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

12.2 Observing system evolution with ocean prediction engagement

The quality of the ocean analysis and forecasts highly relies on observations assimilated for constraining the ocean circulation in ocean forecasting systems. The evolution of the forecasting systems towards increased realism to represent a larger spectrum of ocean processes and scales will be underpinned by the ‘adapted’ in situ and satellite observations that efficiently constrain the different scales of the ocean variability. Close collaboration between ocean forecasting centres and the observation providers is crucial to promote such evolution. Communication ensures the best use of information from the present to the future observation systems. It allows forecasting centres to inform on the observation use and to report on their impacts on analysis and forecasts. In the longer term, it also increases opportunities for the ocean forecasting centres to contribute to evolve ocean observing system designs to optimally meet requirements and enable capabilities of future operational systems. Inclusion of forecasting centres in designing and evaluating the future impact of the GOOS 🔗1 component has started to be recognized as a best practice in the observation and prediction community.

In such a context, OOFS strictly depends on the availability of near-real time observations for assimilation and validation purposes. Accuracy of forecast products is largely impacted by the quality of assimilated observations, so that the effort of the community is to support the forecasters with high quality data in space and time sampling. Le Traon et al. (2019) provides the Copernicus Marine Service strategy for observational network evolutions and the requirements for OOFS to support maritime safety, marine resources, marine and coastal environments, weather, seasonal forecasting, and climate. According to this document, the main priorities are:

  • For satellite data:
    • Guaranteeing continuity of the present operational missions’ capacity of Sentinel for downstream coastal applications, and of Cryosat mission for monitoring of sea ice thickness and sea level in polar regions;
    • Developing new capacity for wide swath altimetry for the future OOFS and services;
    • Developing microwave mission for the improvement of spatial coverage of sea surface temperature, sea ice drift, sea ice thickness, and sea surface salinity;
    • Enforcing R&D for observing sea surface salinity and ocean currents from space.
       
  • For in-situ data:
    • At global scale, the main future challenges are: a) to improve the coverage of biogeochemical measurement, b) the measurement of deep temperature and salinity, and c) measurement of in-situ velocity observations, sea ice observations, and open-ocean wave measurements;
    • At a regional scale, the main priority is to fill gaps for a wide range of variables in the shelf-coastal observational networking, in order to improve monitoring and forecasting capacities. 

Copernicus Marine Service provides specific strategic documents 🔗2 for both satellite and in-situ observations to support monitoring and forecasting activities. The GOOS defines the following strategic objectives for observing systems at global level towards 2030:

  • to deepen engagement and impact by enforcing the connection with forecasting centres;
  • to deliver an integrated fit-for-purposes observing system able to support and expand the implementation of observing systems and ensuring data management according to the FAIR principles;
  • to build future observational networks by supporting innovation in observing technologies and extending systematic observations to understand impacts on the ocean.

12.2.1 Challenges for the current ocean observing systems

Major challenges for the current ocean observing systems include: i) most of the ocean observations made by non-operational oceanography communities (e.g. environment, fishery, research, and industrial sectors) have not been used for operational forecasting; the ocean observations are made by various sectors with different monitoring and data collection standards, and little efforts have been made to harmonise observations from the different sectors; and ii) technologic bottlenecks and significant data gaps in sub-surface, sea bottom, geological and biological observations.

Figure 12.1. Integrated observing. Unlocking the value of ocean observing by integrating observations in three dimensions: fit for purpose, parameter, and instrumental (source: She et al., 2019).

For developing an integrated and unified ocean observing system to support the seamless information service, three pillars are recommended, as shown in Fig.12.1. The first pillar is to maximise the value of existing observations by breaking the institutional and sectorial barrier (She et al., 2019) and fit for the purposes of multi-sectors. This can be implemented by performing multidimensional integration of operational and non-operational ocean observing communities, including operational monitoring, environment monitoring, fishery monitoring, research monitoring, crowd (citizens and NGOs) monitoring and other sectoral monitoring (industrial and socioeconomic). The observations should be “collected once and used for many times” (Martín Míguez et al., 2019). Due to the existing mandate of monitoring entities, either public or private, current ocean observing practices are designed separately to fit for the purpose of individual sectorial service, and observations are hardly shared from different monitoring communities. When designing multidimensional integration on a national and regional scale, unified standards should be applied. The operational and autonomous platform is an efficient framework for the integrated and unified ocean observing, which is highly recommended.

The second pillar is to develop, deploy, and utilise large networks of autonomous, cost-effective, innovative sensors to fill the observation gaps in subsurface and emerging observations, e.g. marine litter, biological variables, and underwater noise. A combination of breakthroughs in underwater communication technology, underwater robotics, and ML/AI may significantly improve the capacity of underwater monitoring, especially for pollutants, biogeochemical and biological variables. Adaptable observations are also needed for characterising key processes underpinning predictability in the marine earth system.

The third pillar is to design and optimise existing ocean observing to fill gaps in the characterizations of processes and sensitive regions that are crucial to the predictability and fit for the purposes in multi-sectors. It is essential that the monitoring capacity is based on an integrated system of in-situ, remote sensing, models, assimilation, and ML/AI tools. Sampling schemes of such a system can then be designed to optimise the integrated monitoring capacity, so that observations would most effectively be used to reduce the earth system prediction uncertainties. It should be noted that dedicated observations should be identified and included to address specific predictability in the UOM (She et al., 2016)

12.2.2 Observing System Evaluation

At present, OS-Eval, based on ocean forecasting systems, are not often conducted in a coordinated manner. The most used techniques of OS-Eval are data denial experiments with real or simulated observations (e.g., OSE and OSSE). Although only observation platforms which are already existing with real observations can be evaluated, simulated observations allow us to evaluate the impact of future platforms or evolution of the observation network. Impact assessment methods will evolve in the future with more sophisticated techniques based on ensemble and adjoint methods, and potentially also AI. Considering that BGC applications and the earth system predictions, including the ocean component, are progressively becoming more important, the development of suitable evaluation methods for those applications is also indispensable. Improving analysis/forecast accuracy and developing methods assimilating new types of observation data will increase the ability to make fair assessments for various platforms. Multi-system evaluation and regular re-assessment of the observation impact to follow the system evolutions are required to improve the robustness of the results by moderating system-dependency.

Enhanced communication and coordination between modelling/data assimilation experts and observation/network experts will be essential for a proper design and interpretation of OS-Eval, especially to extract compelling messages on the ability of the ocean observing system to control processes having different temporal and spatial scales. The provision of regular reports on ocean observation impacts in ocean prediction systems is expected to enhance such communication. It should also be noted that OS-Eval activities require dedicated infrastructures and resources. Cooperation with international partners (e.g. OceanPredict, GOOS/ROOS, WMO, IOC, etc.) is hence essential to establish a substantial value chain between ocean observation networks and ocean prediction systems.

OS-Eval activities require dedicated infrastructures and resources. It is essential to strengthen the capabilities of operational and climate centres to assess the impact of present and future observations to guide observing system agencies but also to improve the use of observations in models.

An observation network cannot be considered by its own but should be evaluated in complementarity with other in-situ and satellite networks. The synergy from a combination of observation platforms’ data with the other existing and planned in-situ and satellite observations should be evaluated. This will be necessary since the model forecasts need to be constrained on a large spectrum of scales, as individual platforms cannot provide it. Optimally leveraging satellite and in-situ observations to improve the ocean predictability is an important research topic with strategic importance. Understanding and being able to showcase and demonstrate the impact of both present and future observing systems in improving ocean prediction (and environmental prediction in general) is important to justify and maintain long term investments for the observation system. Feedback from such efforts enables observation groups to know where to invest their efforts, both technologically and in terms of geographic coverage in density and scope.

To best showcase evaluations of the observing system, prediction impact metrics should be generated in terms of value for: (1) user and application needs; and (2) observing system needs. On the user and application side, elements like the WMO RRR can be used, in which the impact of an observation on the forecast system is framed in terms of impact on a user or application. This can entail further post processing of prediction output, to translate forecasting impact into information that the end user will use directly. For example, for Search and Rescue at sea it may be necessary to know the impact of an observing system on drift prediction, and quantifying how much it would decrease the search area at sea while still ensuring high probability of detection. There is also a need to show the impact of an observing system on a variety of applications, as well as to provide insight into the impact of decreasing or augmenting the number of observations. Additionally, when developing metrics to support observing system needs, the multi-purposeless of the observations (climate, ocean services and health) needs to be covered.

Real-time impact assessment methods should also be developed to monitor and report on the use and impact of the different assimilated observation networks by operational ocean forecasting centres. This will help to detect impacts of changes in the observation network, and take countermeasures against them.

In the next subsections are presented the evolution plans for the observatory component, i.e. ARGO and satellite observations, which will drive the next generation of OOFS.

12.2.3 Argo evolution plans

The international programme Argo (🔗3) is currently the major global initiative for the collection of “information from inside the ocean using a fleet of robotic instruments that drift with the ocean currents and move up and down between the surface and a mid-water level”. In Chapter 4 can be found an overview on the current ARGO operational capabilities for OOFS. Argo design after 2020 is available at 🔗4, including the following major targets:

  • Improved observational capacity in the polar sea-ice regions and marginal seas;
  • Increased resolution in key areas like the Western Boundary Currents in which mesoscale noise is high, and the Equatorial region for which high temporal resolution is needed;
  • Launch of new missions for biogeochemical and deep region variables. 

Next generation Argo programme is also oriented towards validation and deployment of new sensors for measuring ocean turbulence and small-scale mixing, which is fundamental for improving OOFS, numerical models, data assimilation schemes, and validation of forecast products.

Expansion of the observing network requires maintenance and advancements of data management systems among providers and forecasting centers to ensure interoperability and open access to growing data inflow (Roemmich et al., 2019)

12.2.4 Next phase for satellite missions

Satellite observations, together with those in-situ, are the key element for the global ocean observing system. In Chapter 4, it has already been provided a general overview of the type of data used for building OOFS. Next generation of forecasting systems will also exploit the new technological advancements in the observational network, and satellite measurements will play an important role in monitoring the cryosphere, coastal zones, and inland waters to improve the quality of marine services. The International Altimetry Team has recently published a contribution about the future 25 years of progress in altimetry measurements (International Altimetry Team, 2021); according to this work, the main requirements by altimetry for scientific and operational advances of operational oceanography, and more in general for Earth system science, are:

  • Increasing the coverage of satellite measurements to support ocean dynamics understanding, from smaller mesoscale to sub-mesoscale, by means of multi-platform in-situ measurements, multi-satellite and SAR, and SAR-interferometry altimetry;
  • The design of ad-hoc experiments for in-situ data collection guided by remote data;
  • The evaluation of vertical circulation by means for in-situ and high resolution sea surface height measurements;
  • Guaranteeing the continuity of the current operational measurements;
  • Estimating uncertainties on regional sea level trends by comparing tide gauges with GNSS positioning with altimetry;
  • Improving sea level record at coastal scale by using high resolution SAR altimetry, tide gauges with GNSS positioning, and developing GNSS reflectometry (the last is very promising for providing sea level change measurements);
  • Increasing the spatial resolution of altimetry products with advanced techniques like SARIn-based “swath mode” processing and fully focused SAR over polar oceans;
  • Increasing not only spatial but also temporal resolution by means of higher resolving altimeter such as SWOT, accompanied by larger altimetry constellation that includes swath and conventional altimetry, doppler wave and current scatterometer, and integrated altimeter.

To support operational oceanography and marine applications, Copernicus Marine Service has drawn up a document 🔗5 that describes the main requirements for the evolution of the Copernicus Satellite Components. It focuses on the need of a multi-sensor and multi-mission approach for collecting SST, SSS, ocean colour, currents, wind, and wave measurements. This would constrain future high resolution open ocean, coastal models, and coupled ocean/wave models. The document also recognizes the need of improving space/time resolution, to better monitor and forecast the physical and biogeochemical state of the ocean at fine scale, and to improve the monitoring of coastal zones and of rapidly changing polar regions.

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 !