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.

Barystatic and manometric sea level changes represent the mass component of sea level changes at global and regional scales, respectively. Barystatic and manometric sea level changes are estimated using the satellite gravimetry measurements from the GRACE and GRACE-FO missions. Barystatic sea level changes are due to freshwater exchanges with the continents, including ice mass changes from Greenland, Antarctica and continental glaciers, as well as freshwater fluxes from continental areas (i.e. large river basins such as the Amazon or Mississippi). Manometric sea level changes are due to several processes, including the atmosphere - ocean circulation, sea level fingerprints or global water cycle. Climate modes, such as the El Nino Southern Oscillation, Arctic Oscillation, North Atlantic Oscillation or Southern Annular Mode, have also been shown to significantly influence manometric sea level variations (e.g., Pfeffer et al., 2022; https://doi.org/10.1007/s00382-021-05953-z). Two products are distributed: - Barystatic sea level changes from satellite gravimetry with uncertainties at 1-sigma: monthly time series, - Manometric sea level changes from satellite gravimetry with uncertainties at 1-sigma: monthly grids with 1x1 degree. The GRACE and GRACE Follow-On missions monitor the time-variations in the gravity field almost continuously since 2002. Numerous centers distribute time-lapse solutions of the Earth’s gravitational potential, delivered as Stokes coefficients, known as Level-2 solutions. The Level-2 solutions need to be corrected for several geophysical effects and instrumental errors, converted into surface mass anomalies and projected onto the ellipsoid. The resulting gridded surface mass anomalies with appropriate corrections applied are referred to as Level-3 solutions. Several sources of errors affect the solutions of Level-2 and-3, imposed by the satellite configuration, instrumental errors and uncertainties in the geophysical corrections used to process the measurements. We use the ensemble approach of Blazquez et al., (2018; https://doi.org/10.1093/gji/ggy293), to robustly estimate the manometric and barystatic sea level changes and their uncertainties.

The historical Seasonal Sargassum Forecast relies on the system described by Jouanno et al. (2023), operated by at the Laboratoire d’Etude en Géophysique et Océanographie Spatiale (LEGOS) and developed within ANR FORESEA and TOSCA SAREDA projects. This system relies on a mechanistic model of Sargassum populations, the NEMO-Sarg 1.0 model (Jouanno et al. 2021,2023, 2025), which integrates both transport and a physiology model of the macroalgae. The ensemble version of the Sargassum model at 1/4° horizontal resolution has been implemented and allows for the production of 7-month forecasts every month, initialized from Sargassum areal coverage near-real time estimates from the Moderate Resolution Imaging Spectroradiometer MODIS (Berline & Descloitres, 2021). The operational Seasonal Sargassum Forecasts (September 2025 – on going) are produced globally by Mercator Ocean International, building on the same mechanistic NEMO-Sarg 1.0 configuration (Jouanno et al., 2021, 2023, 2025, see details in 2.1). Sargassum biomass is initialized each month using near-real-time estimates derived from OLCI satellite observations (DOI: 10.24400/527896/a01-2022.007), providing an up-to-date representation of surface concentrations. This global operational framework thus complements the IRD regional setup and extends forecasting capability across the entire Atlantic basin. More details on both processing are available in the user manual.

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.

Composite maps of sargassum detection index NFAI (Normalized Floating Algae Index). Daily gridded products provided in delayed time from February 2022 to a month before current date, made available as part of SeSaM SCO project (https://www.spaceclimateobservatory.org/sesam). Each map represents the sargassum presence (through normalized floatting alguae index) of the last 7 days at 0.01° resolution.

Barystatic and manometric sea level changes represent the mass component of sea level changes at global and regional scales, respectively. Barystatic sea level changes are estimated using the sea level budget (SLB) approach combining satellite altimetry with in-situ measurements of the seawater temperature and salinity. This SLB approach is adapted from Barnoud et al., (2023; https://doi.org/10.5194/os-19-321-2023). Manometric sea level variations and their uncertainties are not provided for the SLB approach, due to the high uncertainties associated with the drift of the halosteric component (e.g., Bouih et al., 2025; https://doi.org/10.5194/egusphere-egu24-16813).

Composite maps of sargassum detection index NFAI (Normalized Floating Algae Index). Daily gridded products provided in delayed time from December 2020 to a month before current date, made available as part of SeSaM SCO project (https://www.spaceclimateobservatory.org/sesam) . Each map represents the sargassum presence (through normalized floatting alguae index) at 0.0025° (high) or 0.01° (low) resolutions.

The Ocean Surface Topography Mission (OSTM)/Jason‑2 was a satellite altimetry mission that operated for more than 11 years, from 2008 to 2019. It was a joint endeavour carried out by NASA and CNES. OSTM/Jason‑2 measured a suite of geophysical parameters, including significant wave height (SWH), radar backscatter coefficient (σ⁰), wet tropospheric path delay, and ionospheric delay. These observations were processed to retrieve sea‑surface height (SSH) and sea‑level anomalies (SLA). The mission followed a repeat cycle of roughly 10 days, comprising 254 ground tracks per cycle. The Jason‑2 Geophysical Data Records (GDRs) are Level‑2 products that were distributed with different latencies: - **Operational GDR (OGDR)**: delivered approximately 3 hours after acquisition (near‑real‑time data for operational use). - **Interim GDR (IGDR)**: delivered approximately 2.5 days after acquisition (early‑release data for research and monitoring). - **Standard GDR (GDR)**: delivered approximately 2 months after acquisition (fully validated, higher‑quality data). - **Reprocessed GDRs**: released periodically (typically every few years) with improved processing algorithms and updated auxiliary data. The GDRs are distributed in three file types, each offering a different level of detail: - **GPR**: Reduced product (1 Hz sampling, a subset of the full dataset). - **GPN**: Normal product (both 1 Hz records and 20 Hz high‑rate measurements). - **GPS**: Sensor product (full radar‑echo waveforms, intended for expert users; not available for the OGDR). In 2025 the GDR‑F (“F” standard) was introduced for Jason‑2, bringing a substantial improvement in both the availability and the accuracy of the GDR products.

The SWOT KaRIn Level-3 Wind Wave product (L3_LR_WIND_WAVE) is an innovative product derived from the Unsmoothed L3_LR_SSH product (https://doi.org/10.24400/527896/A01-2024.003), which is based on the algorithm presented by Ardhuin et al. (2024; https://doi.org/10.1029/2024GL109658). L3_LR_WIND_WAVE takes advantage of the KaRIn Low Rate (LR) chain's ability to resolve waves with wavelengths greater than 500 meters (approximately 18 seconds) and provides detailed information on the characteristics of these wave regimes. This includes significant wave height (SWH), dominant wavelength, and wave propagation direction. These regimes are associated with long-period swells and extreme events that play a critical role in ocean dynamics, coastal processes, and maritime operations. The SWOT L3_LR_WIND_WAVE product is organized into two subproducts, "Light" and "Extended". The L2_LR_SSH "Light" product is described in this metadata sheet. The "Light" L3_LR_WIND_WAVE (also known as the "lightweight" product) includes the SWOT L3_LR_SSH 250-m SSHA spectrum, corrected for instrumental effects and expressed in both Cartesian and polar coordinates. It also includes the swell partition of the spectrum and the wave parameters integrated over this partition, for both the WW3 model and the KaRIn model (significant wave height, wavelength, and direction). The "Extended" L3_LR_WIND_WAVE includes the aforementioned variables plus the WW3 spectrum in the same frequency grid as the KaRIn spectrum and the KaRIn transfer functions used for correction, as well as some parameters derived from KaRIn observations (e.g., coherence, mean backscatter).

The CDR-derived Wet Tropospheric Correction (WTC) Product V2 is generated from the Level-2+ along-track altimetry products version 2024 (L2P 2024) distributed by AVISO+ (www.aviso.altimetry.fr). It provides a long-term, homogenized estimation of the wet tropospheric correction based on Climate Data Records (CDRs) of atmospheric water vapour combined with high frequencies MWR data. Two independent CDRs datasets are used: - REMSS V7R2 (coverage until 2022) https://www.remss.com/measurements/atmospheric-water-vapor/tpw-1-deg-product/ - HOAPS V5 precursor CDR from EUMETSAT CM SAF (coverage until 2020) HOAPS V4/V5 data available via https://wui.cmsaf.eu Note: the HOAPS V5 precursor is not yet an official CM SAF product; full validation and public release are pending. The MWR/CDR WTC V2 estimates is derived using spatially varying but temporally constant polynomial coefficients (ai). 1. WTC V2 – Along-track L2P Product Data format: The WTC V2 product is delivered in Level-2+ (L2P) format, along the satellite ground track. Each mission is distributed as a compressed archive (.tar.gz) containing one NetCDF4 CF-1.8 file per mission cycle. Archive naming convention: <mission>_WTC_from_WV_CDR_<version>.tar.gz mission: TP (TOPEX/Poseidon), J1, J2, J3 version: product version (currently V2) File naming convention inside archives: <mission>_C<cycle>.nc cycle: 4-digit cycle index (e.g., C0001) Each NetCDF file contains: 1/ Along-track WTC estimate; 2/ Ancillary information; 3/ Space–time coordinates 2. WTC CDR Uncertainties – Gridded Product: A complementary product is provided, delivering regional trend estimates and associated uncertainties from the WTC Climate Data Record. The uncertainty product is distributed as a single NetCDF4 file: wtc_trend_uncertainties.nc . This file contains global gridded fields of WTC CDR trend and uncertainty parameters. Product content: This is the first dedicated version providing both: WTC CDR (HOAPS) linear trends, and Uncertainty estimates on these trends. Uncertainties are expressed as 1-sigma confidence intervals, and propagated using the methodology described in Section 2.3 of the Product User Manual. The product includes: - Total uncertainty on the WTC trend, propagated from all identified uncertainty sources in the WTC–TCWV regression. - Individual contributions of uncertainty sources (Uncertainties on regression coefficients: a0, a1 and their standard deviations; Uncertainties inherited from the HOAPS TCWV CDR) These fields enable users to assess the relative importance of each uncertainty component and recompute uncertainty propagation with alternative methods. Included regression input variables: To ensure transparency and reproducibility, the product provides: 1/ regression coefficients a0, a1; 2/ their associated uncertainties (std of a0, std of a1); 3/additional diagnostic fields required to recompute uncertainties if needed.