'''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

'''Short description:''' The product MULTIOBS_GLO_PHY_SSS_L4_MY_015_015 is a reformatting and a simplified version of the CATDS L4 product called “SMOS-OI”. This product is obtained using optimal interpolation (OI) algorithm, that combine, ISAS in situ SSS OI analyses to reduce large scale and temporal variable bias, SMOS satellite image, SMAP satellite image, and satellite SST information. Kolodziejczyk Nicolas, Hamon Michel, Boutin Jacqueline, Vergely Jean-Luc, Reverdin Gilles, Supply Alexandre, Reul Nicolas (2021). Objective analysis of SMOS and SMAP Sea Surface Salinity to reduce large scale and time dependent biases from low to high latitudes. Journal Of Atmospheric And Oceanic Technology, 38(3), 405-421. Publisher's official version : https://doi.org/10.1175/JTECH-D-20-0093.1, Open Access version : https://archimer.ifremer.fr/doc/00665/77702/ '''DOI (product) :''' https://doi.org/10.48670/mds-00369

'''Short description''' This product is entirely dedicated to ocean current data observed in near-real time. Current data from 3 different types of instruments are distributed: * The near-surface zonal and meridional velocities calculated along the trajectories of the drifting buoys which are part of the DBCP’s Global Drifter Program. These data are delivered together with wind stress components, surface temperature and a wind-slippage correction for drogue-off and drogue-on drifters trajectories. * The near-surface zonal and meridional total velocities, and near-surface radial velocities, measured by High Frequency radars that are part of the European High Frequency radar Network. These data are delivered together with standard deviation of near-surface zonal and meridional raw velocities, Geometrical Dilution of Precision (GDOP), quality flags and metadata. * The zonal and meridional velocities, at parking depth and in surface, calculated along the trajectories of the floats which are part of the Argo Program. '''DOI (product) :''' https://doi.org/10.48670/moi-00041

"'Short description: ''' Global Ocean - This delayed mode product designed for reanalysis purposes integrates the best available version of in situ data for ocean surface and subsurface currents. Current data from 5 different types of instruments are distributed: * The drifter's near-surface velocities computed from their position measurements. In addition, a wind slippage correction is provided from 1993. Information on the presence of the drogue of the drifters is also provided. * The near-surface zonal and meridional total velocities, and near-surface radial velocities, measured by High Frequency (HF) radars that are part of the European HF radar Network. These data are delivered together with standard deviation of near-surface zonal and meridional raw velocities, Geometrical Dilution of Precision (GDOP), quality flags and metadata. * The zonal and meridional velocities, at parking depth (mostly around 1000m) and at the surface, calculated along the trajectories of the floats which are part of the Argo Program. * The velocity profiles within the water column coming from Acoustic Doppler Current Profiler (vessel mounted ADCP, Moored ADCP, saildrones) platforms * The near-surface and subsurface velocities calculated from gliders (autonomous underwater vehicle) trajectories '''DOI (product) :''' https://doi.org/10.17882/86236

'''Short description:''' This product consists of vertical profiles of the concentration of nutrients (nitrates, phosphates, and silicates) and carbonate system variables (total alkalinity, dissolved inorganic carbon, pH, and partial pressure of carbon dioxide), computed for each Argo float equipped with an oxygen sensor. The method called CANYON is based on a neural network trained using nutrient data (GLODAPv2 database) '''DOI (product) :''' https://doi.org/10.48670/moi-00048

'''DEFINITION''' Estimates of Arctic sea ice extent are obtained from the surface of oceans grid cells that have at least 15% sea ice concentration. These values are cumulated in the entire Northern Hemisphere (excluding ice lakes) and from 1993 up to the year 2019 aiming to: i) obtain the Arctic sea ice extent as expressed in millions of km square (106 km2) to monitor both the large-scale variability and mean state and change. ii) to monitor the change in sea ice extent as expressed in millions of km squared per decade (106 km2/decade), or in sea ice extent loss since the beginning of the time series as expressed in percent per decade (%/decade; reference period being the first date of the key figure b) dot-dashed trend line, Vaughan et al., 2013). These trends are calculated in three ways, i.e. (i) from the annual mean values; (ii) from the March values (winter ice loss); (iii) from September values (summer ice loss). The Arctic sea ice extent used here is based on the “multi-product” (GLOBAL_MULTIYEAR_PHY_ENS_001_031) approach as introduced in the second issue of the Ocean State Report (CMEMS OSR, 2017). Five global products have been used to build the ensemble mean, and its associated ensemble spread. '''CONTEXT''' Sea ice is frozen seawater that floats on the ocean surface. This large blanket of millions of square kilometers insulates the relatively warm ocean waters from the cold polar atmosphere. The seasonal cycle of the sea ice, forming and melting with the polar seasons, impacts both human activities and biological habitat. Knowing how and how much the sea ice cover is changing is essential for monitoring the health of the Earth as sea ice is one of the highest sensitive natural environments. Variations in sea ice cover can induce changes in ocean stratification, in global and regional sea level rates and modify the key rule played by the cold poles in the Earth engine (IPCC, 2019). The sea ice cover is monitored here in terms of sea ice extent quantity. More details and full scientific evaluations can be found in the CMEMS Ocean State Report (Samuelsen et al., 2016; Samuelsen et al., 2018). '''CMEMS KEY FINDINGS''' Since the year 1993 to 2023 the Arctic sea ice extent has decreased significantly at an annual rate of -0.57*106 km2 per decade. This represents an amount of -4.8 % per decade of Arctic sea ice extent loss over the period 1993 to 2023. Over the period 1993 to 2018, summer (September) sea ice extent loss amounts to -1.18*106 km2/decade (September values), which corresponds to -14.85% per decade. Winter (March) sea ice extent loss amounts to -0.57*106 km2/decade, which corresponds to -3.42% per decade. These values slightly exceed the estimates given in the AR5 IPCC assessment report (estimate up to the year 2012) as a consequence of continuing Northern Hemisphere sea ice extent loss. Main change in the mean seasonal cycle is characterized by less and less presence of sea ice during summertime with time. Note: The key findings will be updated annually in November, in line with OMI evolutions. '''DOI (product):''' https://doi.org/10.48670/moi-00190

'''Short description:''' For the '''Global''' Ocean '''Satellite Observations''', Brockmann Consult (BC) is providing '''Bio-Geo_Chemical (BGC)''' products based on the ESA-CCI inputs. * Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP, OLCI-S3A & OLCI-S3B for the '''""multi""''' products. * Variables: Chlorophyll-a ('''CHL'''). * Temporal resolutions: '''monthly'''. * Spatial resolutions: '''4 km''' (multi). * 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-00283

'''This product has been archived''' '''DEFINITION''' Estimates of Ocean Heat Content (OHC) are obtained from integrated differences of the measured temperature and a climatology along a vertical profile in the ocean (von Schuckmann et al., 2018). The regional OHC values are then averaged from 60°S-60°N aiming i) to obtain the mean OHC as expressed in Joules per meter square (J/m2) to monitor the large-scale variability and change. ii) to monitor the amount of energy in the form of heat stored in the ocean (i.e. the change of OHC in time), expressed in Watt per square meter (W/m2). Ocean heat content is one of the six Global Climate Indicators recommended by the World Meterological Organisation for Sustainable Development Goal 13 implementation (WMO, 2017). '''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 ocean shapes our perspectives for the future (von Schuckmann et al., 2020). Variations in OHC can induce changes in ocean stratification, currents, sea ice and ice shelfs (IPCC, 2019; 2021); they set time scales and dominate Earth system adjustments to climate variability and change (Hansen et al., 2011); they are a key player in ocean-atmosphere interactions and sea level change (WCRP, 2018) and they can impact marine ecosystems and human livelihoods (IPCC, 2019). '''CMEMS KEY FINDINGS''' Since the year 2005, the upper (0-700m) near-global (60°S-60°N) ocean warms at a rate of 0.6 ± 0.1 W/m2. Note: The key findings will be updated annually in November, in line with OMI evolutions. '''DOI (product):''' https://doi.org/10.48670/moi-00234

'''Short description:''' Altimeter satellite gridded Sea Level Anomalies (SLA) computed with respect to a twenty-year [1993, 2012] mean. The SLA is estimated by Optimal Interpolation, merging the L3 along-track measurement from the different altimeter missions available. Part of the processing is fitted to the European Sea area. (see QUID document or http://duacs.cls.fr [http://duacs.cls.fr] pages for processing details). The product gives additional variables (i.e. Absolute Dynamic Topography and geostrophic currents (absolute and anomalies)). It serves in delayed-time applications. This product is processed by the DUACS multimission altimeter data processing system. '''DOI (product):''' https://doi.org/10.48670/moi-00141

'''Short description:''' Multi-Year mono-mission satellite-based integral parameters derived from the directional wave spectra. Using linear propagation wave model, only wave observations that can be back-propagated to wave converging regions are considered. The dataset parameters includes partition significant wave height, partition peak period and partition peak or principal direction given along swell propagation path in space and time at a 3-hour timestep, from source to land. Validity flags are also included for each parameter and indicates the valid time steps along propagation (eg. no propagation for significant wave height close to the storm source or any integral parameter when reaching the land). The integral parameters at observation point are also available together with a quality flag based on the consistency between each propagated observation and the overall swell field.This product is processed by the WAVE-TAC multi-mission SAR data processing system. It processes data from the following SAR missions: Sentinel-1A and Sentinel-1B.One file is produced for each mission and is available in two formats: one gathering in one netcdf file all observations related to the same swell field, and for another all observations available in a 3-hour time range, and for both formats, propagated information from source to land. '''DOI (product) :''' https://doi.org/10.48670/moi-00174