Articles | Volume 18, issue 6
https://doi.org/10.5194/os-18-1619-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/os-18-1619-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
21 Nov 2022
Research article |
| 21 Nov 2022
| 21 Nov 2022
Ensemble quantification of short-term predictability of the ocean dynamics at a kilometric-scale resolution: a Western Mediterranean test case
Stephanie Leroux
CORRESPONDING AUTHOR
Univ. Grenoble Alpes, CNRS, IRD, Grenoble INP, IGE, 38000 Grenoble, France
Ocean Next, Grenoble, France
Datlas, Grenoble, France
Jean-Michel Brankart
Univ. Grenoble Alpes, CNRS, IRD, Grenoble INP, IGE, 38000 Grenoble, France
Aurélie Albert
Univ. Grenoble Alpes, CNRS, IRD, Grenoble INP, IGE, 38000 Grenoble, France
Ocean Next, Grenoble, France
Laurent Brodeau
Univ. Grenoble Alpes, CNRS, IRD, Grenoble INP, IGE, 38000 Grenoble, France
Ocean Next, Grenoble, France
Jean-Marc Molines
Univ. Grenoble Alpes, CNRS, IRD, Grenoble INP, IGE, 38000 Grenoble, France
Quentin Jamet
Univ. Grenoble Alpes, CNRS, IRD, Grenoble INP, IGE, 38000 Grenoble, France
Julien Le Sommer
Univ. Grenoble Alpes, CNRS, IRD, Grenoble INP, IGE, 38000 Grenoble, France
Thierry Penduff
Univ. Grenoble Alpes, CNRS, IRD, Grenoble INP, IGE, 38000 Grenoble, France
Pierre Brasseur
Univ. Grenoble Alpes, CNRS, IRD, Grenoble INP, IGE, 38000 Grenoble, France
Related authors
Laurent Bessières, Stéphanie Leroux, Jean-Michel Brankart, Jean-Marc Molines, Marie-Pierre Moine, Pierre-Antoine Bouttier, Thierry Penduff, Laurent Terray, Bernard Barnier, and Guillaume Sérazin
Geosci. Model Dev., 10, 1091–1106, https://doi.org/10.5194/gmd-10-1091-2017, https://doi.org/10.5194/gmd-10-1091-2017, 2017
Short summary
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.
Olivier Narinc, Thierry Penduff, Guillaume Maze, Stéphanie Leroux, and Jean-Marc Molines
EGUsphere, https://doi.org/10.5194/egusphere-2024-1146, https://doi.org/10.5194/egusphere-2024-1146, 2024
Short summary
Short summary
This study examines how the ocean's chaotic variability and atmospheric fluctuations affect yearly changes in North Atlantic Eighteen Degree Water (EDW) properties, using an ensemble of realistic ocean simulations. Results show that while yearly changes in EDW properties are mostly paced by the atmosphere, a notable fraction of these changes are random in phase. This study also illustrates the value of ensemble simulations over single runs in understanding oceanic fluctuations and their causes.
Mikhail Popov, Jean-Michel Brankart, Arthur Capet, Emmanuel Cosme, and Pierre Brasseur
Ocean Sci., 20, 155–180, https://doi.org/10.5194/os-20-155-2024, https://doi.org/10.5194/os-20-155-2024, 2024
Short summary
Short summary
This study contributes to the development of methods to estimate targeted ocean ecosystem indicators, including their uncertainty, in the framework of the Copernicus Marine Service. A simplified approach is introduced to perform a 4D ensemble analysis and forecast, directly targeting selected biogeochemical variables and indicators (phenology, trophic efficiency, downward flux of organic matter). Care is taken to present the methods and discuss the reliability of the solution proposed.
Qiang Wang, Qi Shu, Alexandra Bozec, Eric P. Chassignet, Pier Giuseppe Fogli, Baylor Fox-Kemper, Andy McC. Hogg, Doroteaciro Iovino, Andrew E. Kiss, Nikolay Koldunov, Julien Le Sommer, Yiwen Li, Pengfei Lin, Hailong Liu, Igor Polyakov, Patrick Scholz, Dmitry Sidorenko, Shizhu Wang, and Xiaobiao Xu
Geosci. Model Dev., 17, 347–379, https://doi.org/10.5194/gmd-17-347-2024, https://doi.org/10.5194/gmd-17-347-2024, 2024
Short summary
Short summary
Increasing resolution improves model skills in simulating the Arctic Ocean, but other factors such as parameterizations and numerics are at least of the same importance for obtaining reliable simulations.
Laurent Brodeau, Pierre Rampal, Einar Òlason, and Véronique Dansereau
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-231, https://doi.org/10.5194/gmd-2023-231, 2024
Preprint under review for GMD
Short summary
Short summary
A new sea-ice rheology, known as BBM, has been implemented into the sea-ice component of NEMO. We describe how a new spatial discretization framework was introduced in order to achieve this. A set of realistic ocean/sea-ice simulations of the Arctic have been performed, using BBM and the standard rheology of NEMO. When compared to satellite data, our simulations show that our implementation of BBM leads to a better representation of sea-ice deformations than the standard rheology of NEMO.
Arne Bendinger, Sophie Cravatte, Lionel Gourdeau, Laurent Brodeau, Aurélie Albert, Michel Tchilibou, Florent Lyard, and Clément Vic
Ocean Sci., 19, 1315–1338, https://doi.org/10.5194/os-19-1315-2023, https://doi.org/10.5194/os-19-1315-2023, 2023
Short summary
Short summary
New Caledonia is a hot spot of internal-tide generation due to complex bathymetry. Regional modeling quantifies the coherent internal tide and shows that most energy is converted in shallow waters and on very steep slopes. The region is a challenge for observability of balanced dynamics due to strong internal-tide sea surface height (SSH) signatures at similar wavelengths. Correcting the SSH for the coherent internal tide may increase the observability of balanced motion to < 100 km.
Anne Marie Treguier, Clement de Boyer Montégut, Alexandra Bozec, Eric P. Chassignet, Baylor Fox-Kemper, Andy McC. Hogg, Doroteaciro Iovino, Andrew E. Kiss, Julien Le Sommer, Yiwen Li, Pengfei Lin, Camille Lique, Hailong Liu, Guillaume Serazin, Dmitry Sidorenko, Qiang Wang, Xiaobio Xu, and Steve Yeager
Geosci. Model Dev., 16, 3849–3872, https://doi.org/10.5194/gmd-16-3849-2023, https://doi.org/10.5194/gmd-16-3849-2023, 2023
Short summary
Short summary
The ocean mixed layer is the interface between the ocean interior and the atmosphere and plays a key role in climate variability. We evaluate the performance of the new generation of ocean models for climate studies, designed to resolve
ocean eddies, which are the largest source of ocean variability and modulate the mixed-layer properties. We find that the mixed-layer depth is better represented in eddy-rich models but, unfortunately, not uniformly across the globe and not in all models.
Guillaume Boutin, Einar Ólason, Pierre Rampal, Heather Regan, Camille Lique, Claude Talandier, Laurent Brodeau, and Robert Ricker
The Cryosphere, 17, 617–638, https://doi.org/10.5194/tc-17-617-2023, https://doi.org/10.5194/tc-17-617-2023, 2023
Short summary
Short summary
Sea ice cover in the Arctic is full of cracks, which we call leads. We suspect that these leads play a role for atmosphere–ocean interactions in polar regions, but their importance remains challenging to estimate. We use a new ocean–sea ice model with an original way of representing sea ice dynamics to estimate their impact on winter sea ice production. This model successfully represents sea ice evolution from 2000 to 2018, and we find that about 30 % of ice production takes place in leads.
Takaya Uchida, Julien Le Sommer, Charles Stern, Ryan P. Abernathey, Chris Holdgraf, Aurélie Albert, Laurent Brodeau, Eric P. Chassignet, Xiaobiao Xu, Jonathan Gula, Guillaume Roullet, Nikolay Koldunov, Sergey Danilov, Qiang Wang, Dimitris Menemenlis, Clément Bricaud, Brian K. Arbic, Jay F. Shriver, Fangli Qiao, Bin Xiao, Arne Biastoch, René Schubert, Baylor Fox-Kemper, William K. Dewar, and Alan Wallcraft
Geosci. Model Dev., 15, 5829–5856, https://doi.org/10.5194/gmd-15-5829-2022, https://doi.org/10.5194/gmd-15-5829-2022, 2022
Short summary
Short summary
Ocean and climate scientists have used numerical simulations as a tool to examine the ocean and climate system since the 1970s. Since then, owing to the continuous increase in computational power and advances in numerical methods, we have been able to simulate increasing complex phenomena. However, the fidelity of the simulations in representing the phenomena remains a core issue in the ocean science community. Here we propose a cloud-based framework to inter-compare and assess such simulations.
Clément Bricaud, Julien Le Sommer, Gurvan Madec, Christophe Calone, Julie Deshayes, Christian Ethe, Jérôme Chanut, and Marina Levy
Geosci. Model Dev., 13, 5465–5483, https://doi.org/10.5194/gmd-13-5465-2020, https://doi.org/10.5194/gmd-13-5465-2020, 2020
Short summary
Short summary
In order to reduce the cost of ocean biogeochemical models, a multi-grid approach where ocean dynamics and tracer transport are computed with different spatial resolution has been developed in the NEMO v3.6 OGCM. Different experiments confirm that the spatial resolution of hydrodynamical fields can be coarsened without significantly affecting the resolved passive tracer fields. This approach leads to a factor of 7 reduction of the overhead associated with running a full biogeochemical model.
Yeray Santana-Falcón, Pierre Brasseur, Jean Michel Brankart, and Florent Garnier
Ocean Sci., 16, 1297–1315, https://doi.org/10.5194/os-16-1297-2020, https://doi.org/10.5194/os-16-1297-2020, 2020
Short summary
Short summary
Data assimilation is the most comprehensive strategy to estimate the biogeochemical state of the ocean. Here, surface Chl a data are daily assimilated into a 24-member NEMO–PISCES ensemble configuration to implement a complete 4D assimilation system. Results show the assimilation increases the skills of the ensemble, though a regional diagnosis suggests that the description of model and observation uncertainties needs to be refined according to the biogeochemical characteristics of each region.
Sylvain Watelet, Jean-Marie Beckers, Jean-Marc Molines, and Charles Troupin
Ocean Sci. Discuss., https://doi.org/10.5194/os-2020-79, https://doi.org/10.5194/os-2020-79, 2020
Revised manuscript not accepted
Short summary
Short summary
In this study, we use a numerical hindcast at high resolution (1/12°) to examine the occurrence and properties of Rossby waves in the North Atlantic between 1970–2015. We show evidence of Rossby waves travelling at 39° N at a speed of 4.17 cm s−1. These results are consistent with baroclinic Rossby waves generated by the North Atlantic Oscillation in the central North Atlantic and travelling westward before interacting with the Gulf Stream transport with a time lag of about 2 years.
Pedro Colombo, Bernard Barnier, Thierry Penduff, Jérôme Chanut, Julie Deshayes, Jean-Marc Molines, Julien Le Sommer, Polina Verezemskaya, Sergey Gulev, and Anne-Marie Treguier
Geosci. Model Dev., 13, 3347–3371, https://doi.org/10.5194/gmd-13-3347-2020, https://doi.org/10.5194/gmd-13-3347-2020, 2020
Short summary
Short summary
In the ocean circulation model NEMO, the representation of the overflow of dense Arctic waters through the Denmark Strait is investigated. In this
z-coordinate context, sensitivity tests show that the mixing parameterizations preferably act along the model grid slope. Thus, the representation of the overflow is more sensitive to resolution than to parameterization and is best when the numerical grid matches the local topographic slope.
Ivan Zavialov, Alexander Osadchiev, Roman Sedakov, Bernard Barnier, Jean-Marc Molines, and Vladimir Belokopytov
Ocean Sci., 16, 15–30, https://doi.org/10.5194/os-16-15-2020, https://doi.org/10.5194/os-16-15-2020, 2020
Short summary
Short summary
This study is focused on water exchange between the Sea of Azov and the Black Sea. The Sea of Azov is a small freshened sea that receives a large freshwater discharge and, therefore, can be regarded as a large river estuary connected by narrow Kerch Strait with the Black Sea. In this work we show that water transport through the Kerch Strait is governed by wind forcing and does not depend on the river discharge rate to the Sea of Azov on an intra-annual timescale.
Ann-Sophie Tissier, Jean-Michel Brankart, Charles-Emmanuel Testut, Giovanni Ruggiero, Emmanuel Cosme, and Pierre Brasseur
Ocean Sci., 15, 443–457, https://doi.org/10.5194/os-15-443-2019, https://doi.org/10.5194/os-15-443-2019, 2019
Short summary
Short summary
To better exploit the observational information available for all scales in data assimilation systems, we investigate a new method to introduce scale separation in the algorithm. It consists in carrying out the analysis with spectral localisation for the large scales and spatial localisation for the residual scales. The performance is then checked explicitly and separately for all scales. Results show that accuracy can be improved for the large scales while preserving reliability at all scales.
Florent Garnier, Pierre Brasseur, Jean-Michel Brankart, Yeray Santana-Falcon, and Emmanuel Cosme
Ocean Sci. Discuss., https://doi.org/10.5194/os-2018-153, https://doi.org/10.5194/os-2018-153, 2019
Publication in OS not foreseen
Laurent Bessières, Stéphanie Leroux, Jean-Michel Brankart, Jean-Marc Molines, Marie-Pierre Moine, Pierre-Antoine Bouttier, Thierry Penduff, Laurent Terray, Bernard Barnier, and Guillaume Sérazin
Geosci. Model Dev., 10, 1091–1106, https://doi.org/10.5194/gmd-10-1091-2017, https://doi.org/10.5194/gmd-10-1091-2017, 2017
Short summary
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. M. Treguier, J. Deshayes, J. Le Sommer, C. Lique, G. Madec, T. Penduff, J.-M. Molines, B. Barnier, R. Bourdalle-Badie, and C. Talandier
Ocean Sci., 10, 243–255, https://doi.org/10.5194/os-10-243-2014, https://doi.org/10.5194/os-10-243-2014, 2014
Cited articles
Ajayi, A.: PowerSpec, GitHub [code], https://github.com/adeajayi-kunle/powerspec, last access: 9 November 2022. a
Berner, J., Jung, T., and Palmer, T. N.:
Systematic model error: the impact of increased horizontal
resolution versus improved stochastic and deterministic parameterizations, J. Climate, 25, 4946–4962, https://doi.org/10.1175/JCLI-D-11-00297.1, 2012. a
Bessières, L., Leroux, S., Brankart, J.-M., Molines, J.-M., Moine, M.-P., Bouttier, P.-A., Penduff, T., Terray, L., Barnier, B., and Sérazin, G.: Development of a probabilistic ocean modelling system based on NEMO 3.5: application at eddying resolution, Geosci. Model Dev., 10, 1091–1106, https://doi.org/10.5194/gmd-10-1091-2017, 2017. a
Berloff, P. S. and McWilliams, J. C.:
Material Transport in Oceanic Gyres. Part II: Hierarchy of Stochastic Models, J. Phys. Oceanogr.,
32, 797–830, https://doi.org/10.1175/1520-0485(2002)032<0797:MTIOGP>2.0.CO;2, 2002. a
Brankart, J.-M.:
Impact of uncertainties in the horizontal density gradient
upon low resolution global ocean modelling, Ocean Model., 66, 64–76, https://doi.org/10.1016/j.ocemod.2013.02.004, 2013. a, b, c
Brankart, J.-M.: SESAM, GitHub [code], https://github.com/brankart/sesam, last access: 9 November 2022a.
a
Brankart, J.-M.: EnsDAM, GitHub [code], https://github.com/brankart/ensdam, last access: 9 November 2022b. a
Brankart, J.-M., Candille, G., Garnier, F., Calone, C., Melet, A., Bouttier, P.-A., Brasseur, P., and Verron, J.: A generic approach to explicit simulation of uncertainty in the NEMO ocean model, Geosci. Model Dev., 8, 1285–1297, https://doi.org/10.5194/gmd-8-1285-2015, 2015. a, b, c
Brasseur, P., Blayo, E., and Verron, J.: Predictability experiments in the North Atlantic Ocean: Outcome of a quasi-geostrophic model with assimilation of TOPEX/POSEIDON altimeter data, J. Geophys. Res.-Oceans, 101, 14161–14173, https://doi.org/10.1029/96JC00665, 1996. a
Brodeau, L., Le Sommer, J., and Albert, A.:
Ocean-next/eNATL60: Material describing the set-up and the assessment of NEMO-eNATL60 simulations (Version v1),
Zenodo [code, data set], https://doi.org/10.5281/zenodo.4032732, 2020. a
Buizza, R., Miller, M., and Palmer, T. N.: Stochastic representation of
model uncertainties in the ECMWF ensemble prediction system, Q. J. Roy. Meteor. Soc., 125, 2887–2908, https://doi.org/10.21957/db1w1xzch, 1999. a
Candille G. and Talagrand, O.: Evaluation of probabilistic prediction systems for a scalar variable, Q. J. Roy. Meteor. Soc., 131, 2131–2150, https://doi.org/10.1256/qj.04.71, 2005. a
Candille, G., Côté, C., Houtekamer, P. L., and Pellerin, G.: Verification of an ensemble prediction system against observations, Mon. Weather Rev., 135, 2688–2699, https://doi.org/10.1175/MWR3414.1, 2007. a
Candille, G., Brankart, J.-M., and Brasseur, P.: Assessment of an ensemble system that assimilates Jason-1/Envisat altimeter data in a probabilistic model of the North Atlantic ocean circulation, Ocean Sci., 11, 425–438, https://doi.org/10.5194/os-11-425-2015, 2015. a, b
Chapron B., Dérian, P., Mémin, E., and Resseguieri, V.:
Large scale flows under location uncertainty: a consistent stochastic framework, Q. J. Roy. Meteor. Soc.,
144, 251–260, https://doi.org/10.1002/qj.3198, 2018. a, b
Diaconescu, E. P. and Laprise, R.:
Singular vectors in atmospheric sciences: A review, Earth-Sci. Rev.,
113, 161–175, https://doi.org/10.1016/j.earscirev.2012.05.005, 2012. a
Escudier, R., Renault, L., Pascual, A., Brasseur, P., Chelton, D., and Beuvier, J.: Eddy properties in the Western Mediterranean Sea from satellite altimetry and a numerical simulation, J. Geophys. Res.-Oceans, 121, 3990–4006, https://doi.org/10.1002/2015JC011371, 2016. a
Evensen, G.:
Sequential data assimilation with a non linear quasigeostrophic model
using Monte Carlo methods to forecast error statistics,
J. Geophys. Res., 99, 10143–10162, https://doi.org/10.1029/94JC00572,1994. a
Frederiksen, J., O'Kane, T., and Zidikheri, M.:
Stochastic subgrid parameterizations for atmospheric and oceanic flows, Phys. Scripta, 85, 068202, https://doi.org/10.1088/0031-8949/85/06/068202, 2012. a
Garnier, F., Brankart, J.-M., Brasseur, P., and Cosme, E.:
Stochastic parameterizations of biogeochemical uncertainties
in a 1/4∘ NEMO/PISCES model for probabilistic comparisons
with ocean color data,
J. Marine Syst., 155, 59–72, https://doi.org/10.1016/j.jmarsys.2015.10.012, 2016.
a
Germineaud, C., Brankart, J.-M., and Brasseur, P.: An Ensemble-Based Probabilistic Score Approach to Compare Observation Scenarios: An Application to Biogeochemical-Argo Deployments, J. Atmos. Ocean. Tech., 36, 2307–2326, https://doi.org/10.1175/JTECH-D-19-0002.1, 2019. a
Griffa, A.: Applications of stochastic particle models to oceanographic problems, in: Stochastic modelling in
physical oceanography, edited by: Adler, R., Müller, P., and Rozovskii, B., Birkhuser Boston, 113–140, https://doi.org/10.1007/978-1-4612-2430-3_5, 1996. a
Hawkins, E., Smith, R. S., Gregory, J. M., and Stainforth, D. A.:
Irreducible uncertainty in near-term climate projections, Clim. Dynam., 46, 3807–3819, https://doi.org/10.1007/s00382-015-2806-8, 2016. a
Hersbach, H.: Decomposition of the continuous ranked probability score for ensemble prediction systems, Weather Forecast., 15, 559–570, https://doi.org/10.1175/1520-0434(2000)015<0559:DOTCRP>2.0.CO;2, 2000. a
Juricke, S., Lemke, P., Timmermann, R., and Rackow, T.:
Effects of stochastic ice strength perturbation
on Arctic finite element sea ice modeling,
J. Climate, 26, 3785–3802, https://doi.org/10.1175/JCLI-D-12-00388.1, 2013. a
Juricke, S., MacLeod, D., Weisheimer, A., Zanna, L., and Palmer, T. N.:
Seasonal to annual ocean forecasting skill and the role of model and observational uncertainty,
Q. J. Roy. Meteor. Soc., 144, 1947–1964, https://doi.org/10.1002/qj.3394, 2018. a
Kalnay, E.:
Atmospheric Modeling, Data Assimilation and Predictability,
Cambridge University Press, Cambridge, https://doi.org/10.1017/CBO9780511802270, 2003. a
Lacarra, J. and Talagrand, O.:
Short-range evolution of small perturbations in a barotropic model,
Tellus A, 40, 81–95, https://doi.org/10.1111/j.1600-0870.1988.tb00408.x, 1988. a
Lellouche, J.-M., Greiner, E., Bourdallé-Badie, R., Garric, G., Melet, A., Drévillon, M., Bricaud, M., Hamon, M., Le Galloudec, O., Regnier, C., Candela, T., Testut, C.-E., Gasparin, F., Ruggiero, G., Benkiran, M., Drillet, Y., and Le Traon, P.-Y.:
The Copernicus Global 1/12 Oceanic and Sea Ice GLORYS12 Reanalysis,
Front. Earth Sci., 9, 2296–6463, https://doi.org/10.3389/feart.2021.698876, 2021. a
Leroux, S.: ocean-next/MEDWEST60: v1.0.1 (v1.0.1), Zenodo [code],
https://doi.org/10.5281/zenodo.7220401, 2022. a
Leutbecher, M., Lock, S., Ollinaho, P., Lang, S. T., Balsamo, G., Bechtold, P.,
Bonavita, M., Christensen, H. M., Diamantakis, M., Dutra, E., English, S.,
Fisher, M., Forbes, R. M., Goddard, J., Haiden, T., Hogan, R. J., Juricke, S.,
Lawrence, H., MacLeod, D., Magnusson, L., Malardel, S., Massart, S., Sandu, I.,
Smolarkiewicz, P. K., Subramanian, A., Vitart, F., Wedi, N., and Weisheimer, A.:
Stochastic representations of model uncertainties at ECMWF:
state of the art and future vision, Q. J. Roy. Meteor. Soc.,
143, 2315–2339, https://doi.org/10.1002/qj.3094, 2017. a
Lorenz, E. N.:
A study of the predictability of a 28-variable atmospheric model,
Tellus, 17, 321–333, https://doi.org/10.1111/j.2153-3490.1965.tb01424.x, 1965. a
Lorenz, E. N.:
Atmospheric predictability with a large numerical model,
Tellus, 34, 505–513, https://doi.org/10.1111/j.2153-3490.1982.tb01839.x, 1982.
a
Lorenz, E. N.:
Predictability, a problem partly solved, Proceedings of Predictability ECMWF seminar, Seminar on Predictability, 4–8 September 1995, Shinfield Park, Reading, 1–18, https://www.ecmwf.int/en/elibrary/10829-predictability-problem-partly-solved (last access: 9 November 2022), 1995. a
Lyapunov, A.:
The general problem of the stability of motion, Int. J. Control, 55, 531–534, https://doi.org/10.1080/00207179208934253, 1992. a
Lyard, F. H., Allain, D. J., Cancet, M., Carrère, L., and Picot, N.: FES2014 global ocean tide atlas: design and performance, Ocean Sci., 17, 615–649, https://doi.org/10.5194/os-17-615-2021, 2021. a
Madec, G. and NEMO System Team: NEMO ocean engine: Scientific Notes of Climate Modelling Center, Zenodo, https://doi.org/10.5281/zenodo.6334656, 2002. a
Mémin, E.:
Fluid flow dynamics under location uncertainty,
Geophys. Astro. Fluid,
108, 119–146, https://doi.org/10.1080/03091929.2013.836190, 2014. a, b
Molines J.-M. and the MEOM-IGE group: CDFTOOLS, GitHub [code], https://github.com/meom-group/CDFTOOLS, last access: 9 November 2022. a
Morrow, R., Fu, L.-L., Ardhuin, F., Benkiran, M., Chapron, B., Cosme, E., d'Ovidio, F., Farrar, J. T., Gille, S. T., Lapeyre, G., Le Traon, P.-Y., Pascual, A., Ponte, A., Qiu, B., Rascle, N., Ubelmann, C., Wang, J., and Zaron, E. D.:
Global Observations of Fine-Scale Ocean Surface Topography With the Surface Water and Ocean Topography (SWOT) Mission,
Frontiers in Marine Science,
6, 232, https://doi.org/10.3389/fmars.2019.00232, 2019. a
Palmer, T. and Hagedorn, R. (Eds.):
Predictability of weather and climate, Cambridge University Press, https://doi.org/10.1017/CBO9780511617652, 2006. a
Palmer, T., Shutts, G., Hagedorn, R., Doblas-Reyes, F., Jung, T., and
Leutbecher, M.: Representing model uncertainty in weather and climate
prediction, Annu. Rev. Earth Pl. Sc., 33, 163–193, https://doi.org/10.1146/annurev.earth.33.092203.122552, 2005.
a
Palmer, T. N.:
The economic value of ensemble forecasts as a tool
for risk assessment: From days to decades, Q. J. Roy. Meteor. Soc., 128, 747–774, https://doi.org/10.1256/0035900021643593, 2002. a
Robinson, A. R., Haley, P. J., Lermusiaux, P. F. J., and Leslie, W. G.:
Predictive Skill, Predictive Capability and Predictability in Ocean Forecasting, Proceedings of “The OCEANS 2002 MTS/IEEE” conference, Holland Publications, 787–794, https://doi.org/10.1109/OCEANS.2002.1192070, 2002. a
Sakov, P., Counillon, F., Bertino, L., Lisæter, K. A., Oke, P. R., and Korablev, A.: TOPAZ4: an ocean-sea ice data assimilation system for the North Atlantic and Arctic, Ocean Sci., 8, 633–656, https://doi.org/10.5194/os-8-633-2012, 2012. a
Toth, Z. and Kalnay, E.:
Ensemble Forecasting at NMC: The Generation of Perturbations, B. Am. Meteorol. Soc.,
74, 2317–2330, https://doi.org/10.1175/1520-0477(1993)074<2317:EFANTG>2.0.CO;2, 1993. a
Williams, P. D., Howe, N. J., Gregory, J. M., Smith, R. S., and Joshi, M. M.:
Improved climate simulations through a stochastic parametrization of ocean eddies, J. Climate, 29, 8763–8781, https://doi.org/10.1175/JCLI-D-15-0746.1, 2016. a
Ying, Y. K., Maddison, J. R., and Vanneste, J.:
Bayesian inference of ocean diffusivity from Lagrangian trajectory data,
Ocean Model.,
140, 101401, https://doi.org/10.1016/j.ocemod.2019.101401, 2019. a
Zanna, L., Brankart, J.-M., Huber, M., Leroux, S., Penduff, T., and Williams, P. D.:
Uncertainty and Scale Interactions in Ocean Ensembles:
From Seasonal Forecasts to Multi-Decadal Climate Predictions, Q. J. Roy. Meteor. Soc., 145, 160–175, https://doi.org/10.1002/qj.3397, 2019. a
Short summary
The goal of the study is to evaluate the predictability of the ocean circulation
at a kilometric scale, in order to anticipate the requirements of the future operational forecasting systems. For that purpose, ensemble experiments have been performed with a regional model for the Western Mediterranean (at 1/60° horizontal resolution). From these ensemble experiments, we show that it is possible to compute targeted predictability scores, which depend on initial and model uncertainties.
The goal of the study is to evaluate the predictability of the ocean circulation
at a...
