The mean sea surface (MSS) is an important field in physical oceanography, geophysics, and geodesy. In principle, it corresponds to the time-averaged height of the ocean surface. Auxiliary product : mean sea profile above a reference ellipsoid (T/P or WSG84). This surface is available on a regular grid (1/60°x1/60°, 1 minute). The mean sea surface MSS_CNES_CLS2025 (Charayon et al., in prep) has been computed using a 29-year [1993-2021] period of altimetric data. It was built exploring the use of SWOT KaRIn data to propose a new generation MSS able to better represent small wavelengths gapping state-of-the-art nadir-based MSS. Long wavelengths come from a MSS draft chosen to be the Hybrid 23 MSS (https://doi.org/10.24400/527896/a01-2024.002 ) low pass filtered at 20km since this nadir-based MSS model already captured well long wavelengths. The wavelengths shorter than 100km are improved thanks to a SWOT-KaRIn mean profile anomaly to the MSS draft. This mean profile anomaly is built with: - The SWOT-KaRIn science (L3 v3.0 Expert, cycles 1 to 31 included except cycle 17, https://doi.org/10.24400/527896/a01-2023.018) dataset - The experimental multimission gridded L4 sea level heights and velocities with SWOT using MIOST (https://doi.org/10.24400/527896/a01-2025.001 ). - In SWOT diamonds, since KaRIn data are not available, we used another innovation only based on DT2024 1Hz CryoSat2 and AltiKa SSHA data (https://doi.org/10.48670/moi-00146), period from 2010 to 2025, SSHA data relative to MSS Hybrid 23 low pass filtered at 5km. To build this nadir-based innovation, we used the static part of the mapping from MIOST method.

These gridded products are produced from the following upstream data: - for satellites SARAL/AltiKa, Cryosat-2, HaiYang-2B, Jason-3, Copernicus Sentinel-3A/B, Sentinel-6 MF, SWOT Nadir => NRT (Near-Real-Time) Nadir along-track (or Level-3) SEA LEVEL products (DOI: https://doi.org/10.48670/moi-00147) delivered by the Copernicus Marine Service (http://marine.copernicus.eu/ ). The gridded product is based on near-real-time (NRT) Level-3 Nadir datasets for the period from July 7, 2025, to December 31, 2025. => MY (Multi-Year) Nadir along-track (or Level-3) SEA LEVEL products (DOI: https://doi.org/10.48670/moi-00146 ) delivered by the Copernicus Marine Service (CMEMS, http://marine.copernicus.eu/ ). The gridded product is based on MY Level-3 Nadir datasets for the period from March 28, 2023, to July 6, 2025. - for SWOT KaRIn : the L3_LR_SSH Expert v3.0 product distributed by AVISO (DOI: https://doi.org/10.24400/527896/A01-2023.018) from March 28, 2023 to December 31, 2025. One mapping algorithm is proposed: the MIOST approach which provides which provides global Sea Surface Height (SSH) solutions. The MIOST method is capable of accounting for various modes of ocean surface topography variability (e.g., geostrophic, barotropic, equatorial wave dynamics) by constructing multiple independent components within a predefined covariance model.

This dataset provides an estimation of the climate feedback parameter and the climate sensitivity developed in collaboration by Magellium and LEGOS. It is based on the publication of Meyssignac et al., (2023, https://doi.org/10.1038/s43247-023-00887-2) . The product is a crucial study on Effective Climate Sensitivity (ECS), focusing on the time variation of the climate feedback parameter (λ). Understanding λ is fundamental as it quantifies the Earth’s radiative response to changes in global average temperature. A less negative λ directly implies a greater climate sensitivity to greenhouse gas concentrations. The product addresses the high uncertainty in ECS by providing an observational estimate of λ temporal variations since 1970. Estimates are derived from the global energy balance equation, regressing the radiative response against temperature over periods longer than 25 years. Users are primarily interested in the time series of λ and its associated uncertainties. The input data for computing λ is provided in a secondary "extended product" for transparency, allowing users to rebuild the indicators. The final product, available as a NetCDF file, can be downloaded in open access and is licensed for any project or study.

387 points were surveyed with a SP80 DGPS by Maxime Paschal as part of the La Rochelle Zero Carbon Territory (LRTZC) project on 26/05/23. At each point, the type of vegetation was specified.

Archive de toutes les données de température de surface (SST) satellite produites dans le cadre du projet international GHRSST. Ifremer est un GDAC pour ces données, miroir du GDAC NASA/JPL. Ces données sont utilisées pour la génération de produits multi-capteurs (CMEMS, Medspiration) mais également dans le cadre d'un grand nombre d'études ou projets nécessitant l'utilisation de mesures de SST. L'archive regroupe plusieurs jeux de données provenant de différents satellite ainsi que des données in situ de référence pour leur validation. Elle est mise à jour en temps quasi-réel depuis 10 ans, avec service de diffusion opérationnelle associé (FTP et HTTP). Une fiche sextant (issue du catalogue CERSAT) sera fournie pour chaque dataset dans cette archive.

This dataset provides detections of fronts derived from low resolution optimally interpolated remote sensing microwave SST L4 from REMSS over North Atlantic region. The data are available through HTTP and FTP; access to the data is free and open. In order to be informed about changes and to help us keep track of data usage, we encourage users to register at: https://forms.ifremer.fr/lops-siam/access-to-esa-world-ocean-circulation-project-data/ This dataset was generated by OceanDataLab and is distributed by Ifremer / CERSAT in the frame of the World Ocean Circulation (WOC) project funded by the European Space Agency (ESA).

Level 3, four times a day, sub-skin Sea Surface Temperature derived from AVHRR on Metop satellites and VIIRS or AVHRR on NOAA and NPP satellites, over North Atlantic and European Seas and re-projected on a polar stereographic at 2 km resolution, in GHRSST compliant netCDF format. This catalogue entry presents Suomi-NPP North Atlantic Regional Sea Surface Temperature. SST is retrieved from infrared channels using a multispectral algorithm and a cloud mask. Atmospheric profiles of water vapor and temperature from a numerical weather prediction model, Sea Surface Temperature from an analysis, together with a radiative transfer model, are used to correct the multispectral algorithm for regional and seasonal biases due to changing atmospheric conditions. The quality of the products is monitored regularly by daily comparison of the satellite estimates against buoy measurements. The product format is compliant with the GHRSST Data Specification (GDS) version 2.Users are advised to use data only with quality levels 3,4 and 5.

This daily High-Resolution (HR) Level 3 gridded wind product is derived from Copernicus Sentinel-1 SAR (Synthetic Aperture Radar) observations, over the North Western Atlantic ("ATL" area). It is based on the European Space Agency (ESA) Level-2 OCN products at the highest available resolution. Although L2-OCN products already contain wind vectors, those are calculated using the CMOD5.n Geophysical Model Function (GMF) applied to the co-polarized (co-pol) VV channel (emitting in Vertical polarization and receiving in Vertical polarization). This VV GMF was mapped from scatterometer sensors (Hersbach et al., 2007) which are only able to use co-pol measurements. However, these co-pol GMF are known to lose sensitivity for wind above 20 m/s. Therefore, wind based on such GMF alone, are known to under-estimate wind speed (Polverari et al., 2022). For the L3 products winds based on SAR, we take advantage of the available cross-polarized (cross-pol) VH channel (emitting in Vertical polarization and receiving in Horizontal polarization) for which GMF were specifically derived based on C-Band SAR (Mouche et al., 2017, Mouche et al., 2019). Winds estimated from the combination of both the co-pol and cross-pol channels are referred to as dual-polarization (or dual-pol) winds. As shown in Mouche et al. (2019), taking advantage of the dual polarization strongly improves the wind estimation for high wind conditions thanks to the much greater VH channel sensitivity compared to VV. These new wind estimations are then gridded with a 0.012 degree resolution (between 0.5 and 1.2 km in zonal direction depending on the latitude and 1.3 km in meridional direction) using a cylindrical equidistant projection, independently for ascending and descending satellite passes and for each satellite (so 4 wind fields are available per day for two satellites). This dataset is generated over all Sentinel-1 mission time series starting from March 2018 and updated in delayed mode with a 4-months delay. It is also produced for 4 other different European areas. This dataset is produced and disseminated in the frame of Copernicus Marine Service.

This dataset provide a times series of daily mean fields of Sea Surface Temperature (SST) foundation at ultra-high resolution (UHR) on a 0.02 x 0.02 degree grid (approximately 2 x 2 km) for the Mediterranean Sea, every 24 hours. An Optimal interpolation (OI) technique is used to combine coincident swath measures of SST from different types satellite sensors and to fill gaps where no observations are available or obstructed by clouds. This multi-sensor compositing and interpolation process categorizes this dataset as a Level 4 product. Whereas along swath observation data essentially represent the skin or sub-skin SST, the L4 SST product is defined to represent the SST foundation (SSTfnd). SSTfnd is defined within GHRSST-PP as the temperature at the base of the diurnal thermocline. It is so named because it represents the foundation temperature on which the diurnal thermocline develops during the day. SSTfnd changes only gradually along with the upper layer of the ocean, and by definition it is independent of skin SST fluctuations due to wind- and radiation-dependent diurnal stratification or skin layer response. It is therefore updated at intervals of 24 hrs. SSTfnd corresponds to the temperature of the upper mixed layer which is the part of the ocean represented by the top-most layer of grid cells in most numerical ocean models. It is never observed directly by satellites, but it comes closest to being detected by infrared and microwave radiometers during the night, when the previous day's diurnal stratification can be assumed to have decayed. The processing combines the observations of multiple polar orbiting and geostationary satellites, embedding infrared of microwave radiometers. All these sources are intercalibrated with eachother before merging. The processing is the same as for the Atlantic Near Real Time (NRT) L4 dataset available on Copernicus Marine Service [SST_ATL_SST_L4_NRT_OBSERVATIONS_010_025 dataset] and users can refer to the user manual and quality documents available there for more details. This dataset was developed in the frame of European Space Agency (ESA)'s Medspiration project.

In recent years, large datasets of in situ marine carbonate system parameters (partial pressure of CO2 (pCO2), total alkalinity, dissolved inorganic carbon and pH) have been collated. These carbonate system datasets have highly variable data density in both space and time, especially in the case of pCO2, which is routinely measured at high frequency using underway measuring systems. This variation in data density can create biases when the data are used, for example for algorithm assessment, favouring datasets or regions with high data density. A common way to overcome data density issues is to bin the data into cells of equal latitude and longitude extent. This leads to bins with spatial areas that are latitude and projection dependent (eg become smaller and more elongated as the poles are approached). Additionally, as bin boundaries are defined without reference to the spatial distribution of the data or to geographical features, data clusters may be divided sub-optimally (eg a bin covering a region with a strong gradient). To overcome these problems and to provide a tool for matching in situ data with satellite, model and climatological data, which often have very different spatiotemporal scales both from the in situ data and from each other, a methodology has been created to group in situ data into ‘regions of interest’, spatiotemporal cylinders consisting of circles on the Earth’s surface extending over a period of time. These regions of interest are optimally adjusted to contain as many in situ measurements as possible. All in situ measurements of the same parameter contained in a region of interest are collated, including estimated uncertainties and regional summary statistics. The same grouping is done for each of the other datasets, producing a dataset of matchups. About 35 million in situ datapoints were then matched with data from five satellite sources and five model and re-analysis datasets to produce a global matchup dataset of carbonate system data, consisting of 287,000 regions of interest spanning 54 years from 1957 to 2020. Each region of interest is 100 km in diameter and 10 days in duration. An example application, the reparameterisation of a global total alkalinity algorithm, is shown. This matchup dataset can be updated as and when in situ and other datasets are updated, and similar datasets at finer spatiotemporal scale can be constructed, for example to enable regional studies. This dataset was funded by ESA Satellite Oceanographic Datasets for Acidification (OceanSODA) project which aims at developing the use of satellite Earth Observation for studying and monitoring marine carbonate chemistry. **This version is now superseded by the version 4 with higher spatial and temporal resolution**