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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

12.9 Quality information communication improvements

As described in Chapter 4, PQ assessment is an essential service component for any operational oceanographic centre. In the case of climate and short-term forecasting services, validation of ocean models (physical and biogeochemical) is a crucial issue. Despite the continuous progress of the services towards providing regularly updated quality information, there are still gaps and deficiencies in the operational capacity to assess model solutions. It is still challenging to properly quantify the uncertainties in real time and in a way that is directly understandable and useful to the users. Capet et al. (2020), in their review of the operational modelling capacity in European Seas, pointed out that only 20% of operational coastal model services provide a dynamic uncertainty together with the forecast products. This deficit in terms of operational model validation processes may be mainly linked to the lack of real-time access to a local ocean observation network.

This limitation seems to be partially alleviated within core services that have a regional or global focus. In these services, the PQ processes seems to be favoured by: 1) a wider scope (services dealing not only with forecast models but also with the monitoring component and observational data products); 2) a more integrated data use (for instance through data assimilation in ocean analysis and reanalysis products); and 3) wider spatial coverages (allowing the use of a higher number of observational data sources to validate model predictions). The Copernicus Marine Service is one of these core comprehensive services and in recent years has built some standards for model assessments and delivery of PQ information to end-users. This service, and its evolution roadmap in terms of PQ processes, can illustrate the main expectations for the future evolution of validation and quality information on operational oceanography products.

As described in Sotillo et al. (2021), the Copernicus Marine Service ensures:

  • Standardised processes to assess each product’s scientific quality against appropriate metrics;
  • Product quality information regularly updated and available from a central website, called the “PQ-Dashboard” (https://pqd.mercator-ocean.fr/);
  • Specific PQ documentation delivered with each CopernicusMarine Serviceproduct, completedby regularlyupdated quality summaries, including fit for purpose information, and evolving towards peer reviewed technical reports.

From this baseline, the Copernicus Marine Service Product Quality Strategic Plan 🔗10, identified a list of developments, challenges and opportunities foreseen for the next Copernicus-2 service phase period (2022-2028). The availability of an increasing number of ocean observations should enable and support new developments, and eventually improve the information quality associated with oceanographic products. The three main working lines along which the plan will unfold are discussed in the following subsections and shown in Figure 12.5: future observations, future developments in OO centres, and future quality information. These lines are the way forward for the future development of model validation and quality assessment techniques.

Figure 12.5. New observations enable new developments in operational oceanography centres, which will also benefit from growing computational resources and advanced AI and big data techniques. This will allow significant improvements of the quality information, improving its relevance and its frequency.

12.9.1 New observations for improved quality assessment

The use of new satellite products (e.g. from next Sentinel missions or wide swath altimetry) will enable a significant increase of data coverage towards higher resolution, allowing not only a quality increase but also more validation opportunities for a wide range of operational oceanography products. The continuation of the BGC-Argo and Deep Argo missions and networks are crucial for providing quality information in areas and on variables that are still highly undersampled. The potential extension of Argo coverage towards coastal areas may also be essential for its important socio-economic impact and the benefit for coastal model assessments. In that sense, there are some on-going initiatives in the framework of R&D Projects (such as the Euro-Argo RISE H2020 one) to test Argo on shelf extensions, targeting shallower waters in European marginal seas.

Additionally, operational oceanography centres should improve the effective use of existing observing products and networks through:

  • Upgrade of PQ processes to properly assess high frequency datasets: PQ metrics are generally computed daily. However, currently, and to a greater extent in the future, some near real-time (NRT) model product datasets that are delivered with higher frequency (i.e. every 15 minutes) would need a dedicated assessment.
  • Enhancement of water mass assessment at synoptic scales: at present, sampled only partially. To improve their characterisation in the upper ocean, it is necessary to extend the use of available observational platforms (i.e. more ship of opportunity measurements, thermosalinograph/ferry box data, new glider opportunities, sea mammals). Below 2000m, water mass distributions are still poorly understood, and historical data do not guarantee the reliability of existing climatologies. Deep floats and deep ocean observations also need to be considered to support global prediction assessment.
  • Promote the use of data from specific multi-platform campaigns (specially in hot spots): regular and periodic campaigns in the same waters are necessary for climate monitoring and periodic model assessments (i.e. glider periodic missions along straits); current measurements are also much needed (both Lagrangian and Eulerian observations), not only for temperature and salinity.
  • Ensure easy access to historic observations: there are large amounts of data from research surveys that are either not available or available only in operational catalogues. These independent data (in the sense of not assimilated) can be crucial for assessing model performance. A progressive integration of this kind of data will be advantageous for forecasters, and its “discovery” is foreseen to increase. Access to these sources should be automated, data loss reduced, and the investment on data collection will be recovered. In the context of Copernicus Marine Service, EMODNET, EuroGOOS alliances or other networks, it is crucial for OO centres and data providers to connect initiatives and efforts to better integrate the existing ocean observing systems, as well as the new expected instruments/observations.

12.9.2 Expected development of quality assessment techniques

The use of ensemble data assimilation methods and the expected increase in the use of prediction systems based on model ensembles should significantly improve the quantification of model product uncertainty using probabilistic scores, the evaluation of error propagation, and of model systematic errors and attractors. An increasing number of high-resolution observations will be used to characterise model skill at all observed scales, while advanced statistical techniques (such as deep learning) should contribute to improve cross-validation capabilities between different types of observations, and between observations and models.

Errors in the ocean circulation models, in particular on vertical transport and mixing, strongly impact the coupled biogeochemical model solutions. Thus, monitoring errors in key parameters of the physical forcing should characterise errors (their causes) and subsequent impacts in biogeochemical solutions. The mixed layer depth variable is a typical example of this due to its impact on biogeochemistry processes.

Quality assessment of model downscaling should be eased in the future by advances in integrated systems (following on the idea of monitoring uncertainties “propagating” along the value chain). The added value of downscaling (higher resolution with better representation of the ocean processes) needs to be assessed through a more systematic comparison of global vs. regional and coastal models. To this aim, alternative/innovative validation metrics are needed for model assessment that avoid double penalty when comparing different resolution models (Ebert, 2009). More relevant skill scores are needed for forecasting, implementing new approaches to validate and inter-compare new physical, and biogeochemical model products at very high-resolution.

Finally, there is a growing need to identify and understand long-term trends in ocean parameters and their impact at regional to coastal scale. The validation of such signals is challenging for physical and even more for biogeochemical parameters, such as carbon, oxygen, and ocean acidification, which are of great interest on both regional and global scales. It is crucial to improve the validation methodology and to increase the number of reference observations as much as possible.

12.9.3 Quality assessment for intermediate and end users

There is an increasing demand for regional fit for purpose assessments, especially in coastal areas. The quality information content must evolve following users’ needs. The current OceanPredict product quality metric monitoring has to be complemented with process- (and user-) oriented metrics, and better quantification of uncertainties. Probabilistic scores and robustness assessments with multi-product (model and observed) intercomparisons should help answer many user requirements. The use of application-oriented metrics, such as Lagrangian drift metrics or “event oriented” metrics (e.g. categorical scores based on thresholds) should also be generalised.

The collaboration among forecasting services to agree on international validation standards must continue. Collaboration between forecast services and users should result in the introduction of new user-oriented metrics to be considered as local case studies and validation “benchmarks”.

Operational oceanography centres will have to develop both high-level summarised quality information and high-resolution uncertainty estimates to be delivered alongside the products following FAIR guidelines, as initiated by Peng et al. (2021a, 2021b).

High-level quality summaries, such as product “maturity matrices”, will guide users to choose the most appropriate product for a given use, while the uncertainty information delivered alongside the product will enable the access to tailored product quality information, as a valuable addition to many oceanographic applications.

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Chapter 12

Challenges and future perspectives in ocean prediction

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