'''Short description:''' Le modèle biogéochimique ECO-MARS3D sur la façade Manche Atlantique (PREVIMER_B1-ECOMARS3D-MANGA4000) est un modèle 3D de résolution spatiale 4km qui fournit les concentrations de nutriments et de plancton toutes les heures sur 30 niveaux (fenêtre de prévision à 4 jours). '''Paramètres calculés :''' Les paramètres calculés sont les suivants : * SAL : sea_water_salinity * TEMP : sea_water_temperature * suspended_inorganic_particulate_matter : mass_concentration_of_suspended_matter_in_sea_water * nanopicoplankton_nitrogen : mole_concentration_of_nanoplankton_expressed_as_nitrogen_in_sea_water * diatom_nitrogen : mole_concentration_of_diatoms_expressed_as_nitrogen_in_sea_water * dinoflagellate_nitrogen : mole_concentration_of_dinoflagellates_expressed_as_nitrogen_in_sea_water * microzooplankton_nitrogen : mole_concentration_of_microzooplankton_expressed_as_nitrogen_in_sea_water * mesozooplankton_nitrogen : mole_concentration_of_mesozooplankton_expressed_as_nitrogen_in_sea_water * colonial_phaeocystis_nitrogen : mole_concentration_of_colonial_phaeocystis_expressed_as_nitrogen_in_sea_water * phaeocystis_mucus : concentration_of_phaeocystis_mucus_expressed_as_mass_in_sea_water * ammonium : mole_concentration_of_ammonium_in_sea_water * nitrate : mole_concentration_of_nitrate_in_sea_water * dissolved_silicate : mole_concentration_of_silicate_in_sea_water * dissolved_phosphate : mole_concentration_of_phosphate_in_sea_water * dissolved_oxygen : dissolved_oxygen_in_water_column * cumulative_nanoflagellate_carbon_production : cumulative_nanoflagellate_production_expressed_as_carbon_in_sea_water * cumulative_diatom_carbon_production : cumulative_diatom_production_expressed_as_carbon_in_sea_water * cumulative_dinoflagellate_carbon_production : cumulative_dinoflagellate_production_expressed_as_carbon_in_sea_water * cumulative_phaeocystis_carbon_production : cumulative_phaeocystis_production_expressed_as_carbon_in_sea_water * organic_nitrogen_benth : mole_concentration_of_organic_detritus_expressed_as_nitrogen_in_benthos Les paramètres diagnostiques calculés sont les suivants : * XE : sea_surface_height_above_geoid * maximum_de_diat : maximum_diatom_mass_concentration_in_sea_water * maximum_de_dino : maximum_dinoflagellate_mass_concentration_in_sea_water * maximum_de_nano : maximum_nanoflagellate_mass_concentration_in_sea_water * grad_vert_salinite : maximum_vertical_gradient_of_sea_water_salinity * grad_vert_temp : maximum_vertical_gradient_of_sea_water_temperature * extinction_lumineuse : light_extinction_in_sea_water * prod_diat : cumulated_production_of_diatoms_in_sea_water_column_expressed_in_carbon * prod_dino : cumulated_production_of_dinoflagellates_in_sea_water_column_expressed_in_carbon * prod_nano : cumulated_production_of_nanoflagellates_in_sea_water_column_expressed_in_carbon * chlorophylle_a : chlorophyll_mass_concentration_in_sea_water * prod_cumul_chloro : cumulated_total_production_in_sea_water_column_expressed_in_carbon * maximum_de_phaeocystis : maximum_phaeocystis_mass_concentration_in_sea_water * prod_phaeocystis : cumulated_production_of_phaeocystis_in_sea_water_column_expressed_in_carbon * oxygen_saturation : oxygen_saturation * ammoniumGIRON_tracer_sign: mole_concentration_of_ammonium_in_sea_waterGIRON_tracer_sign * ammoniumGIRON_tracer_age: mole_concentration_of_ammonium_in_sea_waterGIRON_tracer_age * nitrateGIRON_tracer_sign: mole_concentration_of_nitrate_in_sea_waterGIRON_tracer_sign * nitrateGIRON_tracer_age: mole_concentration_of_nitrate_in_sea_waterGIRON_tracer_age * nanopicoplankton_nitrogenGIRON_tracer_sign: mole_concentration_of_nanoplankton_expressed_as_nitrogen_in_sea_waterGIRON_tracer_sign * nanopicoplankton_nitrogenGIRON_tracer_age: mole_concentration_of_nanoplankton_expressed_as_nitrogen_in_sea_waterGIRON_tracer_age * diatom_nitrogenGIRON_tracer_sign: mole_concentration_of_diatoms_expressed_as_nitrogen_in_sea_waterGIRON_tracer_sign * diatom_nitrogenGIRON_tracer_age: mole_concentration_of_diatoms_expressed_as_nitrogen_in_sea_waterGIRON_tracer_age * dinoflagellate_nitrogenGIRON_tracer_sign: mole_concentration_of_dinoflagellates_expressed_as_nitrogen_in_sea_waterGIRON_tracer_sign * dinoflagellate_nitrogenGIRON_tracer_age: mole_concentration_of_dinoflagellates_expressed_as_nitrogen_in_sea_waterGIRON_tracer_age * microzooplankton_nitrogenGIRON_tracer_sign: mole_concentration_of_microzooplankton_expressed_as_nitrogen_in_sea_waterGIRON_tracer_sign * microzooplankton_nitrogenGIRON_tracer_age: mole_concentration_of_microzooplankton_expressed_as_nitrogen_in_sea_waterGIRON_tracer_age * mesozooplankton_nitrogenGIRON_tracer_sign: mole_concentration_of_mesozooplankton_expressed_as_nitrogen_in_sea_waterGIRON_tracer_sign * mesozooplankton_nitrogenGIRON_tracer_age: mole_concentration_of_mesozooplankton_expressed_as_nitrogen_in_sea_waterGIRON_tracer_age * detrital_nitrogenGIRON_tracer_sign: mole_concentration_of_organic_detritus_expressed_as_nitrogen_in_sea_waterGIRON_tracer_sign * detrital_nitrogenGIRON_tracer_age: mole_concentration_of_organic_detritus_expressed_as_nitrogen_in_sea_waterGIRON_tracer_age * colonial_phaeocystis_nitrogenGIRON_tracer_sign: mole_concentration_of_colonial_phaeocystis_expressed_as_nitrogen_in_sea_waterGIRON_tracer_sign * colonial_phaeocystis_nitrogenGIRON_tracer_age: mole_concentration_of_colonial_phaeocystis_expressed_as_nitrogen_in_sea_waterGIRON_tracer_age * phaeocystis_cells_nitrogenGIRON_tracer_sign: mole_concentration_of_phaeocystis_cells_expressed_as_nitrogen_in_sea_waterGIRON_tracer_sign * phaeocystis_cells_nitrogenGIRON_tracer_age: mole_concentration_of_phaeocystis_cells_expressed_as_nitrogen_in_sea_waterGIRON_tracer_age * organic_nitrogen_benthGIRON_tracer_sign: mole_concentration_of_organic_detritus_expressed_as_nitrogen_in_benthosGIRON_tracer_sign * organic_nitrogen_benthGIRON_tracer_age: mole_concentration_of_organic_detritus_expressed_as_nitrogen_in_benthosGIRON_tracer_age * phytoplankton_sign_N_GIRON: nitrogen_fraction_in_phytoplankton_from_source_GIRON * phytoplankton_age_N_GIRON: age_of_nitrogen_fraction_in_phytoplankton_from_source_GIRON * ammoniumLOIRE_tracer_sign: mole_concentration_of_ammonium_in_sea_waterLOIRE_tracer_sign * ammoniumLOIRE_tracer_age: mole_concentration_of_ammonium_in_sea_waterLOIRE_tracer_age * nitrateLOIRE_tracer_sign: mole_concentration_of_nitrate_in_sea_waterLOIRE_tracer_sign * nitrateLOIRE_tracer_age: mole_concentration_of_nitrate_in_sea_waterLOIRE_tracer_age * nanopicoplankton_nitrogenLOIRE_tracer_sign: mole_concentration_of_nanoplankton_expressed_as_nitrogen_in_sea_waterLOIRE_tracer_sign * nanopicoplankton_nitrogenLOIRE_tracer_age: mole_concentration_of_nanoplankton_expressed_as_nitrogen_in_sea_waterLOIRE_tracer_age * diatom_nitrogenLOIRE_tracer_sign: mole_concentration_of_diatoms_expressed_as_nitrogen_in_sea_waterLOIRE_tracer_sign * diatom_nitrogenLOIRE_tracer_age: mole_concentration_of_diatoms_expressed_as_nitrogen_in_sea_waterLOIRE_tracer_age * dinoflagellate_nitrogenLOIRE_tracer_sign: mole_concentration_of_dinoflagellates_expressed_as_nitrogen_in_sea_waterLOIRE_tracer_sign * dinoflagellate_nitrogenLOIRE_tracer_age: mole_concentration_of_dinoflagellates_expressed_as_nitrogen_in_sea_waterLOIRE_tracer_age * microzooplankton_nitrogenLOIRE_tracer_sign: mole_concentration_of_microzooplankton_expressed_as_nitrogen_in_sea_waterLOIRE_tracer_sign * microzooplankton_nitrogenLOIRE_tracer_age: mole_concentration_of_microzooplankton_expressed_as_nitrogen_in_sea_waterLOIRE_tracer_age * mesozooplankton_nitrogenLOIRE_tracer_sign: mole_concentration_of_mesozooplankton_expressed_as_nitrogen_in_sea_waterLOIRE_tracer_sign * mesozooplankton_nitrogenLOIRE_tracer_age: mole_concentration_of_mesozooplankton_expressed_as_nitrogen_in_sea_waterLOIRE_tracer_age * detrital_nitrogenLOIRE_tracer_sign: mole_concentration_of_organic_detritus_expressed_as_nitrogen_in_sea_waterLOIRE_tracer_sign * detrital_nitrogenLOIRE_tracer_age: mole_concentration_of_organic_detritus_expressed_as_nitrogen_in_sea_waterLOIRE_tracer_age * colonial_phaeocystis_nitrogenLOIRE_tracer_sign: mole_concentration_of_colonial_phaeocystis_expressed_as_nitrogen_in_sea_waterLOIRE_tracer_sign * colonial_phaeocystis_nitrogenLOIRE_tracer_age: mole_concentration_of_colonial_phaeocystis_expressed_as_nitrogen_in_sea_waterLOIRE_tracer_age * phaeocystis_cells_nitrogenLOIRE_tracer_sign: mole_concentration_of_phaeocystis_cells_expressed_as_nitrogen_in_sea_waterLOIRE_tracer_sign * phaeocystis_cells_nitrogenLOIRE_tracer_age: mole_concentration_of_phaeocystis_cells_expressed_as_nitrogen_in_sea_waterLOIRE_tracer_age * organic_nitrogen_benthLOIRE_tracer_sign: mole_concentration_of_organic_detritus_expressed_as_nitrogen_in_benthosLOIRE_tracer_sign * organic_nitrogen_benthLOIRE_tracer_age: mole_concentration_of_organic_detritus_expressed_as_nitrogen_in_benthosLOIRE_tracer_age * phytoplankton_sign_N_LOIRE: nitrogen_fraction_in_phytoplankton_from_source_LOIRE * phytoplankton_age_N_LOIRE: age_of_nitrogen_fraction_in_phytoplankton_from_source_LOIRE * ammoniumSEINE_tracer_sign: mole_concentration_of_ammonium_in_sea_waterSEINE_tracer_sign * ammoniumSEINE_tracer_age: mole_concentration_of_ammonium_in_sea_waterSEINE_tracer_age * nitrateSEINE_tracer_sign: mole_concentration_of_nitrate_in_sea_waterSEINE_tracer_sign * nitrateSEINE_tracer_age: mole_concentration_of_nitrate_in_sea_waterSEINE_tracer_age * nanopicoplankton_nitrogenSEINE_tracer_sign: mole_concentration_of_nanoplankton_expressed_as_nitrogen_in_sea_waterSEINE_tracer_sign * nanopicoplankton_nitrogenSEINE_tracer_age: mole_concentration_of_nanoplankton_expressed_as_nitrogen_in_sea_waterSEINE_tracer_age * diatom_nitrogenSEINE_tracer_sign: mole_concentration_of_diatoms_expressed_as_nitrogen_in_sea_waterSEINE_tracer_sign * diatom_nitrogenSEINE_tracer_age: mole_concentration_of_diatoms_expressed_as_nitrogen_in_sea_waterSEINE_tracer_age * dinoflagellate_nitrogenSEINE_tracer_sign: mole_concentration_of_dinoflagellates_expressed_as_nitrogen_in_sea_waterSEINE_tracer_sign * dinoflagellate_nitrogenSEINE_tracer_age: mole_concentration_of_dinoflagellates_expressed_as_nitrogen_in_sea_waterSEINE_tracer_age * microzooplankton_nitrogenSEINE_tracer_sign: mole_concentration_of_microzooplankton_expressed_as_nitrogen_in_sea_waterSEINE_tracer_sign * microzooplankton_nitrogenSEINE_tracer_age: mole_concentration_of_microzooplankton_expressed_as_nitrogen_in_sea_waterSEINE_tracer_age * mesozooplankton_nitrogenSEINE_tracer_sign: mole_concentration_of_mesozooplankton_expressed_as_nitrogen_in_sea_waterSEINE_tracer_sign * mesozooplankton_nitrogenSEINE_tracer_age: mole_concentration_of_mesozooplankton_expressed_as_nitrogen_in_sea_waterSEINE_tracer_age * detrital_nitrogenSEINE_tracer_sign: mole_concentration_of_organic_detritus_expressed_as_nitrogen_in_sea_waterSEINE_tracer_sign * detrital_nitrogenSEINE_tracer_age: mole_concentration_of_organic_detritus_expressed_as_nitrogen_in_sea_waterSEINE_tracer_age * colonial_phaeocystis_nitrogenSEINE_tracer_sign: mole_concentration_of_colonial_phaeocystis_expressed_as_nitrogen_in_sea_waterSEINE_tracer_sign * colonial_phaeocystis_nitrogenSEINE_tracer_age: mole_concentration_of_colonial_phaeocystis_expressed_as_nitrogen_in_sea_waterSEINE_tracer_age * phaeocystis_cells_nitrogenSEINE_tracer_sign: mole_concentration_of_phaeocystis_cells_expressed_as_nitrogen_in_sea_waterSEINE_tracer_sign * phaeocystis_cells_nitrogenSEINE_tracer_age: mole_concentration_of_phaeocystis_cells_expressed_as_nitrogen_in_sea_waterSEINE_tracer_age * organic_nitrogen_benthSEINE_tracer_sign: mole_concentration_of_organic_detritus_expressed_as_nitrogen_in_benthosSEINE_tracer_sign * organic_nitrogen_benthSEINE_tracer_age: mole_concentration_of_organic_detritus_expressed_as_nitrogen_in_benthosSEINE_tracer_age * phytoplankton_sign_N_SEINE: nitrogen_fraction_in_phytoplankton_from_source_SEINE * phytoplankton_age_N_SEINE: age_of_nitrogen_fraction_in_phytoplankton_from_source_SEINE

Combination MPAs and monitoring stations for biodiversity elements

The mission for this tool, as well as its overriding goal, is "Solving problems in capture fisheries and aquaculture using GIS, remote sensing and mapping". Specifically, the objectives of GISFish are to: - Improve the sustainability of aquaculture and capture fisheries by promoting the use of GIS, remote sensing and mapping; - Facilitate the use of GIS, remote sensing, and mapping through easy access to comprehensive information on applications and training opportunities. The Fisheries and Aquaculture Management Division at FAO is actively promoting the use of spatial analytical tools and geo-referenced information for the analysis of Fisheries and Aquaculture data and in the development of Fisheries and Aquaculture management. GISFish is a "one stop" site from which to obtain the global experience on Geographic Information Systems (GIS), Remote Sensing and Mapping as applied to Fisheries and Aquaculture. GISFish sets out the issues in Fisheries and Aquaculture, and demonstrates the benefits of using GIS, remote sensing and mapping to resolve them. The global experience provided by GISFish is captured in Issues, Publications, Activities, Training, Data and Tools, Contacts,Discussions, News and events. GISFish is broadly divided into two domains, GISFish Aquaculture and GISFish Capture Fisheries with a wide range of material and information that is shared between the two domains to address the role of GIS to support the Ecosystem Approach to Fisheries (EAF) and the Ecosystem Approach to Aquaculture (EAF).

The development of the ecosystem approach and models for the management of ocean marine resources requires easy access to standard validated datasets of historical catch data for the main exploited species. They are used to measure the impact of biomass removal by fisheries and to evaluate the models skills, while the use of standard dataset facilitates models inter-comparison. North Atlantic albacore tuna is exploited all year round by longline and in summer and autumn by surface fisheries and fishery statistics compiled by the International Commission for the Conservation of Atlantic Tunas (ICCAT). Catch and effort with geographical coordinates at monthly spatial resolution of 1° or 5° squares were extracted for this species with a careful definition of fisheries and data screening. In total, thirteen fisheries were defined for the period 1956-2010, with fishing gears longline, troll, mid-water trawl and bait fishing. However, the spatialized catch effort data available in ICCAT database represent a fraction of the entire total catch. Length frequencies of catch were also extracted according to the definition of fisheries above for the period 1956-2010 with a quarterly temporal resolution and spatial resolutions varying from 1°x 1° to 10°x 20°. The resolution used to measure the fish also varies with size-bins of 1, 2 or 5 cm (Fork Length). The screening of data allowed detecting inconsistencies with a relatively large number of samples larger than 150 cm while all studies on the growth of albacore suggest that fish rarely grow up over 130 cm. Therefore, a threshold value of 130 cm has been arbitrarily fixed and all length frequency data above this value removed from the original data set.

'''DEFINITION''' The OMI_EXTREME_SL_IBI_slev_mean_and_anomaly_obs indicator is based on the computation of the 99th and the 1st percentiles from in situ data (observations). It is computed for the variable sea level measured by tide gauges along the coast. The use of percentiles instead of annual maximum and minimum values, makes this extremes study less affected by individual data measurement errors. The annual percentiles referred to annual mean sea level are temporally averaged and their spatial evolution is displayed in the dataset omi_extreme_sl_ibi_slev_mean_and_anomaly_obs, jointly with the anomaly in the target year. This study of extreme variability was first applied to sea level variable (Pérez Gómez et al 2016) and then extended to other essential variables, sea surface temperature and significant wave height (Pérez Gómez et al 2018). '''CONTEXT''' Sea level (SLEV) is one of the Essential Ocean Variables most affected by climate change. Global mean sea level rise has accelerated since the 1990’s (Abram et al., 2019, Legeais et al., 2020), due to the increase of ocean temperature and mass volume caused by land ice melting (WCRP, 2018). Basin scale oceanographic and meteorological features lead to regional variations of this trend that combined with changes in the frequency and intensity of storms could also rise extreme sea levels up to one meter by the end of the century (Vousdoukas et al., 2020, Tebaldi et al., 2021). This will significantly increase coastal vulnerability to storms, with important consequences on the extent of flooding events, coastal erosion and damage to infrastructures caused by waves (Boumis et al., 2023). The increase in extreme sea levels over recent decades is, therefore, primarily due to the rise in mean sea level. Note, however, that the methodology used to compute this OMI removes the annual 50th percentile, thereby discarding the mean sea level trend to isolate changes in storminess. The Iberian Biscay Ireland region shows positive sea level trend modulated by decadal-to-multidecadal variations driven by ocean dynamics and superposed to the long-term trend (Chafik et al., 2019). '''COPERNICUS MARINE SERVICE KEY FINDINGS''' The completeness index criteria is fulfilled by 62 stations in 2023, five more than those available in 2022 (57), recently added to the multi-year product INSITU_GLO_PHY_SSH_DISCRETE_MY_013_053. The mean 99th percentiles reflect the great tide spatial variability around the UK and the north of France. Minimum values are observed in the Irish eastern coast (e.g.: 0.66 m above mean sea level in Arklow Harbour) and the Canary Islands (e.g.: 0.93 and 0.96 m above mean sea level in Gomera and Hierro, respectively). Maximum values are observed in the Bristol Channel (e.g.: 6.25 and 5.78 m above mean sea level in Newport and Hinkley, respectively), and in the English Channel (e.g.: 5.16 m above mean sea level in St. Helier). The annual 99th percentiles standard deviation reflects the south-north increase of storminess, ranging between 1-3 cm in the Canary Islands to 15 cm in Hinkley (Bristol Channel). Negative or close to zero anomalies of 2023 99th percentile prevail throughout the region this year, reaching < -20 cm in several stations of the UK western coast and the English Channel (e.g.: -22 cm in Newport; -21 cm in St.Helier). Significantly positive anomaly of 2023 99th percentile is only found in Arcklow Harbour, in the eastern Irish coast. '''DOI (product):''' https://doi.org/10.48670/moi-00253

'''This product has been archived''' For operationnal and online products, please visit https://marine.copernicus.eu '''Short description:''' The Global Ocean Satellite monitoring and marine ecosystem study group (GOS) of the Italian National Research Council (CNR), in Rome, distributes Remote Sensing Reflectances (Rrs), and diffuse attenuation coefficient of light at 490 nm (kd490) for multi-sensor (MODIS-AQUA, NOAA20-VIIRS, NPP-VIIRS, Sentinel3A-OLCI at 300m of resolution) (at 1 km resolution) and Sentinel3A-OLCI observations (at 300m resolution). Exclusively for multi-sensor also the absorption of phytoplankton (aph443), Gelbstoff material (adg443), and the particulate backscattering (bbp443) coefficients at 443 nm are provided. Rrs is defined as the ratio of upwelling radiance and downwelling irradiance at any wavelength (412, 443, 490, 555, and 670 nm for multi-sensor, and 400, 412, 443, 490, 510, 560, 620, 665, 674, 681 and 709 nm for OLCI) and can also be expressed as the ratio of normalized water leaving Radiance (nLw) and the extra-terrestrial solar irradiance (F0). Kd490 is defined as the diffuse attenuation coefficient of light at 490 nm, and is a measure of the turbidity of the water column. It is related to the presence of scattering particles via the ratio between Rrs at 490 and 555 nm (490 and 560 nm for OLCI). For multi-sensor observations Kd490 is achieved via Mediterranean regional algorithm developed by GOS on the basis of MedBiOp in situ dataset (Volpe et al., 2019). The current day data temporal consistency is evaluated as Quality Index (QI): QI=(CurrentDataPixel-ClimatologyDataPixel)/STDDataPixel where QI is the difference between current data and the relevant climatological field as a signed multiple of climatological standard deviations (STDDataPixel). Inherent Optical Properties (aph443, adg443 and bbp443 at 443nm) are derived via QAAv6 model. '''Processing information:''' Multi-sensor products are constituted by MODIS-AQUA, NOAA20-VIIRS, NPP-VIIRS and Sentinel3A-OLCI. For consistency with NASA L2 dataset, BRDF correction was applied to Sentinel3A-OLCI prior to band shifting and multi sensor merging. Hence, the single sensor OLCI data set is also distributed after BRDF correction. Single sensor NASA Level-2 data are destriped and then all Level-2 data are remapped at 1 km spatial resolution (300m for Sentinel3A-OLCI) using cylindrical equirectangular projection. Afterwards, single sensor Rrs fields are band-shifted, over the SeaWiFS native bands (using the QAAv6 model, Lee et al., 2002) and merged with a technique aimed at smoothing the differences among different sensors. This technique is developed by The Global Ocean Satellite monitoring and marine ecosystem study group (GOS) of the Italian National Research Council (CNR, Rome). Then geophysical fields (i.e. chlorophyll, kd490, bbp, aph and adg) are estimated via state-of-the-art algorithms for better product quality. '''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. '''Quality / Accuracy / Calibration information:''' A detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal. '''Suitability, Expected type of users / uses:''' This product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies. '''Dataset names :''' *dataset-oc-med-opt-multi-l3-rrs412_1km_daily-rt-v02 *dataset-oc-med-opt-multi-l3-rrs443_1km_daily-rt-v02 *dataset-oc-med-opt-multi-l3-rrs490_1km_daily-rt-v02 *dataset-oc-med-opt-multi-l3-rrs510_1km_daily-rt-v02 *dataset-oc-med-opt-multi-l3-rrs555_1km_daily-rt-v02 *dataset-oc-med-opt-multi-l3-rrs670_1km_daily-rt-v02 *dataset-oc-med-opt-multi-l3-kd490_1km_daily-rt-v02 *dataset-oc-med-opt-multi-l3-bbp443_1km_daily-rt-v02 *dataset-oc-med-opt-multi-l3-adg443_1km_daily-rt-v02 *dataset-oc-med-opt-multi-l3-aph443_1km_daily-rt-v02 *dataset-oc-med-opt-olci-l3-rrs400_300m_daily-rt-v02 *dataset-oc-med-opt-olci-l3-rrs412_300m_daily-rt-v02 *dataset-oc-med-opt-olci-l3-rrs443_300m_daily-rt-v02 *dataset-oc-med-opt-olci-l3-rrs490_300m_daily-rt-v02 *dataset-oc-med-opt-olci-l3-rrs510_300m_daily-rt-v02 *dataset-oc-med-opt-olci-l3-rrs560_300m_daily-rt-v02 *dataset-oc-med-opt-olci-l3-rrs620_300m_daily-rt-v02 *dataset-oc-med-opt-olci-l3-rrs665_300m_daily-rt-v02 *dataset-oc-med-opt-olci-l3-rrs674_300m_daily-rt-v02 *dataset-oc-med-opt-olci-l3-rrs681_300m_daily-rt-v02 *dataset-oc-med-opt-olci-l3-rrs709_300m_daily-rt-v02 *dataset-oc-med-opt-olci-l3-kd490_300m_daily-rt-v02 '''Files format:''' *CF-1.4 *INSPIRE compliant '''DOI (product) :''' https://doi.org/10.48670/moi-00115

Moving 10-years analysis of nitrates 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 each season. Decades span from 1960-1969 until 2004-2013. Observational data span from 1960 to 2013. Depth range (IODE standard depths): -1500.0, -1400.0, -1300.0, -1200.0, -1100.0, -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 • 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. Units: umol/l

'''This product has been archived''' For operationnal and online products, please visit https://marine.copernicus.eu '''Short description :''' For the '''Global''' Ocean '''Satellite Observations''', ACRI-ST company (Sophia Antipolis, France) is providing '''Chlorophyll-a''' and '''Optics''' products [1997 - present] based on the '''Copernicus-GlobColour''' processor. * '''Chlorophyll and Bio''' products refer to Chlorophyll-a, Primary Production (PP) and Phytoplankton Functional types (PFT). Products are based on a multi sensors/algorithms approach to provide to end-users the best estimate. Two dailies Chlorophyll-a products are distributed: ** one limited to the daily observations (called L3), ** the other based on a space-time interpolation: the '''Cloud Free''' (called L4). * '''Optics''' products refer to Reflectance (RRS), Suspended Matter (SPM), Particulate Backscattering (BBP), Secchi Transparency Depth (ZSD), Diffuse Attenuation (KD490) and Absorption Coef. (ADG/CDM). * The spatial resolution is 4 km. For Chlorophyll, a 1 km over the Atlantic (46°W-13°E , 20°N-66°N) is also available for the '''Cloud Free''' product, plus a 300m Global coastal product (OLCI S3A & S3B merged). *Products (Daily, Monthly and Climatology) are based on the merging of the sensors SeaWiFS, MODIS, MERIS, VIIRS-SNPP&JPSS1, OLCI-S3A&S3B. Additional products using only OLCI upstreams are also delivered. * Recent products are organized in datasets called NRT (Near Real Time) and long time-series in datasets called REP/MY (Multi-Years). The NRT products are provided one day after satellite acquisition and updated a few days after in Delayed Time (DT) to provide a better quality. An uncertainty is given at pixel level for all products. To find the '''Copernicus-GlobColour''' products in the catalogue, use the search keyword '''GlobColour'''. See [http://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-OC-QUID-009-030-032-033-037-081-082-083-085-086-098.pdf QUID document] for a detailed description and assessment. '''DOI (product) :''' https://doi.org/10.48670/moi-00096