As part of the work of the GODAE OceanView Inter-comparison and Validation Task Team (IV-TT), 6 global ocean forecasting systems spread across 5 operational oceanography forecast centres were inter-compared using a common set of observations as a proxy for the truth. The 'Class 4' in the title refers to a set of forecast verification metrics defined in the MERSEA-IP/GODAE internal metrics document (Hernandez 2007), the defining feature of which is that comparisons between forecasts and observations take place in observation space. This approach is seen as a departure from other diagnostic approaches such as analysing model trends or innovation statistics, and is commonly used in the atmospheric community. The physical parameters involved in the comparison are sea surface temperature (SST), sub-surface temperature, sub-surface salinity and sea level anomaly (SLA). SST was measured using in-situ observations obtained from USGODAE, sub-surface conditions were compared to Argo profiles, while sea level anomaly was measured by several satellite altimeters courtesy of AVISO. The 5 forecast centres involved in the project were Met Office, Australian Bureau of Meteorology, Mercator Océan, Environment Canada and NOAA/NWS/NCEP. Combining Met Office, Mercator Océan and Environment Canada forecasts into a mixed resolution multi-model ensemble produces estimates of the ocean state which have better accuracy and associativity properties for SST, SLA and temperature profiles than any individual ensemble component.

(2015) Recent progress in performance evaluations and near real-time assessment of operational ocean products, Journal of Operational Oceanography, 8:sup2, s221-s238,

Abstract. BRAN2020 (2020 version of the Bluelink ReANalysis) is an ocean reanalysis that combines observations with an eddy-resolving, near-global ocean general circulation model to produce a four-dimensional estimate of the ocean state. The data assimilation system employed is ensemble optimal interpolation, implemented with a new multiscale approach that constrains the broad-scale ocean properties and the mesoscale circulation in two steps. There is a separation in the scales that are corrected in the two steps: the high-resolution step corrects the mesoscale dynamics in the same way as previous versions of BRAN, while the extra coarse step is effective at correcting biases that develop at large scales. The reanalysis currently spans January 1993 to December 2019 and assimilates observations of in situ temperature and salinity, as well as of satellite sea-level anomaly and sea surface temperature. BRAN2020 is planned to be updated to within months of real time after this initial release, until an updated version of BRAN is available. Reanalysed fields from BRAN2020 generally show much closer agreement to observations than all previous versions with misfits between reanalysed and observed fields reduced by over 30 % for some variables, for subsurface temperature and salinity in particular. The BRAN2020 dataset is comprised of daily averaged fields of temperature, salinity, velocity, mixed-layer depth and sea level. Reanalysed fields realistically represent all of the major current systems within 75∘ S and 75∘ N, excluding processes relating to sea ice but including boundary currents, equatorial circulation, Southern Ocean variability and mesoscale eddies. BRAN2020 is publicly available at https://doi.org/10.25914/6009627c7af03 (Chamberlain et al., 2021b) and is intended for use by the research community.

Operational oceanography can be described as the provision of routine oceanographic information needed for decision-making purposes. It is dependent upon sustained research and development through the end-to-end framework of an operational service, from observation collection to delivery mechanisms. The core components of operational oceanographic systems are a multi-platform observation network, a data management system, a data assimilative prediction system, and a dissemination/accessibility system. These are interdependent, necessitating communication and exchange between them, and together provide the mechanism through which a clear picture of ocean conditions, in the past, present, and future, can be seen. Ocean observations play a critical role in all aspects of operational oceanography, not only for assimilation but as part of the research cycle, and

[1] The time mean ocean circulation for the southeast Indian Ocean (SEIO) is analyzed using a colorwheel to reveal an "arterial-like" structure. Although quasi-zonal, the mean circulation in this region is distinctly more complex than previously presented in recent literature. The mean flow is derived from an eddy-resolving ocean reanalysis of the past 14 years. The ocean state is constrained through data assimilation with observed sea level anomaly, sea surface temperature and in-situ profiles. Geostrophic currents derived from the reanalyzed mean dynamic topography (MDT) are validated against two other MDT analyses to show a comparable arterial structure and distribution of scales. In particular, the broad scale eastward flow is regularly interspersed by narrow bands of the near westward flow. Meridional sections from the ocean reanalysis reveal a net eastward transport in the surface layer and a net westward flow at mid-depth. The seasonality of the surface layer flow in the SEIO appears to have increased (decreased) eastward mean transport that coincide with seasons of stronger (weaker) Leeuwin Current (LC). Whether the narrow bands of mean current and mass flux are simply the average of the eddies or represent an underlying weak jet remains a topic of current debate. Citation: Divakaran, P., and G. B. Brassington (2011), Arterial ocean circulation of the southeast Indian Ocean,

This paper compares the performance of short-range operational ocean forecasts, using 'observational space' metrics developed under GODAE OceanView (GOV). Best estimates (behind the real-time analysis) and forecasts are intercompared for the Australian region (0-50S, 90-180E) for 2013. Systems considered include those developed in Australia, France, Canada, United Kingdom and USA. Each system is compared to observations of along-track sea level anomaly, sea surface temperature observations from surface drifters and sub-surface Argo profiles of temperature and salinity. The UK operational system generally has the smallest errors for sea surface temperature and sea level anomaly for the Australian region. However, the French systems outperform others in sub-surface temperature and salinity for the region. Of the two products provided by the Australian centre, an ensemble based approach is found to perform better than the deterministic system, having higher skill and lower root mean square errors. Some of the 'better' results of systems can be attributed in part to the lack of independence of the reference observations; however the study does demonstrate the feasibility and robustness of GOV global ocean inter-comparison efforts for regional applications.

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