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 continuously updated version of Copernicus Argo floats realtime currents product is distributed from Copernicus Marine catalogue: - https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=INSITU_GLO_UV_NRT_OBSERVATIONS_013_048 The Argo current product generated by Copernicus in situ TAC is derived from the original trajectory data from Argo GDAC (Global Data Assembly Center) available at: - Argo float data and metadata from Global Data Assembly Centre (Argo GDAC). SEANOE. https://doi.org/10.17882/42182 In 2021, the GDAC distributes data from more than 15,000 Argo floats. Deep ocean current is calculated from floats drift at parking depth, surface current is calculated from float surface drift. An Argo float drifts freely in the global ocean, performing regular observation cycles. An observation cycle usually spreads over 10 days :  - a surface descent to a parking depth (generally 1500 meters deep) - a 10-day drift at this parking depth - an ascent to the surface (vertical profile) - A short surface drift for data transmission The data transmitted at each cycle contain temperature, salinity observations (and additional biogeochemical parameters if applicable), positions (gps or argos), technical data. The ocean current product contains a NetCDF file for each Argo float. It is updated daily in real time by automated processes. For each cycle it contains the surface and deep current variables: - Date (time, time_qc) - Position  (latitude, longitude, position_qc) - Pressure (pres, pres_qc, representative_park_pressure for parking drift, 0 decibar for surface drift) - Current (ewct, ewct_qc, nsct, nsct_qc; the current vector is positioned and dated at the last position of the N-1 cycle) - Duration (days) of the current variable sampling (time_interval) - Grounded indicator - Positions and dates have a QC 1 (good data). Positions and dates that do not have a QC 1 are ignored. The positions are measured during the surface drift (Argos or GPS positioning). For the deep current of cycle N, we take the last good position of cycle N-1 and the first good position of cycle N. For the surface current of cycle N, we take the first and last good position of the N cycle.  

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).

The glider operations in the MOOSE network started to be deployed regularly in 2010 in the North Western Mediterranean Sea, thanks to the setup of national glider facilities at DT-INSU/Ifremer (http://www.dt.insu.cnrs.fr/gliders/gliders.php) and with the support of the European project FP7-PERSEUS. Two endurance lines are operated: MooseT00 (Nice-Calvi; Ligurian Sea) and MooseT02 (Marseille-Menorca; Gulf of Lion). The all dataset here corresponds to raw data in the EGO format.

Observations of Sea surface temperature and salinity are now obtained from voluntary sailing ships using medium or small size sensors. They complement the networks installed on research vessels or commercial ships. The delayed mode dataset proposed here is upgraded annually as a contribution to GOSUD (http://www.gosud.org )

Argo is a global array of 3,000 free-drifting profiling floats that measures the temperature and salinity of the upper 2000 m of the ocean. This allows, for the first time, continuous monitoring of the temperature, salinity, and velocity of the upper ocean, with all data being relayed and made publicly available within hours after collection. The array provides 100,000 temperature/salinity profiles and velocity measurements per year distributed over the global oceans at an average of 3-degree spacing. Some floats provide additional bio-geo parameters such as oxygen or chlorophyll. All data collected by Argo floats are publically available in near real-time via the Global Data Assembly Centers (GDACs) in Brest (France) and Monterey (California) after an automated quality control (QC), and in scientifically quality controlled form, delayed mode data, via the GDACs within six months of collection. The BGC-Argo Sprof snapshot is a subset of the global Argo data snapshot.  It is created to ease BGC-Argo data usage.  The content is the same if you are to download the global Argo data snapshot, and then select all the BGC-Argo Sprof files.  Please use the same DOI and citation as the global Argo data snapshot.

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.  

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.

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.