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.  

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.

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.

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.

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 Southern Ocean plays a fundamental role in regulating the global climate. This ocean also contains a rich and highly productive ecosystem, potentially vulnerable to climate change. Very large national and international efforts are directed towards the modeling of physical oceanographic processes to predict the response of the Southern Ocean to global climate change and the role played by the large-scale ocean climate processes. However, these modeling efforts are greatly limited by the lack of in situ measurements, especially at high latitudes and during winter months. The standard data that are needed to study ocean circulation are vertical profiles of temperature and salinity, from which we can deduce the density of seawater. These are collected with CTD (Conductivity-Temperature-Depth) sensors that are usually deployed on research vessels or, more recently, on autonomous Argo profilers. The use of conventional research vessels to collect these data is very expensive, and does not guarantee access to areas where sea ice is found at the surface of the ocean during the winter months. A recent alternative is the use of autonomous Argo floats. However, this technology is not easy to use in glaciated areas. In this context, the collection of hydrographic profiles from CTDs mounted on marine mammals is very advantageous. The choice of species, gender or age can be done to selectively obtain data in particularly under-sampled areas such as under the sea ice or on continental shelves. Among marine mammals, elephant seals are particularly interesting. Indeed, they have the particularity to continuously dive to great depths (590 ± 200 m, with maxima around 2000 m) for long durations (average length of a dive 25 ± 15 min, maximum 80 min). A Conductivity-Temperature-Depth Satellite Relay Data Logger (CTD-SRDLs) has been developed in the early 2000s to sample temperature and salinity vertical profiles during marine mammal dives (Boehme et al. 2009, Fedak 2013). The CTD-SRDL is attached to the seal on land, then it records hydrographic profiles during its foraging trips, sending the data by satellite ARGOS whenever the seal goes back to the surface.While the principle intent of seal instrumentation was to improve understanding of seal foraging strategies (Biuw et al., 2007), it has also provided as a by-product a viable and cost-effective method of sampling hydrographic properties in many regions of the Southern Ocean (Charrassin et al., 2008; Roquet et al., 2013).

This data set provides a monthly time series of the upper limb of the Meridional Overturning Circulation (MOC) intensity at the A25 Greenland-Portugal OVIDE line from 1993 to 2015. The MOC was derived by combining AVISO altimetry with ISAS temperature and salinity data. The reader is referred to Mercier et al. (2015, Progress in Oceanography) for a full description of the method.

C-RAID: Comprehensive Reprocessing of Drifting Buoy Data (1979-2018) The C-RAID (Copernicus - Reprocessing of Drifting Buoys) project delivers a comprehensive global reprocessing of historical drifting buoy data and metadata, providing climate-quality observations for marine and atmospheric research. Dataset Overview The C-RAID dataset encompasses metadata from 21 858 drifting buoys deployed between 1979 and 2018. Of these, 17 496 buoys have undergone complete reprocessing with scientific validation in delayed mode, including comparison against ERA5 reanalysis. Project Context Managed by the WMO DBCP Drifting Buoys Global Data Assembly Centre (GDAC) through Ifremer, Météo-France, and Ocean Sciences Division of Fisheries and Oceans Canada, C-RAID focuses on enhanced quality control and delivery of climate-quality drifting buoy data for the Marine Climate Data System (MCDS). Objectives - Complete reprocessing and clean-up of the historical drifting buoy data archive - Recovery and rescue of missing datasets - Reprocessing of Argos data with improved positioning using Kalman filter algorithms - Homogenization of quality control procedures across marine and atmospheric parameters Funding & Governance C-RAID was funded by the Copernicus Programme through the European Environment Agency (Contract # EEA/IDM/15/026/LOT1), supporting cross-cutting coordination activities for the in-situ component of Copernicus Services. Stakeholders & Partnerships The project is led by the DB-GDAC consortium (Ifremer, Météo-France) in collaboration with EUMETNET's E-SURFMAR programme, NOAA AOML, and JCOMMOPS. Key Achievements - Reprocessing of approximately 24 000 buoy-years of observations - Recovery of missing datasets and metadata through data rescue efforts - Implementation of homogeneous, rich metadata and data formats - Enhanced Argos location accuracy using Kalman filter reprocessing - Standardized quality control and validation procedures Data Access & FAIR Principles C-RAID provides FAIR (Findable, Accessible, Interoperable, Reusable) data access through: - Web-based data discovery portal for human users - API services for data discovery, subsetting, and download (machine-to-machine access) Target Users The dataset serves major operational and research programmes including: - Copernicus Climate Change Service (C3S) - Copernicus Marine Environment Monitoring Service (CMEMS) - iQuam (in-situ SST Quality Monitor) - ICOADS (International Comprehensive Ocean-Atmosphere Data Set) - GHRSST (Group for High Resolution Sea Surface Temperature) - ISPD (International Surface Pressure Databank) - ICDC (Integrated Climate Data Center)  

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