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This paper reviews the historical development of concepts and practices in the science of ocean predictions. It begins with meteorology, which conducted the first forecasting experiment in 1950, followed by wind waves, and continuing with tidal and storm surge predictions to arrive at the first successful ocean mesoscale forecast in 1983. The work of Professor A. R. Robinson of Harvard University, who produced the first mesoscale ocean predictions for the deep ocean regions is documented for the first time. The scientific and technological developments that made accurate ocean predictions possible are linked with the gradual understanding of the importance of the oceanic mesoscales and their inclusion in the numerical models. Ocean forecasting developed first at the regional level, due to the relatively low computational requirements, but by the end of the 1990s, it was possible to produce global ocean uncoupled forecasts and coupled ocean-atmosphere seasonal forecasts.

The Copernicus Marine Environment Monitoring Service (CMEMS) Ocean State Report (OSR) provides an annual report of the state of the global ocean and European regional seas for policy and decision-makers with the additional aim of increasing general public awareness about the status of, and changes in, the marine environment. The CMEMS OSR draws on expert analysis and provides a 3-D view (through reanalysis systems), a view from above (through remote-sensing data) and a direct view of the interior (through in situ measurements) of the global ocean and the European regional seas. The report is based on the unique CMEMS monitoring capabilities of the blue (hydrography, currents), white (sea ice) and green (e.g. Chlorophyll) marine environment. This first issue of the CMEMS OSR provides guidance on Essential Variables, large-scale changes and specific events related to the physical ocean state over the period 1993–2015. Principal findings of this first CMEMS OSR show a significant increase in global and regional sea levels, thermosteric expansion, ocean heat content, sea surface temperature and Antarctic sea ice extent and conversely a decrease in Arctic sea ice extent during the 1993–2015 period. During the year 2015 exceptionally strong large-scale changes were monitored such as, for example, a strong El Niño Southern Oscillation, a high frequency of extreme storms and sea level events in specific regions in addition to areas of high sea level and harmful algae blooms. At the same time, some areas in the Arctic Ocean experienced exceptionally low sea ice extent and temperatures below average were observed in the North Atlantic Ocean.

In this paper we show that operational oceanography products can be used to develop indicators as required by the Marine Strategy Framework Directive (MSFD). We present a mixing indicator that is calculated using MyOcean Marine Service reanalysis products. Seasonal climatology data of the Brunt–Väisäla frequency (BVF) were computed for 2001–2010 and the vertical mixing coefficient was defined. A vertical mixing indicator was then computed in order to differentiate between different mixing conditions depending on the seasons and differentiating between the shelf and the open ocean in the central Mediterranean Sea.

A new numerical general circulation ocean model for the Mediterranean Sea has been implemented nested within an Atlantic general circulation model within the framework of the Marine Environment and Security for the European Area project (MERSEA, Desaubies, 2006). A 4-year twin experiment was carried out from January 2004 to December 2007 with two different models to evaluate the impact on the Mediterranean Sea circulation of open lateral boundary conditions in the Atlantic Ocean. One model considers a closed lateral boundary in a large Atlantic box and the other is nested in the same box in a global ocean circulation model. Impact was observed comparing the two simulations with independent observations: ARGO for temperature and salinity profiles and tide gauges and along-track satellite observations for the sea surface height. The improvement in the nested Atlantic-Mediterranean model with respect to the closed one is particularly evident in the salinity characteristics of the Modified Atlantic Water and in the Mediterranean sea level seasonal variability.

This study describes a new model implementation for the Mediterranean Sea with what is currently the highest vertical resolution over the Mediterranean basin. The resolution is of 1/16°×1/16° in the horizontal and has 72 unevenly spaced vertical levels. This model has been developed in the frame of the EU-MFSTEP project and is the operational forecast model currently used at the basin scale. The model considers an implicit free surface and this characteristic enhances the model's capability to simulate the sea surface height variability and the net transport at the Strait of Gibraltar. In this study we show the calibration/validation experiments performed before and after the model was used for forecasting. The first experiment consists of a six-year simulation forced by a perpetual year forcing, and the other experiment is a simulation from January 1997 to December 2004, forcing the model with 6-h atmospheric forcing fields from ECMWF. The model Sea Level Anomaly has been compared for the first time with satellite SLA and with ARGO data to provide evidence of the quality of the simulation. The results show that this model is capable of reproducing most of the variability of the general circulation in the Mediterranean Sea. However, some basic model inadequacies stand out and should be corrected in the near future.

This paper describes the operational implementation of the data assimilation scheme for the Mediterranean Forecasting System Pilot Project (MFSPP). The assimilation scheme, System for Ocean Forecast and Analysis (SOFA), is a reduced order Optimal Interpolation (OI) scheme. The order reduction is achieved by projection of the state vector into vertical Empirical Orthogonal Functions (EOF). The data assimilated are Sea Level Anomaly (SLA) and temperature profiles from Expandable Bathy Termographs (XBT). The data collection, quality control, assimilation and forecast procedures are all done in Near Real Time (NRT). The OI is used intermittently with an assimilation cycle of one week so that an analysis is produced once a week. The forecast is then done for ten days following the analysis day. The root mean square (RMS) between the model forecast and the analysis (the forecast RMS) is below 0.7°C in the surface layers and below 0.2°C in the layers deeper than 200 m for all the ten forecast days. The RMS between forecast and initial condition (persistence RMS) is higher than forecast RMS after the first day. This means that the model improves forecast with respect to persistence. The calculation of the misfit between the forecast and the satellite data suggests that the model solution represents well the main space and time variability of the SLA except for a relatively short period of three – four weeks during the summer when the data show a fast transition between the cyclonic winter and anti-cyclonic summer regimes. This occurs in the surface layers that are not corrected by our assimilation scheme hypothesis. On the basis of the forecast skill scores analysis, conclusions are drawn about future improvements. Key words. Oceanography; general (marginal and semi-enclosed seas; numerical modeling; ocean prediction)

This paper describes the first evaluation of the quality of the forecast and analyses produced at the basin scale by the Mediterranean ocean Forecasting System (MFS) (http://gnoo.bo.ingv.it/mfs). The system produces short-term ocean forecasts for the following ten days. Analyses are produced weekly using a daily assimilation cycle. The analyses are compared with independent data from buoys, where available, and with the assimilated data before the data are inserted. In this work we have considered 53 ten days forecasts produced from 16 August 2005 to 15 August 2006. The forecast skill is evaluated by means of root mean square error (rmse) differences, bias and anomaly correlations at different depths for temperature and salinity, computing differences between forecast and analysis, analysis and persistence and forecast and persistence. The Skill Score (SS) is defined as the ratio of the rmse of the difference between analysis and forecast and the rmse of the difference between analysis and persistence. The SS shows that at 5 and 30 m the forecast is always better than the persistence, but at 300 m it can be worse than persistence for the first days of the forecast. This result may be related to flow adjustments introduced by the data assimilation scheme. The monthly variability of SS shows that when the system variability is high, the values of SS are higher, therefore the forecast has higher skill than persistence. We give evidence that the error growth in the surface layers is controlled by the atmospheric forcing inaccuracies, while at depth the forecast error can be interpreted as due to the data insertion procedure. The data, both in situ and satellite, are not homogeneously distributed in the basin; therefore, the quality of the analyses may be different in different areas of the basin.