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Chapter 12

Challenges and future perspectives in ocean prediction

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

12.5 Future of ensemble prediction systems

Numerical ocean, weather, seasonal and climate forecasting systems across the world are tending towards becoming coupled ensemble data assimilation prediction systems (Brassington 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 forecast 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.

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, 0.1o 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.

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 accurate observations, and improved data assimilation practices. 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 vapour, cloud, precipitation, sea-ice, and phytoplankton concentration (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.

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.

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Chapter 12

Challenges and future perspectives in ocean prediction

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