This dataset contains the dynamical outputs of a global ocean simulation coupling dynamics and biogeochemistry at ¼° over the year 2019. The simulation has been performed using the coupled circulation/ecosystem model NEMO/PISCES (https://www.nemo-ocean.eu/), which is here enhanced to perform an ensemble simulation with explicit simulation of modeling uncertainties in the physics and in the biogeochemistry. This dataset is one of the 40 members of the ensemble simulation. This study was part of the Horizon Europe project SEAMLESS (https://seamlessproject.org/Home.html), with the general objective of improving the analysis and forecast of ecosystem indicators.   See Popov et al. (https://os.copernicus.org/articles/20/155/2024/) for more details on the study.

This dataset provides a World Ocean Atlas of Argo inferred statistics. The primary data are exclusively Argo profiles. The statistics are done using the whole time range covered by the Argo data, starting in July 1997. The atlas is provided with a 0.25° resolution in the horizontal and 63 depths from 0 m to 2,000 m in the vertical. The statistics include means of Conservative Temperature (CT), Absolute Salinity, compensated density, compressiblity factor and vertical isopycnal displacement (VID); standard deviations of CT, VID and the squared Brunt Vaisala frequency; skewness and kurtosis of VID; and Eddy Available Potential Energy (EAPE). The compensated density is the product of the in-situ density times the compressibility factor. It generalizes the virtual density used in Roullet et al. (2014). The compressibility factor is defined so as to remove the dependency with pressure of the in-situ density. The compensated density is used in the computation of the VID and the EAPE.

These monthly gridded climatology were produced using MBT, XBT, Profiling floats, Gliders, and ship-based CTD data from different database and carried out in the Med. between 1969 and 2013. The Mixed Layer Depth (MLD) is calculated with a delta T= 0.1 C criterion relative to 10m reference level on individual profiles. The Depth of the Bottom of the Seasonal Thermocline (DBST) is calculated on individual profiles as the maximum value from a vector composed of two elements: 1) the depth of the temperature minimum in the upper 200m; 2) the MLD. This double criterion for the calculation of DBST is necessary in areas where the mixed layer exceed 200m depth. DBST is the integration depth used in the calculation of the upper-ocean Heat Storage Rate. For more details about the data and the methods used, see: Houpert et al. 2015, Seasonal cycle of the mixed layer, the seasonal thermocline and the upper-ocean heat storage rate in the Mediterranean Sea derived from observations, Progress in Oceanography, http://doi.org/10.1016/j.pocean.2014.11.004

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 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.

This dataset contains (1) outputs of Species Distribution Models (SDMs) for marine fish and (2) estimation of species richness using those outputs. SDMs use correlative algorithms to link presences of species to the environment recorded in place and time of their observation, calculate their environmental niche, estimate the geographical location suitable for them (habitat suitability) and in fine their geographical distribution. Here, we downloaded presences of marine fish from two open source databases, GBIF and OBIS and 13 environmental predictors known to be relevant in fish ecology (list below). We used the CEPHALOPOD pipeline, a framework allowing the user the compute a lot of species at the same time, with comparable methods and a verification of quality of inputs and outputs at every steps (Schickele et al., 2025). 3,642 fish made it to the final step and have the habitat suitability estimated for 12 month + annual mean, 10 bootstrap to quantify uncertainty and x algorithms. Those estimation are available in the “L2_marine_fish_*.nc” files, organized by water column position (bathydemersal, bathypelagic, benthopelagic, demersal, pelagic-neritic, pelagic-oceanic, reef-associated). We then used their annual mean to estimate their actual geographic distribution by applying (1) a bathymetric filtration and (2) a cutting procedure which removes isolated patches of high suitability (i.e., potential distribution) with no recorded presences (i.e., considered to be outside of the species dispersion range). Those geographic distribution were then stacked to estimate global species richness of every fish and each water column position.

GOSUD aims at assembling in-situ observations of the world ocean surface collected by a variety of ships and at distributing quality controlled datasets.  At present time the variables considered by GOSUD are temperature and salinity. The GOSUD data are mostly collected using thermosalinographs (TSG) installed on research vessels, on commercial ships and in some cases on sailing exploration ships GOSUD manages both near-real time data and delayed mode (reprocessed) data.

This delayed mode product designed for reanalysis purposes integrates the best available version of in situ data for ocean surface currents and current vertical profiles. It concerns three delayed time datasets dedicated to near-surface currents measurements coming from three platforms (Lagrangian surface drifters, High Frequency radars and Argo floats) and velocity profiles within the water column coming from the Acoustic Doppler Current Profiler (ADCP, vessel mounted only). The latest version of Copernicus surface and sub-surface water velocity product is also distributed from Copernicus Marine catalogue.

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

The observations of campe glider on imedia deployment (Mediterranean Sea - Western basin) are distributed in 4 files: - EGO NetCDF time-series (data, metadata, derived sea water current) - NetCDF profiles extracted from the above time-series - Raw data - JSON metadata used by the decoder The following parameters are provided : - Practical salinity - Sea temperature in-situ ITS-90 scale - Electrical conductivity - Sea water pressure, equals 0 at sea-level