Global mapped climatologies of temperature using the Data Interpolating Variational Analysis (DIVA) mapping method. This product covers all ocean basins over the years 1972 to 2013, and provides vertically interpolated data onto 33 standard depth surfaces, and gridded by bin averaging in each 1° × 1° grid cell. This product is based on the GLODAP (Global Ocean Data Analysis Project) dataset v2.2016b, built with surface-to-bottom ocean biogeochemical bottle data

EMODnet Physics - TSM_NorthSea - Concentration of total suspended matter (conc_tsm, mg/l) is a product of the Coastcolour L2W Concentrations Data, obtained from the OC4 algorithm for clear and moderate turbid waters, and from the CoastColour v1 neural network.The L2W product was then remapped on a regular grid, maintaining the 300 m full resolution, in order to obtain L3 products over the North Sea and monthly averaged. Developed by EMODnet Physics.

EMODnet Physics - TSM_BalticSea - Concentration of total suspended matter (conc_tsm, mg/l) is a product of the Coastcolour L2W Concentrations Data, obtained from the OC4 algorithm for clear and moderate turbid waters, and from the CoastColour v1 neural network.The L2W product was then remapped on a regular grid, maintaining the 300 m full resolution, in order to obtain L3 products over the Baltic Sea and monthly averaged. Developed by EMODnet Physics.

Operational in situ river outflow data. The product includes near real time data from European river stations.

Moving 10-years analysis of ammonium at 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 10-year centered average of the winter season. Decades span from 1977-1986 until 2004-2013. Observational data span from 1970 to 2013. Depth range (IODE standard depths): -1000.0, -900.0, -800.0, -700.0, -600.0, -500.0, -400.0, -300.0, -250.0, -200.0, -150.0, -125.0, -100.0, -75.0, -50.0, -30.0, -20.0, -10.0, -5.0, -0.0. Data Sources: observational data from SeaDataNet/EMODNet Chemistry Data Network. Description of DIVA analysis: Geostatistical data analysis by DIVA (Data-Interpolating Variational Analysis) tool. Profiles were interpolated at standard depths using weighted parabolic interpolation algorithm (Reiniger and Ross, 1968). GEBCO 1min topography is used for the contouring preparation. Analyzed filed masked using relative error threshold 0.3 and 0.5. DIVA settings: A constant value for signal-to-noise ratio was used equal to 3. Correlation length was optimized and filtered vertically and a seasonally-averaged profile was used. Logarithmic transformation applied to the data prior to the analysis. Background field: the data mean value is subtracted from the data. Detrending of data: no. Advection constraint applied: no. Originators of Italian data sets-List of contributors: o Brunetti Fabio (OGS) o Cardin Vanessa, Bensi Manuel doi:10.6092/36728450-4296-4e6a-967d-d5b6da55f306 o Cardin Vanessa, Bensi Manuel, Ursella Laura, Siena Giuseppe doi:10.6092/f8e6d18e-f877-4aa5-a983-a03b06ccb987 o Cataletto Bruno (OGS) o Cinzia Comici Cinzia (OGS) o Civitarese Giuseppe (OGS) o DeVittor Cinzia (OGS) o Giani Michele (OGS) o Kovacevic Vedrana (OGS) o Mosetti Renzo (OGS) o 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 o Celio Massimo (ARPA FVG) o Malaguti Antonella (ENEA) o Fonda Umani Serena (UNITS) o Bignami Francesco (ISAC/CNR) o Boldrini Alfredo (ISMAR/CNR) o Marini Mauro (ISMAR/CNR) o Miserocchi Stefano (ISMAR/CNR) o Zaccone Renata (IAMC/CNR) o 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.

Units: umol/l. Method: spatial interpolation produced with DIVA (Data-Interpolating Variational Analysis). URL: http://modb.oce.ulg.ac.be/DIVA. Comment: Every year of the time dimension corresponds to a 10-year centred average for each season : - winter season (December-February), - spring (March-May), - summer (June-August), - autumn (September-November). Diva settings: Snr=1.0, CL=0.7.

Units: umol/l. Method: spatial interpolation produced with DIVA (Data-Interpolating Variational Analysis). URL: http://modb.oce.ulg.ac.be/DIVA. Comment: Every year of the time dimension corresponds to a 10-year centred average for each season : - winter season (December-February), - spring (March-May), - summer (June-August), - autumn (September-November). Diva settings: Snr=1.0, CL=0.7.

Units: umol/l. Method: spatial interpolation produced with DIVA (Data-Interpolating Variational Analysis). URL: http://modb.oce.ulg.ac.be/DIVA. Comment: Every year of the time dimension corresponds to a 10-year centred average for each season : - winter season (December-February), - spring (March-May), - summer (June-August), - autumn (September-November). Diva settings: Snr=1.0, CL=0.7

Units: umol/l. Method: spatial interpolation produced with DIVA (Data-Interpolating Variational Analysis). URL: http://modb.oce.ulg.ac.be/DIVA. Comment: Every year of the time dimension corresponds to a 10-year centred average for each season : - winter season (December-February), - spring (March-May), - summer (June-August), - autumn (September-November). Diva settings: Snr=1.0, CL=0.7.

Units: umol/l. Method: spatial interpolation produced with DIVA (Data-Interpolating Variational Analysis). URL: http://modb.oce.ulg.ac.be/DIVA. Comment: Every year of the time dimension corresponds to a 10-year centred average for each season : - winter season (December-February), - spring (March-May), - summer (June-August), - autumn (September-November). Diva settings: Snr=1.0, CL=0.7