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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

12.4 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 ofresearch (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.

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Zanna, L., and Bolton, T. (2021). Deep learning of unresolved turbulent ocean processes in climate models. In: “Deep Learning for the Earth Sciences”, G. Camps-Valls, D. Tuia, X. X. Zhu, and M. Reichstein (Eds.), Wiley https://doi.org/10.1002/9781119646181.ch20

Zhang, L., Delworth, T.L., Jia, L. (2017). Diagnosis of Decadal Predictability of Southern Ocean Sea Surface Temperature in the GFDL CM2.1 Model. Journal of Climate, 30(16), 6309-6328, https://doi.org/10.1175/JCLI-D-16-0537.1

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Chapter 12

Challenges and future perspectives in ocean prediction

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