ROCCH, the French Chemical Contaminant Monitoring Network, regularly provides data for assessing the chemical quality of French coastal waters. Concentrations of trace metals and organic compounds are measured in integrative matrices (bivalves and sediments). Surface sediment samples are collected from 200 to 250 monitoring stations in the English Channel, the Bay of Biscay and Mediterranean lagoons every six years. Results concerning approximately 140 historical and emerging chemical substances (metals, PAHs, PCBs, PBDEs, PFAS …) are submitted to international databases of the Regional Sea Convention (OSPAR for the North East Atlantic and the Barcelona Convention for the Mediterranean) and disseminated to public stakeholders. During the ROCCHSED campaign in spring 2022, three sediment cores, each forty to fifty centimetres long, were collected from three different sites in the Bay of Biscay. Horizons of one to two centimetres in length were dated, sieved and freeze-dried for chemical analysis. The concentrations of metals, PAHs and PCBs were determined in horizons aged from over 150 years to the present in order to define the reference concentration of natural levels and describe the temporal profile of contamination.

This product displays the stations where mercury has been measured and the values present in EMODnet Chemistry infrastructure are always above the limit of detection or quantification (LOD/LOQ), i.e quality value equal to 1. It is necessary to take into account that LOD/LOQ can change with time. These products aggregate data by station, producing only one final value for each station (above, below or above/below). EMODnet Chemistry has included the gathering of contaminants data since the beginning of the project in 2009. For the maps for EMODnet Chemistry Phase III, it was requested to plot data per matrix (water,sediment, biota), per biological entity and per chemical substance. The series of relevant map products have been developed according to the criteria D8C1 of the MSFD Directive, specifically focusing on the requirements under the new Commission Decision 2017/848 (17th May 2017). The Commission Decision points to relevant threshold values that are specified in the WFD, as well as relating how these contaminants should be expressed (units and matrix etc.) through the related Directives i.e. Priority substances for Water. EU EQS Directive does not fix any threshold values in sediments. On the contrary Regional Sea Conventions provide some of them, and these values have been taken into account for the development of the visualization products. To produce the maps the following process has been followed: 1. Data collection through SeaDataNet standards (CDI+ODV) 2. Harvesting, harmonization, validation and P01 code decomposition of data 3. SQL query on data sets from point 2 4. Production of map with each point representing at least one record that match the criteria The harmonization of all the data has been the most challenging task considering the heterogeneity of the data sources, sampling protocols. Preliminary processing were necessary to harmonize all the data : • For water: contaminants in the dissolved phase; • For sediment: data on total sediment (regardless of size class) or size class < 2000 μm • For biota: contaminant data will focus on molluscs, on fish (only in the muscle), and on crustaceans • Exclusion of data values equal to 0

Pentadal time-series of the area in the North Atlantic (IHO, 1953) where ice occurred. On a 1 degree grid find all cells that experienced ice in at least 1 month of each 5 year period between 1915 and 2014, and then calculate the total area that these cells covered.

to deliver maps showing the extent of the trawling fishing grounds for identifying the areas which are most disturbed by bottom trawling over the past ten years and identifying the gaps of fishing vessels’ tracking systems in the Mediterranean Sea

Moving 10-years analysis of silicates 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

Google Earth Engine combines a multi-petabyte catalog of satellite imagery and geospatial datasets with planetary-scale analysis capabilities. Scientists, researchers, and developers use Earth Engine to detect changes, map trends, and quantify differences on the Earth's surface. Earth Engine is now available for commercial use, and remains free for academic and research use.

Moving 6-year analysis of Water body dissolved inorganic nitrogen in the NorthEast Atlantic 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 centred average of each season. 6-year periods span from 1971/1976 until 2016/2021. Observation data span from 1971 to 2021. Depth levels (IODE standard depths): [0.0, 5.0, 10.0, 20.0, 30.0, 40.0, 50.0, 75.0, 100.0, 125.0, 150.0, 200.0, 250.0, 300.0, 400.0, 500.0, 600.0, 700.0, 800.0, 900.0, 1000.0, 1100.0, 1200.0, 1300.0, 1400.0, 1500.0, 1750.0, 2000.0]. Data sources: observational data from SeaDataNet/EMODNet Chemistry Data Network. Descrption of DIVAnd analysis: the computation was done with DIVAnd (Data-Interpolating Variational Analysis in n dimensions), version 2.7.4, using GEBCO 30 sec topography for the spatial connectivity of water masses. The horizontal resolution of the produced DIVAnd maps is 0.1 degrees. Horizontal correlation length varies from 400km in open sea regions to 50km at the coast. Vertical correlation length is defined as twice the vertical resolution. Signal-to-noise ratio was fixed to 1 for vertical profiles and 0.1 for time series to account for the redundancy in the time series observations. A logarithmic transformation (DIVAnd.Anam.loglin) was applied to the data prior to the analysis to avoid unrealistic negative values. Background field: a vertically-filtered profile of the seasonal data mean value (including all years) is substracted from the data. Detrending of data: no, advection constraint applied: no. Units: umol/l.

The objective of this tender is to examine the current data collection, observation and data assembly programmes in the Meditterranean Sea, identify gaps and to evaluate how they can be optimised.

'''DEFINITION''' The Strong Wave Incidence index is proposed to quantify the variability of strong wave conditions in the Iberia-Biscay-Ireland regional seas. The anomaly of exceeding a threshold of Significant Wave Height is used to characterize the wave behavior. A sensitivity test of the threshold has been performed evaluating the differences using several ones (percentiles 75, 80, 85, 90, and 95). From this indicator, it has been chosen the 90th percentile as the most representative, coinciding with the state-of-the-art. Two Copernicus Marine products are used to compute the Strong Wave Incidence index: * IBI-WAV-MYP: '''IBI_MULTIYEAR_WAV_005_006''' * IBI-WAV-NRT: '''IBI_ANALYSISFORECAST_WAV_005_005''' The Strong Wave Incidence index (SWI) is defined as the difference between the climatic frequency of exceedance (Fclim) and the observational frequency of exceedance (Fobs) of the threshold defined by the 90th percentile (ThP90) of Significant Wave Height (SWH) computed on a monthly basis from hourly data of IBI-WAV-MYP product: SWI = Fobs(SWH > ThP90) – Fclim(SWH > ThP90) Since the Strong Wave Incidence index is defined as a difference of a climatic mean and an observed value, it can be considered an anomaly. Such index represents the percentage that the stormy conditions have occurred above/below the climatic average. Thus, positive/negative values indicate the percentage of hourly data that exceed the threshold above/below the climatic average, respectively. '''CONTEXT''' Ocean waves have a high relevance over the coastal ecosystems and human activities. Extreme wave events can entail severe impacts over human infrastructures and coastal dynamics. However, the incidence of severe (90th percentile) wave events also have valuable relevance affecting the development of human activities and coastal environments. The Strong Wave Incidence index based on the Copernicus Marine regional analysis and reanalysis product provides information on the frequency of severe wave events. The IBI-MFC covers the Europe’s Atlantic coast in a region bounded by the 26ºN and 56ºN parallels, and the 19ºW and 5ºE meridians. The western European coast is located at the end of the long fetch of the subpolar North Atlantic (Mørk et al., 2010), one of the world’s greatest wave generating regions (Folley, 2017). Several studies have analyzed changes of the ocean wave variability in the North Atlantic Ocean (Bacon and Carter, 1991; Kushnir et al., 1997; WASA Group, 1998; Bauer, 2001; Wang and Swail, 2004; Dupuis et al., 2006; Wolf and Woolf, 2006; Dodet et al., 2010; Young et al., 2011; Young and Ribal, 2019). The observed variability is composed of fluctuations ranging from the weather scale to the seasonal scale, together with long-term fluctuations on interannual to decadal scales associated with large-scale climate oscillations. Since the ocean surface state is mainly driven by wind stresses, part of this variability in Iberia-Biscay-Ireland region is connected to the North Atlantic Oscillation (NAO) index (Bacon and Carter, 1991; Hurrell, 1995; Bouws et al., 1996, Bauer, 2001; Woolf et al., 2002; Tsimplis et al., 2005; Gleeson et al., 2017). However, later studies have quantified the relationships between the wave climate and other atmospheric climate modes such as the East Atlantic pattern, the Arctic Oscillation pattern, the East Atlantic Western Russian pattern and the Scandinavian pattern (Izaguirre et al., 2011, Martínez-Asensio et al., 2016). The Strong Wave Incidence index provides information on incidence of stormy events in four monitoring regions in the IBI domain. The selected monitoring regions (Figure 1.A) are aimed to provide a summarized view of the diverse climatic conditions in the IBI regional domain: Wav1 region monitors the influence of stormy conditions in the West coast of Iberian Peninsula, Wav2 region is devoted to monitor the variability of stormy conditions in the Bay of Biscay, Wav3 region is focused in the northern half of IBI domain, this region is strongly affected by the storms transported by the subpolar front, and Wav4 is focused in the influence of marine storms in the North-East African Coast, the Gulf of Cadiz and Canary Islands. More details and a full scientific evaluation can be found in the CMEMS Ocean State report (Pascual et al., 2020). '''CMEMS KEY FINDINGS''' The trend analysis of the SWI index for the period 1980–2024 shows statistically significant trends (at the 99% confidence level) in wave incidence, with an increase of at least 0.05 percentage points per year in regions WAV1, WAV3, and WAV4. The analysis of the historical period, based on reanalysis data, highlights the major wave events recorded in each monitoring region. In region WAV1 (panel B), the maximum wave event occurred in February 2014, resulting in a 28% increase in strong wave conditions. In region WAV2 (panel C), two notable wave events were identified in November 2009 and February 2014, with increases of 16–18% in strong wave conditions. Similarly, in region WAV3 (panel D), a major event occurred in February 2014, marking one of the most intense events in the region with a 20% increase in storm wave conditions. Additionally, a comparable storm affected the region two months earlier, in December 2013. In region WAV4 (panel E), the most extreme event took place in January 1996, producing a 25% increase in strong wave conditions. Although each monitoring region is generally affected by independent wave events, the analysis reveals several historical events with above-average wave activity that propagated across multiple regions: November–December 2010 (WAV3 and WAV2), February 2014 (WAV1, WAV2, and WAV3), and February–March 2018 (WAV1 and WAV4). The analysis of the near-real-time (NRT) period (from January 2024 onward) identifies a significant event in February 2024 that impacted regions WAV1 and WAV4, resulting in increases of 20% and 15% in strong wave conditions, respectively. For region WAV4, this event represents the second most intense event recorded in the region. '''DOI (product):''' https://doi.org/10.48670/moi-00251

A marine end-to-end ecosystem model and R package. The aim is to represent the entire interconnected marine ecosystem (from physics and chemistry, to whales and fisheries in continental shelf regions) by exploring 'what if' experiments and explore uncertainty. View the application, the website or the latest publication.