## 10.1. Introduction to coupled prediction

In the early days of numerical modelling of the various components of the Earth system, each component was treated individually. Figure 10.1 shows a representation of two systems, ocean and atmosphere, that run independently: the output of one system is used to “force” the other. The interface between the ocean and the atmosphere was considered a phenomenon that had to be modelled independently of the two media.

<figure><img src="image_543.png" /><figcaption>**Figure 10.1.** Traditional modelling platform characterised by Systems (S), like ocean model and atmosphere model, and inputs to each System (V).</figcaption></figure>

This representation of the Earth system interactions is in some sense arbitrary. As the complexity of models grew, attempts were made to integrate the components more tightly, particularly in the field of climate modelling. Weather forecasting has a time scale of days to a couple of weeks (Lorenz, 1967) and, as new forecasts would be initialised regularly (typically every day), excessive diffusivity was never considered a problem. Making the early numerical weather prediction models *conservative* was therefore not a priority. The problem of conserving quantities such as heat, moisture, or momentum to avoid *model drift*, began to manifest itself only with the advent of long integrations of climate models. It became clear that long climate integrations of the atmosphere needed to also consider the impact of a (slowly) changing ocean, not least because the various climate components interact in nonlinear ways. This produces feedback loops that can fundamentally alter the state of each climate component. Numerical weather prediction models also need-

ed to close the energy budget at the top of the atmosphere (or in the case of climate change, get that imbalance right). This led to the first attempts at coupling ocean and atmosphere models. The ice floating on the ocean and the soil in the ground were also separate from the ocean and the atmosphere. The latter was the first to be incorporated into more complex models, leading to the first coupled models.

Figure 10.2 shows a conceptual representation of systems that can interact through a “mechanism” called coupler. Figure 10.3 shows a more detailed and realistic representation of this coupling process.

Theoretical challenges to producing skilful weather forecasts were noted early in the history of NWP. For example, Lorenz (1963) pointed to the phenomenon of sensitive dependence on initial conditions. This means that small changes in our current best guess of the atmosphere or ocean could lead to very large changes in the forecasts. As a consequence, skillful weather prediction is limited to a finite time horizon of around 1-2 weeks. However, this perspective tends to focus on synoptic scale atmospheric dynamics. When a numerical model of the atmosphere is coupled to numerical models of the ocean and other Earth system components, new timescales are introduced into the system. In such multiscale systems,

<figure><img src="image_544.png" /><figcaption>**Figure 10.2.** Coupling modelling platform where Systems (S) communicate with each other through an interface code called “coupler”.</figcaption></figure>

<figure><img src="image_545.png" /></figure>
<figure><img src="image_546.png" /><figcaption>**Figure 10.3.** A schematic of the components (ocean, waves, etc.), the models (NEMO, WWIII, etc.), and the coupling exchanges between them, based on the system described in Lewis et al. (2019). Note the use of the coupler OASIS, the use of input forcing between Jules and the river flow model, direct coupling between Jules and the UM and direct forcing between the NEMO and ERSEM systems. A relatively simple coupled system (no ice) that includes 6 different models and 4 different approaches to coupling between them.</figcaption></figure>

fast growing errors tend to be associated with processes that evolve quickly but saturate at smaller scales (Harlim et al., 2005), while slower growing or decaying errors tend to be associated with larger scale oscillations (Penland and Sardeshmukh, 1995; Penland and Matrosova, 1998; Vannitsem and Duan, 2020).

DA is the process of integrating information from numerical models with observations derived from real world measurements. At operational centres, DA systems have typically been built for each Earth system component independently. Early efforts to produce coupled forecasts maintained this separation of components when applying DA to provide initial conditions (Saha et al., 2006, 2010, and 2014; Zhang et al., 2007), an approach that is now called WCDA. More recently, there have been efforts to treat the entire coupled Earth system as one state and update accordingly. This more integrated approach allows observations to have immediate influence across domain boundaries (e.g. the air-sea interface), and as such is called SCDA. There are also approaches that fall on the spectrum between these extremes, such as the CERA system at the ECMWF that applies different DA systems to the

atmosphere and ocean but still allows influence across the air-sea interface via an iterative cycling over a moving 6-12 hour time window (Laloyaux et al., 2018).

Beyond these theoretical considerations, there are many technical complications involved in transitioning to coupled prediction. Many centres have developed monitoring and prediction tools independently for individual Earth components (e.g. atmosphere, ocean, land, waves, etc.). This is natural based on the historical context of their development and limitations on computing capabilities, but it has created an infrastructure within and across institutions that adds complexity to the task of unifying prediction systems. The major prediction centres are making progress towards an integrated approach by unifying software infrastructure for models and data assimilation capabilities, as well as providing opportunities to increase interactions among the development teams of each system component. Data formats for model output and observational data sets have not been fully standardised across the various Earth system domains, and so this adds further steps before seamless integration.

<figure><img src="image_547.png" /></figure>
A very important practical limitation that has most certainly curtailed research and development in coupled prediction is the extreme demands it places on computational resources. The best performing applications for atmospheric prediction and ocean prediction have already been pushed to their limits of resource consumption. Acknowledging the fact that coupled systems can perform very differently at low resolutions versus high resolutions, there remain very few organisations with the resources needed to explore unanswered questions in coupled prediction at relevant resolutions for operational prediction. For this reason, there are efforts underway to identify methods to reduce the computational demands at bottlenecks within the cycled data assimilation and forecast systems.

## 10.2. Coupling processes

### 10.2.1. Waves and their role in air-sea exchange

Waves have been called the gearbox of the climate system (Semedo et al., 2011). The analogy highlights the mediating role of the wave field between the atmosphere and the ocean interior. It may seem surprising that the sea surface demands its own class of numerical model. The other components (atmosphere, ocean, sea ice, land surface) have real substance, i.e. they each represent a three-dimensional chunk of the Earth system. In contrast, the wave model is a representation of a *surface* between two media, namely the air and the sea (Figure 10.4). There are, however, good practical reasons for this split. If we had access to unlimited computing power, we could model the ocean and the atmosphere with a grid resolution approaching Kolmogorov's microscale. That would mean that the Navier Stokes equations could be solved in the approximative limit known as DNS (Moin and Mahesh,

1998). In this case, the (liquid) ocean would presumably interact with the (gaseous) atmosphere and on their interface would form a wavy surface that, given a sufficiently strong momentum flux (mostly from the atmosphere to the ocean), would form droplets and bubbles as the waves start to break. The computational reality is far from this. At present, we can model the ocean and the atmosphere with models that have grid cells of tens of metres in the horizontal if we limit ourselves to small domains, whereas the waves that form under the influence of the wind have wavelengths of the order of some metres to hundreds of metres and so cannot be explicitly resolved together with the bulk ocean properties.

The behaviour of these waves determines the mass and momentum fluxes between the ocean and the atmosphere. As waves grow under the influence of the wind, they become steeper. In this phase they are also choppier than they will

<figure><img src="image_548.png" /><figcaption>**Figure 10.4.** Representation of AWO coupled models.</figcaption></figure>

<figure><img src="image_549.png" /></figure>
be later on. All this means that the momentum flux between the atmosphere and the ocean is affected by the presence of waves (Janssen et al., 2004; Breivik et al., 2015). There is also very important feedback between the waves and the atmosphere. As waves grow, the sea surface becomes rougher, slowing the near-surface winds and increasing the momentum flux from the atmosphere to the wave field. This has the effect of stemming the deepening of low-pressure systems. This is important in the formation and growth of extratropical lows (Janssen, 1991 and 2004), but also in the evolution of tropical cyclones (discussed further below).

A secondary effect of waves on the air-sea interaction is through their ability to impart momentum and turbulent kinetic energy to the ocean interior (Figure 10.4). As waves grow, they absorb momentum that would otherwise go directly to the formation of ocean currents. As waves break, they part with this momentum, and also inject turbulent kinetic energy into the ocean (Janssen et al., 2004; Rascle et al., 2006; Ardhuin et al., 2008 and 2009). This leads to a redistribution of momentum and kinetic energy in time and space (Ardhuin and Jenkins, 2006; Breivik et al., 2015; Staneva et al., 2017; Wu et al., 2019), and has a profound effect on near-shore processes (Uchiyama et al., 2010; Kumar et al., 2012) where waves interact strongly with the currents. It is also clear that in open ocean conditions the mixed-layer depth is a function of the wave activity, in part sustained by the Langmuir turbulence (McWilliams et al., 1997; Fan and Griffies, 2014; Li et al., 2016 and 2017; Li and Fox-Kemper, 2017; Ali et al., 2019). The enhanced mixing due to waves is thus important for the sea surface temperature, which helps to determine the air-sea heat flux and thus constitutes an important feedback mechanism between the atmosphere and the ocean.

### 10.2.2. Land/sea exchanges

Land-sea interactions take place on a wide range of spatial and temporal scales. The presence of land modifies the weather in the coastal zone, e.g. the daily variations in wind speed and direction due to the sea breeze, and hence the atmosphere provides an indirect link between the land and the ocean. Another example of this indirect coupling is the way large-scale weather systems can influence the transport pathways of river water (Osadchiev et al., 2020).

The physical couplings between land, ocean, and atmosphere are not necessarily equal in strength and importance, and we often observed a lagged response. The runoff from rivers is dependent on the precipitation over a potentially very large catchment area, with significant lag between specific precipitation events and the freshwater discharge to the coastal ocean. This lag is particularly pronounced in temperate and polar regions where the precipitation accumulates as snow during parts of the year. This is reflected by the

state-of-the-art of coupled modelling, as very few systems couple the ocean to the land, but rather use the atmosphere as a mediator.

### 10.2.3. Air-sea exchanges across sea ice

At high latitudes, air-sea exchange is modified by the presence of sea ice. Varying in thickness up to a couple of metres, sea ice is sensitive to forcing from both air and sea and the air, sea, and sea ice are strongly coupled. Geophysical scale sea ice is essentially a mixture of ice floes of varying size and thickness, with the added complexity of being rafted and ridged. Describing accurately the sea ice mechanical behaviour is extremely challenging, although modelling sea ice as plastic materials at the large scale has long been a successful approach (Coon et al., 1974; Hibler, 1979; Hunke and Dukwicz, 1997; Girard et al., 2011). In medium to high model resolutions (≤ 10km), such models can generate small-scale features such as the ice leads (Hutchings et al., 2005; Wang and Wang, 2009; Girard et al., 2011; Sreen et al., 2017). This thin ice cover has a very small heat content and easily melts away during summer, resulting in large seasonal variations of sea ice extent.

In much of the pack ice region, the thermodynamic and dynamic interactions between air and sea are greatly suppressed. During wintertime, the air-sea heat flux through leads is two orders of magnitude larger than that through thick ice (Maykut, 1978). Dynamically, pack ice behaves as a low-pass filter, the air and sea surface stresses act on the ice cover thus driving the advection and deformation of sea ice, while ocean waves are generally suppressed. The MIZ is a highly complex region consisting of ice floes of varying dimensions and shapes. Wave energy propagating into the MIZ can lead to rapid breakup. The damping of waves in sea ice is directly related to the amount of energy imparted on the sea ice. This is a field of active research, and it is presently not fully clear how the MIZ attenuates wave energy (Doble and Bidlot, 2013; Williams et al., 2013; Kohout et al., 2014; Sutherland and Rabault, 2016; Ardhuin et al., 2016; Rabault et al., 2020).

Landfast ice is a special region where the air-sea interaction nearly ceases. It generally appears in winter seasons and often occurs in shallow waters where ridged ice grounds on the seabed (Mahoney et al., 2014), or occurs where islands are close to each other (Divine et al., 2003). Modelling studies have shown that adding base stress due to grounding ridges and increasing ice tensile strength improve the simulation of landfast ice evolution (Lemieux et al., 2016), although in some Arctic shelf seas the time duration needs to be further improved.

In coupled modelling, a key consideration is whether to couple the sea ice directly to the atmosphere or only through the ocean model. In some recent coupled models, particularly

<figure><img src="image_550.png" /></figure>
for high-resolution short-term atmosphere, ocean, and sea ice forecasts, the timestep for coupling has decreased to one hour or less, e.g. the coupled ocean-ice model METROMS at the Norwegian Meteorological Institute (Naughten et al., 2018), or the atmosphere-ice coupled model at UKMO (Ridley et al., 2018). In these cases, the difference between using the atmosphere timestep or ocean timestep is generally negligible.

### 10.2.4. The importance of air-sea exchanges during storms and other extreme events

Air-sea exchange really comes to the fore in the development of tropical cyclones. The sea surface temperature must as a general rule exceed 26.5°C to sustain the growth of the cyclone (Emanuel, 1986). However, the depth to which the ocean's temperature must be above this critical threshold is also important. As the cyclone moves across the sea surface, the Ekman transport will lead to divergence, and vertical Ekman pumping will eventually lead cooler water to the surface. If the cyclone is moving sufficiently slowly, this will eventually kill the cyclone (Mogensen et al, 2017). Thus, it is essential to include an ocean model component that responds to the atmospheric forcing.

Polar lows are of a decidedly less extreme nature than tropical cyclones, but they share the same dependence on sea surface temperature (Rasmussen and Turner, 2003). As winds blow off the sea ice, the air is rapidly warmed by the (relatively) warm ocean surface. Under the appropriate atmospheric conditions (Kolstad, 2015), this can lead to the formation of polar lows. These are small-scale, intense cyclones, typically with gale-force winds. If the cyclone is rather stationary, a shallow layer of warmer water can mix with cooler waters through Ekman pumping. As the ocean temperature is key to sustaining a cyclone, the water mixing can sometimes be enough to inhibit further growth of the polar low.

Examples of instantaneous coupling between land, ocean, and atmosphere also include coastal inundation during landfall of tropical cyclones (Lee et al., 2019). In these cases, heavy precipitation leads to a swelling of local rivers, which is often coincidental with a large storm surge. The result is a rapid sea-level rise that may cause extensive damage to coastal infrastructure, especially when combined with large surface waves and strong winds.

<figure><img src="image_551.png" /></figure>

## 10.3. Benefits expected from coupling

The importance of coupled ocean-atmosphere prediction systems in providing seasonal predictability is well-known (Kim et al., 2012, and references therein). Sources of predictability in seasonal forecasting systems tend to be, by their very nature, coupled systems driven by teleconnections that are functions of climate modes, such as the North Atlantic Oscillation and the El Niño-Southern Oscillation that have geographically far-reaching consequences. However, as timescales shorten and the dominance of these coupled climate modes become less fundamental to predictability of the atmosphere-ocean system, it becomes less obvious whether the benefits of fully coupled systems justify the computational cost or the technical and scientific complexity required. The coupling between atmospheric and wind wave models was first introduced operationally in 1998 at ECMWF. The method based on the theoretical work of Janssen (1991) contributed to an improvement of both atmospheric and surface wave forecasts at the medium range on the global scale. The usual approach of forcing the ocean with atmospheric conditions (Takano et al., 1973), and referred to in this section as “forced”) using bulk parameterisations of

the fluxes (Large and Yeager, 2009) is computationally and structurally far easier and cheaper than coupling approaches. However, the key boundary layer processes (see Section 9.1 for details) are not taken into account and thus the feedback between the atmospheric boundary layer and the upper ocean is not represented. It is necessary to understand how important these processes might be, bearing in mind that coupled models can suffer from systematic errors as a result of positive feedback leading to drifts in the forecast (Hyder et al., 2018).

Ocean forecasting systems have become increasingly high-resolution, resolving coastlines, bathymetry, and eddy-scale processes. The effect of coupling on model predictions becomes more important with increasing grid resolution (Janssen et al., 2004), and so the question of the benefits of coupling to ocean forecasting is perhaps more relevant now than ever. A small but growing body of literature demonstrates the benefits to ocean prediction of coupling at shorter time-ranges (Brassington et al, 2015; Allard et al., 2010; Lewis et al., 2018 and 2019).

<figure><img src="image_552.png" /></figure>
Understanding the advantages of coupled over uncoupled predictions in short-range ocean forecasting is in its infancy. Although the future of advanced systems is clearly coupling, as several processes are better represented, predictive modelling without coupling is however possible thanks to parameterizations and should never be discarded as an option. At a recent science meeting of OceanPredict (Vinayachandran et al., 2020), the need for a careful evaluation of how ocean and atmosphere components interact and impact each other was highlighted. At monthly or shorter timescales, the benefits of running coupled systems need to be evaluated, balancing scientific and service benefits against complexity and computing costs. Intermediate complexity coupling may also be an appropriate approach if full coupling is not viable and the service is not reliant on the atmosphere and ocean information. Lemarié et al. (2021) provided an example of an atmospheric boundary layer approach that gives some of the benefits of coupling whilst being significantly simpler and computationally cheaper.

The potential benefits of using a coupled framework is reinforced by the move towards a multi-hazard approach to predictions. Natural hazards from multiple sources may combine or occur concurrently (Lewis et al., 2015). Large waves, storm surges, high-wind speeds, and extreme precipitation are all hazards that are likely to co-occur, and influence each other through coupled feedbacks that can compound one another (for example through over-topping). Coupled systems that predict these coupled feedbacks may enable an improvement in the range and consistency of actionable information to be provided through hazard warnings and guidance.

When considering providing services in multi-hazards frameworks, the opportunities that coupling provides should be considered alongside the scientific benefits. A coupled system combining the full water-cycle – including consistent precipitation, river runoff, wave, currents, and surge forecasts – can give users mutually consistent products in a joint probability framework. This can be important in coastal flooding, where the impacts for coastal communities or industries can come from high river flows and local heavy precipitation events, alongside overtopping waves and extreme surges. From a service perspective, it is attractive to provide probabilistic frameworks in which the timings and intensities of events are consistently incorporated and interact appropriately; these services increasingly rely on probabilistic information for decision making. An area that has had limited attention but seems likely to prove significant is the impact of feedback among Earth-system components upon ensemble spread, and hence the quality of the probabilistic information.

Ocean phenomena are usefully classified depending on their nature, which determines the timescale for oceanic predictive skill and whether a coupled ocean-atmosphere model would be advantageous. Some phenomena have strong dependence,

and a rapid response, to the atmosphere forcing and can be thought of as forced-dissipative systems. This category includes, surface waves, responses to surface heating and wind in the ocean boundary layer, and storm surges. These systems largely depend upon skill in the atmosphere model, and so the benefits of coupling to the atmosphere can be a leading-order driver of the ocean system skill. The advantage of coupling and its impact upon predictability often focus on the benefits to the atmosphere (Brunet et al., 2010; Belcher et al., 2015). The impact of ocean coupling on tropical meteorology is well documented with tropical cyclones (Bender and Giris 2000; Mogensen et al., 2017; Smith et al., 2018; Neetu et al., 2019), monsoons (Fu, 2007), and the Madden-Julian Oscillation (Bernie et al., 2008; Shelly et al., 2014; Seo et al., 2014), which predictability improved in coupled systems. There is also an increasing body of evidence that the oceans have a significant local (important for short-range forecasts) and non-local (increasingly significant at longer lead-times) influence on the extra tropics (Minobe et al., 2008).

In the literature, there is limited quantification of the impact of the coupled improvement in atmospheric parameters on ocean services but it is an increasing area of study. Guiavarc'h et al. (2019) explored the impact of a coupled (atmosphere-ocean) system on short-range ocean forecast skill and showed that there are benefits in SST predictability at the short-range, but with mixed results for other parameters. Given that the research system they used is at a relatively early stage in development, and the resolution of the atmosphere is significantly lower than in comparable forced systems, these results are encouraging.

Although the importance of coupling the wave-ocean interface for improving forecasts of surge and waves is well documented (Wolf, 2008; Lewis et al., 2018), most storm surge and wave prediction systems remain largely independent. As well as the atmospheric forcing, ocean currents have a significant role in modifying ocean wave properties. The presence of eddies, fronts, and filaments with length scales of tens to hundreds km and ubiquitous in the world's oceans, can be the main source of variability in significant wave heights at these scales. Ardhuin et al. (2017) made a compelling case for the importance of coupling the ocean surface currents to a wave model allowing adequate representation of wave height variability in the world's open oceans. Wave predictions in shelf seas environments are shown to be improved as a result of coupling to an ocean model (Allard et al., 2012; Wahle et al., 2017; Lewis et al., 2018), as well as the predictions of ocean current and other ocean parameters, including upwelling due to stokes drift effects, were enhanced (Wu et al., 2019). Fan et al. (2009) showed that time and spatial variations in the surface wave field, as a result of coupling to winds, are particularly strong in hurricanes, with significant additional feedback from ocean currents and near-surface temperatures.

<figure><img src="image_553.png" /></figure>
The ocean eddy kinetic energy is damped when taking into account the feedbacks between ocean surface current and winds (Oerder et al., 2018; Jullien et al., 2020). As ocean models increasingly resolve the mesoscale explicitly, they are likely to have the tendency to over-predict the eddy activity. In uncoupled systems, there is an option to calculate the wind stress using relative wind speeds (taking into account the eddies and other ocean current interactions). However, in these systems there is no imprint of ocean eddies on the atmospheric wind stress curl (due to the lack of ocean eddies in the uncoupled atmospheric modelling system), and so the feedback onto the wind stress results in over-damping of the eddies. A fully coupled system will correctly allocate the feedback between the winds and currents, allowing the eddy and wind fields to co-evolve correctly. This coupling between the winds and currents can also lead to upscaling to the large scale, e.g. Renault et al. (2016) showed that current/wind feedback, through its eddy killing effect, resolves long-lasting biases in Gulf Stream path.

Marine heatwaves have recently been recognised for their importance (Holbrook et al., 2019). They are high impact events that can be induced by anomalous heating at the ocean surface; their predictability is dependent upon air-sea coupled phenomena (Jacox, 2019). At the other end of the temperature scale, Pellerin et al. (2004) showed that

coupling can also have strong impacts in ice-infested seas even down to sub-daily time scales, due to rapid changes in coastal sea ice cover (i.e. the formation of coastal polynyas). The sea ice acts as a barrier between a relatively warm-wet ocean and cold-dry atmosphere, and changes in the sea ice cover can have dramatic effects on heat and moisture fluxes. The importance of coupling has also been recognized in polar regions (Jung and Vitart, 2006).

Coastal regions are particularly impacted by coupled processes, both between the ocean and atmosphere and coupling with river and estuaries. The impact of freshwater discharges on the ocean circulation is highlighted by Røed and Albretsen (2007) and, more broadly on the coastal marine environment, by Dzwonkowski et al., 2017. The inputs from the land surface, mediated through estuaries and lagoons, are generally poorly represented in ocean forecasting systems due to their scale (time and space) and their complexity. It is extremely difficult to accurately model nutrient inputs, which are mediated strongly by land use and societal factors, and the associated plankton response is therefore compounded. Although this problem is not fundamentally a coupling problem, there is still scope for improving the inputs to the coastal environment through specifying better the river-estuary-ocean interface.

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## 10.4. Ocean Information Services based on Coupled Frameworks

Over the past decades, operational oceanography underwent a rapid transition and gradually became part of core systems of operational centres previously largely focusing on weather. Sufficient observations are now available to improve the estimation of the ocean state, including mesoscale variability, ice cover, or wave spectra for wave systems. The development of weakly coupled data assimilation techniques, the exploration of strongly coupled data assimilation using cross-domain error covariance (Sluka et al., 2016), the ability to assimilate an ever-growing source of observations, the improvements in physics and dynamics of the various components of the Earth system, and rapidly increasing computing capacities, keep pushing forward the quality of forecasts and reanalyses that can be produced. As a result, information available for products and services is continuously expanding and including a rapid increase in the quality and quantity of ocean and marine services. It is now well established that marine services are essential to any nation with coastal assets.

In the late 90s and early 2000s, operational marine services were limited to a few marine weather variables such as waves, tides, and surges. With coupled systems now in place in many operational centres and the continuous push for increased resolution to better reflect local conditions, a wide variety of new services has and continues to emerge. It is now common for service providers to be overwhelmed with information drawn from many prediction systems, and for users to be submerged with products. In the next subsections are discussed the few steps that should be followed to sort through the very large number of products that can be generated numerically, so that services are centred on needs in a fit for purpose and accessible approach. A few simple examples are used to demonstrate ways of tying together all this numerical knowledge and provide forecasts and services that are informative and tailored to various groups of users.

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### 10.4.1. Establishing service needs

The first step when evaluating services' needs, including whether to use or not a coupled or forced system, is to clearly define the service gap and how current capacity can be leveraged to address it. The second step is to identify enough resources required to bring the project to completion. Numerous capacities are required to sustain timely and accurate services: i) reliable and sufficient computing resources including telecommunications, bandwidth, and storage, along with staff to operate and maintain the IT infrastructure; ii) physical scientist to install; optimise; run; validate, and verify numerical systems; iii) physical scientists to produce forecasts; iv) forecasters able to disseminate and explain forecasts; v) the ability to sustain such services through extreme conditions (e.g. during a powerful cyclone); and vi) the capacity to overcome throughout the years the changes in IT infrastructure, complexification of systems, increasing volumes of data, etc. However, it should never be forgotten that, whatever is the capacity and the complexity of a state-of-the-art forecast, it only has value if it reaches the users in the due time.

For those countries that choose to operate regional systems driven with data provided by major operational centres, the capacity to download the required data quickly enough to run regional systems and issue timely regional forecasts is also key. It should be also ensured that sufficient local expertise is available to monitor, and fix any issue with the regional system.

When launching new or improved forecast services, another important step is to identify user groups (e.g. marine engineers, marine transportation industries, search and rescue operations, fisheries and aquaculture, coastal communities) and understand their needs. It should be also kept in mind that within each group there can be considerable modulation of needs and that needs can evolve with time and hence they should be reviewed periodically. See section 4.8 for more details on user requirements.

### 10.4.2. Identifying the required information

Search and rescue and coastal flooding cases are used to illustrate how to select the modelling tools that are required to best address the problem. They are also used to demonstrate how a fit for purpose approach may identify the numerical systems best suited to deliver services.

A search and rescue incident that requires drift predictions is an example of a service to illustrate the choices needed. Forecasts of the trajectory of the drifting object requires knowledge of tides, eddies, inertial oscillations, winds, and waves. Such incidents often occur during high winds and large waves conditions and, as discussed in 9.1, it is under

such conditions that interactions between tides, waves, ocean, and atmosphere are most important. This suggests that coupled predictions could add value (Davidson et al., 2009) to the use of independent ocean, wave, and atmosphere forecasting systems. As already discussed, ensembles are essential to sampling uncertainty in various components of a system. In their comprehensive review of the Deepwater Horizon oil spill event, Barker et al. (2020) made a case for the importance of coupled atmosphere-wave-ocean systems for effective oil spill response. All these considerations point to the use of ensemble coupled ocean-wave-atmosphere systems that are post-processed though tracking systems capable of considering the characteristics of various objects, such as a person in the water or a vessel at drift. However, the simulation overhead (in time and computer resources) of the coupled system needs to be balanced with the need to quickly run ensemble simulations to provide probabilities of the search zone to help optimise search patterns. A case similar to that of search and rescue is the response to oil spill or tracking of nuclear debris, which also requires models to predict particulate dispersion but also need to consider other chemically induced processes, such as fate and behaviour.

Coastal flooding is the other example used here to illustrate how to select the best modelling tools. Local communities typically have precise questions such as: "How much water will there be and for how long?" "Will the water reach my street and my house?" "Will it damage my property?" "Will it erode my land or the cliff my house is perched on?" Local authorities and disaster management agencies might have further considerations such as: "What are the most likely and the worst-case scenarios?" "When should we consider evacuations and through what route?" "What critical infrastructures might be at risk?" However, the nature of the service will depend on local conditions. Consider for example a community living at high latitudes. In the event of a polar low (discussed in 9.1), ice can recede rapidly to expose long stretches of ocean leaving the coastline exposed to large swells. In these areas, wave-ice interactions can lead to rapid changes and coupled ice-ocean-wave-atmosphere systems should be preferred to provide accurate forecasts of the low's evolution, rapidly changing marine conditions, and to warn the coastal communities. On the other hand, locations exposed to tropical cyclones will need a system more focused on predicting ocean-atmosphere interactions in support of track and intensity prediction. However, the concept of a forecast based on total water level at the coast remains, although the fit for purpose numerical guidance to be used might have some differences. It is then particularly important to consider user orientated questions. User groups rarely care about technical issues, such as if the models are coupled or if the surge component is barotropic or baroclinic. They care that scientists put forward the combination that best addresses their concerns. They want to receive a fit for purpose service. Simulations of tide, surge, wave, erosion,

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hydrodynamic, and atmospheric may all be required, but to decide whether they should be coupled or not it is necessary to understand if this improves the specific predictions identified by the user questions outlined above.

Advanced knowledge of the risk of an upcoming event is useful to put in place mitigation measures. An outlook for several days to several weeks is of particular interest, as well as the early identification of upcoming risk for which ensemble systems are relevant. At early stages, the focus should be on identifying risk and uncertainties, and communicating them in a clear manner. As the high impact event nears (e.g. next couple days), ensembles can be replaced with resolution increases, so that the risk forecast is changed into an impact-based forecast (i.e. damage to housing, risk of cars being swept away, risk of cutting off of an evacuation route, etc.). This should make the scientists understand that for the users the waves, surges, tides, and other phenomena are relevant only as much as they affect flooding in their areas of interest. This further highlights the importance of metrics used to evaluate models and forecasts. When it comes

to flooding, having a slightly better RMSD and thus a better representation of the mean state is useless if the total water level peaks are missed. Thus, relying on an overly complicated ocean system that results in little to no added skill in total water level forecasts is useless. Similarly, if a complicated system cannot be operated with sufficient resolution over long enough periods, or with enough ensemble members to sample uncertainties, it is not fit for purpose. In addition, coupling should be considered also in the context of the resources (always limited) of operational centres.

Finally, whether numerical systems are run locally or remotely and whether all systems required to produce such forecasts are coupled or not, the path forward should be one in which the forecasters are experts at providing added value taking into account the perspective of the public (e.g. placing in the context a particular expected extreme, comparing it to previous ones, explaining the subtle differences to be expected with the forecast risk, etc.). As such, the forecasters are the ultimate downscalers bringing added value based on local knowledge and history.

## 10.5. References

* Ali, A., Christensen, K., Breivik, O., Malila, M., Raj, R., Bertino, L., Chassignet, E., Bakhoday-Paskyabi M. (2019). A comparison of Langmuir turbulence parameterizations and key wave effects in a numerical model of the North Atlantic and Arctic Oceans. *Ocean Modelling*, 137, pp 76-97, https://doi.org/10.1016/j.ocemod.2019.02.005

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Takano, K., Mintz, Y., Han, Y.-J. (1973). Numerical simulation of the world ocean circulation. Paper presented at the 2nd Conference on Numerical Prediction. Am. Meteorol. Soc. (1973), Monterey, CA (unpublished).

Uchiyama, Y., McWilliams, J. C., Shchepetkin, A. F. (2010). Wave-current interaction in an oceanic circulation model with a vortex-force formalism: Application to the surf zone. *Ocean Modelling*, 34(1), 16-35, https://doi.org/10.1016/j.ocemod.2010.04.002

Vannitsem, S., and Duan, W. (2020). On the use of near-neutral Backward Lyapunov Vectors to get reliable ensemble forecasts in coupled ocean-atmosphere systems. *Climate Dynamics*, 55, 1125-1139, https://doi.org/10.1007/s00382-020-05313-3

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<figure><img src="image_563.png" /></figure>
11.
# Downstream applications:
## From data to products

<figure><img src="image_564.png" /></figure>

CHAPTER COORDINATOR

Giovani Coppini

CHAPTER AUTHORS (in alphabetical order)

Enrique Alvarez Fanjul, Laurence Crosnier, Tomasz Dabrowski, Pierre Daniel, José Chambel Leitão,

Svitlana Liubartseva, Gianandrea Mannarini, and Glen Nolan

<figure><img src="image_565.png" /></figure>
# 11. Downstream applications: From data to products

<figure><img src="image_566.png" /></figure>

## 11.1. The need of downstream products

11.1.1. Blue Economy

11.1.2. Applications and services

## 11.2. Examples of advanced downstream systems

11.2.1. Sea Situational Awareness

(web pages and other dissemination mechanisms)

11.2.1.1. The Copernicus Marine Service MyOcean Viewer

11.2.1.2. SeaConditions

11.2.1.3. Marine-Analyst

11.2.1.4. NOAA Sea Level Rise viewer

11.2.2. Oil spill forecasting

11.2.2.1. MOTHY

11.2.2.2. WITOIL

11.2.3. Ports

11.2.3.1. SAMOA service for Spanish ports

11.2.3.2. AQUASAFE

11.2.4. Voyage planning

11.2.4.1. VISIR

11.2.5. Fisheries and aquaculture

11.2.5.1. Bluefin tuna

11.2.5.2. Harmful algal bloom warning system

11.2.5.3. Weather window tool

11.2.5.4. Aquaculture site selection

## 11.3. References

<figure><img src="image_567.png" /></figure>
# 11.1.
The need of downstream products

## 11.1.1. Blue Economy

The importance of the ocean for global society and economy is represented in the context and framework of the Blue Economy and is also very relevant for the Sustainable Development Goal 14 (SDG14): Life Below Water 1. The ocean is the single largest natural asset on the planet, contains 97% of all the water on Earth and about 99% of the habitable space on this planet, and delivers numerous benefits to humanity. The ocean is responsible for the oxygen in every other breath we take. It supplies 15% of humanity's protein needs. It helps to slow climate change by absorbing 30% of carbon dioxide emissions and 90% of the excess heat trapped by greenhouse gases. It serves as the highway for some 90% of internationally traded goods, via the shipping sector. If the ocean were a country, with several trillion dollars per year of economic activity it would rank 7th on the list of largest nations by GDP. The ocean is also the source of hundreds of millions of jobs, in sectors such as fisheries, aquaculture, shipping, tourism, energy production, etc. It is also the source of about 30% of the world's oil and gas resources but this percentage will change if the necessary transition to a low carbon development pathway will succeed. Millions of the world's poorest people depend heavily on the ocean and coastal resources for their subsistence and livelihoods. Small-scale fisheries catch about half of the world's seafood but engage 44 times as many jobs per ton of fish as industrial fisheries do.

According to the Water and Ocean Governance Programme of UNDP 2, the Blue Economy is a sustainable ocean economic paradigm and is the natural next step in the overall conceptualization and realisation of sustainable human development. On the other hand, there is the important aspect related to the impact that the Blue Economy might have on the ocean. The European Commission stated 3 that all blue economy sectors, including fisheries, aquaculture, coastal tourism, maritime transport, port activities and shipbuilding, will have to reduce their environmental and climate impact. Tackling the climate and biodiversity crises requires healthy seas and a sustainable use of their

resources, as well as creating alternatives to fossil fuels and traditional food production.

Transitioning to a sustainable Blue Economy requires investing in innovative technologies. Wave and tidal energy, algae production, fisheries management, restoration of marine ecosystems, etc., will create new green jobs and businesses in the Blue Economy.

Downstream services provided by the operational oceanography community should be able to facilitate and support this transition towards a more sustainable Blue Economy worldwide.

## 11.1.2. Applications and services

Operational oceanography is available nowadays to many users through solutions (services and products) dealing with several SDGs, and societal and scientific challenges. Oceanographic products from global to regional scale are produced by national and international forecasting centres. They are then downscaled to sub-regional scales, transformed, and provided to users, private companies, public users, stakeholders, and citizens through an ocean products value chain that includes development of specific solutions, advanced visualisation, usage of multi-channel technological platforms, specific models, and algorithms. Figure 2.1 in Chapter 2 shows a representation of ocean value chain: forecasting centres manages Marine Core Service, which produces Information (e.g. forecast products) that are delivered to Intermediate Users through ad hoc Interfaces managed by the Central Information System. Then, such information is elaborated by Developers and transformed for Multiple Downstream Services that use customised end user information to deliver new information to End Users.

Important steps forwards have been carried out in order to facilitate the dialogue among service providers and users to identify requirements and needs, and to co-develop and test the applications and solutions. Collaborative frameworks like the Copernicus Users Uptake programme, the international initiative Geo Blue Planet, the GOOS Regional Alliances downstream effort, the IOC and WMO working groups, etc., are in support of the Blue Economy's growth.

The private sector has been playing an important role in the development of operational systems, providing operational services in areas that in the past were covered only by public institutions. Some businesses are impacted every day by oceanographic conditions, and sometimes disrupted by extreme events. Accurate and reliable oceanographic and me-

<figure><img src="image_568.png" /></figure>

<footnote n="1">1. https://sdgs.un.org/goals/goal14</footnote>

<footnote n="2">2. https://www.undp.org/blog/blue-economy-sustainable-ocean-economic-paradigm?utm_source=EN&utm_medium=GSR&utm_content=US_UNDP_PaidSearch_Brand English&utm_campaign=CENTRAL&c_src=CENTRAL&c_src2=GS-R&gclid=Cj0KCQiAweaNBhDEARIsAJ5hwbDEFafpkIcJuzlSSld-Lg4UzxIkqOhAo6MCIAjSwEBvBA_ZHi10o_8IaAn_0EALw_wcB</footnote>

<footnote n="3">3. https://ec.europa.eu/commission/presscorner/detail/en/ip_21_2341</footnote>
teorological predictions may increase business productivity, if appropriate standards of safety are adopted.

The public sector mostly focuses on protecting lives and property. Often this is not enough to protect economic operations at sea, in which monitoring and forecast information is needed on a regular basis and representing local scale processes. Downstream applications play a key role addressing (and adjusting to) end users' needs, while simultaneously justifying global scale modelling and monitoring investments done by the public sector in the past.

Most of the developments in downstream applications have been driven by international grants and individual countries. In some areas, end users' willingness to pay for such applications is very low. There is a need to improve the applications but also to promote and disseminate information on existing applications. The main reason is that a reasonable cost/benefit relationship for businesses must be achieved to have a market driven demand for downstream operational services.

It is therefore of paramount importance to show how research and technological development in the different application fields (e.g. advanced visualisation, usage of multi-channels technological platforms, and development of specific models and algorithms) have advanced in recent years, and that more accurate and user friendly applications are available for users.

International standards have been developed and should be further consolidated to support interoperability and a common formatting of ocean application products and services, as well as quality assessment. Downstream applications are an essential component contributing to ocean literacy through the dissemination to the public of operational oceanography knowledge. Economic sustainability and

market exploitation of downstream application is a key aspect to fill the gap towards a fully sustainable development of operational oceanography added value services and products.

It is also of fundamental importance to improve the dialogue among producers and users to identify requirements, as well as co-developing and testing the applications. Engaging end users in the analysis and evaluation process is critical in order to tailor downstream products in the best way.

An effort for gathering and promoting applications by worldwide producers should include different sectors, such as: a) Fishery and aquaculture; b) Tourism and sports activities; c) ICZM and MSP; d) Transport and harbour services; e) Marine digital services; f) Technologies for maritime safety; g) Climate change and anthropogenic impacts on marine environment; and h) Energy from the sea.

To achieve this goal, producers are asked to manage and promote the adoption of an international standard to support interoperability and a common formatting of ocean application products and services, as well as quality assessment. It is fundamental to promote market development of downstream applications as well as liaise with and gather input from the other international and national initiatives (e.g., EuroGOOS, Geo Blue Planet, IOC, WMO).

With a view to the Ocean Decade implementation, in this chapter will be discussed key challenges, best practices, relevant examples of applications, as well as the present advanced capabilities and future challenges in the following applications fields: 1) Sea Situational Awareness (web pages and other dissemination mechanisms); 2) Oil spill observing and forecasting; 3) Ports; 4) Voyage planning; and 5) Fisheries and aquaculture.

<figure><img src="image_569.png" /></figure>
## 11.2.
Examples of advanced downstream systems

<figure><img src="image_570.png" /><figcaption>**Figure 11.1.** Examples of screenshots from MyOcean Viewer web application, including visualisation of sea-water velocity for the Global Ocean and time series/time-depth plots in a specific location (source: 4).</figcaption></figure>

### 11.2.1. Sea Situational Awareness (web pages and other dissemination mechanisms)

Dissemination services provide users with databases on relevant environmental information and set up their formatting to support marine and maritime activities. These services include the collection and validation of existing information and the production of new geo-localised information to users. Mechanisms for data sharing among databases are offered to intermediate and final users, so that the integration with existing services is facilitated.

The mechanisms of access to databases from private and public users is a matter of concern for the dissemination services as it could limit the accessibility of the services. Users should be able to access information in the applications through the Internet, using either desktop or mobile phone.

These issues are quite critical since information on marine and coastal areas is currently dispersed in heterogeneous, not connected, poorly known and not accessible systems. Consequently, it is important to develop systems that widen

availability, improve information sharing, and facilitate access to the data. Public and private users are supported by mechanisms of open and free access, including massive (e.g. FTP, THREDDS, P2P) and system communication services (e.g. web services, tiling services).

#### 11.2.1.1. The Copernicus Marine Service MyOcean Viewer

The Copernicus Marine Service MyOcean Viewer (5) allows exploration of most of the online catalogue with multi-projection maps, graphs vs. time, and elevation and/or distance. Using information from both satellite and in-situ observations, the CMEMS provides state-of-the-art analyses and forecasts daily, which offer the capability to observe, understand, and predict the marine environment state (Figure 11.1). The Copernicus Marine Service MyOcean Viewer has been developed by Lobelia Earth. The Basemap layer contains information from the GSHHG dataset provided by the University of Hawaii and the US NOAA.

4, 5. https://myocean.marine.copernicus.eu/
<figure><img src="image_571.png" /><figcaption>**Figure 11.2.** SeaConditions web service for the Mediterranean Sea.</figcaption></figure>

### 11.2.1.2. SeaConditions

An example of web service for sea situational awareness is SeaConditions (ersh). It is a service providing weather forecasts, including oceanographic and sea state forecasts (sea surface temperature, surface currents, significant wave height and direction, wave period, sea level), meteorological forecasts (air temperature, mean sea level pressure, precipitation, cloud cover, and 10 metre winds), bathymetry and satellite observations of chlorophyll-a, and water transparency. Forecasts are referred to a time period of 5 days and are based on state-of-the-art meteorological, oceanographic, and wave modelling that allow obtaining high quality data (Figure 11.2). SeaConditions includes data from Copernicus Marine Service (including satellite data) and the ECMWF.

SeaConditions covers the Mediterranean Sea: it has commercial and free versions available on the web and downloadable for mobile (Google Play and Apple Store). Its users are more than 100000.

<figure><img src="image_572.png" /><figcaption>**Figure 11.3.** Example of data accessible from Marine-Analyst.eu application.</figcaption></figure>

### 11.2.1.3. Marine-Analyst

The Marine-Analyst (MA) is a platform for searching, viewing, processing, transferring, advertising, and disseminating marine spatial data from numerous data sources (Figure 11.3).

Launched in October 2020, it is powered by WEkEO (8), the Copernicus DIAS. The Marine-Analyst.eu enables users to access data and key analyses for the location of their choice in Europe. It offers a personal dashboard where each user can collate the reports created for the chosen datasets. Generating on-demand data analyses for any location, the platform is able to address multidisciplinary challenges, for example:

<figure><img src="image_573.png" /></figure>

<footnote n="6">6. https://www.sea-conditions.com</footnote>

<footnote n="7">7. http://marine-analyst.eu/</footnote>

<footnote n="8">8. https://www.wekeo.eu/</footnote>
<figure><img src="image_574.png" /><figcaption>**Figure 11.4.** Example of NOAA Viewer.</figcaption></figure>

* **Maritime spatial planning:** it represents a solution for coordinating decision-making on identified areas and specific activities, by considering a basin as a whole or partitioning it, including its ecological functioning and the activities it supports, e.g. zonation based on essential biophysical ocean parameters (such as chlorophyll Chla and light attenuation Kd) (from Copernicus Marine Service), human activities inventory (from EMODnet), etc. ("MSP challenge" at the Ocean Hackathon Brest 2020);

* **Climate change:** addressing climate change not only requires better science, but it is also crucial to inform journalists, interested citizens, and the wider public. The Marine-Analyst facilitates access to information and transparent analyses, e.g. sea surface temperature trends and anomalies (from Copernicus Marine Copernicus), coastal erosion (from EMODnet) ("Climate change at my Beach challenge" at the Hack the Ocean Hackathon 2019).

* **Marine protected areas:** park managers benefit from simplified access to various information comprising key information for the management of their MPA, e.g. MPA bulletins (from Copernicus Marine Service, forecast products), vessel density hierarchical clustering and statistical analysis (from EMODnet) (Environmental impact of Shipping traffic at the Blue Innovation Hackathon 2021).

#### 11.2.1.4. NOAA Sea Level Rise viewer

NOAA provides map viewer and applications for marine data such as the Sea Level Rise - Map Viewer (SKY9), which gives users a way to visualise community-level impacts from coastal flooding or sea level rise (up to 10 feet above average high tides). Photo simulations of how future flooding might impact local landmarks are also provided, as well as data related to water depth, connectivity, flood frequency, socio-economic vulnerability, wetland loss and migration, and mapping confidence. The viewer shows areas along the contiguous United States coast, except for the Great Lakes. The maps are produced using detailed elevation maps with local and regional tidal variability. By using real photos at some specific locations, the viewer is able to show the impact of a certain sea level rise in the "real world" (Figure 11.4).

### 11.2.2. Oil spill forecasting

In the past decade, much effort has been made to support operational oil spill modelling as a crucial component of the response. The most advanced systems are fully operational 24/7, meet the robustness and accuracy criteria, as well as the real-time requirements in terms of performance and dynamic service delivery. Examples of these systems are: OS-CAR (Aamo et al., 1997), GNOME (Zelenke et al., 2012), OilMap

9. https://www.climate.gov/maps-data/dataset/sea-level-rise-map-viewer

<figure><img src="image_575.png" /></figure>
(Spaulding et al., 1992), MOTHY (Daniel, 1996), MEDSLIK-II (De Dominicis et al., 2013), COSMoS (Marcotte et al., 2016), and OpenDrift (Dagestad et al., 2018). Oil spill models' capabilities range from prediction of sea surface and upper layer transport to fully 3D oil and associated gas movement, including intrusion formation and high-pressure physics effects.

Incorporated oil spill models are coupled to up-to-date meteo-ocean monitoring and forecasting systems that provide the fields of ocean circulation, wind, and waves on a regular basis. The Copernicus Marine Service provides such data to most European systems. This information not only allows computing the oil drift, but it also helps to simulate the oil transformation processes and interaction with sediments, both coastal and seabed. The results are typically represented by oil mass balance time series delineating what fraction of the original spilled oil is expected to be kept afloat, evaporated, dispersed in the water column, beached or sedimented.

During the oil spill accidents, tight coordination between the oil spill detection/monitoring unit and operational modeling is vital for starting the spill forecasts, ground-truthing, and fast decision making. Real oil spills provided unique opportunities to examine the predictive skills of operational oil spill models, using observations carried out during oil spill accidents. Such combined studies have been conducted on spills in many geographic regions: the Prestige disaster in the Atlantic Spanish coast in 2002 (Castanedo et al., 2006; Daniel, 2010), the Fu Shan Hai wreck in the Baltic Sea in 2003 (De Carolis et al., 2013), the Lebanon crisis in 2006 (Coppini et al., 2011), the Kerch Strait oil spill in 2007 (Ivanov, 2010), the Deepwater Horizon oil spill in the Gulf of Mexico in 2010 (Liu et al., 2013), the Agia Zoni II oil spill in the Aegean Sea in 2017 (Coppini et al., 2018), the Sanchi spill disaster in the East China Sea in 2018 (Qiao et al., 2019), the Ulysse-Virginia oil spill in the Ligurian Sea in 2018 (Liubartseva et al., 2020a; Daniel et al., 2021), and in the Grande America accident in the Bay of Biscay in 2019 (Daniel et al., 2020).

Since oil transport is mainly controlled by ocean currents, the accuracy of oil spill simulations crucially depends on the resolution of the hydrodynamic models used. To resolve coastal scale processes, downscaling techniques that switch from high-resolution hydrodynamics to ultra-fine scales, with a resolution of around 100 m, are being developed. As an example, an operational decision supporting system for oil spill emergencies addressed to the Italian Coast Guard was developed by Sorgente et al. (2020) in the Western and Central Mediterranean Sea. Progress in downscaling led to operational oil spill modelling in harbour and port areas, such as the Port of Taranto in south Italy (Liubartseva et al., 2020b) and the Port of Tarragona in Spain (Morell Villalonga et al., 2020). In these three examples, the MEDSLIK-II oil spill model was applied in

various configurations. Two examples of oil spill forecasting applications are described in the following subsections.

### 11.2.2.1. MOTHY

MOTHY is a dual system consisting of an ocean model, developed to best represent the surface current, and a slick or object model (Figure 11.5). It operates worldwide, and can be implemented immediately, 24 hours a day, by the marine forecasting service of the National Forecasting Centre of Météo-France (⊕<fnref n="10" />), located in Toulouse. This Centre provides met-ocean support and drift forecasts to assist authorities in charge of accidental marine pollution and search and rescue operations. The oil spill forecasting system was developed by combining the Cedre's (⊕<fnref n="11" />) expertise in oil chemistry with the Météo-France's expertise in weather and ocean forecasting and modelling to provide a robust operational service maintained by Météo-France. The system is operated at Cedre's request in support of oil spill response operations and at the request of the Maritime Rescue Coordination Centres in support of search and rescue operations.

MOTHY has been operational since 1994 and was widely used during the Erika ⊕<fnref n="12" /> (December 1999), Prestige ⊕<fnref n="13" /> (November 2002), and Grande America ⊕<fnref n="14" /> (March 2019) crises. The system is activated about 20 times a week for actual spills or search and rescue operations. The search for objects or people constitutes almost three quarters of the applications.

The model is regularly updated and improved. MOTHY includes a Lagrangian model with the possibility of backtracking. The 3D version takes into account vertical buoyancy- and turbulence driven movement. There are 3 components:

* oil or any substance that drifts like a slick, including vegetable oil or a few chemicals;

* cargo containers;

* search and rescue targets.

Drift predictions depend primarily on reliable and timely access to observed and predicted environmental data. From this point of view, Météo-France is equipped with the best atmospheric models:

* The AROME limited area model covers the entire French coastline, with a 1.3 km resolution around France and 2.5 km around the French overseas territories;

<figure><img src="image_576.png" /></figure>

<footnote n="10">10. https://meteofrance.com/</footnote>

<footnote n="11">11. http://wwz.cedre.fr/en</footnote>

<footnote n="12">12. http://wwz.cedre.fr/en/Resources/Spills/Spills/Erika</footnote>

<footnote n="13">13. http://wwz.cedre.fr/en/Resources/Spills/Spills/Prestige</footnote>

<footnote n="14">14. http://wwz.cedre.fr/en/Resources/Spills/Spills/Grande-America</footnote>
<figure><img src="image_577.png" /><figcaption>**Figure 11.5.** General diagram of the MOTHY system. Blue: static data. Green: data sets stored in the Météo-France database; includes all Météo-France/ECMWF atmospheric models and Copernicus Marine ocean models.</figcaption></figure>

* The ARPEGE 15 global model with variable mesh (1/10 ° or 1/2 ° depending on the area), centred on France, for forecasts over the European seas and near Atlantic;

* The IFS 16 global model of the ECMWF has a resolution of 1/8° and GFS at 1/4° in R&D mode.

and ocean models:

* Global ocean system by Mercator Ocean International 17 with a resolution of 1/12°;

* Regional systems (IBI at 0.028°×0.028° resolution, MedFS at 0.042°×0.042° resolution, Atlantic-European NWS at 0.014°×0.03° resolution);

* SCHISM in New Caledonia (1/200° resolution).

The MOTHY model, which calculates the three-dimensional drift of surface and subsurface oil, is a "superparticles" model.

Superparticles are seeded at each time step, according to the specified location, duration, and rate of release.

Being operated by Météo-France's Marine Forecasting service, MOTHY is free for the French authorities and certain foreign weather services as a part of their public service mission. After requesting an account, users can freely perform modelling in the Channel - North Sea on the Noos-Drift online platform: 18.

#### 11.2.2.2. WITOIL

WITOIL (Where Is The Oil) is a multi-model oil spill forecasting service providing predictions on transport and transformation of actual or hypothetical oil spills for the global ocean, the regional European Seas, and selected coastal areas (Figure 11.6). Being an interactive system, WITOIL requires the input of data about atmospheric winds, sea surface temperatures, and sea currents.

<figure><img src="image_578.png" /></figure>

<footnote n="15">15. http://www.umr-cnrm.fr/gmap/nwp/nwpreport.pdf</footnote>

<footnote n="16">16. https://www.ecmwf.int/</footnote>

<footnote n="17">17. https://www.mercator-ocean.eu/</footnote>

<footnote n="18">18. https://odnature.naturalsciences.be/noosdrift/api/home/</footnote>
<figure><img src="image_579.png" /><figcaption>**Figure 11.6.** Example of the user interface and results of the WITOIL oil spill forecasting application.</figcaption></figure>

WITOIL provides users with spatial-temporal distributions of oil concentration at the sea surface and on the coastline. WITOIL output consists of graphics and animations available through web browser and downloadable products (both ocean and atmospheric fields and oil slick simulation outputs). Moreover, WITOIL provides an interoperable service that can be accessed from and integrated in any GIS system.

Data used in the WITOIL application are: i) the atmospheric wind data from ECMWF provided by Italian Aeronautica Militare at a resolution of 1/10°: 6-hourly analysis, 1 hour for the first 3 days of forecast, 3 hours for the following 3 days of forecast, and 6 hours for the last 4 days of forecast; and ii) the ocean current and SST data are provided by the Copernicus Marine Service.

In particular, the following domains and ocean products are used in WITOIL:

1. The Global Ocean at 1/12° (from Copernicus Marine Service) and 1/16° (Global Ocean Forecasting System by CMCC);

2. Mediterranean Sea using the MedFS products at 1/24° horizontal resolution from Copernicus Marine Service;

3. Black Sea using the BSFS products at 1/40° horizontal resolution from Copernicus Marine Service;

4. Baltic Sea using the Baltic Sea Forecasting System at 0.028°×0.017° horizontal resolution from Copernicus Marine Service;

5. Southern Adriatic and Northern Ionian Sea coastal areas using the CMCC SANIFS (2019), based on the SHYFEM

unstructured grid ocean model, reaching a horizontal resolution of 10-100 m in the coastal areas;

6. Several areas in the Persian Gulf using the CMCC ocean forecasting systems based on SHYFEM, reaching a horizontal resolution of 10-100 m in the coastal areas.

Free-access WITOIL version, a web-based application at 20, is available for the Mediterranean Sea in English, Italian, French, Spanish, Arab, Greek, and Russian. Advanced multi-model WITOIL version is available upon request: 21.

Various systems collaborated to provide information support for a recent oil spill. On 25th July 2020, the MV Wakashio hit a reef and ran aground off Pointe d'Esny on the southeast coast of Mauritius, in the Indian Ocean (Figure 11.7). In the morning of 6th August, oil leaked out of a fuel tank and oil pumping operations were carried out. They were completed on 12th August. On 16th August, the ship broke in two. On 19th August, the front part of the ship was towed about 20 kilometres from the coast. It sank on 24th August.

Following a request from the Mauritian authorities for assistance from France, the Prefect of the Reunion Island, activated a crisis cell. Meteo-France was then involved to forecast the oil drift (Daniel and Virasami, 2021). Also CMCC and MOI produced oil spill simulations and forecasts with WITOIL and sent them to relevant authorities.

<figure><img src="image_580.png" /></figure>

<footnote n="19">19. http://sanifs.cmcc.it</footnote>

<footnote n="20">20. http://www.witoil.com/</footnote>

<footnote n="21">21. witoil@cmcc.it</footnote>
<figure><img src="image_581.png" /><figcaption>**Figure 11.7.** A recent oil spill emergency: the August 2020 incident in Mauritius.</figcaption></figure>

### 11.2.3. Ports

Ports and activities linked to maritime transport, for both commercial and tourism purposes, have a significant economic value for coastal countries. Ports may be affected by very serious environmental issues that have consequences on the safety of port activities, as well as be a vehicle for concentration and dispersal of alien species through ballast waters.

#### 11.2.3.1. SAMOA service for Spanish ports

Approximately 85% of imports and 60% of Spanish exports are channelled through ports, and such figures highlight the vital role they play in the national economy. The ports suffer the extreme events of the main physical variables, in particular wind, waves, and sea level. These phenomena affect the installations throughout all phases of the port life, from design to operation. To respond to these complex needs, the Spanish Puertos del Estado and Port Authorities co-financed the SAMOA initiative.

SAMOA fully integrates a comprehensive set of products in a specific visualisation tool that is managed by administrators in the harbours. It also includes an alert system via e-mail and SMS, fully configurable by the port community users.

In addition, an extended set of applications, such as oil spill models and air pollution monitoring tools, have been fully integrated into the system.

The main functions of the system are: 1) provision of information for knowledge-based operation of infrastructures (e.g. forecast of port closure due to extreme events); 2) aid for pilot operations; 3) safer and more efficient port operations (e.g. crane operations affected by winds and planning of Ro-Ro operations); 4) fight against oil spills in the interior of the harbours; and 5) control of water and air quality.

SAMOA is heavily relying on previously existing Puertos del Estado monitoring networks (25 buoys, 8 HF radars and 40 tide gauges) and it has been used to detect and fill gaps, improving the coverage of meteorological stations on the ports, and the control of the tide gauges by means of continuous GNSS.

The SAMOA wave component has been designed to provide a three-day forecast of agitation (significant wave height in the interior of the port) inside 10 Spanish ports of special interest: Almería (two ports), Gijón, Las Palmas (three ports), Málaga, and Santa Cruz de Tenerife (three ports). Prior to SAMOA, Puertos del Estado was running an operational wave

<figure><img src="image_582.png" /></figure>
<figure><img src="image_583.png" /><figcaption>**Figure 11.8.** The SAMOA visualisation tool, showing the circulation at Barcelona Port (upper panel) and the real-time measurements at Algeciras Harbour (lower panel).</figcaption></figure>

forecast able to provide, using the SWAN model, wave forecasts at the harbours mouth. Thanks to the development of SAMOA, this forecast has now been downscaled to the interi-or of the ports at extremely high resolution (2 m).

The SAMOA circulation component produces daily a short-term (three-day) forecast of three-dimensional currents and other oceanographic variables, such as temperature, salinity, and sea level for nine Spanish ports in the Mediterranean (Barcelona, Tarragona, Almería), the Iberian Atlantic (Bil-

bao, Ferrol), and the Canary Islands (Las Palmas, Tenerife, La Gomera, and Santa Cruz de la Palma). The three-dimensional hydrodynamic model used in the SAMOA circulation compo-nent is the ROMS (Shchepetkin and McWilliams, 2005).

The SAMOA model outputs are freely accessible through the Puertos del Estado's THREDDS catalogue (the THREDDS is a web server that provides access to data and metadata for scientific purposes using a variety of remote data access protocols). Likewise, free access to some products is grant-

<figure><img src="image_584.png" /></figure>
ed via a web interface (ersh). Additionally, a specific tool for port authorities - the CMA shown in Figure 11.8) - has been developed to properly exploit all SAMOA products and so far has been implemented in 46 ports. The CMA, developed by Nologin Ocean Weather Systems (sh), is based on a web interface (e) and provides easy access to all information generated by the SAMOA systems, both in real time and in forecast mode. Users can define thresholds for all spatial points inside the application (model points and measuring stations) that are employed to trigger alerts. The CMA is also capable of creating custom PDF reports for each forecast point. Furthermore, a user-friendly oil spill model and an atmospheric dispersion model have been developed and incorporated into the CMA. Port managers granted access to the tool can define the level of permission allowed to users. For example, some users can get permission for visualisation, but might not have access to the oil spill model. The CMA is also used to configure personalised alert systems, defining the points and the alerts to be triggered, as well as the reception method (e-mail or SMS). The alerts can be defined as a combination of parameters, conditions, and/or thresholds as complex as desired by the user. A very good example of how the CMA tool should be used is the Algeciras harbour, in which it is utilised by a community of 500 users, including the companies located at the facilities.

Building on the success of SAMOA, a SAMOA 2 project is now on its final stage of implementation. This second phase will include new components, such as a wave overtopping forecast and extremely high-resolution wind prediction (2 m). In 2022, once SAMOA 2 is completed, the system will have the following components: 44 CMA implementations in different ports (of a total of 46 ports in the national system), 20 1-km resolution atmospheric forecasts, 21 agitation systems, 31 circulation systems, 19 new meteorological stations, eight GNSS, 15 very high wind forecast systems, plus other additional modules. Work is ongoing on how to use the new models to explore, for example, the wave current interactions.

With the new system in place, the most important challenge for the Spanish port system will be to implement the methodologies necessary to make proper use of all the new available tools. While some ports are very active and are already making good use of the new information, others are still not able to fully exploit it. Several initiatives will be launched to reduce this gap. Making operational oceanography a core part of the port management business is probably the most significant result of SAMOA.

<figure><img src="image_585.png" /><figcaption>**Figure 11.9.** Architecture of the AQUASAFE platform.</figcaption></figure>

#### 11.2.3.2. AQUASAFE

The AQUASAFE platform (ersh) aims to increase efficiency in management of maritime operations, providing real time information and its integration with forecast and diagnostics tools. This platform is operational for all Portuguese Ports and in the Port of Santos (Brazil). It is also used to support aquacultures, inland navigation, irrigation, and water utilities.

Measured data (sensors, remote detection, etc.) and modelled data (meteorology, hydrodynamics, waves, etc.) are integrated in a platform which synchronises all of them in time, space and dimensional units to provide easy and robust access to real time and forecast information. The global architecture of the Platform is presented in Figure 11.9.

AQUASAFE has a micro-service, cloud native architecture. Each one of the platform's components (data download, data formatting, model execution, etc.) is implemented in a completely isolated way, facilitating both its maintenance and scalability. It also uses Container technology (from Docker) so that each component is portable and consistent and can be quickly instantiated.

The Client Applications (User Interfaces) connect to Data services via a state-of-the-art authentication layer. A Data services layer is managing the Data acquisition (ingestion of data from external systems to the platform, and storage) and the Processing Services (which performs computational loads that generate new data). It also manages Reporting and Alarms. Data Processing Services, Reporting and Alarms are processed through a common Scheduler.

Using this system, port operations have better efficiency and safety as it improves the match between tight schedules of container ships, or tight security of liquefied natural

<figure><img src="image_586.png" /></figure>

<footnote n="22">22. https://portus.puertos.es/index.html?locale=en#/</footnote>

<footnote n="23">23. https://www.nologin.es/en/nowsystems</footnote>

<footnote n="24">24. https://cma.puertos.es</footnote>

<footnote n="25">25. https://hidromod.com/?s=aquasafe</footnote>
<figure><img src="image_587.png" /><figcaption>**Figure 11.10.** AQUASAFE platform: wave model at the Port of Leixões (Portugal).</figcaption></figure>

gas (LNG) carriers, and the operational weather limits of each Terminal. Thus, the impact in the logistic chain including ports is extremely positive.

In this context, PIANC's Maritime Navigation Commission Working Group 54 produced a set of recommendations "on the use of hydro/meteo information for navigational channel and port basin operations, including the determination of operational limits" (PIANC, 2012). The AQUASAFE platform was developed along those recommendations.

Hydrodynamics and wave propagation modelling methodology used in the operational systems implemented with AQUASAFE is based on downscaling global models to local scale, to correctly forecast parameters like sea level, currents, or waves (Figure 11.10). Meteorological forecasting also has a similar approach, but institutes or companies specialised in meteorology are usually used as data suppliers (e.g. MeteoGalicia).

This downscaling methodology is also embedded in the concepts behind some modelling systems like WRF (atmospheric model) or SWAN (waves nearshore).

### 11.2.4. Voyage planning

Shipping moves the global commerce as, according to UNCTAD, around 80% of the volume of international trade in

goods is carried by sea ↘<fnref n="26" />. This tremendous effort comes with a significant environmental footprint. According to the Fourth GHG study of the IMO, shipping accounted in 2018 for nearly 3% of global anthropogenic CO2 emissions. Container ships, bulk carriers, and oil tankers are the greatest emitters of the world fleet. When it comes to per-ship emissions, cruise ships also distinguish themselves for their carbon footprint.

Various measures are presently discussed both at IMO ↘<fnref n="27" /> and at the European Union level ↘<fnref n="28" /> for reducing the GHG emission from shipping. In the short term (i.e. the current decade), more efficient operations for both old and newbuild ships would be possible via a blend of design and operational measures. Among the operational ones, both speed limitation and voyage planning could be considered. Speed limitation leads to reduction in emission at the cost of longer voyage durations, and could be viable for delivering non-perishable goods. However, for all ship types, the environmental benefits of speed limitation could be enhanced through voyage planning or weather routing. This wording may refer to quite

<figure><img src="image_588.png" /></figure>

<footnote n="26">26. https://unctad.org/topic/transport-and-trade-logistics/review-of-maritime-transport</footnote>

<footnote n="27">27. https://www.imo.org/en/MediaCentre/MeetingSummaries/Pages/MEPC76meetingsummary.aspx</footnote>

<footnote n="28">28. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A52021DC0550</footnote>
different systems supporting the definition of the route to be sailed. It is possible to distinguish among:

* Strategic/Tactical planning. The former refers to optimally deploy the fleet of a given shipowner to pursue some benefit, while the latter to the shape of a specific route between given ports of call;

* Global/Local optimization. The former refers to optimise some figure of merit integrated along a voyage (such as total voyage GHG emissions), while the latter to locally constrain the vessel behaviour (wind, waves, or ship motions below a given threshold);

* Single/Multiple Objectives. In the former case, the optimization objective (such as emissions, duration, cost, and comfort) is one at time, while in the latter multiple, conflicting objectives can be addressed (usually making use of some mathematical compromise);

* Deterministic/Stochastic. In the former case, an algorithm is used to deliver exactly the same solution for given boundary conditions (usually a proof of optimality is available), while in the latter stochastic approaches (such as genetic ones) are used for speeding up computations at the cost of losing full optimality and reproducibility.

In most items of the above taxonomy, voyage planning is related to weather and ocean state, both observed and predicted, to be used for either constraining the navigational options or defining the route's optimization objectives. A recent joint IMO-WMO symposium 29, highlighted that routing optimization may help ships to increase their operational efficiency and simultaneously reduce emissions, while it is especially relevant for safety of navigation in extreme marine conditions. Voyage planning tools are developed by several research organisations in various countries, such as (non-exhaustive list): Sweden (Li et al., 2021), Portugal (Vettor and Guedes Soares, 2016), Poland (Krata and Szlapczynska, 2018), Italy (Mannarini et al., 2016a), South-Korea (Lee et al., 2018). An attempt to classify ship routing models can be found in (Zis et al., 2020).

Finally, for its contribution to GHG emission reduction, voyage planning embeds a potential to contribute to several targets of the UN Sustainable Development Goals (30), notably to 12.2, 12.4, and 13.

### 11.2.4.1. VISIR

The VISIR is a voyage planning model developed since 2012 by the CMCC and the University of Bologna. It can be defined as a tactical, global-optimization, single-objective, deterministic model system for ship route planning. It is based on an exact graph-search method (Dijkstra's algorithm) modified for dealing with dynamic environmental fields. It is coastline- and sea-bottom-aware, thanks to a masking procedure in preparing the graph. It was used for both motor and sailboat routing in the Mediterranean Sea (Mannarini et al., 2016) and in the Atlantic Ocean (Mannarini and Carelli, 2019). So far, VISIR has been used in conjunction with both analysis and forecast wave and current fields from Copernicus Marine Service, and with wind fields from ECMWF or COSMO-ME (Mannarini et al., 2015).

In its first version, VISIR computed only least-distance and least-time routes. However, starting from version 2, also a least-CO2 route algorithm was embedded into VISIR. VISIR-1 used polar plots for representing sailboat speeds through water (STW) in dependence of wind magnitude and direction (Mannarini et al., 2015), while wave fields were used for motorboat routing (Mannarini et al., 2016). VISIR-1-b made use of sea surface currents for correcting STW for sea current drag and drift (Mannarini and Carelli, 2019). Starting from VISIR-1b, the angular resolution of the routes was enhanced thanks to a higher degree of connectivity of the underlying graph. While all VISIR-1 versions are based on a parametrization of calm water and wave added resistance for motorboats, VISIR-2 can alternatively make use of a ferry response model derived from a ship simulator. This allows for inclusion of wind-wave angle of attack, a more accurate representation of involuntary speed loss in waves, as well as of fuel consumption and CO2 emissions (Mannarini et al., 2021).

The validation of the path planning component of VISIR was performed via both analytical benchmarks (Mannarini and Carelli, 2019) and model inter-comparison (Mannarini et al., 2019).

VISIR is an open-source model and its updates are usually released upon documentation on peer-reviewed publications. In Table 11.1 is provided an overview of VISIR features across its versions (31). So far, only version 1.a and 1.b have been released, which were coded in Matlab. However, the model version currently being developed is coded in python. VISIR-2 powers an operational service for ferries in the Adriatic Sea, available at 32.

In Figure 11.11 an example of optimal routes from the most recent version of VISIR is displayed.

<figure><img src="image_589.png" /></figure>

<footnote n="29">29. https://library.wmo.int/doc_num.php?explnum_id=10305</footnote>

<footnote n="30">30. https://sdgs.un.org/goals</footnote>

<footnote n="31">31. http://www.visir-model.net/</footnote>

<footnote n="32">32. https://gutta-visir.eu</footnote>
<table><caption>**Table 11.1.** Overview of features in VISIR model versions.</caption><thead><tr><td>Version</td><td>Released</td><td>Language</td><td>Ship model</td><td>Fields</td><td>Angular resolution</td><td>Objectives</td></tr></thead><tbody><tr><td>1.a</td><td>2016</td><td>matlab</td><td>Semiempirical parametrization of resistances</td><td>Waves (height, period)</td><td>26.6°</td><td>distance, time</td></tr><tr><td>1.b</td><td>2019</td><td>matlab</td><td>Semiempirical parametrization of resistances</td><td>Waves (height, period) &amp; currents</td><td>7.1° or better</td><td>distance, time</td></tr><tr><td>2.a</td><td>&gt;2021</td><td>python</td><td>Semiempirical parametrization of resistances or fitted ship simulator response</td><td>Waves (height, direction) &amp; currents</td><td>14.0° or better</td><td>distance, time, CO2</td></tr></tbody></table>

<figure><img src="image_590.png" /><figcaption>**Figure 11.11.** Optimal routes for a ferry of 125 m, computed via VISIR-2. Left panel: bundles of East-bound CO2-optimal routes for 25 departure times any 3 hour and three engine load values (70, 85, or 100% of maximum engine power). Right panel: least-distance (cyan), least-time (red), and least-CO2 (green) route from Patras (Greece) to Brindisi (Italy) for departure date and time as in the title and engine load at 70%. Relative CO2 and CII savings with respect to the shortest route are also indicated.</figcaption></figure>

<figure><img src="image_591.png" /></figure>
11.2.5. Fisheries and aquaculture

New technologies are developing for management and mon-
itoring of fisheries, valorisation and protection of fish spe-
cies, enhancing environmental standards for sustainable
aquaculture, reducing wastes and improving safety, develop-
ing commercial platforms for direct selling of fish products,
and tracing products in innovative ways. Another aspect to
take into consideration is the characterization of fisheries
and aquaculture products, with the purpose of diversifying
products and innovating traditional practices. Information
technologies can assist in the development of important
systems to support the economic sustainability of the fishing
industry, which may help fishers to guarantee the quality of
products, as well as get information on sea conditions and
on fishing models. In the following subsection are present-
ed three examples on the use of operational oceanographic
products for stock assessment, and management of fisheries
and aquaculture.

Weather Window
AtlantOS

<figure><img src="image_592.png" /></figure>

Select a Bay
Bantry Bay

Cut-Off Height (m)
0.2

From:
07-06-2022

To:
12-06-2022

LOAD CHARTS

Significant Wave Height

<figure><img src="image_593.png" /></figure>

Mean (Average) Wave Period & Direction To

<figure><img src="image_594.png" /><figcaption>**Figure 11.12.** The Weather window tool
(source: 33).</figcaption></figure>

11.2.5.1. Bluefin tuna

The SOCIB and the IEO have developed a habitat model for predicting spawning distribution of Atlantic bluefin tuna (Thunnus thynnus) in the Western Mediterranean. Observed data for larvae location are incorporated in a predictive system of the habitat model, forced by operational oceanographic products from the Copernicus Marine Service, such as satellite altimetry and chlorophyll, as well as sea surface temperature and salinity from hydrodynamic models for forecasting the Bluefin tuna habitats (Alvarez-Berastegui, 2016; Ingram, 2018). Analysis, satellite data, and forecast by the MedFS, in situ data for OC, SST, and Total Allowable Catch set by ICCAT are used in the application. Description of the application is available at t. 34

11.2.5.2. Harmful algal bloom warning system

The Harmful algal bloom warning system provides near re-
al-time forecast information for the aquaculture industry
along Europe's Atlantic coast, it is of vital importance for mit-
igating the effects of HABs. It was originally developed in the
FP7 Asimuth project (Cusack et al., 2016), subsequently further
developed in H2020 AtlantOS (Cusack et al., 2018) and Inter-
reg Atlantic Area PRIMROSE ( ⚙️<fnref n="35" />). The warning system has
been operational at the Irish Marine Institute since 2013 and
subsequently deployed in Spain by IEO and in Norway by the
NIVA. HAB information is disseminated in the form of a weekly
bulletin to a wide audience with plots easy to interpret and ex-
planatory text. The EOVs are used to develop data products for
the weekly HAB bulletin and include: phytoplankton biomass
and diversity, sea surface temperature, ocean colour, ocean
surface stress, sea surface height, subsurface temperature,
surface currents, subsurface currents, sea surface salinity,
subsurface salinity, and ocean surface heat flux. Other EOVs
required to produce the HAB bulletin are the biotoxins and/
or phycotoxins produced by some phytoplankton species that
can accumulate in shellfish. Numerical models, satellites and
in-situ data are integrated and undergo expert interpretation
before finding their way to the weekly bulletin.

11.2.5.3. Weather window tool

The Weather window tool provides a user-friendly interface to
short-term wave forecasts. It was developed by the Marine In-
stitute, Ireland, in the framework of AtlantOS (Dale et al., 2018)
and is currently developed further in MyCOAST (Interreg Atlan-
tic Area) to include more locations along the European Atlantic
coasts. Availability of “coordinated” coastal observatories and
forecasting models is the prerequisite for the transferability

<figure><img src="image_595.png" /></figure>

<footnote n="33">33. https://www.digitalocean.ie</footnote>

<footnote n="34">34. http://www.socib.es/index.php?seccion= detalle_noticia&id_noticia=191</footnote>

<footnote n="35">35. https://www.shellfish-safety.eu/</footnote>
<table><thead><tr><th>PHYSICS</th><th>BIOGEOCHEMISTRY</th><th>BIOLOGY & ECOSYSTEMS</th></tr></thead><tbody><tr><td>Sea State*</td><td>Oxygen</td><td>Phytoplankton Biomass & Diversity</td></tr><tr><td>Ocean Surface Stress</td><td>Nutrients</td><td>Zooplankton Biomass & Diversity</td></tr><tr><td>Sea Ice</td><td>Inorganic carbon</td><td>Fish Abundance & Distribution</td></tr><tr><td>Sea Surface Height</td><td>Transient Tracers</td><td>Marine turtles, birds, mammals abundance & Distribution</td></tr><tr><td>Sea Surface Temperature</td><td>Suspended Particulates</td><td>Live Coral</td></tr><tr><td>Subsurface Temperature</td><td>Nitrous Oxide</td><td>Sea Grass Cover</td></tr><tr><td>Surface Currents</td><td>Carbon Isotope (°C)</td><td>Mangrove Cover</td></tr><tr><td>Subsurface Currents</td><td>Dissolved Organic Carbon</td><td>Macroalgal Canopy</td></tr><tr><td>Sea Surface Salinity</td><td>Ocean Colour</td><td></td></tr><tr><td>Subsurface Salinity</td><td></td><td></td></tr><tr><td>Ocean surface heat flux</td><td></td><td></td></tr></tbody></table>

*Wave height is essential for site selection

<table><caption>**Table 11.2.** List of physical variables for building the aquaculture site selection (source: Dale et al., 2017).</caption><tr><td>Mature</td><td>Pilot</td><td>Concept</td><td>Useful for site selection</td></tr></table>

of this tool to other locations. To this aim, MyCOAST project developed a guideline to the standardisation of data from the observing and forecasting systems based on the NcML 36. The user of the tool selects a location from a dropdown menu,

inputs a cut-off wave height that is deemed by the user as safe for specific field activity at the site, and selects the time period up to a week (limited by the duration of wave forecast). Two graphs are then displayed: i) significant wave height time series with the cut-off height marked and safe dates and times coloured green; and ii) wave period and direction as a supplementary information. A snapshot of the tool is presented in Figure 11.12.

<figure><img src="image_596.png" /><figcaption>**Figure 11.13.** A conceptual model of aquaculture site selection approach using GIS (source: Dale et al. 2017).</figcaption></figure>

#### 11.2.5.4. Aquaculture site selection

Aquaculture site selection is a key factor in aquaculture operation, including offshore aquaculture, and proper site selection is a prerequisite for the economic sustainability of the operation, for animal welfare, and for product quality. It is possible to base the identification of suitable sites for offshore aquaculture on observational and modelling systems. In response to the industry requirement, a relevant demonstration of this service was performed in the framework of AtlantOS (Dale et al., 2017). GIS approach has been used as it offers a means to organise, process, and analyse different data types and data models, as well as to perform a spatial multi-criteria evaluation. A set of criteria needs to be fulfilled for a site to be suitable for offshore aquaculture. These criteria, presented in Table 11.2, are related to physical factors like temperature, waves, and currents, as well as legal or administrative factors such as distance to ship routes, marine protected areas, offshore windmills, etc. A conceptual model of the aquaculture site selection approach using GIS is presented in Figure 11.13.

<figure><img src="image_597.png" /></figure>

<footnote n="36">36. https://www.unidata.ucar.edu/software/netcdf-java/v4.6/ncml/Tutorial.html</footnote>
11.3.
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<figure><img src="image_600.png" /></figure>
12.
# Challenges and future perspectives in ocean prediction

<figure><img src="image_601.png" /></figure>

CHAPTER COORDINATOR

Fraser Davidson

CHAPTER AUTHORS (in alphabetical order)

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.

<figure><img src="image_602.png" /></figure>
# 12. Challenges and future perspectives in ocean prediction

<figure><img src="image_603.png" /></figure>

## 12.1. Introduction

## 12.2. Observing system evolution with ocean prediction engagement

12.2.1. Challenges for the current ocean observing systems

12.2.2. Observing System Evaluation

12.2.3. Argo evolution plans

12.2.4. Next phase for satellite missions

## 12.3. Numerical models planned evolutions, including adaptation to new HPC systems

## 12.4. Future evolutions in ocean data assimilation for operational ocean forecasting

## 12.5. Future of ensemble prediction systems

## 12.6. Opportunities of artificial intelligence for ocean forecasting systems

12.6.1. Expected contributions of machine learning to ocean forecasting pipelines

12.6.2. Designing fully trainable ocean forecasting systems core engines

12.6.3. Towards user-centric, ocean digital twins leveraging lightweight emulators

## 12.7. Seamless prediction

12.7.1. Optimal use of modelling workforce and model consolidation

12.7.2. Development of seamless UOM for multiple temporal scales

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

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

<figure><img src="image_604.png" /></figure>
12.8. Operational forecasting and scenarios in a digital ocean

12.8.1. Construction of an openDTO service platform

12.8.2. Underlying architecture

12.9. Quality assessment for intermediate and end users

12.9.1. New observations for improved quality assessment

12.9.2. Expected development of quality assessment techniques

12.9.3. Quality information communication improvements

12.10. Expected future evolution of Copernicus Marine Service
products and services

12.11. The United Nations Decade of Ocean Science
for Sustainable Development

12.11.1. The Decade Collaborative Centre for Ocean Prediction

12.11.2. CoastPredict Program

12.11.3. ForeSea Program

12.12. References

<figure><img src="image_605.png" /></figure>
## 12.1.

### Introduction

The growth of ocean prediction research, capability, applicability, availability, maturity, and user uptake from an initial idea 25 years ago, while gradual, has been unrelenting. Today's capacity and maturity in ocean prediction goes beyond what was initially conceived and provides a strong basis for advancement of societal benefits. Over the next 10 years, ocean prediction systems will continue to gradually rival weather prediction systems in the sense of ubiquitous use, protecting lives, economic impact, and supporting custodianship of the environment. Building a framework with standards and best practices for the full operational oceanography value chain will enable further harnessing of prediction systems in supporting a healthy ocean at the same time of a blue economic growth for all countries. This will further awareness and accessibility of the marine environment through digital platforms underpinning increases in ocean prediction literacy, capacity building, applications, and services (Figure 4.1).

Herein we outline the expected advances of ocean prediction and other supporting components of operational oceanography over the next decade. An underlying theme is the integration

of ocean prediction systems within the larger context of operational oceanography, seamless environmental prediction, and the blue economy. This requires a transparent framework approach of standards and best practices, enabling all countries, particularly those with the least resources, to engage and benefit.

This chapter introduces the key drivers for the next generation of OOFS, spanning from global to coastal scale observing systems (Section 12.2) to numerical models evolution (Section 12.3), data assimilation (Section 12.4) and ensemble systems for prediction (Section 12.5), from the growing AI techniques for understanding physical processes (Section 12.6) to seamless approach (Section 12.7) andDTO (Section 12.8), including as well the evolution in quality assessment (Section 12.9). The last sections focus on planned evolution for state-of-the-art services like the Copernicus Marine Service (Section 12.10) and international initiatives promoted by the UN Decade of the Ocean (Section 12.11).

<figure><img src="image_606.png" /></figure>

## 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 🌐<fnref n="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:

<figure><img src="image_607.png" /></figure>

<footnote n="1">1. https://www.goosocean.org/</footnote>
* 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) technological bottlenecks and significant data gaps in sub-surface, sea bottom, geological and biological observations.

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).

<figure><img src="image_608.png" /></figure>

<footnote n="2">2. https://marine.copernicus.eu/about/observation-requirements</footnote>
<figure><img src="image_609.png" /><figcaption>**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).</figcaption></figure>

### 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 modeling/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 observa-

<figure><img src="image_610.png" /></figure>
tion 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 (®<fnref n="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 ®<fnref n="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.

<figure><img src="image_611.png" /></figure>

<footnote n="3">3. https://argo.ucsd.edu/</footnote>

<footnote n="4">4. https://argo.ucsd.edu/argo-beyond-2020/</footnote>
To support operational oceanography and marine appli-
cations, Copernicus Marine Service has drawn up a doc-
ument 5 that describes the main requirements for the
evolution of the Copernicus Satellite Components. It fo-
cuses 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 fu-

ture high resolution open ocean, coastal models, and cou-
pled ocean/wave models. The document also recognizes
the need of improving space/time resolution, to better mon-
itor 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.

<figure><img src="image_612.png" /></figure>

12.3.

Numerical models planned evolutions, including adaptation to
new HPC systems

Ocean models are one of the pillars for OOFS. Chapter 4 provides information on current modelling capacities while Chapters from 5 to 10 deepen the theoretical aspects, but still remain a main question to be answered: *What is expected by ocean models for the future OOFS?* Fox-Kemper et al. (2019) provided an extensive review on challenges and perspectives in ocean models, touching many scientific open questions and issues to solve. In particular, evolving the core models to address adequate scales in space and time, accurately representing physical processes, and running fastly is the baseline for improving predictability, as well as past reconstruction of the blue, green and white ocean. These are the challenges that have to be tackled for the improvement of future OOFS.

Le Sommer et al. (2018) showed that the evolution in ocean
modelling for operational oceanography is strictly connect-
ed to resolve physical processes down to the submesoscale
(Chassignet and Xu, 2021) and to describe internal wave and
internal tides at a global scale thanks to increase in comput-
er power and improved physical parameterization (Shriver et
al., 2012). Increasing resolution in space and time is not the
only way to address high quality operational products: mod-
ularity of modern geoscientific models is key for addressing
modelling complexity (Le Sommer et al., 2018).

Modelling complexity and modularity for the next generation
of OOFS have a computational cost that needs to be account-
ed for once we consider evolutions in numerics. Evolutions in
High Performance Computing is then another pillar on which
establishing OOFS; scientific questions to be solved require
also to face technological challenges. Le Sommer et al. (2018)
highlighted how the main current limitations in the modelling
framework capacity is not due to computational speed of the

processors, but on access to memory and latency in input/
output. Such limits require a deep revision on the way devel-
opments are carried on, but sustained collaboration between
ocean modellers and computer scientists is also key.

The usage of graphics processing units (GPU) is progressively
accelerating the Earth system modelling the atmosphere and
the ocean. This transition to modern massive supercomput-
ers requires re-design numerical codes and HPC optimiza-
tion/parallelization strategies. In the oceanographic commu-
nity, codes have been progressively ported on hybrid CPU/
GPU architectures: for example, Xu et al. (2015) provided a
first example of porting of the POM on GPU architecture, fo-
cusing on adopted strategy for memory access optimization,
new design of communications, boundary optimization over-
lapping approach, and I/O optimization, achieving over 400x
speedup against a single CPU core, reducing energy con-
sumption by about seven times. Liu et al. (2019) provided a
description of the first parallel implementation and optimi-
zation of the ROMS on a *many-processor* system, the Sunway
sw26010: the result showed that the speedup of optimised
hotspot program can be up to 3.69x with respect to original
ROMS one. Such examples demonstrate how future complex
computing architectures can be exploited for accelerating
ocean models execution, benefiting operational systems,
and opening new frontiers in numerical modelling.

Growing application requirements push from petascale to
exascale: in the near future larger datasets, more parame-
ters, much more computing, more need for parallelism, and
large power consumption will be available. These improve-
ments are strictly connected to evolutions in climate and
ocean modelling that aim to represent real-world systems
characterised by multi-physics and multi-scale interaction in
space and time, opening to predictive science.

5. https://marine.copernicus.eu/sites/default/files/media/pdf/2020-10/CMEMS-requirements-satellites.pdf

<figure><img src="image_613.png" /></figure>
Exascale computing is then the next frontier to build global climate systems at the optimal model resolution that requires a high level of performance capabilities but remaining within a specific power budget. Operational centres need to account for heterogeneous computing resources: heterogeneous computing aims to match the requirements of each application to the strengths of CPU/GPU architectures (Mittal and Vetter, 2015). The collaborative framework among different hardware components is an open research field that aims at:

* Port large-scale codes written in CPU or GPU-suited languages into heterogeneous computing systems minimising overhead and error-prone;

* Design new suitable data-access strategies to take full advantage of fused CPU-GPU systems;

* Reduce use of more classical programming languages like Fortran in favour of more modern computing languages such as Python;

* Increase data analytics capacities;

* Decrease energy consumption towards Green Computing.

<figure><img src="image_614.png" /></figure>

## 12.4.

### Future evolutions in ocean data assimilation for operational ocean forecasting

Emerging observing technologies provide impetus to the development of DA systems. Operational ocean DA systems are constantly evolving their application of improved data assimilation methods, their use with increased resolution models and models with increased complexity, their use of new and upcoming observing technology, and their use of new community DA software and computer hardware infrastructures. Below is a summary of some of the areas in which DA is expected to evolve in operational forecasting systems over the next 10 years.

In terms of the DA methodology, the most immediate development is the merging of ensemble and variational methods. Drawing on the strengths of both approaches, the “hybrid” approach is being developed in a number of forecasting centres. The static or parametrized version of the background error covariances used in variational methods and the flow-dependent estimates from an ensemble are combined. Experience from NWP suggests that the hybrid approach performs better than an either pure variational or pure ensemble method (Lorenc and Jardak, 2018); efforts are underway to implement similar capability in global and regional ocean forecasting systems. These are likely to reach some maturity over the coming few years. More sophisticated DA methods, which do not rely on the assumption that forecast errors have an unbiased Gaussian distribution (such as particle filters, van Leeuwen et al., 2015), are being actively pursued to deal with, for instance, biogeochemical variables. Another growing area of methodological development is the application of machine learning to the data assimilation problem (Bonavita et al., 2021), particularly in regard to model error estimation, model parameter estimation, and the estimation of forecast error covariance statistics.

Ocean model resolution is constantly being increased as more computer resources become available. DA systems need to evolve to make sure they can deal with the larger range of scales in the models. The complexity of models is also increasing in both the ocean models themselves and the different types of coupled models being used. Applying DA methods to ocean/sea-ice models, physical-biogeochemical models, acoustic-physical models, and more complete earth system models that include many different earth system components, is an active area of research (Penny et al., 2019). Models used for operational ocean, sea-ice, and atmosphere forecasting on short timescales are increasingly becoming coupled together and the data assimilation methods needed to effectively initialise these systems are being developed. Most operational coupled weather forecasting systems do not currently use strongly coupled data assimilation methods, whereby ocean observations can directly influence the atmospheric analysis and vice versa, but they are expected to be developed and implemented over the next decade.

The software infrastructure needed to apply the data assimilation is also under development by several new community DA software systems, including the DART (Anderson et al., 2009), the OOPS, the JEDI, EnKF-C (Sakov, 2014), and the PDAF (Nerger et al., 2020). The computer hardware used to run forecasting systems is also evolving with different architectures such as GPUs, which will become a strong computational candidate for operational forecasting systems in a 10-year timeframe along with the evolution of numerical codes. The community software systems provide the opportunity for more collaboration between operational forecasting groups, and between operational and research groups.

<figure><img src="image_615.png" /></figure>
Future of ensemble prediction systems

Numerical ocean, weather, seasonal and climate forecasting
systems across the world are tending towards becoming cou-
pled ensemble data assimilation prediction systems (Brass-
ington et al., 2015; Barton, 2021; Buizza, 2021; Frolov, 2021; Fujii
et al., 2021; Komaromi, 2021), including a better coverage of
the inter-relationships among the geophysical domains of
the ocean, atmosphere, sea ice, land, and biogeochemistry
(Sandery et al., 2020; O’Kane et al., 2021). Forecasting systems
are also increasingly applied to finer spatiotemporal scales.

The need to quantify the probability distribution of fore-
cast error in coupled and downscaled models, as well as the
reliability and accuracy of forecasts, will be served well by
ensemble prediction systems, such as those using the EnKF
(e.g., Sandery et al., 2020; O’Kane et al., 2021; Sun et al., 2020;
Minamide and Posselt, 2022).

Ensemble prediction systems enable synthesis of models and observations leading to data that can be used to provide best estimates of geophysical variables and quantify the dynamics of their uncertainty (Sandery et al., 2019) (Figure 12.2). Uncertainty quantification will become as important in forecasts as the forecasts themselves, providing guidance on reliability and insight into fast growing disturbances in the geophysical environment. As described in other sections of this chapter, advances in ensemble prediction will also be coupled to improvements in models, observations, data assimilation, computer resources and technology.

There is an associated loss of predictability towards finer
scales (Jacobs et al., 2021). Prediction systems using coupled
data assimilation and finer spatial resolution will require
larger ensembles, more frequent, representative and accu-
rate observations, and improved data assimilation practic-
es. Extending the range of predictability will be facilitated
by advances to ensemble prediction systems. Operational
ensemble systems will incorporate improved methods for
data assimilation in the presence of model error and strong
non-linearities, such as the iterative EnKF (Sakov et al., 2017),
hybrid covariance methods (Kotsuki and Bishop, 2022), and
assimilation of non-linear observations such as water va-
pour, cloud, precipitation, sea-ice, and phytoplankton con-
centration (Bishop, 2016; Posselt and Bishop, 2018).

Combining ensemble prediction with machine learning and artificial intelligence will also play an increasing role in forecasting (Brajard et al., 2021; Weyn et al., 2021). In some instances, forward models with reduced order low dimensional and data-driven differentiable emulators (Maulik et al., 2021) will be able to replace full non-linear models to reduce computational cost and assist in searches for initial conditions, patterns, parameterisations and ensemble perturbations appropriate for particular forecasts. Ensemble prediction systems will be used to identify initial states, forcing and dynamics that contribute to regime transitions (O’Kane et al 2019; Quinn et al., 2020) and in the forecasting of extreme events (Hawcroft et al., 2021).

Forecast model parameters will continue to be poorly known, subject to uncertainty, dependent on grid resolution, and a source of model bias requiring joint state and parameter estimation (Kitsios et al., 2021). With this approach, predictability of certain geophysical processes may be improved (Zhang et al., 2017). Future ensemble prediction systems will be optimised with model parameters that minimise bias in the ensemble mean but that adequately represent the parameter’s error probability distribution in the ensemble (Gao et al., 2021). Coupled model forecasts will be able to be optimised in state and parameter space. Model error minimization will be multi-variate and simultaneous across the geophysical realms with respect to the global network of observations (Sandery et al., 2020).

Ensemble prediction systems will play an increasing role in the future design of observation systems (Sandery et al., 2019 and 2020). Coupled ensemble prediction provides insight into unobserved variables through cross domain covariances. Future applications of coupled ensemble prediction systems will provide improved reanalysis products with tighter constraints on carbon, sea-ice volume, air-sea fluxes, ocean heat storage and transport, using optimally designed observing systems.

<figure><img src="image_616.png" /></figure>
<figure><img src="image_617.png" /><figcaption>**Figure 12.2.** Quantifying the dynamics of system uncertainty. This image shows forecast ensemble spread in sea surface temperature (K) and sea ice concentration on 28th September 2017 (in observation space) from a 96 member, 0.10 horizontal resolution coupled ocean-sea-ice EnKF prediction system, known as ACCESS-OM2-EnKF-C (Sakov, 2014; Kiss et al., 2020). SST spread is related to uncertainty: the forecast dynamical state of Tropical Instability Waves and sea ice spread shows that forecast uncertainty at this time of year is greatest in certain areas.</figcaption></figure>

Unstructured mesh models that enhance resolution towards the coastline for detailed hydrodynamic and biogeochemical forecasting of coastal and river, lake and estuarine circulation processes (Herzfeld et al., 2020) will be run as ensemble prediction systems. Meshes that adapt resolution according to areas of most rapidly growing geophysical instabilities, such as in tropical cyclone, tsunami, and flood forecasting (Beisiegel et al., 2021) will also be run as ensemble prediction systems.

As systems continue to be developed, improving the accuracy of forecast error covariance estimates will deliver coupled downscaled analyses and forecasts with greater skill. With advances to observation systems, relatively higher resolution monitoring and ensemble prediction of sea-ice, waves, currents, sea-levels, temperatures, biogeochemistry, and the tracing of river plumes containing sediments, contaminants, and pollutants may be made possible using ensemble prediction systems. Access to future higher resolution ocean in-situ and satellite data may enable prediction of the

ocean sub-mesoscale circulation and near-field currents for search and rescue, ship-routing, safety, and recreation. As science, technology, networking, and connectivity improves, real-time assimilation of user-supplied observations into ensemble prediction systems to augment local predictability may become possible.

<figure><img src="image_618.png" /></figure>
12.6.

Opportunities of artificial intelligence for ocean forecasting systems

Recent developments in AI open many interesting opportu-
nities in the context of operational oceanography and ocean
forecasting systems. Operational forecasting systems are
indeed not only based on observational data but also on
algorithms. These algorithms gather and encode our under-
standing of physical systems and their dynamics, as well as
of observation networks and associated uncertainties. They
also reflect our collective knowledge on the relevant criteria
for evaluating ocean data products. As in many activities re-
lying on algorithms, the emergence of artificial intelligence,
and especially of deep learning, opens a number of new pos-
sibilities, and is therefore the subject of growing interest in
our community.

The ML generally refers to all the methods used to build
algorithms whose components and parameters are not
defined a priori but are trained according to a given ob-
jective. This field encompasses a large number of different
methods, algorithms, and training strategies. It is a wide
and fast-moving research field that includes, but is not re-
stricted to, deep learning. ML is also intimately linked to a
technological landscape and a software ecosystem in con-
stant evolution. These technologies allow researchers and
engineers to assemble complex algorithms from elementary
building blocks in a very versatile and modular way, with in-
teresting performances compared to state-of-the-art meth-
ods in many disciplines.

Applications of artificial intelligence are currently in vogue but, beyond the hype, artificial intelligence and machine learning can help us to overcome some of the current limitations of ocean forecasting systems. Ocean models and data assimilation methods, which are the scientific underpinning of current ocean forecasting systems, are indeed facing important challenges. Performing large ensemble simulations with full ocean models at increasingly fine spatial resolution is becoming more and more difficult computationally. We still do not know how to fully exploit hybrid computing architectures in our systems. We do not have a robust and plug-and-play framework to adapt their complexity to new custom applications. Although they are constantly being improved, our systems are also becoming increasingly difficult to modify and maintain. As developed in the following subsections, AI and ML may well help us to overcome these limitations and may even deeply impact on the structure of our operational systems.

12.6.1. Expected contributions of machine
learning to ocean forecasting pipelines

Machine learning has long been used in ocean sciences and operational oceanography. However, these applications have so far mostly been limited to data retrieval algorithms upstream of forecasting systems (remote sensing, quality control), or to data processing and analysis in downstream applications (data mining, data fusion). In this context, ML algorithms have been essentially seen as black boxes without much physical basis. This perception is fundamentally renewed with the emergence of physics based machine learning and differentiable programming, which now allow to bridge physical sciences, scientific computing, uncertainty quantification, and machine learning (Carleo et al., 2019).

If we adopt a data-centric viewpoint, ocean forecasting sys-
tems can indeed be described as a succession of independent
data processing steps in sequential pipelines (see Figure 4.1).
These pipelines include the collection of past observation-
al data, data-assimilation to reconstruct the current state
of the ocean, forecasting with a physics-based model, and
eventually the post-processing and dissemination to users.
Data is being processed with algorithms at each step of the
pipelines. It is now obvious that modern machine learning
has the potential to impact each step of the data-processing
pipelines of operational oceanography and ocean forecast-
ing systems.

As mentioned above, many applications can be identified
upstream or downstream of the core engines of ocean fore-
casting systems. Typical applications of ML upstream of core
engines include, for instance, algorithms for alleviating ob-
servational noise, for retrieving parameters (Malmgren-Han-
sen, 2021), or for data quality control (Castelão, 2021). ML can
thus be used for detecting outliers in Argo profiles (Maze et
al., 2017). The range of possible downstream uses of core
forecasting engines is even wider. ML is here expected to help
design tailored services addressing key challenges (Persello
et al., 2022), such as improving the prediction of Lagrangian
drift or detecting anomalous extreme events.

However, what is probably more difficult to perceive is how
machine learning may soon affect the core engine of ocean
forecasting systems, and eventually all the services to users.
Machine learning and differentiable programming are in-
deed opening many opportunities in computational fluid dy-

<figure><img src="image_619.png" /></figure>
namics (Vinuesa and Brunton, 2021), while deeply renewing inverse methods in many areas (Cranmer et al., 2020). These recent advances could be leveraged for improving ocean models, e.g. for better accounting for unresolved processes (Brunton et al., 2020; Zanna and Bolton, 2021). They could also help improve data assimilation schemes (Bonavita and Laloyaux, 2020), or even possibly replace full inversion pipelines (Fablet et al., 2021).

These recent advances open the possibility to design and train our core forecasting engines in such a way that their complexity and performance could be optimised for specific applications, ultimately improving our ability to meet the diversity of user needs.

### 12.6.2. Designing fully trainable ocean forecasting systems core engines

The core engines of current ocean forecasting systems are based on two types of objects that are still quite independent, namely ocean circulation models and data assimilation methods. Ocean models, data assimilation methods, and their implementation in forecasting systems are being continuously improved. But our core forecasting engines are still rather static in their design and structure, due to technological, organisational and historical reasons. For instance, ocean models are generally developed without taking into account how they will be implemented with data assimilation. As such, there is no guarantee of the optimality of the overall design of our systems and its fit for purpose in specific contexts.

Recent developments at the interface of machine learning and scientific computing could open the possibility of optimising the design of our core prediction engines according to predefined objectives. Indeed, beyond the improvements of specific components of ocean models or data assimilation schemes, the real benefit to be expected from machine learning in forecasting systems is the ability to optimise entire pipelines with end-to-end strategies. The term end-to-end here refers to the ability to optimise components of processing pipelines based on metrics measuring the performance of the entire pipeline. End-to-end strategies may eventually allow the design of fit for purpose and user-centric processing chains and products.

There are obviously technological conditions to realise this potential. Integrating trainable components in core forecasting engines is indeed greatly facilitated if these engines are already composed of independent modules with robust and stable interfaces. It is therefore necessary a gradual evolution to make the system more modular and composable. Moreover, if we want to take advantage of end-to-end strategies, the core engines should be fully differentiable. This would allow to back-propagate a misfit in the prediction into

an increment in the parameters of the engine. This is only possible if the core engine is written in a high-level differentiable language or programming framework.

Such prerequisites may at first appear daunting, but a gradual evolution towards modular, composable, and differentiable core engines would also have important side benefits. First, this effort to redesign our core engines, may actually provide a viable strategy for exploiting upcoming computing architectures, starting from GPUs (Kochkov et al., 2021). It may also simplify the maintenance of our engines, as for instance the development of adjoint models (Hatfield et al., 2021), therefore speeding up the transfer from research to operation (R2O). Another benefit is also the built-in treatment of uncertainties, thanks to recent advances in probabilistic programming (van de Meent et al., 2021) and Bayesian Machine Learning 6.

### 12.6.3. Towards user-centric, ocean digital twins leveraging lightweight emulators

Looking further ahead, it can be guessed what future digital twins of the ocean will eventually look like. The integration of AI components may indeed gradually change the underlying paradigm of ocean forecasting systems. While current systems essentially implement “single-core engines” with a predefined level of complexity, future systems may be based on collections of core engines, tailored to the specific needs of particular users. These tailored core engines would instantiate core methods and building blocks in a versatile and user-centric way, providing fit for purpose tools and products to users.

Whatever form digital twins will eventually take, a key methodology will be the ability to train emulators of existing systems at reduced costs and with controlled complexity. As described above, a gradual evolution of our core forecasting engines will be needed for leveraging the full potential of AI and ML. This transition may in particular leverage DDEs. They provide approximations of pre-existing algorithms (Kasim et al., 2021) and can be integrated in data assimilation schemes (Nonnenmacher and Greenberg, 2021). As such, DDEs offer a good solution for building upon existing expertise and tools, while benefiting from the pace of scientific and technological advances in AI.

<figure><img src="image_620.png" /></figure>

<footnote n="6">6. https://jorisbaan.nl/2021/03/02/introduction-to-bayesian-deep-learning.html</footnote>
In conclusion, it appears that we are at the beginning of an exciting phase in the evolution of ocean forecasting systems, which could deeply transform the entire service offered to users. The integration of AI in ocean forecasting systems will require a gradual but profound change of the algorithms that constitute their underpinnings. This transition will take advantage of the

wealth of expertise on ocean physics, observing networks, and user needs available in ocean forecasting centres. It will also require developing and nurturing new collaborations with the broader AI technological and scientific community, and benefit from the adoption of open science practices.

<figure><img src="image_621.png" /></figure>

## 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.

7. https://eurogoos.eu/models/

<figure><img src="image_622.png" /></figure>
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 devel-
opers is still far from sufficient for most of the models. There-
fore, 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 op-
tions 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 co-
operation is carried out at regional level. The global co-de-
velopment of models is probably less active due to both ad-
ministrative 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 2011ª: "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 devel-
opment 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 fil-
ters, 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 im-
portant 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 de-
terministic prediction; iii) shorter-scale phenomena, although
constrained by longer-scale ones, are also a statistical forc-
ing 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 re-
quirements 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 sea-
sons. For the regional scale coupled UOMs, geographic cov-
erage 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, flex-
ible grid and high-performance computing are two basic re-
quirements. 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 res-

<figure><img src="image_623.png" /></figure>

<footnote n="8">8. https://public.wmo.int/en/bulletin/predictability-beyond-deterministic-limit</footnote>
olution 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 im-
provement of prediction skills of the marine earth system
models with a forecast range of 10 days to 1 month. The re-
search should focus on: (i) establishing UOM global coopera-
tion framework to harmonise, coordinate, and further evolve
existing UOM development work; (ii) designing the UOM con-
cept, 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 pre-
diction skills in time scales from months to seasons for cli-
mate, 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 atmo-
sphere-ocean-wave-ice coupling and ocean-optics-SPM-bio-
geochemical coupling will be developed for ensemble pre-
diction. UOM code will also be optimised for efficient hybrid
parallel computing.

<figure><img src="image_624.png" /></figure>

12.8.

Operational forecasting and scenarios in a digital ocean

A Digital Twin of the Ocean (DTO) is a highly accurate model of the ocean to monitor and predict environmental change, human impact, and vulnerability, with the support of an openly accessible and interoperable dataspace that can function as a central hub for informed decision making (Figure 12.3) (see for example 👤). Such an information system consists of one or more digital replicas of the state and temporal evolution of the oceanic system constrained by the available observations and the laws of physics, making imperative to integrate a set of models or software that pairs the digital world with physical assets, and to feed this set with information from sensors.

A DTO aims to deliver a holistic and cost-effective solution for the integration of all information sources related to seas and oceans, like in situ-data and satellite information combined with IoT techniques, Citizen science, state-of-the-art

ocean modelling together with AI and HPC resources into a
digital, consistent, high-resolution, multi-dimensional, and
near real-time representation of the ocean. This will result
in a shared capacity to access, manipulate, analyse, and vi-
sualise marine information. The knowledge generated by
theDTO platform will empower scientists, citizens, govern-
ments, and industries to collectively share the responsibility
to monitor, preserve and enhance marine and coastal hab-
itats, while promoting action and sustainable measures in
the framework of the blue economy (tourism, fishing, aqua-
culture, transport, renewable energy, etc.), contributing to a
healthy and productive ocean.

9. https://digitaltwin.ocean.mercator-ocean.eu/

<figure><img src="image_625.png" /></figure>
<figure><img src="image_626.png" /><figcaption>**Figure 12.3.** Schematic representation of Digital Twin of the Ocean concept.</figcaption></figure>

### 12.8.1. Construction of an openDTO service platform

To properly address the construction of a digital twin, breakthroughs are needed in various aspects of the digital twin information system, including information completeness and quality, information access and intervention, as well as the underlying supporting infrastructure, tools, and services. The operational pilot ofDTO, under development at European level, will encompass the production of a new quality of information, incorporating human systems in the prediction problem and leveraging advances in information theory and digital technologies. Ensembles of simulations combining models from different disciplines, informed by spatial correlations determined from high-resolution observations and by data-driven learning of unknown processes and missing constraints, will enable theDTO to reduce uncertainty in estimation and forecasting of ocean states, changes, and impacts.

Enhancing information quality requires a step change in computational complexity. This means adequate infrastructure including support of very high computing throughputs, concurrency, and extreme-scale hardware. However, it is important to conceal this complexity so that users can run and configure involved workflows and access the information but without requiring expert intervention. In addition, the underlying models and data need to be scientifically sound.

This will require a multi-layered software framework where tasks like simulations, observational data ingestion, and post-processing are treated as objects that are executed on federated computing infrastructures, feed data into virtual data repositories with standardised metadata, and from which a heavily machine-learning-based toolkit extracts information that can be manipulated in any possible way. The result should be the provision of on-demand, conveniently accessible modelling and simulation products, data and processes or MSaaS.

<figure><img src="image_627.png" /></figure>
<figure><img src="image_628.png" /><figcaption>**Figure 12.4.** DIDO Architecture.</figcaption></figure>

12.8.2. Underlying architecture

The multi-layered framework enabling this digital twin ocean pilot operational service comprises 3 major interrelated structural elements (Figure 12.4):

* ADTO data access layer that mixes results and tools from ongoing projects and existing infrastructures with new developments targeting data ingestion, and data harmonising into a Data lake for subsequent use in theDTO engine;

* A DIDO engine comprising a set of modelling capabilities, including on-demand modelling and what-if scenario modelling that fill the observational gaps in space and time in a physically consistent way, and observation-driven learning of unknown processes and missing

constraints, which will enable to reduce uncertainty in estimation and forecasting;

* A DIDO interactive service layer supplying tools, libraries, and interfaces to simplify running and configuration of workflows, as well as access to information, including its analysis and visualisation.

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