The Black Sea Monitoring and Forecasting Center (BS-MFC) is the European reference service for the provision of ocean analyses, forecasts, and reanalyses in the Black Sea basin. It is part of the Copernicus Marine Environment and Monitoring Service (CMEMS) and ensures a high level of efficiency in terms of operations, science, and technology for predictions and the monitoring of physical and biogeochemical processes in the Black Sea. The operational BS-MFC framework is based on state-of-the-art numerical models for hydrodynamics, biogeochemistry, and waves; analysis, forecast, and reanalysis are provided on a spatial grid with about 3 km of horizontal resolution that covers the whole Black Sea basin (the Azov Sea is not included). The scientific assessment of BS-MFC products is performed by implementing a product quality dashboard that provides pre-qualification and operational model skills according to GODAE/OceanPredict standards. Novel interfaces based on high-resolution models are part of the scientific development plan to ensure a strong connection with the nearest seas from a modelling point of view, in particular with the Mediterranean Sea. To improve forecasting skills, dedicated online coupled systems are being developed, which involve physics, biogeochemistry, and waves together with the atmosphere and, in the future, with ensemble forecasting methodologies and river-ocean interfaces.

This work describes the design, implementation and validation of the Black Sea physics analysis and forecasting system, developed by the Black Sea Physics production unit within the Black Sea Monitoring and Forecasting Center as part of the Copernicus Marine Environment and Monitoring Service. The system provides analyses and forecasts of the temperature, salinity, sea surface height, mixed layer depth and currents for the whole Black Sea basin, excluding the Azov Sea, and has been operational since 2016. The system is composed of the NEMO (v 3.4) numerical model and an OceanVar scheme, which brings together real time observations (in-situ temperature and salinity profiles, sea level anomaly and sea surface temperature satellite data). An operational quality assessment framework is used to evaluate the accuracy of the products which set the basic standards for the future upgrades, highlighting the strengths and weaknesses of the model and the observing system in the Black Sea.

On 7 March 2014 (UTC), Malaysia Airlines flight 370 vanished without a trace. The aircraft is believed to have crashed in the southern Indian Ocean, but despite extensive search operations the location of the wreckage is still unknown. The first tangible evidence of the accident was discovered almost 17 months after the disappearance. On 29 July 2015, a small piece of the right wing of the aircraft was found washed up on the island of Réunion, approximately 4000 km from the assumed crash site. Since then a number of other parts have been found in Mozambique, South Africa and on Rodrigues Island. This paper presents a numerical simulation using high-resolution oceanographic and meteorological data to predict the movement of floating debris from the accident. Multiple model realisations are used with different starting locations and wind drag parameters. The model realisations are combined into a superensemble, adjusting the model weights to best represent the discovered debris. The superensemble is then used to predict the distribution of marine debris at various moments in time. This approach can be easily generalised to other drift simulations where observations are available to constrain unknown input parameters. The distribution at the time of the accident shows that the discovered debris most likely originated from the wide search area between 28 and 35° S. This partially overlaps with the current underwater search area, but extends further towards the north. Results at later times show that the most probable locations to discover washed-up debris are along the African east coast, especially in the area around Madagascar. The debris remaining at sea in 2016 is spread out over a wide area and its distribution changes only slowly.

The diurnal cycle of sea surface temperature (SST) is an important component of the ocean‐atmosphere system and is necessary for accurately computing air‐sea heat fluxes. Ocean temperatures in the near‐surface are highly sensitive to atmospheric conditions and can vary significantly depending on time of day. Ocean general circulation models are unable to fully capture the near‐surface diurnal SST variability, because they do not possess the necessary vertical structure and resolution. Furthermore, SST observations come from a number of sources that represent the temperature at various near‐surface depths. This presents difficulties when assimilating SST observations as well as constructing robust climate records of SST. In this study we model the fine‐scale near‐surface structure allowing SST comparisons between foundation SST, SST at depth, subskin SST, and skin SST. Hourly model results, forced and initialized using readily available reanalysis data, are from a 2‐year period, 2013–2014, over the Mediterranean Sea. Various solar absorption parameterizations are examined, and the resulting SSTs are compared to Spinning Enhanced Visible and InfraRed Imager‐derived observations of the skin temperature.

A new web-based and mobile decision support system (DSS) for search-and-rescue (SAR) at sea is presented, and its performance is evaluated using real case scenarios. The system, named OCEAN-SAR, is accessible via the website http://www.ocean-sar.com. In addition to the website, dedicated applications for iOS and Android have been created to optimise the user experience on mobile devices. OCEAN-SAR simulates drifting objects at sea, using as input ocean currents and wind data provided, respectively, by the CMEMS and ECMWF. The modelling of the drifting objects is based on the leeway model, which parameterises the wind drag of an object using a series of coefficients. These coefficients have been measured in field experiments for different types of objects, ranging from a person in the water to a coastal freighter adrift. OCEAN-SAR provides the user with an intuitive interface to run simulations and to visualise their results using Google Maps. The performance of the service is evaluated by comparing simulations to data from the Italian Coast Guard pertaining to actual incidents in the Mediterranean Sea.