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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

12.7 Seamless prediction

Palmer et al. (2008) used “seamless” to refer to predictions across the range of weather and climate time scales, e.g. ranging from forecast in days to projections in decades. The WMO, in its document “Seamless prediction of the Earth system: from minutes to months” (WMO, 2015), further developed this concept, with a main focus on the weather component but also starting to consider its importance for the ocean. Then, within EuroGOOS this concept has been expanded to promote next generation of ocean services able to seamlessly span spatially from global ocean to coastal areas and estuaries as a continuum with high resolution information (She et al., 2021). To achieve the objectives of the seamless approach, numerical ocean models need to evolve (Chassignet and Xu, 2021; Fox-Kemper et al., 2019) towards:

  • Use of nested and regional downscaling simulations, by means of high-resolution spatial grid spacing or using variable-resolution and multi-scale modelling;
  • New parameterizations and improvement of the existing ones (e.g. air-sea parameterization, turbulence and mixing, internal tides, vertical convection, coastal estuaries interface with open ocean);
  • More direct simulation of sea level changes and tides.

Seamless is also connected to coupling as global coupled ocean-atmosphere-land-ice modelling systems are used to perform climate change projections and studies, from decadal to seasonal timescales (Hewitt et al., 2017). The overall advancements of numerics in ocean dynamics, biogeochemistry, weather modelling, and hydrology open new opportunities for coupled systems to address predictions on short-range timescales from regional to coastal scales.

In order to establish a seamless marine information service, integrated and unified ocean observing systems and seamless unified modelling and forecasting systems should be developed. Integrated ocean observing implies that ocean observations made by multiple sectors for all subsystems with multiple means - remote sensing, robotics, and in-situ - are integrated, while monitoring schemes and data management are designed in an unified way, so that the observations, after being integrated with the seamless models, will be able to fit users’ purposes. Furthermore, ocean observing should be cost effective and sustainable.

The seamless models can be based on mathematical equations or statistical and AI algorithms, which simulate or emulate marine physical-chemical-geological-biological systems. There are still significant gaps in current forecasting capacity to reach seamless predictability. The development of a seamless modelling capacity will be discussed in the next subsections from three aspects: space, time, and system of systems. The seamless ocean earth system prediction models should be based on UOMs and including atmospheric models. Development of UOMs has been identified as one of the four EuroGOOS research priorities (She et al., 2016).

12.7.1 Optimal use of modelling workforce and model consolidation

A seamless UOM modelling framework should be developed to leverage global efforts to enable joint code development. One notable feature of the ocean modelling community is the great diversity of the models but the very limited research workforce for each model. An incomplete survey of ocean circulation modelling by EuroGOOS (🔗7) showed that EU countries use 32 ocean models for operational and/or ocean climate modelling, among which 24 were developed in the EU and 8 from the US. Twenty ocean circulation models have been used in Europe for operational forecasting (Capet et al., 2020). In the US, at least ten ocean models are currently used for operational forecasts. If this count would be extended to ocean circulation models developed and used in other countries (i.e. Australia, Canada, China, and Japan) the number of ocean models in use could be huge. It is well-known that a significant workforce is needed to keep an ocean model at the state-of-the-art.

However, each ocean modelling group has only a very limited workforce for ocean model development. Even though joint or community model development has improved the situation for a small number of models, the number of ocean model developers is still far from sufficient for most of the models. Therefore, it is necessary to optimise the use of ocean modelling workforces focusing only on a limited number of models. The future UOMs can be made so that one model would have options with multiple coordinates and parameterizations, hence emulating different model behaviours.

Optimal use of modelling workforce should be coordinated in national, regional (such as the GRAs), and global scales so that the UOMs in different scales can be well addressed and consolidated with a critical mass of model developers. However, it is not always possible to have a critical mass of model developers at the national level, as only countries with strong national investment in ocean science have such a capacity. It is easier to reach a critical mass at the regional or global levels. In fact, most of the effective modelling cooperation is carried out at regional level. The global co-development of models is probably less active due to both administrative and political barriers. It is highly recommended to strengthen global collaboration on UOM development

12.7.2 Development of seamless UOM for multiple temporal scales

Predictability in an ocean earth system has a multi-scale feature, relating to the spatiotemporal scales of its subsystems as well as their interactions, which can be divided into forcing-based predictability, self-constrained subsystem predictability, and coupled system predictability. For atmospheric systems, according to the high-resolution global forecast model experiments, the upper limit of the self-constrained predictability for deterministic prediction is two weeks. Longer-scale predictability is related to blocking events with time scales ranging from weeks to years, e.g. MJO, PNA, NAO, AO, ENSO, QBO, which relies on interaction between atmosphere and ocean-ice systems and solar radiation. It is well-known that the surface ocean is mainly dominated by forcing-based predictability, i.e. variability of waves, ice and sea level in synoptic scale are largely determined by weather conditions. Subsurface ocean and sea ice can store forcing signals and release them to affect the atmosphere at a “slower” pace. This generates longer predictability in the coupled ocean-ice-atmospheric system. MJO, PNA, NAO, AO, and ENSO are all phenomena generated in such a coupled system. As stated by Brian Hoskins in the WMO Lecture 2011 🔗8: “The background provided by the longer time-scales and by external conditions, and the phenomena that occur on each range of time-scales in the seamless weather-climate prediction problem, give the promise of some predictive power on all time-scales”.

Most of these long-scale processes can still not be predicted successfully by current coupled-system models. UOM development is a key to improve the earth system predictability in the current stage as it will provide insight knowledge, as well as simulate the processes that the ocean-ice system filters, absorbs, and transfers the atmospheric signals into a slow-motion signal and then feeds back to the atmosphere.

To reach breakthroughs in longer-scale predictability, it is important to consider that: i) ocean earth system forecast is a probability prediction problem; ii) multi-model ensemble has shown expanded atmospheric forecasting skills than the deterministic prediction; iii) shorter-scale phenomena, although constrained by longer-scale ones, are also a statistical forcing to the longer-scale, thus should not be treated only as noise; and iv) solar radiation, volcano eruption, and changes of pollutants in both ocean and atmosphere can affect the intrinsic signals in the system and then should be included. UOM development should address these issues.

12.7.3 Geographic configurations and seamless UOM in space and in a marine system of systems

For a coordinated UOM development, proper geographic scales should be defined as well, so that both scientific requirements and collaboration needs are met. Three types of forecast UOMs can be expected: i) global-scale coupled UOMs aiming at longer-scale prediction of the earth system, which is not necessarily high resolution but should be able to use short-scale as a statistical forcing; ii) global and regional scale coupled models aiming at produce refined forecast within a “foreseeable” time, e.g. a month, for which high resolution will be important; and iii) for“touchable” spatiotemporal scale, i.e. inland water-estuary-coastal-regional sea in space and a few days in time. It should also be noted that the smaller-scale UOMs can be easily applied to long-term forecast applications when forecasts at boundaries are well defined.

The coupled UOMs will mainly be developed for global and regional scale to address longer scales from months to seasons. For the regional scale coupled UOMs, geographic coverage should be sufficiently large to reflect impacts of the atmosphere-ocean coupling. The resolution of the coupled UOMs can be a few kilometres (mesoscale resolving) for global scale and hundreds to thousands metres for regional scale to resolve sub-mesoscale eddies and narrow straits connecting sea basins. Therefore for regional scale coupled UOMs, flexible grid and high-performance computing are two basic requirements. For one regional scale there might be more than one coupled UOM.

High resolution is required to provide a seamless prediction in space. For example, narrow straits connecting two large water bodies and archipelago water areas may need a resolution of 100-1000 m; inland waters-estuary-coastal-open sea continuum, essential for pollutant transport modelling, nutrient cycle, and carbon cycle modelling, needs also a similar model resolution. An even higher resolution (10-100 m) may be required when dealing with river inputs to the sea, impact of flooding, hydropower, barriers to pollutant transport, coastal inundation, compound flooding-surge events, and port management. Hence, a spatial seamless UOM should have flexible grids, either unstructured grid or dynamic two-way nested grid.

12.7.4 Evolution in short-, mid- and long-term perspectives

In short- to mid-term (3-5 years) perspectives, the objective would be to develop a UOM framework and continuous improvement of prediction skills of the marine earth system models with a forecast range of 10 days to 1 month. The research should focus on: (i) establishing UOM global cooperation framework to harmonise, coordinate, and further evolve existing UOM development work; (ii) designing the UOM concept, framework, and multiple configurations for different scales, considering international cooperation and sharing of best practices, optimal use of workforce, critical mass for UOM development, code portability, relocatability, scalability, flexibility, resolvability, and reducing the redundancy of models; (iii) improving model process description, so that each UOM sub-model can effectively model major features in the subsystem; (iv) investigate possibility for establishing forecasting capacity in emerging modelling areas, such as SPM, marine litter, underwater noise, and fisheries, and also develop prototype pre-operational models in these areas; v) improving high-performance computing through code modernization; (vi) improving the UOM subsystem coupling; and (vii) develop high-resolution models with flexible grids and interfaces with basin and global scale models, as well as resolving coastal processes for downstream applications.

In the long-term (10 years), the objective is to improve prediction skills in time scales from months to seasons for climate, physical, and biogeochemical systems, establish and improve forecasting capacity in emerging areas such as SPM, marine litter, underwater noise, and fisheries. For the ESP in seasonal and longer scales, coupled UOMs including atmosphere-ocean-wave-ice coupling and ocean-optics-SPM-biogeochemical coupling will be developed for ensemble prediction. UOM code will also be optimised for efficient hybrid parallel computing.

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Chapter 12

Challenges and future perspectives in ocean prediction

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