'''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), merged multi-sensor (L3S), 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-L3S 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-00314

'''DEFINITION''' Ocean heat content (OHC) is defined here as the deviation from a reference period (1993-2014) and is closely proportional to the average temperature change from z1 = 0 m to z2 = 700 m depth: OHC=∫_(z_1)^(z_2)ρ_0 c_p (T_yr-T_clim )dz [1] with a reference density of = 1030 kgm-3 and a specific heat capacity of cp = 3980 J kg-1 °C-1 (e.g. von Schuckmann et al., 2009). Time series of annual mean values area averaged ocean heat content is provided for the Mediterranean Sea (30°N, 46°N; 6°W, 36°E) and is evaluated for topography deeper than 300m. '''CONTEXT''' Knowing how much and where heat energy is stored and released in the ocean is essential for understanding the contemporary Earth system state, variability and change, as the oceans shape our perspectives for the future. The quality evaluation of MEDSEA_OMI_OHC_area_averaged_anomalies is based on the “multi-product” approach as introduced in the second issue of the Ocean State Report (von Schuckmann et al., 2018), and following the MyOcean’s experience (Masina et al., 2017). 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 degree 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 degree horizontal resolution (GLOBAL_MULTIYEAR_PHY_ENS_001_031): GLORYS, C-GLORS, ORAS5, FOAM • Two observation based products: CORA (INSITU_GLO_PHY_TS_OA_MY_013_052) and ARMOR3D (MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012). Details on the products are delivered in the PUM and QUID of this OMI. '''CMEMS KEY FINDINGS''' The ensemble mean ocean heat content anomaly time series over the Mediterranean Sea shows a continuous increase in the period 1993-2022 at rate of 1.38±0.08 W/m2 in the upper 700m. After 2005 the rate has clearly increased with respect the previous decade, in agreement with Iona et al. (2018). '''DOI (product):''' https://doi.org/10.48670/moi-00261

'''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

'''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 surface chlorophyll of the European region by merging the daily chlorophyll regional products over the Atlantic Ocean, the Baltic Sea, the Black Sea, and the Mediterranean Sea. Single chlorophyll daily images are the Case I – Case II products, which are produced accounting for bio-optical differences in these two water types. The mosaic is built using the following datasets: • dataset-oc-atl-chl-multi_cci-l3-chl_1km_daily-rt-v01 for the North Atlantic Ocean • dataset-oc-bal-chl-modis_a-l3-nn_1km_daily-rt-v01 for the Baltic Sea • dataset-oc-bs-chl-multi-l3-chl_1km_daily-rt-v02 for the Black Sea • dataset-oc-med-chl-multi-l3-chl_1km_daily-rt-v02 for the Mediterranean Sea. '''Processing information:''' All details about the processing can be found in relevant product description: *OCEANCOLOUR_ATL_CHL_L3_NRT_OBSERVATIONS_009_036 *OCEANCOLOUR_BAL_CHL_L3_NRT_OBSERVATIONS_009_049 *OCEANCOLOUR_BS_CHL_L3_NRT_OBSERVATIONS_009_044 *OCEANCOLOUR_MED_CHL_L3_NRT_OBSERVATIONS_009_040 '''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-eur-chl-multi-l3-chl_1km_daily-rt-v02 '''DOI (product) :''' https://doi.org/10.48670/moi-00095

'''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''' Time mean meridional Eulerian streamfunctions are computed using the velocity field estimate provided by the Copernicus Marine Mediterranean Sea reanalysis over the period from 1987 to the year preceding the current one [-1Y], operationally extended yearly. The Eulerian meridional streamfunction is evaluated by integrating meridional velocity daily data first in a vertical direction, then in a meridional direction, and finally averaging over the reanalysis period. The Mediterranean overturning indices are derived for the eastern and western Mediterranean Sea by computing the annual streamfunction in the two areas separated by the Strait of Sicily around 36.5°N, and then considering the associated maxima. In each case a geographical constraint focused the computation on the main region of interest. For the western index, we focused on deep-water formation regions, thus excluding both the effect of shallow physical processes and the Gibraltar net inflow. For the eastern index, we investigate the Levantine and Cretan areas corresponding to the strongest meridional overturning cell locations, thus only a zonal constraint is defined. Time series of annual mean values is provided for the Mediterranean Sea using the Mediterranean 1/24o eddy resolving reanalysis (Escudier et al., 2020, 2021). More details can be found in the Copernicus Marine Ocean State Report issue 4 (OSR4, von Schuckmann et al., 2020) Section 2.4 (Lyubartsev et al., 2020) and in the QUID. '''CONTEXT''' The western and eastern Mediterranean clockwise meridional overturning circulation is connected to deep-water formation processes. The Mediterranean Sea 1/24o eddy resolving reanalysis (MEDSEA_MULTIYEAR_PHY_006_004, Escudier et al., 2020, 2021) is used to show the interannual variability of the Meridional Overturning Index. Details on the product are delivered in the PUM and QUID of this OMI. The Mediterranean Meridional Overturning Index is defined here as the maxima of the clockwise cells in the eastern and western Mediterranean Sea and is associated with deep and intermediate water mass formation processes that occur in specific areas of the basin: Gulf of Lion, Southern Adriatic Sea, Cretan Sea and Rhodes Gyre (Pinardi et al., 2015). As in the global ocean, the overturning circulation of the western and eastern Mediterranean are paramount to determine the stratification of the basins (Cessi, 2019). In turn, the stratification and deep water formation mediate the exchange of oxygen and other tracers between the surface and the deep ocean (e.g., Johnson et al., 2009; Yoon et al., 2018). In this sense, the overturning indices are potential gauges of the ecosystem health of the Mediterranean Sea, and in particular they could instruct early warning indices for the Mediterranean Sea to support the Sustainable Development Goal (SDG) 13 Target 13.3. '''CMEMS KEY FINDINGS''' The western and eastern Mediterranean overturning indices (WMOI and EMOI) are synthetic indices of changes in the thermohaline properties of the Mediterranean basin related to changes in the main drivers of the basin scale circulation. The western sub-basin clockwise overturning circulation is associated with the deep-water formation area of the Gulf of Lion, while the eastern clockwise meridional overturning circulation is composed of multiple cells associated with different intermediate and deep-water sources in the Levantine, Aegean, and Adriatic Seas. On average, the EMOI shows higher values than the WMOI indicating a more vigorous overturning circulation in eastern Mediterranean. The difference is mostly related to the occurrence of the eastern Mediterranean transient (EMT) climatic event, and linked to a peak of the EMOI in 1992 (Roether et al. 1996, 2014, Gertman et al. 2006). In 1999, the difference between the two indices started to decrease because EMT water masses reached the Sicily Strait flowing into the western Mediterranean Sea (Schroeder et al., 2016). The western peak in 2006 is discussed to be linked to anomalous deep-water formation during the Western Mediterranean Transition (Smith, 2008; Schroeder et al., 2016). Thus, the WMOI and EMOI indices are a useful tool for long-term climate monitoring of overturning changes in the Mediterranean Sea. '''DOI (product):''' https://doi.org/10.48670/mds-00317

'''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:''' The Global Ocean Satellite monitoring and marine ecosystem study group (GOS) of the Italian National Research Council (CNR) in Rome distributes reprocessed surface chlorophyll concentration (Chl) and phytoplankton functional types (PFT). Input 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. Reprocessed (multi-year) products are consistent and homogeneous in terms of format, algorithms and processing software. Chl is obtained by means of the Mediterranean regional algorithms: an updated version of the MedOC4 (Volpe et al., 2019) and AD4 (Berthon and Zibordi, 2004). Discrimination between the two water types is performed by comparing the satellite spectrum with the average spectrum from in situ measurements. Reference insitu dataset is MedBiOp (Volpe et al., 2019) where Case II spectra are selected with a k-mean cluster analysis (Melin et al., 2015). Merging of Case I and Case II information is performed estimating the Mahalanobis distance between observed and reference spectra and using it as weight for the final value. The PFT provides estimates of Chl concentration of 9 phytoplankton groups: Micro, Nano, Pico, Diato, Dino, Crypto, Hapto, Green and Prokar. Micro consists of Diato and Dino, Nano includes Crypto and Hapto and Pico is referred to Green and Prokar with the adjustment of Brewin et al. (2010) in the ultra-oligotrophic water for Pico and Nano. These classes are estimated via empirical regional functions, correlating Chl concentration with each in-situ PFT fraction computed by a regional diagnostic pigment analysis (Di Cicco et al. 2017). '''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) 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-chl-multi-l3-chl_1km_daily-rep-v02 * dataset-oc-med-pft-multi-l3-pft_1km_daily-rep-v02 '''Files format:''' *CF-1.4 *INSPIRE compliant '''DOI (product) :''' https://doi.org/10.48670/moi-00112

'''DEFINITION''' The CMEMS MEDSEA_OMI_tempsal_extreme_var_temp_mean_and_anomaly OMI indicator is based on the computation of the annual 99th percentile of Sea Surface Temperature (SST) from model data. Two different CMEMS products are used to compute the indicator: The Iberia-Biscay-Ireland Multi Year Product (MEDSEA_MULTIYEAR_PHY_006_004) and the Analysis product (MEDSEA_ANALYSISFORECAST_PHY_006_013). 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 over the whole period (1987-2019). * Anomaly of the 99th percentile in 2020: The 99th percentile of the year 2020 is computed from the Near Real Time product. The anomaly is obtained by subtracting the mean percentile from the 2020 percentile. This indicator is aimed at monitoring the extremes of sea surface temperature every year and at checking their variations in space. The use of percentiles instead of annual maxima, makes this extremes study less affected by individual data. This study of extreme variability was first applied to the 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 Alvarez Fanjul et al., 2019). More details and a full scientific evaluation can be found in the CMEMS Ocean State report (Alvarez Fanjul et al., 2019). '''CONTEXT''' The Sea Surface Temperature is one of the Essential Ocean Variables, hence the monitoring of this variable is of key importance, since its variations can affect the ocean circulation, marine ecosystems, and ocean-atmosphere exchange processes. As the oceans continuously interact with the atmosphere, trends of sea surface temperature can also have an effect on the global climate. In recent decades (from mid ‘80s) the Mediterranean Sea showed a trend of increasing temperatures (Ducrocq et al., 2016), which has been observed also by means of the CMEMS SST_MED_SST_L4_REP_OBSERVATIONS_010_021 satellite product and reported in the following CMEMS OMI: MEDSEA_OMI_TEMPSAL_sst_area_averaged_anomalies and MEDSEA_OMI_TEMPSAL_sst_trend. The Mediterranean Sea is a semi-enclosed sea characterized by an annual average surface temperature which varies horizontally from ~14°C in the Northwestern part of the basin to ~23°C in the Southeastern areas. Large-scale temperature variations in the upper layers are mainly related to the heat exchange with the atmosphere and surrounding oceanic regions. The Mediterranean Sea annual 99th percentile presents a significant interannual and multidecadal variability with a significant increase starting from the 80’s as shown in Marbà et al. (2015) which is also in good agreement with the multidecadal change of the mean SST reported in Mariotti et al. (2012). Moreover the spatial variability of the SST 99th percentile shows large differences at regional scale (Darmariaki et al., 2019; Pastor et al. 2018). '''CMEMS KEY FINDINGS''' The Mediterranean mean Sea Surface Temperature 99th percentile evaluated in the period 1987-2019 (upper panel) presents highest values (~ 28-30 °C) in the eastern Mediterranean-Levantine basin and along the Tunisian coasts especially in the area of the Gulf of Gabes, while the lowest (~ 23–25 °C) are found in the Gulf of Lyon (a deep water formation area), in the Alboran Sea (affected by incoming Atlantic waters) and the eastern part of the Aegean Sea (an upwelling region). These results are in agreement with previous findings in Darmariaki et al. (2019) and Pastor et al. (2018) and are consistent with the ones presented in CMEMS OSR3 (Alvarez Fanjul et al., 2019) for the period 1993-2016. The 2020 Sea Surface Temperature 99th percentile anomaly map (bottom panel) shows a general positive pattern up to +3°C in the North-West Mediterranean area while colder anomalies are visible in the Gulf of Lion and North Aegean Sea . This Ocean Monitoring Indicator confirms the continuous warming of the SST and in particular it shows that the year 2020 is characterized by an overall increase of the extreme Sea Surface Temperature values in almost the whole domain with respect to the reference period. This finding can be probably affected by the different dataset used to evaluate this anomaly map: the 2020 Sea Surface Temperature 99th percentile derived from the Near Real Time Analysis product compared to the mean (1987-2019) Sea Surface Temperature 99th percentile evaluated from the Reanalysis product which, among the others, is characterized by different atmospheric forcing). Note: The key findings will be updated annually in November, in line with OMI evolutions. '''DOI (product):''' https://doi.org/10.48670/moi-00266

'''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