'''This product has been archived'''                For operationnal and online products, please visit https://marine.copernicus.eu '''Short description:''' Experimental altimeter satellite along-track sea surface heights anomalies (SLA) computed with respect to a twenty-year [1993, 2012] mean with a 5Hz (~1.3km) sampling. All the missions are homogenized with respect to a reference mission (see QUID document or http://duacs.cls.fr [http://duacs.cls.fr] pages for processing details). The product gives additional variables (e.g. Mean Dynamic Topography, Dynamic Atmosphic Correction, Ocean Tides, Long Wavelength Errors, Internal tide, …) that can be used to change the physical content for specific needs This product was generated as experimental products in a CNES R&D context. It was processed by the DUACS multimission altimeter data processing system. '''DOI (product) :''' https://doi.org/10.48670/moi-00137

'''This product has been archived''' For operationnal and online products, please visit https://marine.copernicus.eu '''Short description:''' For the European Ocean, the L4 multi-sensor daily satellite product is a 2km horizontal resolution subskin sea surface temperature analysis. This SST analysis is run by Meteo France CMS and is built using the European Ocean L3S products originating from bias-corrected European Ocean L3C mono-sensor products at 0.02 degrees resolution. This analysis uses the analysis of the previous day at the same time as first guess field. '''DOI (product) :''' https://doi.org/10.48670/moi-00161

'''Short description:''' In wavenumber spectra, the 1hz measurement error is the noise level estimated as the mean value of energy at high wavenumbers (below ~20km in term of wavelength). The 1hz noise level spatial distribution follows the instrumental white-noise linked to the Surface Wave Height but also connections with the backscatter coefficient. The full understanding of this hump of spectral energy (Dibarboure et al., 2013, Investigating short wavelength correlated errors on low-resolution mode altimetry, OSTST 2013 presentation) still remain to be achieved and overcome with new retracking, new editing strategy or new technology. '''DOI (product) :''' https://doi.org/10.48670/moi-00144

'''Short description:''' Global sea ice thickness from merged L-Band radiometer (SMOS ) and radar altimeter (CryoSat-2, Sentinel-3A/B) observations during freezing season between October and April in the northern hemisphere and April to October in the southern hemisphere. The SMOS mission provides L-band observations and the ice thickness-dependency of brightness temperature enables to estimate the sea-ice thickness for thin ice regimes. Radar altimeters measure the height of the ice surface above the water level, which can be converted into sea ice thickness assuming hydrostatic equilibrium. '''DOI (product) :''' https://doi.org/10.48670/moi-00125

'''This product has been archived''' For operationnal and online products, please visit https://marine.copernicus.eu '''Short description:''' For the Global Ocean- In-situ observation delivered in delayed mode. This In Situ delayed mode product integrates the best available version of in situ oxygen, chlorophyll / fluorescence and nutrients data '''DOI (product) :''' https://doi.org/10.17882/86207

'''This product has been archived''' For operationnal and online products, please visit https://marine.copernicus.eu '''Short description:''' For the European Ocean- The L3 multi-sensor (supercollated) product is built from bias-corrected L3 mono-sensor (collated) products at the resolution 0.02 degrees. If the native collated resolution is N and N < 0.02 the change (degradation) of resolution is done by averaging the best quality data. If N > 0.02 the collated data are associated to the nearest neighbour without interpolation nor artificial increase of the resolution. A synthesis of the bias-corrected L3 mono-sensor (collated) files remapped at resolution R is done through a selection of data based on the following hierarchy: AVHRR_METOP_B, VIIRS_NPP, SLSTRA, SEVIRI, AVHRRL-19, MODIS_A, MODIS_T, AMSR2. This hierarchy can be changed in time depending on the health of each sensor. '''DOI (product) :''' https://doi.org/10.48670/moi-00163

'''DEFINITION''' The Copernicus Marine IBI_OMI_seastate_extreme_var_swh_mean_and_anomaly OMI indicator is based on the computation of the annual 99th percentile of Significant Wave Height (SWH) from model data. Two different CMEMS products are used to compute the indicator: The Iberia-Biscay-Ireland Multi Year Product (IBI_MULTIYEAR_WAV_005_006) and the Analysis product (IBI_ANALYSISFORECAST_WAV_005_005). Two parameters have been considered for this OMI: * Map of the 99th mean percentile: It is obtained from the Multi-Year Product, the annual 99th percentile is computed for each year of the product. The percentiles are temporally averaged in the whole period (1980-2023). * Anomaly of the 99th percentile in 2024: The 99th percentile of the year 2024 is computed from the Analysis product. The anomaly is obtained by subtracting the mean percentile to the percentile in 2024. This indicator is aimed at monitoring the extremes of annual significant wave height and evaluate the spatio-temporal variability. The use of percentiles instead of annual maxima, makes this extremes study less affected by individual data. This approach was first successfully applied to sea level variable (Pérez Gómez et al., 2016) and then extended to other essential variables, such as sea surface temperature and significant wave height (Pérez Gómez et al 2018 and Álvarez-Fanjul et al., 2019). Further details and in-depth scientific evaluation can be found in the CMEMS Ocean State report (Álvarez- Fanjul et al., 2019). '''CONTEXT''' The sea state and its related spatio-temporal variability affect dramatically maritime activities and the physical connectivity between offshore waters and coastal ecosystems, impacting therefore on the biodiversity of marine protected areas (González-Marco et al., 2008; Savina et al., 2003; Hewitt, 2003). Over the last decades, significant attention has been devoted to extreme wave height events since their destructive effects in both the shoreline environment and human infrastructures have prompted a wide range of adaptation strategies to deal with natural hazards in coastal areas (Hansom et al., 2015). Complementarily, there is also an emerging question about the role of anthropogenic global climate change on present and future extreme wave conditions (Young and Ribal, 2019). The Iberia-Biscay-Ireland region, which covers the North-East Atlantic Ocean from Canary Islands to Ireland, is characterized by two different sea state wave climate regions: whereas the northern half, impacted by the North Atlantic subpolar front, is of one of the world’s greatest wave generating regions (Mørk et al., 2010; Folley, 2017), the southern half, located at subtropical latitudes, is by contrast influenced by persistent trade winds and thus by constant and moderate wave regimes. The North Atlantic Oscillation (NAO), which refers to changes in the atmospheric sea level pressure difference between the Azores and Iceland, is a significant driver of wave climate variability in the Northern Hemisphere. The influence of North Atlantic Oscillation on waves along the Atlantic coast of Europe is particularly strong in and has a major impact on northern latitudes wintertime (Gleeson et al., 2017; Martínez-Asensio et al. 2016; Wolf et al., 2002; Bauer, 2001; Kushnir et al., 1997; Bouws et al., 1996; Bacon and Carter, 1991). Swings in the North Atlantic Oscillation index produce changes in the storms track and subsequently in the wind speed and direction over the Atlantic that alter the wave regime. When North Atlantic Oscillation index is in its positive phase, storms usually track northeast of Europe and enhanced westerly winds induce higher than average waves in the northernmost Atlantic Ocean. Conversely, in the negative North Atlantic Oscillation phase, the track of the storms is more zonal and south than usual, with trade winds (mid latitude westerlies) being slower and producing higher than average waves in southern latitudes (Marshall et al., 2001; Wolf et al., 2002; Wolf and Woolf, 2006). Additionally, a variety of previous studies have uniquevocally determined the relationship between the sea state variability in the IBI region and other atmospheric climate modes such as the East Atlantic pattern, the Arctic Oscillation, the East Atlantic Western Russian pattern and the Scandinavian pattern (Izaguirre et al., 2011, Martínez-Asensio et al., 2016). In this context, long‐term statistical analysis of reanalyzed model data is mandatory not only to disentangle other driving agents of wave climate but also to attempt inferring any potential trend in the number and/or intensity of extreme wave events in coastal areas with subsequent socio-economic and environmental consequences. '''CMEMS KEY FINDINGS''' The climatic mean of 99th percentile (1980-2023) reveals a north-south gradient of Significant Wave Height with the highest values in northern latitudes (above 8m) and lowest values (2-3 m) detected southeastward of Canary Islands, in the seas between Canary Islands and the African Continental Shelf. This north-south pattern is the result of the two climatic conditions prevailing in the region and previously described. The 99th percentile anomalies in 2024 show that during this period, virtually the entire IBI region was affected by positive anomalies in maximum SWH, which exceeded the standard deviation of the historical record in the waters west of the Iberian Peninsula, the Spanish coast of the Bay of Biscay, and the African coast south of Cape Ghir. Anomalies reaching twice the standard deviation of the time series were also observed in coastal regions of the English Channel. '''DOI (product):''' https://doi.org/10.48670/moi-00249

'''Short description:''' The IBI-MFC provides the biogeochemical multi-year (non assimilative) product for the Iberia-Biscay-Ireland region starting in 01/01/1993, extended every year to use available reprocessed upstream data and regularly updated on a monthly basis to cover the period up to month M-4 using an interim processing system. The model system is designed, developed and run by Mercator Ocean International, while the operational product post-processing and interim processing system are run by NOW Systems with the support of CESGA supercomputing centre. The biogeochemical model PISCES is run simultaneously with the ocean physical NEMO model, generating products at 1/36° horizontal resolution. The PISCES model is able to simulate the first levels of the marine food web, from nutrients up to mesozooplankton and it has 24 state variables. The product provides daily, monthly and yearly averages of the main biogeochemical variables. Additionally, climatological parameters (monthly mean and standard deviation) of these variables for the period 1993-2016 are delivered. '''DOI (Product)''': https://doi.org/10.48670/moi-00028

"''Short description:''' The IBI-MFC provides a high-resolution ocean analysis and forecast product (daily run by Nologin with the support of CESGA in terms of supercomputing resources), covering the European waters, and more specifically the Iberia–Biscay–Ireland (IBI) area. The last 2 years before now (historic best estimates) as well as forecasts of different temporal resolutions with a horizon of 10 days (updated on a daily basis) are available on the catalogue. The system is based on a eddy-resolving NEMO model application at 1/36º horizontal resolution, being Mercator-Ocean in charge of the model code development. The hydrodynamic forecast includes high frequency processes of paramount importance to characterize regional scale marine processes: tidal forcing, surges and high frequency atmospheric forcing, fresh water river discharge, wave forcing in forecast, etc. A weekly update of IBI downscaled analysis is also delivered as historic IBI best estimates. The product offers 3D daily and monthly ocean fields, as well as hourly mean and 15-minute instantaneous values for some surface variables. Daily and monthly averages of 3D Temperature, 3D Salinity, 3D Zonal, Meridional and vertical Velocity components, Mix Layer Depth, Sea Bottom Temperature and Sea Surface Height are provided. Additionally, hourly means of surface fields for variables such as Sea Surface Height, Mix Layer Depth, Surface Temperature and Currents, together with Barotropic Velocities are delivered. Doodson-filtered detided mean sea level and horizontal surface currents are also provided. Finally, 15-minute instantaneous values of Sea Surface Height and Currents are also given. '''DOI (Product)''': https://doi.org/10.48670/moi-00027

'''This product has been archived''' For operationnal and online products, please visit https://marine.copernicus.eu '''Short description:''' The Global Ocean Satellite monitoring and marine ecosystem study group (GOS) of the Italian National Research Council (CNR), in Rome, distributes Level-4 product including the daily interpolated chlorophyll field with no data voids starting from the multi-sensor (MODIS-Aqua, NOAA-20-VIIRS, NPP-VIIRS, Sentinel3A-OLCI at 300m of resolution) (at 1 km resolution) and the monthly averaged chlorophyll concentration for the multi-sensor (at 1 km resolution) and Sentinel-OLCI Level-3 (at 300m resolution). Chlorophyll field are obtained by means of the Mediterranean regional algorithms: an updated version of the MedOC4 (Case 1 waters, Volpe et al., 2019, with new coefficients) and AD4 (Case 2 waters, Berthon and Zibordi, 2004). Discrimination between the two water types is performed by comparing the satellite spectrum with the average water type spectral signature from in situ measurements for both water types. Reference insitu dataset is MedBiOp (Volpe et al., 2019) where pure Case II spectra are selected using a k-mean cluster analysis (Melin et al., 2015). Merging of Case I and Case II information is performed estimating the Mahalanobis distance between the observed and reference spectra and using it as weight for the final merged value. The interpolated gap-free Level-4 Chl concentration is estimated by means of a modified version of the DINEOF algorithm by GOS (Volpe et al., 2018). DINEOF is an iterative procedure in which EOF are used to reconstruct the entire field domain. As first guess, it uses the SeaWiFS-derived daily climatological values at missing pixels and satellite observations at valid pixels. The other Level-4 dataset is the time averages of the L3 fields and includes the standard deviation and the number of observations in the monthly period of integration. '''Processing information:''' Multi-sensor products are constituted by MODIS-AQUA, NOAA20-VIIRS, NPP-VIIRS and Sentinel3A-OLCI. For consistency with NASA L2 dataset, BRDF correction was applied to Sentinel3A-OLCI prior to band shifting and multi sensor merging. Hence, the single sensor OLCI data set is also distributed after BRDF correction. Single sensor NASA Level-2 data are destriped and then all Level-2 data are remapped at 1 km spatial resolution (300m for Sentinel3A-OLCI) using cylindrical equirectangular projection. Afterwards, single sensor Rrs fields are band-shifted, over the SeaWiFS native bands (using the QAAv6 model, Lee et al., 2002) and merged with a technique aimed at smoothing the differences among different sensors. This technique is developed by The Global Ocean Satellite monitoring and marine ecosystem study group (GOS) of the Italian National Research Council (CNR, Rome). Then geophysical fields (i.e. chlorophyll, kd490, bbp, aph and adg) are estimated via state-of-the-art algorithms for better product quality. Level-4 includes both monthly time averages and the daily-interpolated fields. Time averages are computed on the delayed-time data. The interpolated product starts from the L3 products at 1 km resolution. At the first iteration, DINEOF procedure uses, as first guess for each of the missing pixels the relative daily climatological pixel. A procedure to smooth out spurious spatial gradients is applied to the daily merged image (observation and climatology). From the second iteration, the procedure uses, as input for the next one, the field obtained by the EOF calculation, using only a number of modes: that is, at the second round, only the first two modes, at the third only the first three, and so on. At each iteration, the same smoothing procedure is applied between EOF output and initial observations. The procedure stops when the variance explained by the current EOF mode exceeds that of noise. '''Description of observation methods/instruments:''' Ocean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the pre+D2sence of phytoplankton. '''Quality / Accuracy / Calibration information:''' A detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal. '''Suitability, Expected type of users / uses:''' This product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies. '''Dataset names:''' *dataset-oc-med-chl-multi-l4-chl_1km_monthly-rt-v02 *dataset-oc-med-chl-multi-l4-interp_1km_daily-rt-v02 *dataset-oc-med-chl-olci-l4-chl_300m_monthly-rt-v02 '''Files format:''' *CF-1.4 *INSPIRE compliant '''DOI (product) :''' https://doi.org/10.48670/moi-00113