'''This product has been archived''' For operationnal and online products, please visit https://marine.copernicus.eu '''Short description:''' For the European Ocean- The L3 multi-sensor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''' This product includes the Mediterranean Sea satellite chlorophyll trend map based on regional chlorophyll reprocessed (MY) product as distributed by CMEMS OC-TAC (OCEANCOLOUR_MED_BGC_L3_NRT_009_141). 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. This OMI has been introduced since the 2nd issue of Ocean State Report in 2017. '''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. '''KEY FINDINGS''' The chlorophyll trend in the Mediterranean Sea for the 1997–2024 period confirms the findings of the previous release, with predominantly negative values observed across most of the basin. On average, the region shows a trend of approximately -0.77% per year, slightly more negative than the overall trend reported previously. As in earlier assessments, weak positive trends persist in specific areas such as the northern Aegean Sea and the Sicily Channel. Compared to the 1997–2023 period, new positive trends are now evident in the Gulf of Lion. In contrast to the findings of Salgado-Hernanz et al. (2019), which were based on satellite observations from 1998 to 2014, this analysis does not reveal a distinct difference between the western and eastern Mediterranean basins. Notably, the Ligurian Sea now exhibits a negative trend, diverging from the positive trends identified by Colella et al. (2016) for the 1998–2009 period and by Salgado-Hernanz et al. (2019) for 1998–2014. Similarly, the waters of the Northern Adriatic Sea show weak positive trends, differing from the strong negative trend previously reported by Colella et al. (2016), and also representing a shift from the positive values observed by Salgado-Hernanz et al. (2019). '''DOI (product):''' https://doi.org/10.48670/moi-00260

'''DEFINITION''' The time series are derived from the regional chlorophyll reprocessed (MY) product as distributed by CMEMS (OCEANCOLOUR_MED_BGC_L3_NRT_009_141). This dataset, derived from multi-sensor (SeaStar-SeaWiFS, AQUA-MODIS, NOAA20-VIIRS, NPP-VIIRS, Envisat-MERIS and Sentinel3-OLCI) 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). Monthly regional mean values are calculated by performing the average of 2D monthly mean (weighted by pixel area) over the region of interest. The deseasonalized time series is obtained by applying the X-11 seasonal adjustment methodology on the original time series as described in Colella et al. (2016), and then the Mann-Kendall test (Mann, 1945; Kendall, 1975) and Sens’s method (Sen, 1968) are subsequently applied to obtain the magnitude of trend. This OMI has been introduced since the 2nd issue of Ocean State Report in 2017. '''CONTEXT''' Phytoplankton and 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). Therefore, it is of critical importance to monitor chlorophyll concentration at multiple temporal and spatial scales, in order to be able to separate potential long-term climate signals from natural variability in the short term. In particular, phytoplankton in the Mediterranean Sea is known to respond to climate variability associated with the North Atlantic Oscillation (NAO) and El Niño Southern Oscillation (ENSO) (Basterretxea et al. 2018, Colella et al. 2016). '''KEY FINDINGS''' In the Mediterranean Sea, the average chlorophyll trend for the 1997–2024 period is slightly negative, at -0.77 ± 0.59% per year, reinforcing the findings of the previous releases. This result contrasts with the analysis by Sathyendranath et al. (2018), which reported increasing chlorophyll concentrations across all European seas. From around 2010–2011 onward, excluding the 2018–2019 period, a noticeable decline in chlorophyll levels is evident in the deseasonalized time series (green line) and in the observed maxima (grey line), particularly from 2015. This sustained decline over the past decade contributes to the overall negative trend observed in the Mediterranean Sea. '''DOI (product):''' https://doi.org/10.48670/moi-00259

'''Short description:''' The Reprocessed (REP) Mediterranean (MED) dataset provides a stable and consistent long-term Sea Surface Temperature (SST) time series over the Mediterranean Sea (and the adjacent North Atlantic box) developed for climate applications. This product consists of daily (nighttime), optimally interpolated (L4), satellite-based estimates of the foundation SST (namely, the temperature free, or nearly-free, of any diurnal cycle) at 0.05° resolution grid covering the period from 1st January 1981 to present (approximately one month before real time). The MED-REP-L4 product is built from a consistent reprocessing of the collated level-3 (merged single-sensor, L3C) climate data record (CDR) v.3.0, provided by the ESA Climate Change Initiative (CCI) and covering the period up to 2021, and its interim extension (ICDR) that allows the regular temporal extension for 2022 onwards. '''DOI (product) :''' https://doi.org/10.48670/moi-00173

'''DEFINITION''' Ocean salt content (OSC) is defined and represented here as the volume average of the integral of salinity in the Mediterranean Sea from z1 = 0 m to z2 = 300 m depth: ¯S=1/V ∫V S dV Time series of annual mean values area averaged ocean salt content are provided for the Mediterranean Sea (30°N, 46°N; 6°W, 36°E) and are evaluated in the upper 300m excluding the shelf areas close to the coast with a depth less than 300 m. The total estimated volume is approximately 5.7e+5 km3. '''CONTEXT''' The freshwater input from the land (river runoff) and atmosphere (precipitation) and inflow from the Black Sea and the Atlantic Ocean are balanced by the evaporation in the Mediterranean Sea. Evolution of the salt content may have an impact in the ocean circulation and dynamics which possibly will have implication on the entire Earth climate system. Thus monitoring changes in the salinity content is essential considering its link to changes in: the hydrological cycle, the water masses formation, the regional halosteric sea level and salt/freshwater transport, as well as for their impact on marine biodiversity. The OMI_CLIMATE_OSC_MEDSEA_volume_mean is based on the “multi-product” approach introduced in the seventh issue of the Ocean State Report (contribution by Aydogdu et al., 2023). Note that the estimates in Aydogdu et al. (2023) are provided monthly while here we evaluate the results per year. Six global products and a regional (Mediterranean Sea) product have been used to build an ensemble mean, and its associated ensemble spread. The reference products are: • The Mediterranean Sea Reanalysis at 1/24°horizontal resolution (MEDSEA_MULTIYEAR_PHY_006_004, DOI: https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_PHY_006_004_E3R1, Escudier et al., 2020) • Four global reanalyses at 1/4°horizontal resolution (GLOBAL_REANALYSIS_PHY_001_031, GLORYS, C-GLORS, ORAS5, FOAM, DOI: https://doi.org/10.48670/moi-00024, Desportes et al., 2022) • Two observation-based products: CORA (INSITU_GLO_TS_REP_OBSERVATIONS_013_001_b, DOI: https://doi.org/10.17882/46219, Szekely et al., 2022) and ARMOR3D (MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012, DOI: https://doi.org/10.48670/moi-00052, Grenier et al., 2021). Details on the products are delivered in the PUM and QUID of this OMI. '''CMEMS KEY FINDINGS''' The Mediterranean Sea salt content shows a positive trend in the upper 300 m with a continuous increase over the period 1993-2021 at rate of 7.0*10-3 ±3.1*10-4 psu yr-1. The overall ensemble mean of different products is 38.57 psu. During the early 1990s in the entire Mediterranean Sea there is a large spread in salinity with the observational based datasets showing a higher salinity, while the reanalysis products present relatively lower salinity. The maximum spread between the period 1993–2021 occurs in the 1990s with a value of 0.12 psu, and it decreases to as low as 0.02 psu by the end of the 2010s. '''DOI (product):''' https://doi.org/10.48670/mds-00325

'''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

'''Short description:''' The Mean Dynamic Topography MDT-CMEMS_2024_EUR is an estimate of the mean over the 1993-2012 period of the sea surface height above geoid for the European Seas. This is consistent with the reference time period also used in the SSALTO DUACS products '''DOI (product) :''' https://doi.org/10.48670/mds-00337

'''Short description:''' Near Real-Time mono-mission satellite-based 2D full wave spectral product. These very complete products enable to characterise spectrally the direction, wave length and multiple sea Sates along CFOSAT track (in boxes of 70km/90km left and right from the nadir pointing). The data format are 2D directionnal matrices. They also include integrated parameters (Hs, direction, wavelength) from the spectrum with and without partitions. '''DOI (product) :''' N/A