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 2023. Each region of interest is 50 km in diameter and 5 days in duration, improving the spatial and temporal resolution of the previous version (v3.4). The list of sources added in this dataset includes: - sea surface temperature and sea ice concentration from Copernicus Climate Service Multi-satellite L4 (http://dx.doi.org/10.5285/62c0f97b1eac4e0197a674870afe1ee6) - sea surface salinity from the Copernicus Marine service Multi Observation Global Ocean Sea Surface Salinity and Sea Surface Density (https://doi.org/10.48670/moi-00051) - Surface ocean sea-air CO2 fluxes and total alkalinity from ETH Zurich OceanSODA-ETHZ-v2 gridded dataset (https://doi.org/10.5281/zenodo.11206366) - salinity and mixed layer depth from SODA v3.4.2 reanalysis (https://doi.org/10.1175/JCLI-D-18-0149.1) - chlorophyll-A from ESA Ocean Colour CCI v6 (doi:10.3390/s19194285) - wind at 10 m and mean sea level pressure from ERA5 reanalysis - nitrate, silicate, phosphate, oxygen, temperature and salinity from World Ocean Atlas 2018 - sea level anomaly from Global Ocean Gridded L4 Sea Surface Heights And Derived Variables Multi-Year dataset by Copernicus Marine Service Information (https://doi.org/10.48670/moi-00148) - sea surface salinity from ESA Salinity CCI L4 v3.2.1 (https://dx.doi.org/10.5285/5920a2c77e3c45339477acd31ce62c3c) - sea surface salinity from JPL SMAP Level 3 CAP Sea Surface Salinity (https://doi.org/10.5067/SMP40-3SPCS) - temperature and salinity from Coriolis Observation Re-Analysis CORA5.2 by Copernicus Marine Service (https://doi.org/10.17882/46219) - subskin sea surface temperature from NOAA OISST SST (http://doi.org/10.5067/GHAAO-4BC01) - sea surface salinity from the Arctic salinity dataset (https://doi.org/10.20350/digitalCSIC/9065) the Barcelona Expert Center (http://bec.icm.csic.es/) - pH and spCO2 from Copernicus Marine Service Surface Ocean Carbon Dataset (https://doi.org/10.48670/moi-00047) 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 OceanHealth / Ocean Acidification project which aims at developing the use of satellite Earth Observation for studying and monitoring marine carbonate chemistry.

This set of data documents the radiocarbon dates (n=19) obtained thanks to the accelerator mass spectrometry method (AMS) at the LMC14/ARTEMIS French national facility on the cores (Multicorer, Kullenberg) retrieved from the West-Gironde mud patch (WGMP) during the JERICObent-7 cruise (10-15 July 2019; NR Côtes de la Manche, https://doi.org/10.17600/18001022). The WGMP registers very high sedimentation rates since the last 600 years (≥ 0.3 cm/yr) and is thus of great interest for palaeoceanographic investigations. At present, this depocenter marks the mid-shelf of the temperate Bay of Biscay off major French rivers from the Aquitaine basin. The fine mud deposits of the WGMP are of 3 to 4 meters thick and lie on palimpsest levels rich in gravels and shells. They cover a V-shaped structure, oriented SW-NE, which is attributed to the incision(s) of a paleovalley in the Cenozoic substrate, mainly linked to the paleo-Gironde routing changes during past glacials/interglacials, and its potential past convergences with the paleo-rivers of the Antioche perthuis (Seudre, Charente paleovalleys?) at that times. Detailed information on each sample is presented with the 14C results obtained by the Artemis AMS facility at LMC14 laboratory (Dumoulin et al. 2017- https://doi.org/10.1017/RDC.2016.116, Beck et al. 2024- https://doi.org/10.1017/RDC.2023.23). Raw ages are indicated together with calibration calculations using the last two versions of the Calib software (http://calib.org/, Calib 7 and 8) to show the dispersion of ages linked to the updating of calibration curves (Marine13, Intcal13, Marine20, Intcal 20). The calibrated ages finally retained for publications (used in the related Seanoe document - https://doi.org/10.17882/104237 - and published in Eynaud et al., 2025 for the ST3c core, https://doi.org/10.1016/j.gloplacha.2025.105039) are those obtained with the last Calib 8.1 version. Raw 14C ages were calibrated and converted to calendar ages using the IntCal20 calibration curve with a reservoir age correction of 400 years deduced from Radionuclide analyses (137Cs and 210Pb) at the top of the studied cores (see Schmidt, 2025, https://www.seanoe.org/data/00968/107979/). 

This visualization product displays beaches locations where the Marine Strategy Framework Directive (MSFD) monitoring protocol has been applied to collate data on macrolitter (> 2.5 cm). Reference lists associated with these protocols have been indicated with different colors in the map. EMODnet Chemistry included the collection of marine litter in its 3rd phase. Since the beginning of 2018, data of beach litter have been gathered and processed in the EMODnet Chemistry Marine Litter Database (MLDB). The harmonization of all the data has been the most challenging task considering the heterogeneity of the data sources, sampling protocols and reference lists used on a European scale. Preliminary processings were necessary to harmonize all the data: - Exclusion of OSPAR 1000 protocol: in order to follow the approach of OSPAR that it is not including these data anymore in the monitoring; - Selection of MSFD surveys only (exclusion of other monitoring, cleaning and research operations); - Exclusion of beaches without coordinates; - Some categories & some litter types like organic litter, small fragments (paraffin and wax; items > 2.5cm) and pollutants have been removed. This list was created using EU Marine Beach Litter Baselines, the European Threshold Value for Macro Litter on Coastlines and the Joint list of litter categories for marine macro-litter monitoring from JRC (these three documents are attached to this metadata). More information is available in the attached documents. Warning: the absence of data on the map does not necessarily mean that they do not exist, but that no information has been entered in the Marine Litter Database for this area.

This dataset contains the biological outputs of a global ocean simulation coupling dynamics and biogeochemistry at ¼° over the year 2019. The simulation has been performed using the coupled circulation/ecosystem model NEMO/PISCES (https://www.nemo-ocean.eu/), which is here enhanced to perform an ensemble simulation with explicit simulation of modeling uncertainties in the physics and in the biogeochemistry. This dataset is one of the 40 members of the ensemble simulation. This study was part of the Horizon Europe project SEAMLESS (https://seamlessproject.org/Home.html), with the general objective of improving the analysis and forecast of ecosystem indicators.   See Popov et al. (https://os.copernicus.org/articles/20/155/2024/) for more details on the study.

This visualization product displays the cigarette related items abundance of marine macro-litter (> 2.5cm) per beach per year from non-MSFD monitoring surveys, research & cleaning operations without UNEP-MARLIN data. EMODnet Chemistry included the collection of marine litter in its 3rd phase. Since the beginning of 2018, data of beach litter have been gathered and processed in the EMODnet Chemistry Marine Litter Database (MLDB). The harmonization of all the data has been the most challenging task considering the heterogeneity of the data sources, sampling protocols and reference lists used on a European scale. Preliminary processings were necessary to harmonize all the data: - Exclusion of OSPAR 1000 protocol: in order to follow the approach of OSPAR that it is not including these data anymore in the monitoring; - Selection of surveys from non-MSFD monitoring, cleaning and research operations; - Exclusion of beaches without coordinates; - Selection of plastic bags related items only. The list of selected items is attached to this metadata. This list was created using EU Marine Beach Litter Baselines, the European Threshold Value for Macro Litter on Coastlines and the Joint list of litter categories for marine macro-litter monitoring from JRC (these three documents are attached to this metadata); - Exclusion of surveys without associated length; - Normalization of survey lengths to 100m & 1 survey / year: in some case, the survey length was not 100m, so in order to be able to compare the abundance of litter from different beaches a normalization is applied using this formula: Number of plastic bags related items of the survey (normalized by 100 m) = Number of plastic bags related items of the survey x (100 / survey length) Then, this normalized number of plastic bags related items is summed to obtain the total normalized number of plastic bags related items for each survey. Finally, the median abundance of plastic bags related items for each beach and year is calculated from these normalized abundances of plastic bags related items per survey. Percentiles 50, 75, 95 & 99 have been calculated taking into account plastic bags related items from other sources data for all years. More information is available in the attached documents. Warning: the absence of data on the map does not necessarily mean that they do not exist, but that no information has been entered in the Marine Litter Database for this area.

This visualization product displays the spatial distribution of litter density normalized per km² per year from research and monitoring protocols. EMODnet Chemistry included the collection of marine litter in its 3rd phase. Before 2021, there was no coordinated effort at the regional or European scale for micro-litter. Given this situation, EMODnet Chemistry proposed to adopt the data gathering and data management approach as generally applied for marine data, i.e., populating metadata and data in the CDI Data Discovery and Access service using dedicated SeaDataNet data transport formats. EMODnet Chemistry is currently the official EU collector of micro-litter data from Marine Strategy Framework Directive (MSFD) National Monitoring activities (descriptor 10). A series of specific standard vocabularies or standard terms related to micro-litter have been added to SeaDataNet NVS (NERC Vocabulary Server) Common Vocabularies to describe the micro-litter. European micro-litter data are collected by the National Oceanographic Data Centres (NODCs). Micro-litter map products are generated from NODCs data after a test of the aggregated collection including data and data format checks and data harmonization. A filter is applied to represent only micro-litter sampled according to research and monitoring protocols as MSFD monitoring. Densities were calculated for each net using the following calculation: Density (number of particles per km²) = Micro-litter count / (Sampling effort (km) * Net opening (cm) * 0.00001) When the number of microlitters or the net opening was not filled, it was not possible to calculate the density. The spatial distribution was then determined by calculating the weighted average of the densities in each cell using the following formula: Weighted average of the cell c (number of particles per km²) = ( (Sampling effort (km) of net 1 * Density (number of particles per km²) of net 1) + … + (Sampling effort (km) of net N * Density (number of particles per km²) of net N) ) / ∑ Sampling effort (km) of all nets in the cell c. Percentiles 50, 75, 95 & 99 have been calculated taking into account data for all years. Warning: the absence of data on the map does not necessarily mean that they do not exist, but that no information has been entered in the National Oceanographic Data Centre (NODC) for this area. This work is based on the work presented in the following scientific article: O. Gerigny, M. Brun, M.C. Fabri, C. Tomasino, M. Le Moigne, A. Jadaud, F. Galgani, Seafloor litter from the continental shelf and canyons in French Mediterranean Water: Distribution, typologies and trends, Marine Pollution Bulletin, Volume 146, 2019, Pages 653-666, ISSN 0025-326X, https://doi.org/10.1016/j.marpolbul.2019.07.030.

The ESA Sea State Climate Change Initiative (CCI) project has produced global daily merged multi-sensor time-series of along-track satellite altimeter significant wave height data (referred to as Level 3 (L3) data) with a particular focus for use in climate studies. This dataset contains the Version 4 Remote Sensing Significant Wave Height product, which provides along-track data at approximately 6 km spatial resolution. It has been generated from upstream Sea State CCI L2P products, edited and merged into daily products, retaining only valid and good quality measurements from all altimeters over one day, with simplified content (only a few key parameters). This is close to what is delivered in Near-Real Time by the CMEMS (Copernicus - Marine Environment Monitoring Service) project. It covers the date range from 1991-2023. The altimeter data used in the Sea State CCI dataset v3 come from multiple satellite missions (ERS-1, ERS-2,TOPEX-Poseidon, Envisat, CryoSat-2, Jason-1, Jason-2, Jason-3, SARAL, Sentinel-3 A, Sentinel-3 B, Sentinel-6 A), therefore spanning over a wider time range than previous version 3. The missions already retracked (with WHALES) in version 3 were not reprocessed, but extended when applicable. Many altimeters are bi-frequency (Ku-C or Ku-S) and only measurements in Ku band were used, for consistency reasons, being available on each altimeter but SARAL (Ka band). The version 4 of this dataset now supersedes the previous version 3.

Moving 6-year analysis of Water body phosphate in the Mediterranean Sea for each season: - winter: January-March, - spring: April-June, - summer: July-September, - autumn: October-December. Every year of the time dimension corresponds to the 6-year centered average of the season. 6-years periods span from 1970-1975 until 2018-2023. Description of DIVA analysis: The computation was done with the DIVAnd (Data-Interpolating Variational Analysis in n dimensions), version 2.7.12, using GEBCO 30sec topography for the spatial connectivity of water masses. The horizontal resolution of the produced DIVAnd maps grids is dx=dy=0.125 degrees (around 13.5km and 10.9km accordingly). The vertical resolution is 20 depth levels: [0.,5.,10.,20.,30.,50.,75.,100.,125.,150.,200.,250.,300.,400.,500.,600.,700.,800.,900.,1000.]. The horizontal correlation length is 200km. The vertical correlation length (in meters) was set twices the vertical resolution: [10.,10.,20.,20.,40.,50.,50.,50.,50.,100.,100.,100.,200.,200.,200.,200.,200.,200.,200.,200.]. Duplicates check was performed using the following criteria for space and time: dlon=0.001deg., dlat=0.001deg., ddepth=1m, dtime=1hour, dvalue=0.1. The error variance (epsilon2) was set equal to 1 for profiles and 10 for time series to reduce the influence of close data near the coasts. An anamorphosis transformation was applied to the data (function DIVAnd.Anam.loglin) to avoid unrealistic negative values: threshold value=200. A background analysis field was used for all years (1970-2023) with correlation length equal to 600km and error variance (epsilon2) equal to 20. Quality control of the observations was applied using the interpolated field (QCMETHOD=3). Residuals (differences between the observations and the analysis (interpolated linearly to the location of the observations) were calculated. Observations with residuals outside the minimum and maximum values of the 99% quantile were discarded from the analysis. Originators of Italian data sets-List of contributors: - Brunetti Fabio (OGS) - Cardin Vanessa, Bensi Manuel doi:10.6092/36728450-4296-4e6a-967d-d5b6da55f306 - Cardin Vanessa, Bensi Manuel, Ursella Laura, Siena Giuseppe doi:10.6092/f8e6d18e-f877-4aa5-a983-a03b06ccb987 - Cataletto Bruno (OGS) - Cinzia Comici Cinzia (OGS) - Civitarese Giuseppe (OGS) - DeVittor Cinzia (OGS) - Giani Michele (OGS) - Kovacevic Vedrana (OGS) - Mosetti Renzo (OGS) - Solidoro C.,Beran A.,Cataletto B.,Celussi M.,Cibic T.,Comici C.,Del Negro P.,De Vittor C.,Minocci M.,Monti M.,Fabbro C.,Falconi C.,Franzo A.,Libralato S.,Lipizer M.,Negussanti J.S.,Russel H.,Valli G., doi:10.6092/e5518899-b914-43b0-8139-023718aa63f5 - Celio Massimo (ARPA FVG) - Malaguti Antonella (ENEA) - Fonda Umani Serena (UNITS) - Bignami Francesco (ISAC/CNR) - Boldrini Alfredo (ISMAR/CNR) - Marini Mauro (ISMAR/CNR) - Miserocchi Stefano (ISMAR/CNR) - Zaccone Renata (IAMC/CNR) - Lavezza, R., Dubroca, L. F. C., Ludicone, D., Kress, N., Herut, B., Civitarese, G., Cruzado, A., Lefèvre, D.,Souvermezoglou, E., Yilmaz, A., Tugrul, S., and Ribera d'Alcala, M.: Compilation of quality controlled nutrient profiles from the Mediterranean Sea, doi:10.1594/PANGAEA.771907, 2011.

Moving 6-year analysis of Water body chlorophyll-a in the Mediterranean Sea for each season: - winter: January-March, - spring: April-June, - summer: July-September, - autumn: October-December. Every year of the time dimension corresponds to the 6-year centered average of the season. 6-years periods span from 1990-1995 until 2019-2024. Description of DIVA analysis: The computation was done with the DIVAnd (Data-Interpolating Variational Analysis in n dimensions), version 2.7.12, using GEBCO 30sec topography for the spatial connectivity of water masses. The horizontal resolution of the produced DIVAnd maps grids is dx=dy=0.125 degrees (around 13.5km and 10.9km accordingly). The vertical resolution is 20 depth levels: [0.,5.,10.,20.,30.,50.,75.,100.,125.,150.,200.,250.,300.,400.,500.,600.,700.,800.,900.,1000.]. The horizontal correlation length is 200km. The vertical correlation length (in meters) was set twices the vertical resolution: [10.,10.,20.,20.,40.,50.,50.,50.,50.,100.,100.,100.,200.,200.,200.,200.,200.,200.,200.,200.]. Duplicates check was performed using the following criteria for space and time: dlon=0.001deg., dlat=0.001deg., ddepth=1m, dtime=1hour, dvalue=0.1. The error variance (epsilon2) was set equal to 1 for profiles and 10 for time series to reduce the influence of close data near the coasts. An anamorphosis transformation was applied to the data (function DIVAnd.Anam.loglin) to avoid unrealistic negative values: threshold value=200. A background analysis field was used for all years (1990-2024) with correlation length equal to 600km and error variance (epsilon2) equal to 20. Quality control of the observations was applied using the interpolated field (QCMETHOD=3). Residuals (differences between the observations and the analysis (interpolated linearly to the location of the observations) were calculated. Observations with residuals outside the minimum and maximum values of the 99% quantile were discarded from the analysis. Originators of Italian data sets-List of contributors: - Brunetti Fabio (OGS) - Cardin Vanessa, Bensi Manuel doi:10.6092/36728450-4296-4e6a-967d-d5b6da55f306 - Cardin Vanessa, Bensi Manuel, Ursella Laura, Siena Giuseppe doi:10.6092/f8e6d18e-f877-4aa5-a983-a03b06ccb987 - Cataletto Bruno (OGS) - Cinzia Comici Cinzia (OGS) - Civitarese Giuseppe (OGS) - DeVittor Cinzia (OGS) - Giani Michele (OGS) - Kovacevic Vedrana (OGS) - Mosetti Renzo (OGS) - Solidoro C.,Beran A.,Cataletto B.,Celussi M.,Cibic T.,Comici C.,Del Negro P.,De Vittor C.,Minocci M.,Monti M.,Fabbro C.,Falconi C.,Franzo A.,Libralato S.,Lipizer M.,Negussanti J.S.,Russel H.,Valli G., doi:10.6092/e5518899-b914-43b0-8139-023718aa63f5 - Celio Massimo (ARPA FVG) - Malaguti Antonella (ENEA) - Fonda Umani Serena (UNITS) - Bignami Francesco (ISAC/CNR) - Boldrini Alfredo (ISMAR/CNR) - Marini Mauro (ISMAR/CNR) - Miserocchi Stefano (ISMAR/CNR) - Zaccone Renata (IAMC/CNR) - Lavezza, R., Dubroca, L. F. C., Ludicone, D., Kress, N., Herut, B., Civitarese, G., Cruzado, A., Lefèvre, D.,Souvermezoglou, E., Yilmaz, A., Tugrul, S., and Ribera d'Alcala, M.: Compilation of quality controlled nutrient profiles from the Mediterranean Sea, doi:10.1594/PANGAEA.771907, 2011.

A 3D numerical model of the Adour Estuary and the Basque Coast was developed with TELEMAC3D (v8p0) from the open TELEMAC-MASCARET system. The hydrodynamic model was validated with field data and reproduced satisfactorily tide, current and salinity observations. Please refer to the following references for further details : - Defontaine, S., Walther, R., & Sous, D. (2022). Variability of subtidal flow in a narrow meandering stratified estuary. Estuarine, Coastal and Shelf Science, 266, 107716. https://doi.org/10.1016/j.ecss.2021.107716 - Defontaine, S. (2019). Saline structure, circulation and suspended sediment transport in a channelized salt-wedge estuary: the Adour river estuary (Doctoral dissertation, Université de Pau et des Pays de l'Adour).