'''Short description:''' The iceberg product contains 9 (6+3) datasets: Six gridded datasets in netCDF format: IW, EW and RCMNL modes and mosaic for the two modes) describing iceberg concentration as number of icebergs counted within 10x10 km grid cells. The iceberg concentration is derived by applying a Constant False Alarm Rate (CFAR) algorithm on data from Synthetic Aperture Radar (SAR) satellite sensors. Three datasets – individual iceberg positions – in shapefile format: The shapefile format allows the best representation of the icebergs. Each shapefile-dataset also includes a shapefile holding the polygonized satellite coverage Despite its precision (individual icebergs are proposed), this product is a generic and automated product and needs expertise to be correctly used. For all applications concerning marine navigation, please refer to the national Ice Service of the country concerned. '''DOI (product) :''' https://doi.org/10.48670/moi-00129

'''This product has been archived''' For operationnal and online products, please visit https://marine.copernicus.eu '''Short description:''' The Global Ocean Satellite monitoring and marine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''' The Mediterranean water mass formation rates are evaluated in 4 areas as defined in the Ocean State Report issue 2 (OSR2, von Schuckmann et al., 2018) section 3.4 (Simoncelli and Pinardi, 2018): (1) the Gulf of Lions for the Western Mediterranean Deep Waters (WMDW); (2) the Southern Adriatic Sea Pit for the Eastern Mediterranean Deep Waters (EMDW); (3) the Cretan Sea for Cretan Intermediate Waters (CIW) and Cretan Deep Waters (CDW); (4) the Rhodes Gyre, the area of formation of the so-called Levantine Intermediate Waters (LIW) and Levantine Deep Waters (LDW). Annual water mass formation rates have been computed using daily mixed layer depth estimates (density criteria Δσ = 0.01 kg/m3, 10 m reference level) considering the annual maximum volume of water above mixed layer depth with potential density within or higher the specific thresholds specified in Table 1 then divided by seconds per year. Annual mean values are provided using the Mediterranean 1/24o eddy resolving reanalysis (Escudier et al. 2020, 2021). Time spans from 1987 to the year preceding the current one [-1Y], operationally extended yearly. '''CONTEXT''' The formation of intermediate and deep water masses is one of the most important processes occurring in the Mediterranean Sea, being a component of its general overturning circulation. This circulation varies at interannual and multidecadal time scales and it is composed of an upper zonal cell (Zonal Overturning Circulation) and two main meridional cells in the western and eastern Mediterranean (Pinardi and Masetti 2000). The objective is to monitor the main water mass formation events using the eddy resolving Mediterranean Sea Reanalysis (MEDSEA_MULTIYEAR_PHY_006_004, Escudier et al. 2020, 2021) and considering Pinardi et al. (2015) and Simoncelli and Pinardi (2018) as references for the methodology. The Mediterranean Sea Reanalysis can reproduce both Eastern Mediterranean Transient and Western Mediterranean Transition phenomena and catches the principal water mass formation events reported in the literature. This will permit constant monitoring of the open ocean deep convection process in the Mediterranean Sea and a better understanding of the multiple drivers of the general overturning circulation at interannual and multidecadal time scales. Deep and intermediate water formation events reveal themselves by a deep mixed layer depth distribution in four Mediterranean areas: Gulf of Lions, Southern Adriatic Sea Pit, Cretan Sea and Rhodes Gyre. '''KEY FINDINGS''' The Western Mediterranean Deep Water (WMDW) formation events in the Gulf of Lion appear to be larger after 1999 consistently with Schroeder et al. (2006, 2008) related to the Eastern Mediterranean Transient event. This modification of WMDW after 2005 has been called Western Mediterranean Transition. WMDW formation events are consistent with Somot et al. (2016) and the event in 2009 is also reported in Houpert et al. (2016). The Eastern Mediterranean Deep Water (EMDW) formation in the Southern Adriatic Pit region displays a period of water mass formation between 1988 and 1993, in agreement with Pinardi et al. (2015), in 1996, 1999 and 2000 as documented by Manca et al. (2002). Weak deep water formation in winter 2006 is confirmed by observations in Vilibić and Šantić (2008). An intense deep water formation event is detected in 2012-2013 (Gačić et al., 2014). Last years are characterized by large events starting from 2017 (Mihanovic et al., 2021). Cretan Intermediate Water formation rates present larger peaks between 1989 and 1993 with the ones in 1992 and 1993 composing the Eastern Mediterranean Transient phenomena. The Cretan Deep Water formed in 1992 and 1993 is characterized by the highest densities of the entire period in accordance with Velaoras et al. (2014). The Levantine Deep Water formation rate in the Rhode Gyre region presents the largest values between 1992 and 1993 in agreement with Kontoyiannis et al. (1999). '''DOI (product):''' https://doi.org/10.48670/mds-00318

'''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

'''Short description:''' For the NWS/IBI Ocean- Sea Surface Temperature L3 Observations . This product provides daily foundation sea surface temperature from multiple satellite sources. The data are intercalibrated. This product consists in a fusion of sea surface temperature observations from multiple satellite sensors, daily, over a 0.02° resolution grid. It includes observations by polar orbiting and geostationary satellites . The L3S SST data are produced selecting only the highest quality input data from input L2P/L3P images within a strict temporal window (local nightime), to avoid diurnal cycle and cloud contamination. The observations of each sensor are intercalibrated prior to merging using a bias correction based on a multi-sensor median reference correcting the large-scale cross-sensor biases. 3 more datasets are available that only contain "per sensor type" data : Polar InfraRed (PIR), Polar MicroWave (PMW), Geostationary InfraRed (GIR) '''DOI (product) :''' https://doi.org/10.48670/moi-00310

'''DEFINITION''' OMI_CLIMATE_SST_BAL_area_averaged_anomalies product includes time series of monthly mean SST anomalies over the period 1982-2024, relative to the 1991-2020 climatology, averaged for the Baltic Sea. The SST Level 4 analysis products that provide the input to the monthly averages are taken from the reprocessed product SST_BAL_SST_L4_REP_OBSERVATIONS_010_016 with a recent update to include 2023. The product has a spatial resolution of 0.02 in latitude and longitude. The OMI time series runs from Jan 1, 1982 to December 31, 2024 and is constructed by calculating monthly averages from the daily level 4 SST analysis fields of the SST_BAL_SST_L4_REP_OBSERVATIONS_010_016 product . The climatology period from 1991 to 2020 (30 years) is selected according to WMO recommendations (WMO, 2017) and the most recent practice from the U.S. National Oceanic and Atmospheric Administration practice (https://wmo.int/media/news/updated-30-year-reference-period-reflects-changing-climate). See the Copernicus Marine Service Ocean State Reports (section 1.1 in Von Schuckmann et al., 2016; section 3 in Von Schuckmann et al., 2018) for more information on the OMI product. '''CONTEXT''' Sea Surface Temperature (SST) is an Essential Climate Variable (GCOS) that is an important input for initialising numerical weather prediction models and fundamental for understanding air-sea interactions and monitoring climate change (GCOS 2010). The Baltic Sea is a region that requires special attention regarding the use of satellite SST records and the assessment of climatic variability (Høyer and She 2007; Høyer and Karagali 2016). The Baltic Sea is a semi-enclosed basin with natural variability and it is influenced by large-scale atmospheric processes and by the vicinity of land. In addition, the Baltic Sea is one of the largest brackish seas in the world. When analysing regional-scale climate variability, all these effects have to be considered, which requires dedicated regional and validated SST products. Satellite observations have previously been used to analyse the climatic SST signals in the North Sea and Baltic Sea (BACC II Author Team 2015; Lehmann et al. 2011). Recently, Høyer and Karagali (2016) demonstrated that the Baltic Sea had warmed 1-2 oC from 1982 to 2012 considering all months of the year and 3-5 °C when only July-September months were considered. This was corroborated in the Ocean State Reports (section 1.1 in Von Schuckmann et al., 2016; section 3 in Von Schuckmann et al., 2018). '''CMEMS KEY FINDINGS''' The basin-average trend of SST anomalies for Baltic Sea region amounts to 0.039±0.003°C/year over the period 1982-2024 which corresponds to an average warming of 1.68°C. Adding the North Sea area, the average trend amounts to 0.026±0.002°C/year over the same period, which corresponds to an average warming of 1.19°C for the entire region since 1982. '''DOI (product):''' https://doi.org/10.48670/moi-00205

'''This product has been archived''' For operational and online products, please visit https://marine.copernicus.eu '''Short description:''' For the '''North Atlantic''' Ocean '''Satellite Observations''', Plymouth Marine Laboratory (PML) is providing '''Bio-Geo_Chemical (BGC)''' products based on the ESA-CCI reflectance inputs. * Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP, OLCI-S3A & OLCI-S3B for the '''""multi""''' products, and S3A & S3B only for the '''""olci""''' products. * Variables: Chlorophyll-a ('''CHL''') and Diffuse Attenuation ('''KD490'''). * Temporal resolutions:'''monthly'''. * Spatial resolutions: '''1 km''' (multi) or '''300 meters''' (olci). * Recent products are organized in datasets called Near Real Time ('''NRT''') and long time-series (from 1997) in datasets called Multi-Years ('''MY'''). To find these products in the catalogue, use the search keyword '''""ESA-CCI""'''. '''DOI (product) :''' https://doi.org/10.48670/moi-00285

'''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:''' For the Baltic Sea- The DMI Sea Surface Temperature L3S aims at providing daily multi-sensor supercollated data at 0.03deg. x 0.03deg. horizontal resolution, using satellite data from infra-red radiometers. Uses SST satellite products from these sensors: NOAA AVHRRs 7, 9, 11, 14, 16, 17, 18 , Envisat ATSR1, ATSR2 and AATSR. '''DOI (product) :''' https://doi.org/10.48670/moi-00154

'''This product has been archived''' For operationnal and online products, please visit https://marine.copernicus.eu '''DEFINITION''' Oligotrophic subtropical gyres are regions of the ocean with low levels of nutrients required for phytoplankton growth and low levels of surface chlorophyll-a whose concentration can be quantified through satellite observations. The gyre boundary has been defined using a threshold value of 0.15 mg m-3 chlorophyll for the Atlantic gyres (Aiken et al. 2016), and 0.07 mg m-3 for the Pacific gyres (Polovina et al. 2008). The area inside the gyres for each month is computed using monthly chlorophyll data from which the monthly climatology is subtracted to compute anomalies. A gap filling algorithm has been utilized to account for missing data. Trends in the area anomaly are then calculated for the entire study period (September 1997 to December 2020). '''CONTEXT''' Oligotrophic gyres of the oceans have been referred to as ocean deserts (Polovina et al. 2008). They are vast, covering approximately 50% of the Earth’s surface (Aiken et al. 2016). Despite low productivity, these regions contribute significantly to global productivity due to their immense size (McClain et al. 2004). Even modest changes in their size can have large impacts on a variety of global biogeochemical cycles and on trends in chlorophyll (Signorini et al. 2015). Based on satellite data, Polovina et al. (2008) showed that the areas of subtropical gyres were expanding. The Ocean State Report (Sathyendranath et al. 2018) showed that the trends had reversed in the Pacific for the time segment from January 2007 to December 2016. '''CMEMS KEY FINDINGS''' The trend in the North Atlantic gyre area for the 1997 Sept – 2020 December period was positive, with a 0.39% year-1 increase in area relative to 2000-01-01 values. This trend has decreased compared with the 1997-2019 trend of 0.45%, and is statistically significant (p<0.05). During the 1997 Sept – 2020 December period, the trend in chlorophyll concentration was positive (0.24% year-1) inside the North Atlantic gyre relative to 2000-01-01 values. This time series extension has resulted in a reversal in the rate of change, compared with the -0.18% trend for the 1997-209 period and is statistically significant (p<0.05). Note: The key findings will be updated annually in November, in line with OMI evolutions. '''DOI (product):''' https://doi.org/10.48670/moi-00226