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.

Ensemble simulations of the ecosystem model Apecosm (https://apecosm.org) forced by the IPSL-CM6-LR climate model with the climate change scenario SSP1-2.6. The output files contain yearly mean biomass density for 3 communities (epipelagic, mesopelagic migratory and mesopelagic redidents) and 100 size classes (ranging from 0.12cm to 1.96m) The model grid file is also provided. Units are in J/m2 and can be converted in kg/m2 by dividing by 4e6. These outputs are associated with the "Assessing the time of emergence of marine ecosystems from global to local scales using IPSL-CM6A-LR/APECOSM climate-to-fish ensemble simulations" paper from the Earth's Future "Past and Future of Marine Ecosystems" Special Collection.

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 dataset is an aggregation of all availale in situ data from Coriolis and Copernicus in situ data centres, observed in the French DCSMM area. It contains 5167 NetCDF CF files from 1903 to 2017. Each file contains the observations of a specific platform (e.g. vessel, mooring site, sea level station). Observed parameters are temperature, salinity, pressure, oxygen, nitrate, chlorophyll (and other bio-geo-chemicals), current, wave, sea level, river flow.  

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.

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 DBCP – Data Buoy Cooperation Panel - is an international program coordinating the use of autonomous data buoys to observe atmospheric and oceanographic conditions, over ocean areas where few other measurements are taken. DBCP coordinates the global array of 1 600 active drifting buoys (August 2020) and historical observation from 14 000 drifting buoys. Data and metadata collected by drifting buoys are publically available in near real-time via the Global Data Assembly Centers (GDACs) in Coriolis-Ifremer (France) and MEDS (Canada) after an automated quality control (QC). In long term, scientifically quality controlled delayed mode data will be distributed on the GDACs. Disclaimer: the DB-GDAC is under construction. It is currently (January 2020) aggregating data from the Coriolis DAC (E-Surfmar, Canada). Additional DACs are considered. An interim provision from GTS real-time data to GDAC may be provided from Coriolis DAC.  

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.

The upper ocean pycnocline (UOP) monthly climatology is based on the ISAS20 ARGO dataset containing Argo and Deep-Argo temperature and salinity profiles on the period 2002-2020. Regardless of the season, the UOP is defined as the shallowest significant stratification peak captured by the method described in Sérazin et al. (2022), whose detection threshold is proportional to the standard deviation of the stratification profile. The three main characteristics of the UOP are provided -- intensity, depth and thickness -- along with hydrographic variables at the upper and lower edges of the pycnocline, the Turner angle and density ratio at the depth of the UOP. A stratification index (SI) that evaluates the amount of buoyancy required to destratify the upper ocean down to a certain depth, is also included. When evaluated at the bottom of the UOP, this gives the upper ocean stratification index (UOSI) as discussed in Sérazin et al. (2022). Three mixed layer depth variables are also included in this dataset, including the one using the classic density threshold of 0.03 kg.m-3, along with the minimum of these MLD variables. Several statistics of the UOP characteristics and the associated quantities are available in 2°×2° bins for each month of the year, whose results were smoothed using a diffusive gaussian filter with a 500 km scale. UOP characteristics are also available for each profile, with all the profiles sorted in one file per month.

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).