Back
Chapter 12

Challenges and future perspectives in ocean prediction

CHAPTER
COORDINATORS
Fraser Davidson
CHAPTER
AUTHORS
Enrique Alvarez Fanjul, Alain Arnaud, Stefania Ciliberti, Marie Drevillon, Ronan Fablet, Yosuke Fujii, Isabel Garcia-Hermosa, Stéphanie Guinehut, Emma Heslop, Villy Kourafalou, Julien Le Sommer, Matt Martin, Andrew M. Moore, Nadia Pinardi, Elizabeth Remy, Paul Sandery, Jun She, Marcos G. Sotillo, and Joaquin Tintorè

12.10 Expected future evolution of Copernicus Marine Service products and services

The first operational phase 2014-2021 of the Copernicus Marine Service has successfully implemented a service chain devoted to ocean information, involving committed producers throughout Europe, and serving expert users worldwide. The Copernicus Marine Service will develop an ambitious 7-year plan (Copernicus 2, 2021-2027) with staged implementation that answers to increasing user and policy (e.g. EU Green Deal) needs. The objective is to fully embrace the capabilities of new digital services and implement the next generation of ocean monitoring and forecasting for the Blue/White/Green ocean.

Copernicus Marine Service products and services are delivered by means of state-of-the-art, user-oriented, scientific and technical methodologies, which induces openness to newly developing ideas and associated capacities. Apart from guaranteeing service continuity, the Copernicus Marine Service is continuously evolving to ensure that its services and products remain state-of-the-art and meet a wide range of existing and emerging user and policy needs related to all marine and maritime sectors: maritime safety, coastal environment monitoring, trade and marine navigation, fishery, aquaculture, marine renewable energy, marine conservation and biodiversity, ocean health, climate and climate adaptation, recreation, education, science and innovation.

The following major improvements of current products, as well as new products benefiting from science and technology advances, are already planned to ensure an enhanced continuity of the service, keeping the service at the state-of-art and at internationally competitive and fit for purpose standards, considering the European policies’ priorities (Green Deal, Common Fisheries Policy, Marine Strategy Framework Directive, and Convention on Biological Diversity):

  • High resolution monitoring, modelling, and forecasting of the blue ocean with an increase of the horizontal resolutions of the current systems by a factor of at least 3 (e.g. global 1/36°, regional 1/108°). Coupling and interaction with waves, sea ice, atmosphere, biogeochemistry, and rivers will also be implemented for improved ocean forecasts. New high-resolution sea level observations from the SWOT wide swath altimeter mission, new ocean topography, sea surface temperature, salinity from the Sentinel, HPMC, CRISTAL, and CIMR missions will be included as observational products. These improvements will impact the different Copernicus Marine Service areas and their key applications: maritime security and safety, maritime transport, pollution monitoring and offshore operations, and coastal zone monitoring and forecasting.
  • Probabilistic forecasting and extended (1-month) forecasts based on model ensembles, allowing a better characterization of model uncertainties in analyses and forecast. Data assimilation techniques will evolve toward more multivariate schemes to constrain in a more extended and coherent way the different inanimate components of the marine environment (physics, sea ice, and biogeochemistry). Coupled ocean/atmosphere data assimilation will also be implemented. Probabilistic forecasts will be instrumental for early warning systems, and to support decision-making based on operational products by better characterising the confidence level associated with the provided information.
  • Reanalyses of the 20th century physical and biogeochemical data for the global ocean and the European regional seas, assimilating historical in-situ observations (e.g. sea surface temperature and tige gauges mainly for the first half of the century and temperature and salinity profiles from 1950 onwards). The purpose of these reanalyses is to better assess the past evolution of the ocean in response to climate change and to better monitor Essential Ocean Variables and Essential Climate Variables related to the ocean.
  • Step changes in Arctic Ocean monitoring, modelling, and forecasting through upgrade in sea-ice models, improved coupling with the atmosphere and hydrology (river discharge and nutrient loads), higher-resolution, extended forecasting ranges from a week to a month, and ensemble forecasting for an improved characterization of forecasting uncertainties. Provision of icebergs’ forecasts will complement the information produced for ice services. Improved satellite products on sea-ice detection and a pan-Arctic ice chart will complete the offer. These evolutions will address user needs regarding maritime transport (e.g. ship routine) and marine safety in sea-ice and iceberg infested regions, marine resources (fisheries and conservation) and climate change impact in the Arctic.
  • Air/sea fluxes of CO2 monitoring and modelling, including advanced modelling/data assimilation systems at global and regional scales as well as including error estimations. Foreseen developments also include processing and quality control of novel in-situ observations from the BGC Argo array and improvement of observation-based products derived from Neural Network methods. These evolutions are required by the Copernicus anthropogenic CO2 service as well as for blue carbon monitoring.
  • Coastal zone monitoring and forecasting with improved capacities to link and co-production between coastal systems with Copernicus Marine Service upstream systems. Consistency and river-ocean continuity will be ensured by using standardised methods to couple hydrological models (for river run-offs) with global, regional, and coastal ocean models. Time-series (past, present, forecasts) of standardised modelled river discharges of freshwater, nutrients, particulate, and dissolved matter will be provided. Coastal zone monitoring will also be enhanced through satellite observations – based on Sentinel (especially S1, S2, S3, and S6) and other missions - for nearshore bathymetry and shoreline position and their evolution, high-resolution winds, spectral wave information, detection of plastic debris, monitoring of marine litter, ecosystems, water quality, and sea surface temperature. Given the huge social, economic, and biological value of coastal zones, these improvements will contribute to a wide range of applications (coastal zone management, climate adaptation, coastal modelling, aquaculture and fisheries, navigation and shipping, marine renewable energy, oil spill management and search and rescue), supporting various policies and resilience to climate change.
  • Marine biology monitoring and forecasting with major improvement in numerical models to represent processes (e.g. benthic/pelagic coupling, riverine inputs) increasing accuracy, advanced data assimilation techniques (e.g. combining state and parameter estimation), and new modules linking optical properties in the near-surface ocean to biomass to better couple ocean colour and subsurface data from in-situ such as BGC Argo. End-to-end ecosystem modelling will also be included to link along the food web low trophic levels (e.g. plankton) to mid-trophic levels (e.g. micronekton), and to high-trophic levels (e.g. predator fishes and marine mammals). Marine biology monitoring will also be enhanced through the improvement of gathering, processing, quality control, and characterization of biogeochemical and marine biology in-situ (e.g. optical and acoustic sensors) and satellite (e.g. S2, S3 and hyperspectral) observations in open and coastal oceans. These products will support international and European Union objectives in terms of biodiversity, development of sustainable food resources, water quality, assessment of blue carbon in the overall carbon stake accounting.
  • Long-term projections of the marine environment (both physics, biogeochemistry, and marine ecosystems) under climate change from global to regional scales (downscaling of climate scenarios), and associated consequences for main stocks of exploited fishes. These products will support climate assessments for decision-making on adaptation and mitigation of climate risks (e.g. coastal floods, surges, etc.).
  • Enhanced digital services with online cloud processing capabilities for manipulating and processing data with advanced analytics and scientific computing software (e.g. artificial intelligence toolboxes), access to Sentinel Level 1&2 data, marine data (e.g. from EMODnet, SAF, etc.), and connection to HPC computing nodes. This will consolidate the Copernicus Marine Service as a one-stop shop for operational and digital ocean services.

A document 🔗11 presenting the Copernicus Marine Service Evolution Strategy for R&D priorities has been prepared by its STAC 🔗12 and reviewed by MOI. This document details the expected future products and services by Copernicus Marine Service and the required developments. It is a living document, as it is updated periodically according to feedback from users and policy needs, the status of scientific developments achieved within and outside the Copernicus Marine Service community, and to the high-level Copernicus Marine Service evolution strategy.

Previous section Next section

References

Anderson, J., Hoar, T. J., Raeder, K., Liu, H., Collins, N., Torn, R., et al. (2009). The data assimilation research test bed: a community facility. Bulletin of the American Meteorological Society, 90,1283-1296, https://doi.org/10.1175/2009BAMS2618.1

Barton, N., Metzger, E.J., Reynolds, C.A., Ruston, B., Rowley, C., Smedstad, O.M., Ridout, J.A., Wallcraft, A., Frolov, S., Hogan, P. and Janiga, M.A. (2021). The Navy’s Earth System Prediction Capability: A new global coupled atmosphere-ocean-sea ice prediction system designed for daily to subseasonal forecasting. Earth and Space Science, 8(4), e2020EA001199, https://doi.org/10.1029/2020EA001199

Beisiegel, N., Castro, C.E., and Behrens, J. (2021). Metrics for Performance Quantification of Adaptive Mesh Refinement. Journal of Scientific Computing, 87(1), 1-24, https://doi.org/10.1007/s10915-021-01423-0

Bishop, C.H. (2016). The GIGG-EnKF: ensemble Kalman filtering for highly skewed non-negative uncertainty distributions. Quarterly Journal of the Royal Meteorological Society,142(696),1395-1412, https://doi.org/10.1002/qj.2742

Bonavita, M., and Laloyaux, P. (2020). Machine learning for model error inference and correction. Journal of Advances in Modeling Earth Systems, 12(12), e2020MS002232, https://doi.org/10.1029/2020MS002232

Bonavita, M., Arcucci, R., Carrassi, A., Dueben, P., Geer, A. J., Le Saux, B., Longépé, N., Mathieu, P., and Raynaud, L. (2021). Machine Learning for Earth System Observation and Prediction. Bulletin of the American Meteorological Society, 102(4), E710-E716, https://doi.org/10.1175/BAMS-D-20-0307.1

Brajard, J., Carrassi, A., Bocquet, M. and Bertino, L. (2021). Combining data assimilation and machine learning to infer unresolved scale parametrization. Philosophical Transactions of the Royal Society A, 379(2194), https://doi.org/10.1098/rsta.2020.0086

Brassington, G.B., Martin, M.J., Tolman, H.L., Akella, S., Balmeseda, M., Chambers, C.R.S., Chassignet, E., Cummings, J.A., Drillet, Y., Jansen, P.A.E.M. and Laloyaux, P. (2015). Progress and challenges in short-to medium-range coupled prediction. Journal of Operational Oceanography, 8(sup2), s239-s258, https://doi.org/10.1080/1755876X.2015.1049875

Brunton, S. L., Noack, B. R., and Koumoutsakos, P. (2020). Machine learning for fluid mechanics. Annual Review of Fluid Mechanics, 52(1), 477-508 https://doi.org/10.1146/annurev-fluid-010719-060214

Buizza, R. (2021). Probabilistic view of numerical weather prediction and ensemble prediction. In “Uncertainties in Numerical Weather Prediction”, Editors: H. Ólafsson and J.-W. Bao, Elsevier, https://doi.org/10.1016/C2017-0-03301-3

Carleo, G., Cirac, I., Cranmer, K., Daudet, L., Schuld, M., Tishby, N., Vogt-Maranto, L., and Zdeborová, L. (2019). Machine learning and the physical sciences. Reviews of Modern Physics, 91(4), 045002, https://doi.org/10.1103/RevModPhys.91.045002

Capet, A., Fernández, V., She, J., Dabrowski, T., Umgiesser, G., Staneva, J., Mészáros, L., Campuzano, F., Ursella, L., Nolan, G., El Serafy, G. (2020). Operational Modeling Capacity in European Seas - An EuroGOOS Perspective and Recommendations for Improvement. Frontiers in Marine Science, 7,129, https://doi.org/10.3389/fmars.2020.00129

Castelão, G. P. (2021). A machine learning approach to quality control oceanographic data. Computers & Geosciences, 155, 104803, https://doi.org/10.1016/j.cageo.2021.104803

Chassignet, E.P., and Xu, X. (2021). On the Importance of High-Resolution in Large-Scale Ocean Models. Advances in Atmospheric Sciences, 38, 1621-1634, https://doi.org/10.1007/s00376-021-0385-7

Cranmer, K., Brehmer, J., and Louppe, G. (2020). The frontier of simulation-based inference. Proceedings of the National Academy of Sciences, 117(48), 30055-30062 https://doi.org/10.1073/pnas.1912789117

Ebert, E.E. (2009). Neighborhood verification - a strategy for rewarding close forecasts. Weather and Forecasting 24(6), 1498-1510, https://doi.org/10.1175/2009WAF2222251.1

Fablet, R., Chapron, B., Drumetz, L., Mémin, E., Pannekoucke, O., and Rousseau, F. (2021). Learning variational data assimilation models and solvers. Journal of Advances in Modeling Earth Systems, 13(10), e2021MS002572, https://doi.org/10.1029/2021MS002572

Fox-Kemper, B., Adcroft, A., Böning, C.W., Chassignet, E.P., Curchitser, E., Danabasoglu, G., Eden, C., England, M.H., Gerdes, R., Greatbatch, R.J., Griffies, S.M., Hallberg, R.W., Hanert, E., Heimbach, P., Hewitt, E.T., Hill, C.N., Komuro, Y., Legg, S., Le Sommer, J., Masina, S., Marsland, S.J., Penny, S.G., Qiao, F., Ringler, T.D., Treguier, A.M., Tsujino, H., Uotila, P., Yeager, S.G. (2019). Challenges and Prospects in Ocean Circulation Models. Frontiers in Marine Science, 6, https://doi.org/10.3389/fmars.2019.00065

Frolov, S., Reynolds, C.A., Alexander, M., Flatau, M., Barton, N.P., Hogan, P. and Rowley, C. (2021). Coupled Ocean-Atmosphere Covariances in Global Ensemble Simulations: Impact of an Eddy-Resolving Ocean. Monthly Weather Review, 149(5), 1193-1209, https://doi.org/10.1175/MWR-D-20-0352.1

Fujii, Y., Ishibashi, T., Yasuda, T., Takaya, Y., Kobayashi, C. and Ishikawa, I. (2021). Improvements in tropical precipitation and sea surface air temperature fields in a coupled atmosphere ocean data assimilation system. Quarterly Journal of the Royal Meteorological Society, 147(735), 1317-1343, https://doi.org/10.1002/qj.3973

Gao, Y., Tang, Y., Song, X. and Shen, Z. (2021). Parameter Estimation Based on a Local Ensemble Transform Kalman Filter Applied to El Niño-Southern Oscillation Ensemble Prediction. Remote Sensing, 13(19), 3923, https://doi.org/10.3390/rs13193923

Hatfield, S., Chantry, M., Dueben, P., Lopez, P., Geer, A., and Palmer, T. (2021). Building Tangent-Linear and Adjoint Models for Data Assimilation With Neural Networks. Journal of Advances in Modeling Earth Systems, 13(9), https://doi.org/10.1029/2021MS002521

Hawcroft, M., Lavender, S., Copsey, D., Milton, S., Rodríguez, J., Tennant, W., Webster, S. and Cowan, T. (2021). The benefits of ensemble prediction for forecasting an extreme event: The Queensland Floods of February 2019. Monthly Weather Review, 149(7), 2391-2408, https://doi.org/10.1175/MWR-D-20-0330.1

Herzfeld, M., Engwirda, D., and Rizwi, F. (2020). A coastal unstructured model using Voronoi meshes and C-grid staggering. Ocean Modelling, 148, 101599, https://doi.org/10.1016/j.ocemod.2020.101599

Hewitt, C. D., Garrett, N., Newton, P. (2017). Climateurope - Coordinating and supporting Europe’s knowledge base to enable better management of climate-related risks. Climate Services, 6, 77-79, https://doi.org/10.1016/j.cliser.2017.07.004

International Altimetry Team (2021). Altimetry for the future: Building on 25 years of progress. Advances in Space Research, 68, 319-363, https://doi.org/10.1016/j.asr.2021.01.022

Jacobs, G., D’Addezio, J.M., Ngodock, H. and Souopgui, I. (2021). Observation and model resolution implications to ocean prediction. Ocean Modelling, 159, 101760, https://doi.org/10.1016/j.ocemod.2021.101760

Kasim, M. F., Watson-Parris, D., Deaconu, L., Oliver, S., Hatfield, P., Froula, D. H., Gregori, G., Jarvis, M., Khatiwala, S., Korenaga, J., Topp-Mugglestone, J., Viezzer, E., and Vinko, S. M. (2021). Building high accuracy emulators for scientific simulations with deep neural architecture search. Machine Learning: Science and Technology, 3(1), 015013, https://doi.org/10.1088/2632-2153/ac3ffa

Kiss, A.E., Hogg, A.M., Hannah, N., Boeira Dias, F., Brassington, G.B., Chamberlain, M.A., Chapman, C., Dobrohotoff, P., Domingues, C.M., Duran, E.R. and England, M.H. (2020). ACCESS OM2 v1.0: A global ocean-sea ice model at three resolutions. Geosci. Model Dev., 13, 401–442, 2020, https://doi.org/10.5194/gmd-13-401-2020

Kitsios, V., Sandery, P., O’Kane, T.J. and Fiedler, R. (2021). Ensemble Kalman filter parameter estimation of ocean optical properties for reduced biases in a coupled general circulation model. Journal of Advances in Modeling Earth Systems, 13(2), https://doi.org/10.1029/2020MS002252

Kochkov, D., Smith, J. A., Alieva, A., Wang, Q., Brenner, M. P., and Hoyer, S. (2021). Machine learning–accelerated computational fluid dynamics. Proceedings of the National Academy of Sciences, 118(21), e2101784118, https://doi.org/10.1073/pnas.2101784118

Komaromi, W.A., Reinecke, P.A., Doyle, J.D., and Moskaitis, J.R. (2021). The Naval Research Laboratory’s Coupled Ocean–Atmosphere Mesoscale Prediction System-Tropical Cyclone Ensemble (COAMPS-TC Ensemble). Weather and Forecasting, 36(2), 499-517, https://doi.org/10.1175/WAF-D-20-0038.1

Kotsuki, S., and Bishop, C.H. (2022). Implementing Hybrid Background Error Covariance into the LETKF with Attenuation-based Localization: Experiments with a Simplified AGCM. Monthly Weather Review, 150(1), 283-302, https://doi.org/10.1175/MWR-D-21-0174.1

Le Sommer, J., Chassignet, E.P. Wallcraft, A.J. (2018). Ocean circulation modeling for operational oceanography: Current status and future challenges. In “New Frontiers in Operational Oceanography”, E. Chassignet, A. Pascual, J. Tintoré, and J. Verron, Eds., GODAE OceanView, 289-306, https://doi.org/10.17125/gov2018.ch12

Le Traon, P.Y., Reppucci, A., Alvarez Fanjul, E., Aouf, L., Behrens, A., Belmonte, M., Bentamy, A., Bertino, L., Brando, V.E., Kreiner, M.B., Benkiran, M., Carval, T., Ciliberti, S.A., Claustre, H., Clementi, E., Coppini, G., Cossarini, G., De Alfonso Alonso-Munoyerro, M., Delamarche, A., Dibarboure, G., Dinessen, F., Drevillon, M., Drillet, Y., Faugere, Y., Fernandez, V., Fleming, A., Garcia-Hermosa M.I., Sotillo, M.G., Garric, G., Gasparin, F., Giordan, C., Gehlen, M., Grégoire, M.L., Guinehut, S., Hamon, M., Harris, C., Hernandez, F., Hinkler, J.B., Hoyer, J., Karvonen, J., Kay, S., King, R., Lavergne, T., Lemieux-Dudon, B., Lima, L., Mao, C., Martin, M.J., Masina, S., Melet, A., Buongiorno Nardelli, B., Nolan, G., Pascual, A., Pistoia, J., Palazov, A., Piolle, J.F., Pujol, M.I., Pequignet, A.C., Peneva, E., Perez Gomez, B., Petit de la Villeon, L., Pinardi, N., Pisano, A., Pouliquen, S., Reid, R., Remy, E., Santoleri, R., Siddorn, J., She, J., Staneva, J., Stoffelen, A., Tonani, M., Vandenbulcke, L., von Schuckmann, K., Volpe, G., Wettre, C., Zacharioudaki, A. (2019) From Observation to Information and Users: The Copernicus Marine Service Perspective. Frontiers in Marine Science, 6, 234, https://doi.org/10.3389/fmars.2019.00234

Liu, T., Zhuang, Y., Tian, M., Pan, J., Zeng, T., Guo, Y., Yang, M. (2019). Parallel Implementation and Optimization of Regional Ocean Modeling System (ROMS) Based on Sunway SW26010 Many-core Processor. IEEE Access, 7, 146170-146182, doi:10.1109/ACCESS.2019.2944922

Lorenc, A., and Jardak, M. (2018). A comparison of hybrid variational data assimilation methods for global NWP. Quarterly Journal of the Royal Meteorological Society, 144(717), 2748-2760, https://doi.org/10.1002/qj.3401

Malmgren-Hansen, D. (2021). Deep learning for high-dimensional parameter retrieval. In: “Deep Learning for the Earth Sciences”, G. Camps-Valls, D. Tuia, X. X. Zhu, and M. Reichstein Eds., Wiley, https://doi.org/10.1002/9781119646181.ch16

Martín Míguez, B., Novellino, A., Vinci, M., Claus, S., Calewaert, J.-B., Vallius, H. et al. (2019). The European Marine Observation and Data Network (EMODnet): Visions and Roles of the Gateway to Marine Data in Europe. Frontiers in Marine Science, 6, 313, https://doi.org/10.3389/fmars.2019.00313

Maulik, R., Rao, V., Wang, J., Mengaldo, G., Constantinescu, E., Lusch, B., Balaprakash, P., Foster, I. and Kotamarthi, R. (2021). Efficient high-dimensional variational data assimilation with machine-learned reduced-order models. Geoscientific Model Development, https://doi.org/10.5194/gmd-2021-415

Maze, G., Mercier, H., Fablet, R., Tandeo, P., Lopez Radcenco, M., Lenca, P., Feucher, C., and Le Goff, C. (2017). Coherent heat patterns revealed by unsupervised classification of Argo temperature profiles in the North Atlantic Ocean. Progress in Oceanography, 151, 275-292, https://doi.org/10.1016/j.pocean.2016.12.008

Minamide, M., and Posselt, D.J. (2022). Using Ensemble Data Assimilation to Explore the Environmental Controls on the Initiation and Predictability of Moist Convection. Journal of the Atmospheric Sciences, 79(4), 1151-1169, https://doi.org/10.1175/JAS-D-21-0140.1

Mittal, S., Vetter, J.S. (2015). A Survey of CPU-GPU Heterogeneous Computing Techniques. ACM Computing Surveys, 47(4), 1-35, https://doi.org/10.1145/2788396

Nerger, L., Tang, Q., and Mu, L. (2020). Efficient ensemble data assimilation for coupled models with the Parallel Data Assimilation Framework: example of AWI-CM (AWI-CM-PDAF 1.0). Geoscientific Model Development, 13, 4305-4321, https://doi.org/10.5194/gmd-13-4305-2020

Nonnenmacher, M., and Greenberg, D. S. (2021). Deep emulators for differentiation, forecasting, and parametrization in Earth science simulators. Journal of Advances in Modeling Earth Systems, 13, e2021MS002554, https://doi.org/10.1029/2021MS002554

O’Kane, T.J., Sandery, P.A., Kitsios, V., Sakov, P., Chamberlain, M.A., Squire, D.T., Collier, M.A., Chapman, C.C., Fiedler, R., Harries, D. and Moore, T.S. (2021). CAFE60v1: A 60-year large ensemble climate reanalysis. Part II: Evaluation. Journal of Climate, 34(13), 5171-5194, https://doi.org/10.1175/JCLI-D-20-0518.1

O’Kane, T.J., Sandery, P.A., Monselesan, D.P., Sakov, P., Chamberlain, M.A., Matear, R.J., Collier, M.A., Squire, D.T. and Stevens, L. (2019). Coupled data assimilation and ensemble initialisation with application to multi-year ENSO prediction. Journal of Climate, 32(4), 997-1024, https://doi.org/10.1175/JCLI-D-18-0189.1

Palmer, T. N., Doblas-Reyes, F. J., Weisheimer, A., Rodwell, M. J. (2008). Toward Seamless Prediction: Calibration of Climate Change Projections Using Seasonal Forecasts. Bulletin of the American Meteorological Society, 89(4), 459-470, https://doi.org/10.1175/BAMS-89-4-459

Peng, G., Downs, R.R., Lacagnina, C., Ramapriyan, H., Ivánová, I., Moroni, D., Wei, Y., Larnicol, G., Wyborn, L., Goldberg, M., Schulz, J., Bastrakova, I., Ganske, A., Bastin, L., Khalsa, S.J.S., Wu, M., Shie, C.-L., Ritchey, N., Jones, D., Habermann, T., Lief, C., Maggio, I., Albani, M., Stall, S., Zhou, L., Drévillon, M., Champion, S., Hou, C.S., Doblas-Reyes, F., Lehnert, K., Robinson, E. and Bugbee, K., (2021a). Call to Action for Global Access to and Harmonization of Quality Information of Individual Earth Science Datasets. Data Science Journal, 20(1), 19, http://doi.org/10.5334/dsj-2021-019

Peng, G., Lacagnina, C., Ivánová, I., Downs, R.R., Ramapriyan, H., Ganske, A., Jones, D. et al. (2021b). International Community Guidelines for Sharing and Reusing Quality Information of Individual Earth Science Datasets. AGU Fall Meeting, 1-17 December 2020, https://doi.org/10.1002/essoar.10508481.1

Persello, C., Wegner, J. D., Hänsch, R., Tuia, D., Ghamisi, P., Koeva, M., and Camps-Valls, G. (2022). Deep learning and earth observation to support the sustainable development goals. IEEE Geoscience and Remote Sensing Magazine, 2-30, https://doi.org/10.48550/arXiv.2112.11367

Posselt, D.J., Bishop, C.H. (2018). Nonlinear Data Assimilation for Clouds and Precipitation using a Gamma-Inverse Gamma Ensemble Filter. Quarterly Journal of the Royal Meteorological Society,144(716), 2331-2349, https://doi.org/10.1002/qj.3374

Quinn, C., O’Kane, T.J., Kitsios, V. (2020). Application of a local attractor dimension to reduced space strongly coupled data assimilation for chaotic multiscale systems. Nonlinear Processes in Geophysics, 27 (1), 51-74, https://doi.org/10.5194/npg-27-51-2020

Robinson A. R., and K. Brink (Eds.). 2006. The Sea. Vols. 10 to 14. Harvard University Press.

Roemmich, D., Alford, M.J., Hervé, C., Johnson, K., King, B., Moum, J., Oke, P., Owens, W.B., Pouliquen, S., Purkey, S., Scanderbeg, M., Suga, T., Wijffels, S., Zilberman, N., Bakker, D., Baringer, M., Belbeoch, M., Bittig, H.C., Boss, E., Calil, P., Carse, F., Carval, T., Chai, F., Conchubhair, D.Ó., d’Ortenzio, F., Dall’Olmo, G., Desbruyeres, D., Fennel, K., Fer, I., Ferrari, R., Forget, G., Freeland, H., Fujiki, T., Gehlen, M., Greenan, B., Hallberg, R., Hibiya, T., Hosoda, S., Jayne, S., Jochum, M., Johnson, G.C., Kang, K., Kolodziejczyk, N., Körtzinger, A., Le Traon, P.-Y., Lenn, Y.-D., Maze, G., Mork, K.A., Morris, T., Nagai T., Nash, J., Garabato, A.N., Olsen, A., Pattabhi, R.R., Prakash, S., Riser, S., Schmechtig, C., Schmid, C., Shroyer, E., Sterl, A., Sutton, P., Talley, L., Tanhua, T., Thierry, V., Thomalla, S., Toole, J., Troisi, A., Trull, T.W., Turton, J., Velez-Belchi, P. J., Walczowski, W., Wang, H., Wanninkhof, R., Waterhouse, A.F., Waterman, S., Watson, A., Wilson, C., Wong, A.P.S., Xu, J., Yasuda, I.(2019). On the Future of Argo: A Global, Full-Depth, Multi-Disciplinary Array. Frontiers in Marine Science, 6, https://doi.org/10.3389/fmars.2019.00439

Sakov, P. (2014). EnKF-C User Guide, https://doi.org/10.48550/arXiv.1410.1233

Sakov, P.,Haussaire, J.-M., Bocquet, M. (2017). An iterative ensemble Kalman filterin presence of additivemodel error. Quarterly Journal of the Royal Meteorological Society,144(713), https://doi.org/10.48550/arXiv.1711.06110

Sandery, P., Brassington, G., Colberg, F., Sakov, P., Herzfeld, M., Maes, C., Tuteja, N. (2019). An ocean reanalysis of the western Coral Sea and Great Barrier Reef. Ocean Modelling, 144, 101495, http://dx.doi.org/10.1016/j.ocemod.2019.101495

Sandery, P., O’Kane, T., Kitsios, V., and Sakov, P. (2020). Climate model state estimation using variants of EnKF coupled data assimilation. Monthly Weather Review,148(6), 2411-2431, https://doi.org/10.1175/MWR-D-18-0443.1

She, J, Muñiz Piniella, Á, Benedetti-Cecchi, L, Boehme, L, Boero, F, Christensen, A, et al. (2019). An integrated approach to coastal and biological observations. Frontiers in Marine Science, 6, 314, https://doi.org/10.3389/fmars.2019.00314

She, J., Allen, I., Buch, E., Crise, A., Johannessen, J.A., Le Traon P.-Y. et al. (2016). Developing European operational oceanography for Blue Growth, climate change adaptation and mitigation, and ecosystem-based management. Ocean Science, 12, 953-976, https://doi.org/10.5194/os-12-953-2016

She, J., Bethers, U., Cardin, V., Christensen, K.H., Dabrowski, T., et al. (2021). Develop EuroGOOS marine climate service with a seamless earth system approach. 9th EuroGOOS International conference, Shom; Ifremer; EuroGOOS AISBL, May 2021, Brest (Virtual), France, 554-561.

Shriver, J. F., Arbic, B. K., Richman, J. G., Ray, R. D., Metzger, E. J., Wallcraft, A. J., Timko, P. G. (2012), An evaluation of the barotropic and internal tides in a high-resolution global ocean circulation model. Journal of Geophysical Research: Oceans, 117(C10), https://doi.org/10.1029/2012JC008170

Sotillo, M.G., Garcia-Hermosa, I., Drévillon, M., Régnier, C., Szczypta, C., Hernandez, F., Melet, A., Le Traon, P-Y. (2021). Communicating CMEMS Product Quality: evolution & achievements along Copernicus-1 (2015-2021). Included in “Special Issue: The Copernicus Marine Service from 2015 to 2021: six years of achievements”, by LeTraon et al., Mercator Océan Journal #57, https://doi.org/10.48670/moi-cafr-n813

Sun, Y., Bao, W., Valk, K., Brauer, C. C., Sumihar, J., and Weerts, A. H. (2020). Improving forecast skill of lowland hydrological models using ensemble Kalman filter and unscented Kalman filter. Water Resources Research, 56, e2020WR027468, https://doi.org/10.1029/2020WR027468

van de Meent, J.-W., Paige, B., Yang, H., AND Wood, F. (2021). An introduction to probabilistic programming, https://doi.org/10.48550/arXiv.1809.10756

van Leeuwen, S., Tett, P., Mills, D., van der Molen, J. (2015). Stratified and non stratified areas in the North Sea: Long-term variability and biological and policy implications. Journal of Geophysical Research: Oceans, 120(7), 4670-4686, https://doi.org/10.1002/2014JC010485

Vinuesa, R., and Brunton, S. L. (2021). The potential of machine learning to enhance computational fluid dynamics, https://doi.org/10.48550/arXiv.2110.02085

Weyn, J.A., Durran, D.R., Caruana, R., Cresswell-Clay, N. (2021). Sub-Seasonal Forecasting With a Large Ensemble of Deep-Learning Weather Prediction Models. Journal of Advances in Modeling Earth Systems, 13(7), e2021MS002502, https://doi.org/10.1029/2021MS002502

WMO (2015). Seamless prediction of the Earth system: from minutes to months. WMO-No. 1156, ISBN 978- 92-63-11156-2.

Xu, S., Huang, X., Oey, L.-Y., Xu, F., Fu, H., Zhang, Y., Yang, G. (2015). POM.gpu-v1.0: a GPU-based Princeton Ocean Model. Geoscientific Model Development, 8, 2815-2827, https://doi.org/10.5194/gmd-8-2815-2015

Zanna, L., and Bolton, T. (2021). Deep learning of unresolved turbulent ocean processes in climate models. In: “Deep Learning for the Earth Sciences”, G. Camps-Valls, D. Tuia, X. X. Zhu, and M. Reichstein (Eds.), Wiley https://doi.org/10.1002/9781119646181.ch20

Zhang, L., Delworth, T.L., Jia, L. (2017). Diagnosis of Decadal Predictability of Southern Ocean Sea Surface Temperature in the GFDL CM2.1 Model. Journal of Climate, 30(16), 6309-6328, https://doi.org/10.1175/JCLI-D-16-0537.1

Previous chapter 11
Chapter 12

Challenges and future perspectives in ocean prediction

To start contributing, sharing knowledge and editing the WIKI, please login !