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 product contains observations and gridded files from two up-to-date carbon and biogeochemistry community data products: Surface Ocean Carbon ATlas SOCATv2023 and GLobal Ocean Data Analysis Project GLODAPv2.2023. The SOCATv2023-OBS dataset contains >25 million observations of fugacity of CO2 of the surface global ocean from 1957 to early 2023. The quality control procedures are described in Bakker et al. (2016). These observations form the basis of the gridded products included in SOCATv2023-GRIDDED: monthly, yearly and decadal averages of fCO2 over a 1x1 degree grid over the global ocean, and a 0.25x0.25 degree, monthly average for the coastal ocean. GLODAPv2.2023-OBS contains >1 million observations from individual seawater samples of temperature, salinity, oxygen, nutrients, dissolved inorganic carbon, total alkalinity and pH from 1972 to 2021. These data were subjected to an extensive quality control and bias correction described in Olsen et al. (2020). GLODAPv2-GRIDDED contains global climatologies for temperature, salinity, oxygen, nitrate, phosphate, silicate, dissolved inorganic carbon, total alkalinity and pH over a 1x1 degree horizontal grid and 33 standard depths using the observations from the previous major iteration of GLODAP, GLODAPv2. SOCAT and GLODAP are based on community, largely volunteer efforts, and the data providers will appreciate that those who use the data cite the corresponding articles (see References below) in order to support future sustainability of the data products.

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

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 )

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.

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 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.

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).

Satellite altimetry missions provide a quasi-global synoptic view of sea level over more than 25 years. The satellite altimetry constellation is used to build sea level maps and regional sea level indicators such as trends and accelerations. Estimating realistic uncertainties on these quantities is crucial to address some current climate science questions such as climate change detection and attribution or regional sea level budget closure for example. Previous studies have estimated the uncertainty for the global mean sea level (GMSL), but no uncertainty information is available at regional scales. In this study we estimate a regional satellite altimetry error budget and use it to derive maps of confidence intervals for local sea rise rates and accelerations. We analyze 27 years of satellite altimetry maps and derive the satellite altimetry error variance-covariance matrix at each grid point, prior to the estimation of confidence intervals on local trends and accelerations at the 90% confidence level using extended least squares estimators. Over 1993–2019, we find that the average local sea level trend uncertainty is 0.83 mm.yr-1 with local values ranging from 0.78 to 1.22 mm.yr-1. For accelerations, uncertainties range from 0.057 to 0.12 mm.yr-2, with a mean value of 0.063 mm.yr-2.   Change history: - 2020/07/08: initial dataset submission over 1993-2018 - 2020/10/21: 1993-2019 update and addition of error levels

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.