'''This product has been archived''' For operationnal and online products, please visit https://marine.copernicus.eu '''Short description:''' The Global Ocean Satellite monitoring and marine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

'''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:''' For the Mediterranean Sea - The product contains daily Level-3 sea surface wind with a 1km horizontal pixel spacing using Synthetic Aperture Radar (SAR) observations and their collocated European Centre for Medium-Range Weather Forecasts (ECMWF) model outputs. Products are processed homogeneously starting from the L2OCN products. '''DOI (product) :''' https://doi.org/10.48670/mds-00342

'''DEFINITION:''' The regional annual chlorophyll anomaly is computed by subtracting a reference climatology (1997-2014) from the annual chlorophyll mean, on a pixel-by-pixel basis and in log10 space. Both the annual mean and the climatology are computed employing the regional products as distributed by CMEMS, derived by application of the regional chlorophyll algorithms over remote sensing reflectances (Rrs) provided by the ESA Ocean Colour Climate Change Initiative (ESA OC-CCI, Sathyendranath et al., 2018a). '''CONTEXT:''' Phytoplankton – and chlorophyll concentration as their proxy – respond rapidly to changes in their physical environment. In the North Atlantic region these changes present a distinct seasonality and are mostly determined by light and nutrient availability (González Taboada et al., 2014). By comparing annual mean values to a climatology, we effectively remove the seasonal signal at each grid point, while retaining information on potential events during the year (Gregg and Rousseaux, 2014). In particular, North Atlantic anomalies can then be correlated with oscillations in the Northern Hemisphere Temperature (Raitsos et al., 2014). Chlorophyll anomalies also provide information on the status of the North Atlantic oligotrophic gyre, where evidence of rapid gyre expansion has been found for the 1997-2012 period (Polovina et al. 2008, Aiken et al., 2017, Sathyendranath et al., 2018b). '''CMEMS KEY FINDINGS:''' The average chlorophyll anomaly in the North Atlantic is -0.02 log10(mg m-3), with a maximum value of 1.0 log10(mg m-3) and a minimum value of -1.0 log10(mg m-3). That is to say that, in average, the annual 2019 mean value is slightly lower (96%) than the 1997-2014 climatological value. A moderate increase in chlorophyll concentration was observed in 2019 over the Bay of Biscay and regions close to Iceland and Greenland, such as the Irminger Basin and the Denmark Strait. In particular, the annual average values for those areas are around 160% of the 1997-2014 average (anomalies > 0.2 log10(mg m-3)). While the significant negative anomalies reported for 2016-2017 (Sathyendranath et al., 2018c) in the area west of the Ireland and Scotland coasts continued to manifest, the Irish and North Seas returned to their normative regime during 2019, with anomalies close to zero. A change in the anomaly sign (positive to negative) was also detected for the West European Basin, with annual values as low as 60% of the 1997-2014 average. This reduction in chlorophyll might be matched with negative anomalies in sea level during the period, indicating a dominance of upwelling factors over stratification.

'''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

'''Short description:''' For the Mediterranean Sea - The product contains daily Level-3 sea surface wind with a 1km horizontal pixel spacing using Near Real-Time Synthetic Aperture Radar (SAR) observations and their collocated European Centre for Medium-Range Weather Forecasts (ECMWF) model outputs. Products are updated several times daily to provide the best product timeliness. '''DOI (product) :''' https://doi.org/10.48670/mds-00334

'''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 distributes Remote Sensing Reflectances (Rrs) and diffuse attenuation coefficient of light at 490 nm (kd490) data. These datasets derived from Rrs multi-sensor (MODIS-AQUA, NOAA20-VIIRS, NPP-VIIRS, Sentinel3A-OLCI) spectra at the state-of-the-art algorithms for multi-sensor merging. 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. Reprocessed (multi-year) products are consistent and homogeneous in terms of format, algorithms and processing software. Rrs is defined as the ratio of upwelling radiance and downwelling irradiance at any wavelength (412, 443, 490, 555, and 670 nm). Kd490 is defined as 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 within the water column. It is directly related to the presence of scattering particles in the water column and is estimated through the ratio between Rrs at 490 and 555 nm. Kd490 is achieved via Mediterranean regional algorithm developed by GOS on the basis of MedBiOp in situ dataset (Volpe et al., 2019). The current day data temporal consistency is evaluated as Quality Index (QI): QI=(CurrentDataPixel-ClimatologyDataPixel)/STDDataPixel where QI is the difference between current data and the relevant climatological field as a signed multiple of climatological standard deviations (STDDataPixel). Inherent Optical Properties (aph443, adg443 and bbp443 at 443nm) are derived via QAAv6 model. '''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 and kd490, bbp, aph and adg) are estimated via state-of-the-art algorithms for better product quality. The entire data set is consistent and processed in one-shot mode (with an unique software version and identical configurations). '''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-l3-rrs412_1km_daily-rep-v02 * dataset-oc-med-opt-multi-l3-rrs443_1km_daily-rep-v02 * dataset-oc-med-opt-multi-l3-rrs490_1km_daily-rep-v02 * dataset-oc-med-opt-multi-l3-rrs510_1km_daily-rep-v02 * dataset-oc-med-opt-multi-l3-rrs555_1km_daily-rep-v02 * dataset-oc-med-opt-multi-l3-rrs670_1km_daily-rep-v02 * dataset-oc-med-opt-multi-l3-kd490_1km_daily-rep-v02 * dataset-oc-med-opt-multi-l3-bbp443_1km_daily-rep-v02 * dataset-oc-med-opt-multi-l3-adg443_1km_daily-rep-v02 * dataset-oc-med-opt-multi-l3-aph443_1km_daily-rep-v02 '''Files format:''' *CF-1.4 *INSPIRE compliant '''DOI (product) :''' https://doi.org/10.48670/moi-00116

'''DEFINITION''' We have derived an annual eutrophication and eutrophication indicator map for the North Atlantic Ocean using satellite-derived chlorophyll concentration. Using the satellite-derived chlorophyll products distributed in the regional North Atlantic CMEMS MY Ocean Colour dataset (OC- CCI), we derived P90 and P10 daily climatologies. The time period selected for the climatology was 1998-2017. For a given pixel, P90 and P10 were defined as dynamic thresholds such as 90% of the 1998-2017 chlorophyll values for that pixel were below the P90 value, and 10% of the chlorophyll values were below the P10 value. To minimise the effect of gaps in the data in the computation of these P90 and P10 climatological values, we imposed a threshold of 25% valid data for the daily climatology. For the 20-year 1998-2017 climatology this means that, for a given pixel and day of the year, at least 5 years must contain valid data for the resulting climatological value to be considered significant. Pixels where the minimum data requirements were met were not considered in further calculations. We compared every valid daily observation over 2021 with the corresponding daily climatology on a pixel-by-pixel basis, to determine if values were above the P90 threshold, below the P10 threshold or within the [P10, P90] range. Values above the P90 threshold or below the P10 were flagged as anomalous. The number of anomalous and total valid observations were stored during this process. We then calculated the percentage of valid anomalous observations (above/below the P90/P10 thresholds) for each pixel, to create percentile anomaly maps in terms of % days per year. Finally, we derived an annual indicator map for eutrophication levels: if 25% of the valid observations for a given pixel and year were above the P90 threshold, the pixel was flagged as eutrophic. Similarly, if 25% of the observations for a given pixel were below the P10 threshold, the pixel was flagged as oligotrophic. '''CONTEXT''' Eutrophication is the process by which an excess of nutrients – mainly phosphorus and nitrogen – in a water body leads to increased growth of plant material in an aquatic body. Anthropogenic activities, such as farming, agriculture, aquaculture and industry, are the main source of nutrient input in problem areas (Jickells, 1998; Schindler, 2006; Galloway et al., 2008). Eutrophication is an issue particularly in coastal regions and areas with restricted water flow, such as lakes and rivers (Howarth and Marino, 2006; Smith, 2003). The impact of eutrophication on aquatic ecosystems is well known: nutrient availability boosts plant growth – particularly algal blooms – resulting in a decrease in water quality (Anderson et al., 2002; Howarth et al.; 2000). This can, in turn, cause death by hypoxia of aquatic organisms (Breitburg et al., 2018), ultimately driving changes in community composition (Van Meerssche et al., 2019). Eutrophication has also been linked to changes in the pH (Cai et al., 2011, Wallace et al. 2014) and depletion of inorganic carbon in the aquatic environment (Balmer and Downing, 2011). Oligotrophication is the opposite of eutrophication, where reduction in some limiting resource leads to a decrease in photosynthesis by aquatic plants, reducing the capacity of the ecosystem to sustain the higher organisms in it. Eutrophication is one of the more long-lasting water quality problems in Europe (OSPAR ICG-EUT, 2017), and is on the forefront of most European Directives on water-protection. Efforts to reduce anthropogenically-induced pollution resulted in the implementation of the Water Framework Directive (WFD) in 2000. '''CMEMS KEY FINDINGS''' The coastal and shelf waters, especially between 30 and 400N that showed active oligotrophication flags for 2020 have reduced in 2021 and a reversal to eutrophic flags can be seen in places. Again, the eutrophication index is positive only for a small number of coastal locations just north of 40oN in 2021, however south of 40oN there has been a significant increase in eutrophic flags, particularly around the Azores. In general, the 2021 indicator map showed an increase in oligotrophic areas in the Northern Atlantic and an increase in eutrophic areas in the Southern Atlantic. The Third Integrated Report on the Eutrophication Status of the OSPAR Maritime Area (OSPAR ICG-EUT, 2017) reported an improvement from 2008 to 2017 in eutrophication status across offshore and outer coastal waters of the Greater North Sea, with a decrease in the size of coastal problem areas in Denmark, France, Germany, Ireland, Norway and the United Kingdom. '''DOI (product):''' https://doi.org/10.48670/moi-00195

'''Short description:''' The Mean Dynamic Topography MDT-CMEMS_2020_MED is an estimate of the mean over the 1993-2012 period of the sea surface height above geoid for the Mediterranean Sea. This is consistent with the reference time period also used in the SSALTO DUACS products '''DOI (product) :''' https://doi.org/10.48670/moi-00151

'''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