'''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

'''This product has been archived'''                For operationnal and online products, please visit https://marine.copernicus.eu '''Short description:''' The low resolution ocean physics analysis and forecast for the North-West European Shelf is produced using a forecasting ocean assimilation model, with tides, at 7 km horizontal resolution. The ocean model is NEMO (Nucleus for European Modelling of the Ocean), using the 3DVar NEMOVAR system to assimilate observations. These are surface temperature, vertical profiles of temperature and salinity, and along track satellite sea level anomaly data. The model is forced by lateral boundary conditions from the UK Met Office North Atlantic Ocean forecast model and by the CMEMS Baltic forecast product [https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=BALTICSEA_ANALYSISFORECAST_PHY_003_006 BALTICSEA_ANALYSISFORECAST_PHY_003_006]. The atmospheric forcing is given by the operational UK Met Office Global Atmospheric model. The river discharge is from a daily climatology. Further details of the model, including the product validation are provided in the [http://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-NWS-QUID-004-001.pdf CMEMS-NWS-QUID-004-001]. Products are provided as hourly instantaneous and daily 25-hour, de-tided, averages. The datasets available are temperature, salinity, horizontal currents, sea level, mixed layer depth, and bottom temperature. Temperature, salinity and currents, as multi-level variables, are interpolated from the model 51 hybrid s-sigma terrain-following system to 24 standard geopotential depths (z-levels). Grid-points near to the model boundaries are masked. The product is updated daily, providing a 6-day forecast and the previous 2-day assimilative hindcast. See [http://catalogue.marine.copernicus.eu/documents/PUM/CMEMS-NWS-PUM-004-001_002.pdf CMEMS-NWS-PUM-004-001_002] for further details. '''Associated products:''' This model is coupled with a biogeochemistry model (ERSEM) available as CMEMS product [https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NWSHELF_ANALYSISFORECAST_BGC_004_002 NWSHELF_ANALYSISFORECAST_BGC_004_002] A reanalysis product is available from: [https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NWSHELF_MULTIYEAR_PHY_004_009 NWSHELF_MULTIYEAR_PHY_004_009]. '''DOI (product) :''' https://doi.org/10.48670/moi-00057

'''This product has been archived''' '''Short description: ''' For the '''Global''' Ocean '''Satellite Observations''', ACRI-ST company (Sophia Antipolis, France) is providing '''Bio-Geo-Chemical (BGC)''' products based on the '''Copernicus-GlobColour''' processor. * Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the '''"multi"''' products, and S3A & S3B only for the '''"olci"''' products. * Variables: Chlorophyll-a ('''CHL'''), Phytoplankton Functional types and sizes ('''PFT'''), Primary Production ('''PP'''), Suspended Matter ('''SPM'''), Secchi Transparency Depth ('''ZSD'''), Diffuse Attenuation ('''KD490'''), Particulate Backscattering ('''BBP'''), Absorption Coef. ('''CDM''') and Reflectance ('''RRS'''). * Temporal resolutions: '''monthly''' plus, for some variables, '''daily gap-free''' based on a space-time interpolation to provide a "cloud free" product. * Spatial resolutions: '''4 km''' and a finer resolution based on olci '''300 meters''' inputs. * 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 the '''Copernicus-GlobColour''' products in the catalogue, use the search keyword '''"GlobColour"'''. '''DOI (product) :''' https://doi.org/10.48670/moi-00281

'''DEFINITION''' The temporal evolution of thermosteric sea level in an ocean layer (here: 0-700m) is obtained from an integration of temperature driven ocean density variations, which are subtracted from a reference climatology (here 1993-2014) to obtain the fluctuations from an average field. The regional thermosteric sea level values from 1993 to close to real time are then averaged from 60°S-60°N aiming to monitor interannual to long term global sea level variations caused by temperature driven ocean volume changes through thermal expansion as expressed in meters (m). '''CONTEXT''' The global mean sea level is reflecting changes in the Earth’s climate system in response to natural and anthropogenic forcing factors such as ocean warming, land ice mass loss and changes in water storage in continental river basins (IPCC, 2019). Thermosteric sea-level variations result from temperature related density changes in sea water associated with volume expansion and contraction (Storto et al., 2018). Global thermosteric sea level rise caused by ocean warming is known as one of the major drivers of contemporary global mean sea level rise (WCRP, 2018). '''CMEMS KEY FINDINGS''' Since the year 1993 the upper (0-700m) near-global (60°S-60°N) thermosteric sea level rises at a rate of 1.5±0.1 mm/year.

'''Short description:''' Global sea ice thickness from merged L-Band radiometer (SMOS ) and radar altimeter (CryoSat-2, Sentinel-3A/B) observations during freezing season between October and April in the northern hemisphere and April to October in the southern hemisphere. The SMOS mission provides L-band observations and the ice thickness-dependency of brightness temperature enables to estimate the sea-ice thickness for thin ice regimes. Radar altimeters measure the height of the ice surface above the water level, which can be converted into sea ice thickness assuming hydrostatic equilibrium. '''DOI (product) :''' https://doi.org/10.48670/moi-00125

'''DEFINITION''' The indicator of the Kuroshio extension phase variations is based on the standardized high frequency altimeter Eddy Kinetic Energy (EKE) averaged in the area 142-149°E and 32-37°N and computed from the DUACS delayed-time (CMEMS SEALEVEL_GLO_PHY_L4_MY_008_047) and near real-time (CMEMS SEALEVEL_GLO_PHY_L4_NRT _008_046) altimeter sea level gridded products. ""CONTEXT"" The Kuroshio Extension is an eastward-flowing current in the subtropical western North Pacific after the Kuroshio separates from the coast of Japan at 35°N, 140°E. Being the extension of a wind-driven western boundary current, the Kuroshio Extension is characterized by a strong variability and is rich in large-amplitude meanders and energetic eddies (Niiler et al., 2003; Qiu, 2003, 2002). The Kuroshio Extension region has the largest sea surface height variability on sub-annual and decadal time scales in the extratropical North Pacific Ocean (Jayne et al., 2009; Qiu and Chen, 2010, 2005). Prediction and monitoring of the path of the Kuroshio are of huge importance for local economies as the position of the Kuroshio extension strongly determines the regions where phytoplankton and hence fish are located. Unstable (contracted) phase of the Kuroshio enhance the production of Chlorophyll (Lin et al., 2014). ""CMEMS KEY FINDINGS"" The different states of the Kuroshio extension phase have been presented and validated by (Bessières et al., 2013) and further reported by Drévillon et al. (2018) in the Copernicus Ocean State Report #2. Two rather different states of the Kuroshio extension are observed: an ‘elongated state’ (also called ‘strong state’) corresponding to a narrow strong steady jet, and a ‘contracted state’ (also called ‘weak state’) in which the jet is weaker and more unsteady, spreading on a wider latitudinal band. When the Kuroshio Extension jet is in a contracted (elongated) state, the upstream Kuroshio Extension path tends to become more (less) variable and regional eddy kinetic energy level tends to be higher (lower). In between these two opposite phases, the Kuroshio extension jet has many intermediate states of transition and presents either progressively weakening or strengthening trends. In 2018, the indicator reveals an elongated state followed by a weakening neutral phase since then. '''DOI (product):''' https://doi.org/10.48670/moi-00222

'''This product has been archived'''                For operationnal and online products, please visit https://marine.copernicus.eu '''DEFINITION''' The ocean monitoring indicator of regional mean sea level is derived from the DUACS delayed-time (DT-2021 version) altimeter gridded maps of sea level anomalies based on a stable number of altimeters (two) in the satellite constellation. These products are distributed by the Copernicus Climate Change Service and the Copernicus Marine Service (SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057). The mean sea level evolution estimated in the Mediterranean Sea is derived from the average of the gridded sea level maps weighted by the cosine of the latitude. The annual and semi-annual periodic signals are removed (least square fit of sinusoidal function) and the time series is low-pass filtered (175 days cut-off). The curve is corrected for the regional mean effect of the Glacial Isostatic Adjustment (GIA) using the ICE5G-VM2 GIA model (Peltier, 2004). During 1993-1998, the Global men sea level (hereafter GMSL) has been known to be affected by a TOPEX-A instrumental drift (WCRP Global Sea Level Budget Group, 2018; Legeais et al., 2020). This drift led to overestimate the trend of the GMSL during the first 6 years of the altimetry record (about 0.04 mm/y at global scale over the whole altimeter period). A correction of the drift is proposed for the Global mean sea level (Legeais et al., 2020). Whereas this TOPEX-A instrumental drift should also affect the regional mean sea level (hereafter RMSL) trend estimation, this empirical correction is currently not applied to the altimeter sea level dataset and resulting estimated for RMSL. Indeed, the pertinence of the global correction applied at regional scale has not been demonstrated yet and there is no clear consensus achieved on the way to proceed at regional scale. Additionally, the estimate of such a correction at regional scale is not obvious, especially in areas where few accurate independent measurements (e.g. in situ)- necessary for this estimation - are available. The trend uncertainty is provided in a 90% confidence interval (Prandi et al., 2021). This estimate only considers errors related to the altimeter observation system (i.e., orbit determination errors, geophysical correction errors and inter-mission bias correction errors). The presence of the interannual signal can strongly influence the trend estimation considering to the altimeter period considered (Wang et al., 2021; Cazenave et al., 2014). The uncertainty linked to this effect is not taken into account. '''CONTEXT''' The indicator on area averaged sea level is a crucial index of climate change, and individual components contribute to sea level rise, including expansion due to ocean warming and melting of glaciers and ice sheets (WCRP Global Sea Level Budget Group, 2018). According to the recent IPCC 6th assessment report, global mean sea level (GMSL) increased by 0.20 (0.15 to 0.25) m over the period 1901 to 2018 with a rate 25 of rise that has accelerated since the 1960s to 3.7 (3.2 to 4.2) mm yr-1 for the period 2006–2018. Human activity was very likely the main driver of observed GMSL rise since 1970 (IPCC WGII, 2021). The weight of the different contributions evolves with time and in the recent decades the mass change has increased, contributing to the on-going acceleration of the GMSL trend (IPCC, 2022a; Legeais et al., 2020; Horwath et al., 2022). At regional scale, sea level does not change homogenously, and RMSL rise can also be influenced by various other processes, with different spatial and temporal scales, such as local ocean dynamic, atmospheric forcing, Earth gravity and vertical land motion changes (IPCC WGI, 2021). Rising sea level can strongly affect population and infrastructures in coastal areas, increase their vulnerability and risks for food security, particularly in low lying areas and island states. Adverse impacts from floods, storms and tropical cyclones with related losses and damages have increased due to sea level rise, and increase their vulnerability and increase risks for food security, particularly in low lying areas and island states (IPCC, 2022b). Adaptation and mitigation measures such as the restoration of mangroves and coastal wetlands, reduce the risks from sea level rise (IPCC, 2022c). Beside a clear long-term trend, the regional mean sea level variation in the Mediterranean Sea shows an important interannual variability, with a high trend observed before 1999 and lower values afterward. This variability is associated with a variation of the different forcing. Steric effect has been the most important forcing before 1999 (Fenoglio-Marc, 2002; Vigo et al., 2005). Important change of the deep-water formation site also occurred in 1995. The latest is preconditioned by an important change of the sea surface circulation observed in the Ionian Sea in 1997-1998 (e.g. Gačić et al., 2011), under the influence of the North Atlantic Oscillation (NAO) and negative Atlantic Multidecadal Oscillation (AMO) phases (Incarbona et al., 2016). They may also impact the sea level trend in the basin (Vigo et al., 2005). In 2010-2011, high regional mean sea level has been related to enhanced water mass exchange at Gibraltar, under the influence of wind forcing during the negative phase of NAO (Landerer and Volkov, 2013). '''CMEMS KEY FINDINGS''' Over the [1993/01/01, 2021/08/02] period, the basin-wide RMSL in the Mediterranean Sea rises at a rate of 2.7  0.83 mm/year. '''DOI (product):''' https://doi.org/10.48670/moi-00264

'''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

'''This product has been archived''' For operationnal and online products, please visit https://marine.copernicus.eu '''Short description:''' Mediterranean Sea - near real-time (NRT) in situ quality controlled observations, hourly updated and distributed by INSTAC within 24-48 hours from acquisition in average '''DOI (product) :''' https://doi.org/10.48670/moi-00044

'''This product has been archived''' For operationnal and online products, please visit https://marine.copernicus.eu '''Short description:''' Near-Real-Time gridded multi-mission merged satellite significant wave height. Only valid data are included. This product is processed in Near-Real-Time by the WAVE-TAC multi-mission altimeter data processing system and is based on CMEMS level-3 SWH datasets (see the product WAVE_GLO_WAV_L3_SWH_NRT_OBSERVATIONS_014_001). It merges along-track SWH data from the following missions: Jason-3, Sentinel-3A, Sentinel-3B, SARAL/AltiKa, Cryosat-2, CFOSAT and HaiYang-2B. The resulting gridded product has a 2° horizontal resolution and is produced daily. Different SWH fields are produced: VAVH_DAILY fields are daily statistics computed from all available level 3 along-track measurements from 00 UTC until 23:59 UTC ; VAVH_INST field provides an estimate of the instantaneous wave field at 12:00UTC (noon), using all available Level 3 along-track measurements and accounting for their spatial and temporal proximity. '''DOI (product) :''' https://doi.org/10.48670/moi-00177