'''This product has been archived''' '''DEFINITION''' The temporal evolution of thermosteric sea level in an ocean layer is obtained from an integration of temperature driven ocean density variations, which are subtracted from a reference climatology to obtain the fluctuations from an average field. The regional thermosteric sea level values 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. Thermosteric sea-level variations result from temperature related density changes in sea water associated with volume expansion and contraction. Global thermosteric sea level rise caused by ocean warming is known as one of the major drivers of contemporary global mean sea level rise (Cazenave et al., 2018; Oppenheimer et al., 2019). '''CMEMS KEY FINDINGS''' Since the year 2005 the upper (0-2000m) near-global (60°S-60°N) thermosteric sea level rises at a rate of 1.3±0.2 mm/year. Note: The key findings will be updated annually in November, in line with OMI evolutions. '''DOI (product):''' https://doi.org/10.48670/moi-00240

'''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

'''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

"''Short description:''' The IBI-MFC provides a high-resolution ocean analysis and forecast product (daily run by Nologin with the support of CESGA in terms of supercomputing resources), covering the European waters, and more specifically the Iberia–Biscay–Ireland (IBI) area. The last 2 years before now (historic best estimates) as well as forecasts of different temporal resolutions with a horizon of 10 days (updated on a daily basis) are available on the catalogue. The system is based on a eddy-resolving NEMO model application at 1/36º horizontal resolution, being Mercator-Ocean in charge of the model code development. The hydrodynamic forecast includes high frequency processes of paramount importance to characterize regional scale marine processes: tidal forcing, surges and high frequency atmospheric forcing, fresh water river discharge, wave forcing in forecast, etc. A weekly update of IBI downscaled analysis is also delivered as historic IBI best estimates. The product offers 3D daily and monthly ocean fields, as well as hourly mean and 15-minute instantaneous values for some surface variables. Daily and monthly averages of 3D Temperature, 3D Salinity, 3D Zonal, Meridional and vertical Velocity components, Mix Layer Depth, Sea Bottom Temperature and Sea Surface Height are provided. Additionally, hourly means of surface fields for variables such as Sea Surface Height, Mix Layer Depth, Surface Temperature and Currents, together with Barotropic Velocities are delivered. Doodson-filtered detided mean sea level and horizontal surface currents are also provided. Finally, 15-minute instantaneous values of Sea Surface Height and Currents are also given. '''DOI (Product)''': https://doi.org/10.48670/moi-00027

'''Short description:''' The NWSHELF_ANALYSISFORECAST_PHY_004_013 is produced by a coupled hydrodynamic-wave model system with tides, implemented over the North East Atlantic and Shelf Seas at 1.5 km of horizontal resolution and 33 vertical levels. The product is updated daily, providing 7-day forecast for temperature, salinity, currents, sea level and mixed layer depth. Products are provided at quarter-hourly, hourly, daily de-tided (with Doodson filter), and monthly frequency. '''DOI (product) :''' https://doi.org/10.48670/moi-00054

'''Short description:''' Altimeter satellite along-track sea surface heights anomalies (SLA) computed with respect to a twenty-year [1993, 2012] mean with a 1Hz (~7km) sampling. It serves in delayed-time applications. This product is processed by the DUACS multimission altimeter data processing system. It processes data from all altimeter missions available (e.g. Sentinel-6A, Jason-3, Sentinel-3A, Sentinel-3B, Saral/AltiKa, Cryosat-2, Jason-1, Jason-2, Topex/Poseidon, ERS-1, ERS-2, Envisat, Geosat Follow-On, HY-2A, HY-2B, etc.). The system exploits the most recent datasets available based on the enhanced GDR/NTC production. All the missions are homogenized with respect to a reference mission. Part of the processing is fitted to the Global ocean. (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 Atmospheric Correction, Ocean Tides, Long Wavelength Errors) that can be used to change the physical content for specific needs (see PUM document for details) '''Associated products''' A time invariant product https://resources.marine.copernicus.eu/product-detail/SEALEVEL_GLO_PHY_NOISE_L4_STATIC_008_033/INFORMATION describing the noise level of along-track measurements is available. It is associated to the sla_filtered variable. It is a gridded product. One file is provided for the global ocean and those values must be applied for Arctic and Europe products. For Mediterranean and Black seas, one value is given in the QUID document. '''DOI (product)''': https://doi.org/10.48670/moi-00146

'''DEFINITION''' The OMI_EXTREME_WAVE_IBI_swh_mean_and_anomaly_obs indicator is based on the computation of the 99th and the 1st percentiles from in situ data (observations). It is computed for the variable significant wave height (swh) measured by in situ buoys. The use of percentiles instead of annual maximum and minimum values, makes this extremes study less affected by individual data measurement errors. The percentiles are temporally averaged, and the spatial evolution is displayed, jointly with the anomaly in the target year. This study of extreme variability was first applied to sea level variable (Pérez Gómez et al 2016) and then extended to other essential variables, sea surface temperature and significant wave height (Pérez Gómez et al 2018). '''CONTEXT''' Projections on Climate Change foresee a future with a greater frequency of extreme sea states (Stott, 2016; Mitchell, 2006). The damages caused by severe wave storms can be considerable not only in infrastructure and buildings but also in the natural habitat, crops and ecosystems affected by erosion and flooding aggravated by the extreme wave heights. In addition, wave storms strongly hamper the maritime activities, especially in harbours. These extreme phenomena drive complex hydrodynamic processes, whose understanding is paramount for proper infrastructure management, design and maintenance (Goda, 2010). In recent years, there have been several studies searching possible trends in wave conditions focusing on both mean and extreme values of significant wave height using a multi-source approach with model reanalysis information with high variability in the time coverage, satellite altimeter records covering the last 30 years and in situ buoy measured data since the 1980s decade but with sparse information and gaps in the time series (e.g. Dodet et al., 2020; Timmermans et al., 2020; Young & Ribal, 2019). These studies highlight a remarkable interannual, seasonal and spatial variability of wave conditions and suggest that the possible observed trends are not clearly associated with anthropogenic forcing (Hochet et al. 2021, 2023). In the North Atlantic, the mean wave height shows some weak trends not very statistically significant. Young & Ribal (2019) found a mostly positive weak trend in the European Coasts while Timmermans et al. (2020) showed a weak negative trend in high latitudes, including the North Sea and even more intense in the Norwegian Sea. For extreme values, some authors have found a clearer positive trend in high percentiles (90th-99th) (Young, 2011; Young & Ribal, 2019). '''COPERNICUS MARINE SERVICE KEY FINDINGS''' The mean 99th percentiles showed in the area present a wide range from 2-3.5m in the Canary Island with 0.1-0.3 m of standard deviation (std), 3.5m in the Gulf of Cadiz with 0.5m of std, 3-6m in the English Channel and the Irish Sea with 0.5-0.6m of std, 4-7m in the Bay of Biscay with 0.4-0.9m of std to 8-10m in the West of the British Isles with 0.7-1.4m of std. Results for this year show slight negative anomalies in the Canary Island (-0.4/0.0m) and in the Gulf of Cadiz (-0.8m) barely out of the standard deviation range in both areas, slight positive or negative anomalies in the West of the British Isles (-0.6/+0.4m) and in the English Channel and the Irish Sea (-0.6/+0.3m) but inside the range of the standard deviation and a general positive anomaly in the Bay of Biscay reaching +1.0m but close to the limit of the standard deviation. '''DOI (product):''' https://doi.org/10.48670/moi-00250

'''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

'''Short description:''' For the Global Ocean - The product contains monthly Level-4 sea surface wind and stress fields at 0.25 degrees horizontal spatial resolution. The monthly averaged wind and stress fields are based on monthly average ECMWF ERA5 reanalysis fields, corrected for persistent biases using all available Level-3 scatterometer observations from the Metop-A, Metop-B and Metop-C ASCAT, QuikSCAT SeaWinds and ERS-1 and ERS-2 SCAT satellite instruments. The applied bias corrections, the standard deviation of the differences and the number of observations used to calculate the monthly average persistent bias are included in the product. '''DOI (product) :''' https://doi.org/10.48670/moi-00181

'''Short description''': You can find here the OMEGA3D observation-based quasi-geostrophic vertical and horizontal ocean currents developed by the Consiglio Nazionale delle RIcerche. The data are provided weekly over a regular grid at 1/4° horizontal resolution, from the surface to 1500 m depth (representative of each Wednesday). The velocities are obtained by solving a diabatic formulation of the Omega equation, starting from ARMOR3D data (MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012 ) and ERA5 surface fluxes. '''Citation''': Buongiorno Nardelli, B. (2020). CNR global observation-based OMEGA3D quasi-geostrophic vertical and horizontal ocean currents (1993-2018) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service (CMEMS). https://doi.org/10.25423/CMCC/MULTIOBS_GLO_PHY_W_REP_015_007 '''DOI (product) :''' https://doi.org/10.25423/cmcc/multiobs_glo_phy_w_rep_015_007