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
No articles found.
Alexis Barge, Julien Le Sommer, Andrea Storto, and Sophie Valcke
EGUsphere, https://doi.org/10.5194/egusphere-2026-854, https://doi.org/10.5194/egusphere-2026-854, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
Scientists use programs, called Earth System Models, to study and predict climate. These models are based on physical theories but can be completed with AI tools. However, combining these tools with traditional models is difficult due to their different nature. Our research introduces a new method that connects these AI tools with existing climate models. We tested this method by integrating it with an ocean model. This work should help scientists explore new ways of making climate predictions.
Arthur Coquereau, Florian Sévellec, Thierry Huck, Joël J.-M. Hirschi, and Quentin Jamet
Earth Syst. Dynam., 17, 209–233, https://doi.org/10.5194/esd-17-209-2026, https://doi.org/10.5194/esd-17-209-2026, 2026
Short summary
Short summary
Using statistical methods and a set of ensemble climate models, we decompose the sources of Atlantic Meridional Overturning Circulation (AMOC) variance. Three distinct phases of physical variability are identified: from 1850 to 1990, internal variability dominates; from 1990 to 2050, dynamical adjustment related to AMOC decline takes over; after 2050, differences between forcing scenarios become dominant. Beyond these physical factors, model variability remains a major source of uncertainty.
Damien Héron, Thierry Penduff, Jean-Michel Brankart, Pierre Brasseur, Samuel Somot, Robin Waldman, and Romain Pennel
Ocean Sci., 22, 531–547, https://doi.org/10.5194/os-22-531-2026, https://doi.org/10.5194/os-22-531-2026, 2026
Short summary
Short summary
Our study used realistic ocean simulations to determine how much of the Mediterranean’s circulation is due to natural randomness rather than atmospheric forcing. We found that spontaneous ocean variability is strong in several regions and can persist for years or even decades. This randomness influences how well models and observations can capture the Mediterranean’s response to climate change.
Vadim Bertrand, Julien Le Sommer, Victor Vianna Zaia De Almeida, Adeline Samson, and Emmanuel Cosme
Ocean Sci., 22, 241–255, https://doi.org/10.5194/os-22-241-2026, https://doi.org/10.5194/os-22-241-2026, 2026
Short summary
Short summary
Understanding ocean surface currents is essential for navigation and environmental monitoring. Traditionally, currents are estimated from satellite sea surface height using a simplified balance. We developed a new method that goes beyond this approximation, providing more accurate currents estimates. Using a model simulation, satellite observations, and drifters data, we show that these corrections become increasingly important at finer spatial scales, especially in energetic regions.
Lohenn Fiol, Stephanie Leroux, Pierre Rampal, and Jean-Michel Brankart
EGUsphere, https://doi.org/10.5194/egusphere-2025-6379, https://doi.org/10.5194/egusphere-2025-6379, 2026
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
We examine how uncertainty in the initial position of sea ice features (leads, ridges), affects daily-to-weekly winter sea-ice forecasts. Using ensemble simulations with a sea ice–ocean model, we compare two formulations of sea ice mechanics. We show that pack-ice dynamics are highly sensitive to this choice: one formulation strongly amplifies small initial errors, while the other damps them. Our results highlight the need for ensemble forecasts to capture uncertainty and risks in the Arctic.
Claudio M. Pierard, Siren Rühs, Laura Gómez-Navarro, Michael Charles Denes, Florian Meirer, Thierry Penduff, and Erik van Sebille
Nonlin. Processes Geophys., 32, 411–438, https://doi.org/10.5194/npg-32-411-2025, https://doi.org/10.5194/npg-32-411-2025, 2025
Short summary
Short summary
Particle-tracking simulations compute how ocean currents transport material. However, initializing these simulations is often ad hoc. Here, we explore how two different strategies (releasing particles over space or over time) compare. Specifically, we compare the variability in particle trajectories to the variability of particles computed in a 50-member ensemble simulation. We find that releasing the particles over 20 weeks gives variability that is most like that in the ensemble.
Loïc Macé, Luc Vandenbulcke, Jean-Michel Brankart, Jean-François Grailet, Pierre Brasseur, and Marilaure Grégoire
EGUsphere, https://doi.org/10.5194/egusphere-2025-4973, https://doi.org/10.5194/egusphere-2025-4973, 2025
Short summary
Short summary
In this paper, we propose a three-stream radiative transfer model and present a use case for the Black Sea. The model is able to simulate in-water irradiance and sea surface reflectance in a wide spectral range. When coupled with an ecosystem model, the simulated irradiances can be used to update water temperature and drive primary production in a consistent way. A stochastic version of this model is also proposed to inform on uncertainties in the optical properties of seawater.
Loïc Macé, Luc Vandenbulcke, Jean-Michel Brankart, Pierre Brasseur, and Marilaure Grégoire
Biogeosciences, 22, 3747–3768, https://doi.org/10.5194/bg-22-3747-2025, https://doi.org/10.5194/bg-22-3747-2025, 2025
Short summary
Short summary
The representation of light propagation in seawater is critical for modelling marine biogeochemistry. We analyse results from a radiative transfer model that accounts for the absorption and scattering of light in the ocean with their respective uncertainties. We compare these results with in situ and remote-sensed data. Our analysis highlights the benefits of accounting for model uncertainties while using advanced representations of light in modelling frameworks.
Lara Börger, Michael Schindelegger, Mengnan Zhao, Rui M. Ponte, Anno Löcher, Bernd Uebbing, Jean-Marc Molines, and Thierry Penduff
Earth Syst. Dynam., 16, 75–90, https://doi.org/10.5194/esd-16-75-2025, https://doi.org/10.5194/esd-16-75-2025, 2025
Short summary
Short summary
Flows in the ocean are driven either by atmospheric forces or by small-scale internal disturbances that are inherently chaotic. We use computer simulation results to show that these chaotic oceanic disturbances can attain spatial scales large enough to alter the motion of Earth's pole of rotation. Given their size and unpredictable nature, the chaotic signals are a source of uncertainty when interpreting observed year-to-year polar motion changes in terms of other processes in the Earth system.
Rémy Lapere, Louis Marelle, Pierre Rampal, Laurent Brodeau, Christian Melsheimer, Gunnar Spreen, and Jennie L. Thomas
Atmos. Chem. Phys., 24, 12107–12132, https://doi.org/10.5194/acp-24-12107-2024, https://doi.org/10.5194/acp-24-12107-2024, 2024
Short summary
Short summary
Elongated open-water areas in sea ice, called leads, can release marine aerosols into the atmosphere. In the Arctic, this source of atmospheric particles could play an important role for climate. However, the amount, seasonality and spatial distribution of such emissions are all mostly unknown. Here, we propose a first parameterization for sea spray aerosols emitted through leads in sea ice and quantify their impact on aerosol populations in the high Arctic.
Olivier Narinc, Thierry Penduff, Guillaume Maze, Stéphanie Leroux, and Jean-Marc Molines
Ocean Sci., 20, 1351–1365, https://doi.org/10.5194/os-20-1351-2024, https://doi.org/10.5194/os-20-1351-2024, 2024
Short summary
Short summary
This study examines how the ocean's chaotic variability and atmospheric fluctuations affect yearly changes in North Atlantic Subtropical Mode Water (STMW) properties, using an ensemble of realistic ocean simulations. Results show that while yearly changes in STMW properties are mostly paced by the atmosphere, a notable part 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.
Laurent Brodeau, Pierre Rampal, Einar Ólason, and Véronique Dansereau
Geosci. Model Dev., 17, 6051–6082, https://doi.org/10.5194/gmd-17-6051-2024, https://doi.org/10.5194/gmd-17-6051-2024, 2024
Short summary
Short summary
A new brittle sea ice rheology, BBM, has been implemented into the sea ice component of NEMO. We describe how a new spatial discretization framework was introduced to achieve this. A set of idealized and realistic ocean and sea ice simulations of the Arctic have been performed using BBM and the standard viscous–plastic rheology of NEMO. When compared to satellite data, our simulations show that our implementation of BBM leads to a fairly good representation of sea ice deformations.
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.
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.
Sophie Cravatte, Guillaume Serazin, Thierry Penduff, and Christophe Menkes
Ocean Sci., 17, 487–507, https://doi.org/10.5194/os-17-487-2021, https://doi.org/10.5194/os-17-487-2021, 2021
Short summary
Short summary
The various currents in the southwestern Pacific Ocean contribute to the redistribution of waters from the subtropical gyre equatorward and poleward. The drivers of their interannual variability are not completely understood but are usually thought to be related to well-known climate modes of variability. Here, we suggest that oceanic chaotic variability alone, which is by definition unpredictable, explains the majority of this interannual variability south of 20° S.
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...
