Gridded multi-mission merged satellite significant wave height in Near-Real-Time. It merges along-track SWH data from the following missions: Jason-3, Sentinel-3A, Sentinel-3B, SARAL/AltiKa, Cryosat-2 and CFOSAT.

For the Global Ocean - Multimission altimeter satellite gridded sea surface heights and derived variables computed with respect to a twenty-year mean. Previously distributed by Aviso+, no change in the scientific content. All the missions are homogenized with respect to a reference mission which is currently Jason-3. The acquisition of various altimeter data is a few days at most. The sla is computed with a non-centered computation time window (6 weeks before the date).

Multimission altimeter products "Experimental" with a finer resolution in preparation to the SWOT Era with sea surface heights computed with respect to a twenty-year mean and Geostrophic velocities, resulting from specific processes, available in delayed time. Use: regional studies, ocean variability (mesoscale circulation,...),

The MIOST (Multiscale Interpolation Ocean Science Topography) experimental altimeter product provides grids at delayed-time, at global scale, 1/10° spatial resolution, the sea surface height (MSLA and MADT) as well as the geostrophic currents, resulting from specific processing. Use for regional studies, ocean variability (mesoscale circulation,...).

Multimission altimeter satellite gridded sea surface heights and derived variables computed with respect to a twenty-year mean. Previously distributed by Aviso+, no change in the scientific content. All the missions are homogenized with respect to a reference mission which is currently Jason-3. The acquisition of various altimeter data is a few days at most. The sla is computed with a non-centered computation time window (6 weeks before the date).

These gridded products are produced from the along-track (or Level-3) SEA LEVEL products (DOI: doi.org/10.48670/moi-00147) delivered by the Copernicus Marine Service (CMEMS, marine.copernicus.eu) for satellites SARAL/AltiKa, Cryosat-2, HaiYang-2B, Jason-3, Copernicus Sentinel-3A&B, Sentinel 6A, SWOT nadir, and SWOT Level-3 KaRIn sea level products (DOI: https://doi.org/10.24400/527896/A01-2023.018). Three mapping algorithms are proposed: MIOST, 4DvarNET, 4DvarQG: - the MIOST approach which give the global SSH solutions: the MIOST method is able of accounting for various modes of variability of the ocean surface topography (e.g., geostrophic, barotrope, equatorial waves dynamic …) by constructing several independent components within an assumed covariance model. - the 4DvarNET approach for the regional SSH solutions: the 4DvarNET mapping algorithm is a data-driven approach combining a data assimilation scheme associated with a deep learning framework. - the 4DvarQG approach for the regional SSH solutions: the 4DvarQG mapping technique integrates a 4-Dimensional variational (4DVAR) scheme with a Quasi-Geostrophic (QG) model. References: - Ballarotta, M., Ubelmann, C., Bellemin-Laponnaz, V., Le Guillou, F., Meda, G., Anadon, C., Laloue, A., Delepoulle, A., Faugère, Y., Pujol, M.-I., Fablet, R., and Dibarboure, G., 2024: Integrating wide swath altimetry data into Level-4 multi-mission maps, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2024-2345 - Beauchamp, M., Febvre, Q., Georgenthum, H., and Fablet, R., 2023: 4DVarNet-SSH: end-to-end learning of variational interpolation schemes for nadir and wide-swath satellite altimetry, Geosci. Model Dev., 16, 2119–2147, https://doi.org/10.5194/gmd-16-2119-2023 - Fablet, R., Beauchamp, M., Drumetz, L., and Rousseau, F., 2021: Joint Interpolation and Representation Learning for Irregularly Sampled Satellite-Derived Geophysical Fields, Front. Appl. Math. Stat., 7, 655224, https://doi.org/10.3389/fams.2021.655224 - Le Guillou, F., Metref, S., Cosme, E., Ubelmann, C., Ballarotta, M. Le Sommer, J. Verron, J., 2021: Mapping Altimetry in the Forthcoming SWOT Era by Back-and-Forth Nudging a One-Layer Quasigeostrophic Model, J. Atmos. Oceanic Technol., 38, 697–710, https://doi.org/10.1175/JTECH-D-20-0104.1 - Ubelmann, C., Dibarboure, G., Gaultier, L., Ponte, A., Ardhuin, F., Ballarotta, M., & Faugère, Y., 2021: Reconstructing ocean surface current combining altimetry and future spaceborne Doppler data. Journal of Geophysical Research: Oceans, 126, e2020JC016560. https://doi.org/10.1029/2020JC016560

'''This product has been archived''' For operationnal and online products, please visit https://marine.copernicus.eu '''Short description:''' The KD490 product identifies the turbidity of the water column, i.e., how visible light in the blue-green region of the spectrum penetrates within the water column. It is directly related to the presence of scattering particles in the water column. This product is derived from OLCI and remapped at nominal 300m spatial resolution using cylindrical equirectangular projection. '''Description of observation methods/instruments:''' Ocean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so called ocean colour which is affected by the presence of phytoplankton. By comparing reflectances at different wavelengths and calibrating the result against in-situ measurements, an estimate of in water absorption parameters can be derived. '''Quality / Accuracy / Calibration information:''' Detailed description of cal/val is given in the relevant QUID, associated validation reports and quality documentation. '''DOI (product) :''' https://doi.org/10.48670/moi-00078

Experimental monomission and multimission altimeter products Experimental Ssalto/Duacs altimetry product, monomission and multimission, including sea level anomalies (SLA) over the Arctic Ocean, with finer spatial resolution. Three mono-mission products (one for each satellite CryoSat-2, SARAL/AltiKa and Sentinel-3A), a multi-mission product combining these three altimeters are available.

This dataset provide a times series of gap free map of Sea Surface Temperature (SST) foundation at high resolution on a 0.10 x 0.10 degree grid (approximately 10 x 10 km) for the Global Ocean, every 24 hours. Whereas along swath observation data essentially represent the skin or sub-skin SST, the Level 4 SST product is defined to represent the SST foundation (SSTfnd). SSTfnd is defined within GHRSST 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 each other before merging. A ranking procedure is used to select the best sensor observation for each grid point. An optimal interpolation is used to fill in where observations are missing. '''DOI (product) :''' https://doi.org/10.48670/mds-00321

'''This product has been archived''' "''DEFINITION''' Marine primary production corresponds to the amount of inorganic carbon which is converted into organic matter during the photosynthesis, and which feeds upper trophic layers. The daily primary production is estimated from satellite observations with the Antoine and Morel algorithm (1996). This algorithm modelized the potential growth in function of the light and temperature conditions, and with the chlorophyll concentration as a biomass index. The monthly area average is computed from monthly primary production weighted by the pixels size. The trend is computed from the deseasonalised time series (1998-2022), following the Vantrepotte and Mélin (2009) method. The trend estimate is not shown because the length of the time series does not allow to completely differentiate the climate trend to the natural variability of the primary production. More details are provided in the Ocean State Reports 4 (Cossarini et al. ,2020). '''CONTEXT''' Marine primary production is at the basis of the marine food web and produce about 50% of the oxygen we breath every year (Behrenfeld et al., 2001). Study primary production is of paramount importance as ocean health and fisheries are directly linked to the primary production (Pauly and Christensen, 1995, Fee et al., 2019). Changes in primary production can have consequences on biogeochemical cycles, and specially on the carbon cycle, and impact the biological carbon pump intensity, and therefore climate (Chavez et al., 2011). Despite its importance for climate and socio-economics resources, primary production measurements are scarce and do not allow a deep investigation of the primary production evolution over decades. Satellites observations and modelling can fill this gap. However, depending of their parametrisation, models can predict an increase or a decrease in primary production by the end of the century (Laufkötter et al., 2015). Primary production from satellite observations presents therefore the advantage to dispose an archive of more than two decades of global data. This archive can be assimilated in models, in addition to direct environmental analysis, to minimise models uncertainties (Gregg and Rousseaux, 2019). In the Ocean State Reports 4, primary production estimate from satellite and from modelling are compared at the scale of the Mediterranean Sea. This demonstrates the ability of such a comparison to deeply investigate physical and biogeochemical processes associated to the primary production evolution (Cossarini et al., 2020) '''CMEMS KEY FINDINGS''' Global primary production does not show specific trend and remain relatively constant over the archive 1998-2022. The temporal variability of the primary production appears to be mainly driven by the seasonal variation. However, some specific inter-annual event may induce noticeable increase or decrease in primary production, as for example in the second part of 2011. '''DOI (product):''' https://doi.org/10.48670/moi-00225