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Since 2016, the Copernicus Marine Environment Monitoring Service (CMEMS) has produced and disseminated an ensemble of four global ocean reanalyses produced at eddy-permitting resolution for the period from 1993 to present, called GREP (Global ocean Reanalysis Ensemble Product). This dataset offers the possibility to investigate the potential benefits of a multi-system approach for ocean reanalyses, since the four reanalyses span by construction the same spatial and temporal scales. In particular, our investigations focus on the added value of the information on the ensemble spread, implicitly contained in the GREP ensemble, for temperature, salinity, and steric sea level studies. It is shown that in spite of the small ensemble size, the spread is capable of estimating the flow-dependent uncertainty in the ensemble mean, although proper re-scaling is needed to achieve reliability. The GREP members also exhibit larger consistency (smaller spread) than their predecessors, suggesting advancement with time of the reanalysis vintage. The uncertainty information is crucial for monitoring the climate of the ocean, even at regional level, as GREP shows consistency with CMEMS high-resolution regional products and complement the regional estimates with uncertainty estimates. Further applications of the spread include the monitoring of the impact of changes in ocean observing networks; the use of multi-model ensemble anomalies in hybrid ensemble-variational retrospective analysis systems, which outperform static covariances and represent a promising application of GREP. Overall, the spread information of the GREP product is found to significantly contribute to the crucial requirement of uncertainty estimates for climatic datasets.

Subtropical gyres are the oceanic regions where plastic litter accumulates over long timescales, exposing surrounding oceanic islands to plastic contamination, with potentially severe consequences on marine life. Islands’ exposure to such contaminants, littered over long distances in marine or terrestrial habitats, is due to the ocean currents that can transport plastic over long ranges. Here, this issue is addressed for the Easter Island ecoregion (EIE). High-resolution ocean circulation models are used with a Lagrangian particle-tracking tool to identify the connectivity patterns of the EIE with industrial fishing areas and coastline regions of the Pacific basin. Connectivity patterns for “virtual” particles either floating (such as buoyant macroplastics) or neutrally-buoyant (smaller microplastics) are investigated. We find that the South American shoreline between 20°S and 40°S, and the fishing zone within international waters off Peru (20°S, 80°W) are associated with the highest probability for debris to reach the EIE, with transit times under 2 years. These regions coincide with the most-densely populated coastal region of Chile and the most-intensely fished region in the South Pacific. The findings offer potential for mitigating plastic contamination reaching the EIE through better upstream waste management. Results also highlight the need for international action plans on this important issue.

The global high resolution monitoring and forecasting system PSY4 at Mercator Ocean, initialized in October 2006, has achieved 11 years of global ocean state estimation. Based on the NEMO global 1/12° configuration, PSY4 includes data assimilation of satellite and multi-instrument in situ observations. In parallel to this monitoring system, a twin-free simulation (with no assimilation) has been performed for the period 2007–2015. In this study, monthly-averaged fields of both ocean state estimates are compared with observation products for the period 2007–2015, to examine the consistency of PSY4 fields with related observations for representing large-scale variability and to provide a baseline that is mainly focused on in situ comparisons for validation/qualification of on-going system developments. Observations play a major role in correctly positioning the main energetic structures, both in space and time. In addition, data assimilation appears to overcome the other deficiencies of models by reducing SST bias in upwelling regions and by increasing the thermocline gradient in the tropics. Generally, the amplitude of the total-resolved variability in both PSY4 estimates is consistent with observation data sets. Annual cycle and longer-term variability in temperature, salinity and sea surface height are significantly improved with data assimilation, but some progress is still needed to better represent the amplitude of changes of ocean heat and freshwater contents on long timescales. Finally, the PSY4 system's ability to capture the large scale variability is further investigated by using as a case study the northward pathways of El Nino anomalies in the tropical North Pacific in 2014 and 2015 in order to illustrate how such systems can be used to answer relevant scientific questions.

Operational evolution of global and regional ocean forecasting systems has been extremely significant in recent years. Global Ocean Data Assimilation Experiment (GODAE) Oceanview supports the natio...

The availability of GODAE Oceanview-type ocean forecast systems provides the opportunity to develop high-resolution, short- to medium-range coupled prediction systems. Several groups have undertake...

This study is aimed at exploring the errors of a regional model of the Bay of Biscay, a regional zoom of the IBI configuration of the ocean model NEMO, with the ultimate objective of guiding the ch...

All climate models predict a freshening of the North Atlantic at high latitude that may induce an abrupt change of the Atlantic Meridional Overturning Circulation (hereafter AMOC) if it resides in the bistable regime, where both a strong and a weak state coexist. The latter remains uncertain as there is no consensus among observations and ocean reanalyses, where the AMOC is bistable, versus most climate models that reproduce a mono-stable strong AMOC. A series of four hindcast simulations of the global ocean at 1/12° resolution, which is presently unique, are used to diagnose freshwater transport by the AMOC in the South Atlantic, an indicator of AMOC bistability. In all simulations, the AMOC resides in the bistable regime: it exports freshwater southward in the South Atlantic, implying a positive salt advection feedback that would act to amplify a decreasing trend in subarctic deep water formation as projected in climate scenarios.

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.

The feasibility of global ocean weather prediction was just emerging as the Global Ocean Data Assimilation Experiment (GODAE) began in 1997. Ocean weather includes phenomena such as meandering currents and fronts, eddies, the surface mixed layer and sea surface temperature (SST), equatorial and coastally trapped waves, upwelling of cold water, and Rossby waves, all influencing ocean variables such as temperature (T), salinity (S), currents, and sea surface height (SSH). Adequate real-time data input, computing power, numerical ocean models, data assimilation capabilities, atmospheric forcing, and bathymetric/boundary constraints are essential to make such prediction possible. The key observing systems and real-time data inputs are SSH from satellite altimetry, satellite and in situ SST, T or T and S profiles (e.g., Argo, TAO/Triton, PIRATA moored array in the Atlantic, bathythermographs), and atmospheric forcing. The ocean models dynamically interpolate data in conjunction with data assimilation, convert atmospheric forcing into oceanic responses, and forecast the ocean weather, applying bathymetric/boundary constraints in the process. The results are substantially influenced by ocean model simulation skill and it is advantageous to use an ocean model that is eddy-resolving (nominally 1/10º or finer), not just eddy-permitting. Because the most abundant ocean observations are satellite surface data, and subsurface data are very sparse in relation to the spatial scales of the mesoscale ocean features that dominate the ocean interior, downward projection of surface data is a key challenge in ocean data assimilation. The need for accurate prediction of ocean features that are inadequately observed, such as mixed layer depth, places a major burden on the ocean model, data assimilation, and atmospheric forcing. The sensitivity of ocean phenomena to atmospheric forcing and the time scale for response affect the time scale for oceanic predictive skill, sensitivity to the initial state versus the atmospheric forcing as a function of forecast length, and thus oceanic data requirements and prediction system design. Outside of surface boundary layers and shallow regions, forecast skill is about one month globally and over many subregions, and is only modestly reduced by using climatological forcing after the end of atmospheric forecasts versus using analysis-quality forcing for the duration. In addition, global ocean prediction systems must demonstrate the ability to provide initial and boundary conditions to nested regional and coastal models that enhance their predictive skill. Demonstrations of feasibility in relation to the preceding phenomena, requirements, and challenges are drawn from the following global and basin-scale ocean prediction systems: BLUElink> (Australia), the HYbrid Coordinate Ocean Model (HYCOM; USA), Mercator Ocean (France), Multivariate Ocean Variational Estimation/Meteorological Research Institute Community Ocean Model (MOVE/MRI.COM; Japan), and the Naval Research Laboratory Layered Ocean Model (NLOM; USA).

The French research community in the Mediterranean Sea modeling and the French operational ocean forecasting center Mercator Océan have gathered their skill and expertise in physical oceanography, ocean modeling, atmospheric forcings and data assimilation to carry out a MEDiterranean sea ReanalYsiS (MEDRYS) at high resolution for the period 1992-2013. The ocean model used is NEMOMED12, a Mediterranean configuration of NEMO with a 1=12° (∼7 km) horizontal resolution and 75 vertical z levels with partial steps. At the surface, it is forced by a new atmospheric-forcing data set (ALDERA), coming from a dynamical downscaling of the ERA-Interim atmospheric reanalysis by the regional climate model ALADIN-Climate with a 12 km horizontal and 3 h temporal resolutions. This configuration is used to carry a 34-year hindcast simulation over the period 1979-2013 (NM12-FREE), which is the initial state of the reanalysis in October 1992. MEDRYS uses the existing Mercator Océan data assimilation system SAM2 that is based on a reduced-order Kalman filter with a threedimensional (3-D) multivariate modal decomposition of the forecast error. Altimeter data, satellite sea surface temperature (SST) and temperature and salinity vertical profiles are jointly assimilated. This paper describes the configuration we used to perform MEDRYS. We then validate the skills of the data assimilation system. It is shown that the data assimilation restores a good average temperature and salinity at intermediate layers compared to the hindcast. No particular biases are identified in the bottom layers. However, the reanalysis shows slight positive biases of 0.02 psu and 0.15 °C above 150m depth. In the validation stage, it is also shown that the assimilation allows one to better reproduce water, heat and salt transports through the Strait of Gibraltar. Finally, the ability of the reanalysis to represent the sea surface high-frequency variability is shown., This research has received funding from the French National Research Agency (ANR) project REMEMBER (contract ANR-12-SENV-001).

Since December 2010, the global analysis and forecast of the MyOcean system consists in the Mercator Océan NEMO global 1/4° configuration with a 1/12° "zoom" over the Atlantic and Mediterranean Sea. The zoom open boundaries come from the global 1/4° at 20° S and 80° N. The data assimilation uses a reduced order Kalman filter with a 3-D multivariate modal decomposition of the forecast error. It includes an adaptative error and a localization algorithm. A 3D-Var scheme corrects for the slowly evolving large-scale biases in temperature and salinity. Altimeter data, satellite temperature and in situ temperature and salinity vertical profiles are jointly assimilated to estimate the initial conditions for the numerical ocean forecasting. This paper gives a description of the recent systems. The validation procedure is introduced and applied to the current and future systems. This paper shows how the validation impacts on the quality of the systems. It is shown how quality check (in situ, drifters) and data source (satellite temperature) impacts as much as the systems design (model physics and assimilation parameters). The validation demonstrates the accuracy of the MyOcean global products. Their quality is stable in time. The future systems under development still suffer from a drift. This could only be detected with a 5 yr hindcast of the systems. This emphasizes the need for continuous research efforts in the process of building future versions of MyOcean2 forecasting capacities.

International audience; European eel (Anguilla anguilla) larvae achieve one of the longest larval migrations of the marine realm, i.e., more than 6000 km from their spawning grounds in the Sargasso Sea to European continental shelves. The duration of this migration remains debated, between 7 months and 3 years. This information is, however, crucial since it determines the period over which larvae are affected by environmental conditions and hence the subsequent recruitment success. We investigate the pathways and duration of trans-Atlantic connections using 3 years of high-resolution (daily, 1/12°) velocity fields available from a Mercator-Océan model configuration without data assimilation. We study specifically the effect of spatial and temporal resolutions on our estimates by applying various filters in time (from daily to 12-day averages) and space (from 1/12° to 1° gridcell aggregation) to the nominal model outputs. Numerical particles are released in the presumed European eel spawning area and considered as passive tracers at three specific depths (around 0, 50, and 200 m). We diagnose particularly the intensity of the water transfer between suitable control sections that encompass the eel larva distribution. Transit ages are also investigated, with a particular focus on the pathways that minimize the connection times between the western and eastern North Atlantic. We show that small-scale structures (eddies and filaments) contribute to faster connections though they also correspond to additional complexity in trajectories. The shortest pathways mostly follow the Gulf Stream and the North Atlantic Drift, whereas interior connections require longer transfers that prove less compatible with biological observations.