Multimission altimeter satellite gridded sea surface heights and derived variables computed with respect to a twenty-year mean. Previously distributed by Aviso+, no change in the scientific content. All the missions are homogenized with respect to a reference mission which is currently Jason-3. The acquisition of various altimeter data is a few days at most. The sla is computed with a non-centered computation time window (6 weeks before the date).

Gridded multi-mission merged satellite significant wave height in Near-Real-Time. It merges along-track SWH data from the following missions: Jason-3, Sentinel-3A, Sentinel-3B, SARAL/AltiKa, Cryosat-2 and CFOSAT.

For the Global Ocean - Multimission altimeter satellite gridded sea surface heights and derived variables computed with respect to a twenty-year mean. Previously distributed by Aviso+, no change in the scientific content. All the missions are homogenized with respect to a reference mission which is currently Jason-3. The acquisition of various altimeter data is a few days at most. The sla is computed with a non-centered computation time window (6 weeks before the date).

The MIOST (Multiscale Interpolation Ocean Science Topography) experimental altimeter product provides grids at delayed-time, at global scale, 1/10° spatial resolution, the sea surface height (MSLA and MADT) as well as the geostrophic currents, resulting from specific processing. Use for regional studies, ocean variability (mesoscale circulation,...).

'''This product has been archived''' For operationnal and online products, please visit https://marine.copernicus.eu '''Short description:''' The Global Ocean Satellite monitoring and marine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 operationally produces Level-4 product includes monthly averaged datasets of the diffuse attenuation coefficient of light at 490 nm (Kd490) for multi-sensor (MODIS-AQUA, NOAA20-VIIRS, NPP-VIIRS, Sentinel3A-OLCI at 300m of resolution) (at 1 km resolution) and Sentinel3A-OLCI observations (at 300m resolution). Kd490 is the diffuse attenuation coefficient of light at 490 nm, and is a measure of the turbidity of the water column, i.e., how visible light in the blue-green region of the spectrum penetrates the water column. It is directly related to the presence of absorbing and scattering matter in the water column and is estimated through the ratio between Rrs at 490 and 555 nm. For the multi-sensor dataset, 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 GOS. The QAA allows the inversion of the radiative transfer equations to compute the Inherent Optical Properties. Level-4 product includes monthly averages along with the standard deviation and the number of observations in the 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. Time averages are computed on the delayed-time data. '''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 presence 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-opt-multi-l4-kd490_1km_monthly-rt-v02 *dataset-oc-med-opt-olci-l4-kd490_300m_monthly-rt '''Files format:''' *CF-1.4 *INSPIRE compliant '''DOI (product) :''' https://doi.org/10.48670/moi-00117

'''DEFINITION''' This product includes the Mediterranean Sea satellite chlorophyll trend map based on regional chlorophyll reprocessed (MY) product as distributed by CMEMS OC-TAC. This dataset, derived from multi-sensor (SeaStar-SeaWiFS, AQUA-MODIS, NOAA20-VIIRS, NPP-VIIRS, Envisat-MERIS and Sentinel3-OLCI) (at 1 km resolution) Rrs spectra produced by CNR using an in-house processing chain, is obtained by means of the Mediterranean Ocean Colour regional algorithms: an updated version of the MedOC4 (Case 1 (off-shore) waters, Volpe et al., 2019, with new coefficients) and AD4 (Case 2 (coastal) waters, Berthon and Zibordi, 2004). The processing chain and the techniques used for algorithms merging are detailed in Colella et al. (2023). The trend map is obtained by applying Colella et al. (2016) methodology, where the Mann-Kendall test (Mann, 1945; Kendall, 1975) and Sens’s method (Sen, 1968) are applied on deseasonalized monthly time series, as obtained from the X-11 technique (see e. g. Pezzulli et al. 2005), to estimate, trend magnitude and its significance. The trend is expressed in % per year that represents the relative changes (i.e., percentage) corresponding to the dimensional trend [mg m-3 y-1] with respect to the reference climatology (1997-2014). Only significant trends (p < 0.05) are included. '''CONTEXT''' Phytoplankton are key actors in the carbon cycle and, as such, recognised as an Essential Climate Variable (ECV). Chlorophyll concentration - as a proxy for phytoplankton - respond rapidly to changes in environmental conditions, such as light, temperature, nutrients and mixing (Colella et al. 2016). The character of the response depends on the nature of the change drivers, and ranges from seasonal cycles to decadal oscillations (Basterretxea et al. 2018). The Mediterranean Sea is an oligotrophic basin, where chlorophyll concentration decreases following a specific gradient from West to East (Colella et al. 2016). The highest concentrations are observed in coastal areas and at the river mouths, where the anthropogenic pressure and nutrient loads impact on the eutrophication regimes (Colella et al. 2016). The the use of long-term time series of consistent, well-calibrated, climate-quality data record is crucial for detecting eutrophication. Furthermore, chlorophyll analysis also demands the use of robust statistical temporal decomposition techniques, in order to separate the long-term signal from the seasonal component of the time series. '''CMEMS KEY FINDINGS''' Chlorophyll trend in the Mediterranean Sea, for the period 1997-2022, generally confirm trend results of the previous release with negative values over most of the basin. In Ligurian Sea, Gulf of Lion and Adriatic Sea, negative trend are slightly emphasized. Weak positive trend areas of the previous release are confirmed in the southern part of the western Mediterranean basin, Rhode Gyre and in the northern coast of the Aegean Sea. On average the trend in the Mediterranean Sea is about -0.69% per year. Contrary to what shown by Salgado-Hernanz et al. (2019) in their analysis (related to 1998-2014 satellite observations), western and eastern part of the Mediterranean Sea do not show differences. In the Ligurian Sea, the trend switch to negative values, differing from the positive regime observed in the trend maps of both Colella et al. (2016) and Salgado-Hernanz et al. (2019), referred, respectively, to 1998-2009 and 1998-2014 period, respectively. The waters offshore the Po River mouth show weak negative trend values, partially differing from the markable negative regime observed in the 1998-2009 period (Colella et al., 2016), and definitely moving from the positive trend observed by Salgado-Hernanz et al. (2019). '''Figure caption''' Mediterranean Sea satellite chlorophyll trend over the period 1997-2022, based on CMEMS product OCEANCOLOUR_MED_BGC_L4_MY_009_144. Trend are expressed in % per year, with positive trends in red and negative trends in blue. '''DOI (product):''' https://doi.org/10.48670/moi-00260

'''Short description:''' The ESA SST CCI and C3S global Sea Surface Temperature Reprocessed product provides gap-free maps of daily average SST at 20 cm depth at 0.05deg. x 0.05deg. horizontal grid resolution, using satellite data from the (A)ATSRs, SLSTR and the AVHRR series of sensors (Merchant et al., 2019). The ESA SST CCI and C3S level 4 analyses were produced by running the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) system (Good et al., 2020) to provide a high resolution (1/20deg. - approx. 5km grid resolution) daily analysis of the daily average sea surface temperature (SST) at 20 cm depth for the global ocean. Only (A)ATSR, SLSTR and AVHRR satellite data processed by the ESA SST CCI and C3S projects were used, giving a stable product. It also uses reprocessed sea-ice concentration data from the EUMETSAT OSI-SAF (OSI-450 and OSI-430; Lavergne et al., 2019). '''DOI (product) :''' https://doi.org/10.48670/moi-00169

'''This product has been archived'''                For operationnal and online products, please visit https://marine.copernicus.eu '''Description:''' This product is a NRT L4 global total velocity field at 0m and 15m. It consists of the zonal and meridional velocity at a 6h frequency and at 1/4 degree regular grid produced on a daily basis. These total velocity fields are obtained by combining CMEMS NRT satellite Geostrophic Surface Currents and modelled Ekman current at the surface and 15m depth (using ECMWF NRT wind). 6 hourly product, daily and monthly mean are available. This product has been initiated in the frame of CNES/CLS projects. Then it has been consolidated during the Globcurrent project (funded by the ESA User Element Program). '''DOI (product) :''' https://doi.org/10.48670/moi-00049

'''This product has been archived''' For operational and online products, please visit https://marine.copernicus.eu '''Short description:''' For the Global Ocean - the OSTIA diurnal skin Sea Surface Temperature product provides daily gap-free maps of: *Hourly mean skin Sea Surface Temperature at 0.25° x 0.25° horizontal resolution, using in-situ and satellite data from infra-red radiometers. The Operational Sea Surface Temperature and Ice Analysis (OSTIA) system is run by the Met Office. A 1/4° (approx. 28 km) hourly analysis of skin Sea Surface temperature (SST) is produced daily for the global ocean. The skin temperature of the ocean is the temperature measured by satellite infra-red radiometers and can experience a large diurnal cycle. The skin SST L4 product is created by combining: 1. the OSTIA foundation SST analysis which uses in-situ and satellite observations; 2. the OSTIA diurnal warm layer analysis which uses satellite observations; and 3. a cool skin model. OSTIA uses satellite data provided by the GHRSST project. '''DOI (product) :''' https://doi.org/10.48670/moi-00167

'''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), the monthly averaged chlorophyll concentration for the multi-sensor and climatological fields, all at 1 km 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. Monthly 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. SeaWiFS daily climatology provides reference for the calculation of Quality Indices (QI) for Chl observations. '''Processing information:''' Multi-sensor product is 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. Single sensor NASA Level-2 data are destriped and then all Level-2 data are remapped at 1 km spatial resolution 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) 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. The entire data set is consistent and processed in one-shot mode (with an unique software version and identical configurations). For the climatology Rrs data are derived from the latest SeaWiFS NASA reprocessing (R2018.0) and converted to chlorophyll concentrations with the same algorithm as the one used for other L4 and/or L3 products. '''Description of observation methods/instruments:''' Ocean color technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so called ocean color which is affected by the presence of phytoplankton. '''Quality / Accuracy / Calibration information:''' A detailed description of both the cal/val and a more in depth view of the method is given in QUID-009-038to045-071-073-078-079-095-096.pdf over Copernicus 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-rep-v02 *dataset-oc-med-chl-multi-l4-interp_1km_daily-rep *dataset-oc-med-chl-seawifs-l4-chl_1km_daily-climatology-v02 '''Files format:''' *CF-1.4 *INSPIRE compliant. '''DOI (product) :''' https://doi.org/10.48670/moi-00114