15 years of L-Band remote sensing Sea Surface Salinity (SSS) measurements have proven the capability of satellite SSS to resolve large scale to mesoscale SSS features in tropical to subtropical ocean. In mid to high latitude, L-Band measurements still suffer from large scale and time varying biases. Here, a simple method is proposed to mitigate the large scale and time varying biases. First, in order to estimate these biases, an Optimal Interpolation (OI) using a large correlation scale is used to map SMOS and SMAP L3 products and is compared to equivalent mapping of in situ observations. Then, a second mapping is performed on corrected SSS at scale of SMOS/SMAP resolution (~45 km). This procedure allows to correct and merge both products, and to increase signal to noise ratio of the absolute SSS estimates. Using thermodynamic equation of state (TEOS-10), the resulting L4 SSS product is combined with microwave satellite SST products to produce sea surface density and spiciness, useful to fully characterize the surface ocean water masses. The new L4 SSS products is validated against independent in situ measurements from low to high latitudes. The L4 products exhibits a significant improvement in mid-and high latitude in comparison to the existing SMOS and SMAP L3 products.

This product integrates observations aggregated and validated from the Regional EuroGOOS consortium (Arctic-ROOS, BOOS, NOOS, IBI-ROOS, MONGOOS and Black Sea GOOS) as well as from National Data Centers (NODCs) and JCOMM global systems (Argo, GOSUD, OceanSITES, GTSPP, DBCP) and the Global telecommunication system (GTS) used by the Met Offices. Data are available in a dedicated directory to waves (INSITU_GLO_WAV_REP_OBSERVATIONS_013_045) of GLOBAL Distribution Unit in one file per platform. This directory is updated twice a year. Data are distributed in two datasets, one with original time sampling and the other with hourly data and rounded timestamps. The information distributed includes wave parameters and wave spectral information. The latest version of Copernicus delayed-mode wave product is distributed from Copernicus Marine catalogue. Additional credits: The American wave data are collected from US NDBC (National Data Buoy Center). The Australian wave data are collected from Integrated Marine Observing System (IMOS); IMOS is enabled by the National Collaborative Research Infrastructure Strategy (NCRIS); It is operated by a consortium of institutions as an unincorporated joint venture, with the University of Tasmania as Lead Agent. The Canadian data are collected from Fisheries and Oceans Canada.

This dataset provides a global Look-Up Table (LUT) of physiological ratios for the real-time adjustment of chlorophyll-a fluorescence measured by biogeochemical Argo (BGC-Argo) profiling floats. The physiological ratios aim to account for the global variability in the relationship between fluorescence and chlorophyll-a concentration, as influenced by phytoplankton physiology. The LUT was developed using two different gap-filled observational Argo-based products (SOCA machine learning-based methodology ; Sauzède et al., 2016; Sauzède et al., 2024). The first product provides gap-filled chlorophyll-a data derived from fluorescence corrected for dark signal and non-photochemical quenching (NPQ) following Schmechtig et al. (2023), while the second product provides chlorophyll-a concentrations derived from light attenuation. The latter is based on the downward irradiance at 490 nm (ED490) derived from the SOCA-light method (Renosh et al., 2023). From this, the diffuse attenuation coefficient (KD490) is computed, which is subsequently used to estimate the chlorophyll-a concentration through the bio-optical relationships described by Morel et al. (2007). These two products, based on fluorescence and radiometry, enable the derivation of spatially varying correction factors, or physiological ratios. These ratios provide a validated grounded framework for adjusting real-time fluorescence observations from OneArgo floats into chlorophyll-a concentrations. The LUT is distributed in NetCDF format and is provided on a regular 1°×1° latitude–longitude grid covering the global ocean. Each grid cell contains the temporal mean, averaged over the water column (from the surface to 1.5 times the euphotic depth), of the physiological ratio. The file also includes metadata describing variable definitions, units, and other relevant information. Variables included: - physiological_ratio — fluorescence-to-radiometry-based chlorophyll correction factor (dimensionless) - physiological_ratio_sd — temporal standard deviation (over the twelve months) of the fluorescence-to-radiometry-based chlorophyll correction factor (dimensionless) - lat, lon — spatial coordinates (degrees north/east) - Global attributes — dataset description, reference citation, and contact information

Mesoscale eddy detection from 2000 to 2021 are computed using the AMEDA algorithm applied on AVISO L4 absolute dynamic topography at 1/8th degree. Eddy numbers correspond to tracks referenced in the DYNED atlas (https://doi.org/10.14768/2019130201.2). Detection is based on AVISO delyed-time product from 2000 to 2019 and on day+6 near-real-time altimetry from 2020 to 2021. Colocalisation is then made with available in situ profiles from Coriolis Ocean Dataset for Reanalysis (CORA) delayed-time data (113486 profiles) and Copernicus near-real-time profiles (43567).

This dataset is composed by the climatological seasonal field of the Ocean Salinity Stratification as defined from the Brunt-Vaisala frequency limited to the upper 300 m depth. The details are given in Maes, C., and T. J. O’Kane (2014), Seasonal variations of the upper ocean salinity stratification in the Tropics, J. Geophys. Res. Oceans, 119, 1706–1722, doi:10.1002/2013JC009366.

This data set contains the gridded hydrographic and transport data for the biennial Go-Ship A25 Greenland–Portugal OVIDE section from 2002 to 2012. The properties and transports are mapped on a 7km x 1m grid. Using a common grid facilitates the comparison between the different occupations of the line and the averaging. This data set was used in Daniault et al. (2016, Progress in Oceanography) to which the reader is referred for a description of the gridding method.

The In Situ delayed mode product designed for reanalysis purposes integrates the best available version of in situ data for ocean surface currents. The data are collected from the Surface Drifter Data Assembly Centre (SD-DAC at NOAA AOML). All surface drifters data have been processed to check for drogue loss. Drogued and undrogued drifting buoy surface ocean currents are provided with a drogue presence flag as well as a wind slippage correction for undrogued buoys. Altimeter and wind data have been used to extract the direct wind slippage from the total drifting buoy velocities. This product is designed to be assimilated into or for validation purposes of operational models operated by ocean forecasting centers for reanalysis purposes or for research community. These users need data aggregated and quality controlled in a reliable and documented manner.

The Coriolis Ocean Dataset for Reanalysis for the Ireland-Biscay-Iberia region (hereafter CORA-IBI) product is a regional dataset of in situ temperature and salinity measurements. The latest version of the product covers the period 1950-2014. The CORA-IBI observations comes from many different sources collected by Coriolis data centre in collaboration with the In Situ Thematic Centre of the Copernicus Marine Service (CMEMS INSTAC).  The observations integrated in the CORA-IBI product have been acquired both by autonomous platforms (Argo profilers, fixed moorings, gliders, drifters, sea mammals, fishery observing system from the RECOPESCA program), research or opportunity vessels ( CTDs, XBTs, ferrybox).  This CORA-IBI product has been controlled using an objective analysis (statistical tests) method and a visual quality control (QC). This QC procedure has been developed with the main objective to improve the quality of the dataset to the level required by the climate application and the physical ocean re-analysis activities. It provides T and S individual profiles on their original level with QC flags. The reference level of measurements is immersion (in meters) or pressure (in decibars). It is a subset on the IBI (Iberia-Bay-of-Biscay Ireland) of the CMEMS product referenced hereafter. The main new features of this regional product compared with previous global CORA products are the incorporation of coastal profiles from fishery observing system (RECOPESCA programme) in the Bay of Biscay and the English Channel as well as the use of an historical dataset collected by the Service hydrographique de la Marine (SHOM).

A quantitative understanding of the integrated ocean heat content depends on our ability to determine how heat is distributed in the ocean and what are the associated coherent patterns. This dataset contains the results of the Maze et al., 2017 (Prog. Oce.) study demonstrating how this can be achieved using unsupervised classification of Argo temperature profiles. The dataset contains: - A netcdf file with classification~results (labels and probabilities) and coordinates (lat/lon/time) of 100,684 Argo temperature profiles in North Atlantic. - A netcdf file with a Profile Classification Model (PCM) that can be used to classify new temperature profiles from observations or numerical models. The classification method used is a Gaussian Mixture Model that decomposes the Probability Density Function of the dataset into a weighted sum of Gaussian modes. North Atlantic Argo temperature profiles between 0 and 1400m depth were interpolated onto a regular 5m grid, then compressed using Principal Component Analysis and finally classified using a Gaussian Mixture Model. To use the netcdf PCM file to classify new data, you can checkout our PCM Matlab and Python toolbox here: https://github.com/obidam/pcm

This dataset contains OAC-P results from application to Argo data in the World Ocean : - the 2000-2015 climatology of OAC-P results mapped onto a 0.5x0.5 grid with mapping error estimates; - the 2000-2015 probability density function of the permanent pycnocline potential density referenced to the sea surface vs Brunt-Väisälä frequency squared.OAC-P is an "Objective Algorithm for the Characterization of the permanent Pycnocline" developed to characterize subtropical gyre stratification features with both observed and modeled potential density profiles. OAC-P estimates the following properties: - for the permanent pycnocline: depth, upper and lower thicknesses, Brunt-Väisälä frequency squared, potential density, temperature and salinity; - for the surface mode water overlying the permanent pycnocline: depth, Brunt-Väisälä frequency squared, potential density, temperature and salinity. Argo data were download from Coriolis Argo GDAC on February, 8th 2016. Only Argo data with QC=1, 2, 5 or 8 were used.