. 10.1175/JCLI-D-17-0168.1
- 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
- Imprints of Ocean Chaotic Intrinsic Variability on Bottom Pressure and Implications for Data and Model Analyses M. Zhao et al. 10.1029/2021GL096341
- Ensemble quantification of short-term predictability of the ocean dynamics at a kilometric-scale resolution: a Western Mediterranean test case S. Leroux et al. 10.5194/os-18-1619-2022
- Representation of the Denmark Strait overflow in a <i>z</i>-coordinate eddying configuration of the NEMO (v3.6) ocean model: resolution and parameter impacts P. Colombo et al. 10.5194/gmd-13-3347-2020
Latest update: 16 Apr 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