Map of seasonal averages of dissolved inorganic Nitrogen (uM) indicator for eutrophication for the past 10 years (2005-2014) in the Atlantic basin. It will be generated using in situ measurements of the different parameteres required to assess the dissolved inorganic Nitrogen indicator and the OSPAR Convention Common procedure methodology (OSPAR 2013, Common Procedure for the Identification of the Eutrophication Status of the OSPAR Maritime Area. Agreement 2013-08. 67 pp).

This product attempt to follow up on the sea level rise per stretch of coast of the North Atlantic, over past 50 years as follows: • Characterization of absolute sea level trend at annual resolution, along the coasts of EU Member States (including Outermost Regions), Canada, Faroes, Greenland, Iceland, Mexico, Morocco, Norway and USA; The stretchs or coast are defined by the administrative regions of the Atlantic Coast: • from NUTS3** administrative division for EU countries (see Eurostat), and • from GADM*** administrative divisions for non-EU countries. ** Third level of Nomenclature of Territorial Units for Statistics *** Global Administrative Areas For absolute sea level trend for 50 years we extract the information from grided sea level reconstruction datasets (using a combination of satellite and tide gauges) and extrapolate it to the nearest strecth of coast. The product is Provided in tabular form and as a map layer.

The All-Atlantic Ocean Research and Innovation Alliance (AAORIA) is the result of science diplomacy efforts involving countries from both sides of the Atlantic Ocean. It builds upon the success of two existing cooperative agreements – the Galway Statement on Atlantic Ocean Cooperation which was signed by the European Union, United States, and Canada in 2013; and the Belem Statement on Atlantic Ocean Research and Innovation Cooperation which was signed by the European Union, Brazil, and South Africa in 2017 as well as on several other bilateral and multilateral agreements. AAORIA aims to enhance marine research and innovation cooperation along and across the Atlantic Ocean. In 2022, the “All-Atlantic Declaration” was signed to revitalize collaboration among current initiatives and enhance the coordination between the Galway Working Groups, All-Atlantic Joint Pilot Actions, and related projects. Additionally, it aims to engage new partners and initiatives to join the All-Atlantic community.

'''This product has been archived''' For operationnal and online products, please visit https://marine.copernicus.eu '''DEFINITION''' The time series are derived from the regional chlorophyll reprocessed (REP) product as distributed by CMEMS. This dataset, derived from multi-sensor (SeaStar-SeaWiFS, AQUA-MODIS, NOAA20-VIIRS, NPP-VIIRS, Envisat-MERIS and Sentinel3A-OLCI) Rrs spectra produced by CNR using an in-house processing chain, is obtained by means of the Mediterranean Ocean Colour regional algorithms: an updated version of the MedOC4 (Case 1 (off-shore) waters, Volpe et al., 2019, with new coefficients) and AD4 (Case 2 (coastal) waters, Berthon and Zibordi, 2004). The processing chain and the techniques used for algorithms merging are detailed in Colella et al. (2021). Monthly regional mean values are calculated by performing the average of 2D monthly mean (weighted by pixel area) over the region of interest. The deseasonalized time series is obtained by applying the X-11 seasonal adjustment methodology on the original time series as described in Colella et al. (2016), and then the Mann-Kendall test (Mann, 1945; Kendall, 1975) and Sens’s method (Sen, 1968) are subsequently applied to obtain the magnitude of trend. '''CONTEXT''' Phytoplankton and chlorophyll concentration as a proxy for phytoplankton respond rapidly to changes in environmental conditions, such as light, temperature, nutrients and mixing (Colella et al. 2016). The character of the response depends on the nature of the change drivers, and ranges from seasonal cycles to decadal oscillations (Basterretxea et al. 2018). Therefore, it is of critical importance to monitor chlorophyll concentration at multiple temporal and spatial scales, in order to be able to separate potential long-term climate signals from natural variability in the short term. In particular, phytoplankton in the Mediterranean Sea is known to respond to climate variability associated with the North Atlantic Oscillation (NAO) and El Niño Southern Oscillation (ENSO) (Basterretxea et al. 2018, Colella et al. 2016). '''CMEMS KEY FINDINGS''' In the Mediterranean Sea, the trend average for the 1997-2020 period is slightly negative (-0.580.62% per year). Due to the change in processing techniques and chlorophyll retrieval, this trend estimate cannot be compared directly to those previously reported. The observations time series (in grey) shows minima values have been quite constant until 2015 and then there is a little decrease up to 2020, when an absolute minimum occurs with values lower than 0.04 mg m-3. Throughout the time series, maxima are variable year by year (with absolute maximum in 2015, >0.14 mg m-3), showing an evident reduction since 2016. In the last years of the series, the decrease of chlorophyll concentrations is also observed in the deseasonalized timeseries (in green) with a marked step in 2020. This attenuation of chlorophyll values in the last years results in an overall negative trend for the Mediterranean Sea. Note: The key findings will be updated annually in November, in line with OMI evolutions. '''DOI (product):''' https://doi.org/10.48670/moi-00259

The challenge attempts to collect data on landings for the North Atlantic sea basin (i.e. north of the equator, excluding Caribe, Baltic, North Sea and Artic) and to compute: mass and number of discards by species and year, including fish, mammals, reptiles and seabirds. Data are presented in an Excel spreadsheet.

This dataset represents the regions for levels 1, 2 and 3 of the Nomenclature of Territorial Units for Statistics (NUTS) for 2016. The NUTS nomenclature is a hierarchical classification of statistical regions and subdivides the EU economic territory into regions of four different levels (NUTS , 1, 2 and 3, moving respectively from larger to smaller territorial units). NUTS 1 is the most aggregated level. An additional Country level (NUTS 0) is also available for countries where the the nation at statistical level does not coincide with the administrative boundaries. For example Mt Athos in Greece and Mellum and Minsener Ogg in Germany. The NUTS classification has been officially established through Regulation (EC) No 2016/2066 of the European Parliament and of the Council and its amendments. A non-official NUTS-like classification has been defined for the EFTA countries and candidate countries. An introduction to the NUTS classification is available here: http://ec.europa.eu/eurostat/web/nuts/overview. This dataset has been created mainly from the EuroBoundary Map v 12 (Eurogeographics) and geographic information from TurkStat for Turkey. The public dataset is available under the Download link indicated below. Available scales are 1M, 3M, 10M, 20M, 60M). The full dataset is available via the EC restricted download link under GISCO.NUTS_2016. Here six scale ranges (100K, 1M, 3M, 10M and 20M, 60M) are available. Coverage is the economic territory of the EU, EFTA countries and candidate countries as in 2013.

'''DEFINITION''' The time series are derived from the regional chlorophyll reprocessed (MY) product as distributed by CMEMS (OCEANCOLOUR_MED_BGC_L3_NRT_009_141). This dataset, derived from multi-sensor (SeaStar-SeaWiFS, AQUA-MODIS, NOAA20-VIIRS, NPP-VIIRS, Envisat-MERIS and Sentinel3-OLCI) Rrs spectra produced by CNR using an in-house processing chain, is obtained by means of the Mediterranean Ocean Colour regional algorithms: an updated version of the MedOC4 (Case 1 (off-shore) waters, Volpe et al., 2019, with new coefficients) and AD4 (Case 2 (coastal) waters, Berthon and Zibordi, 2004). The processing chain and the techniques used for algorithms merging are detailed in Colella et al. (2023). Monthly regional mean values are calculated by performing the average of 2D monthly mean (weighted by pixel area) over the region of interest. The deseasonalized time series is obtained by applying the X-11 seasonal adjustment methodology on the original time series as described in Colella et al. (2016), and then the Mann-Kendall test (Mann, 1945; Kendall, 1975) and Sens’s method (Sen, 1968) are subsequently applied to obtain the magnitude of trend. This OMI has been introduced since the 2nd issue of Ocean State Report in 2017. '''CONTEXT''' Phytoplankton and chlorophyll concentration as a proxy for phytoplankton respond rapidly to changes in environmental conditions, such as light, temperature, nutrients and mixing (Colella et al. 2016). The character of the response depends on the nature of the change drivers, and ranges from seasonal cycles to decadal oscillations (Basterretxea et al. 2018). Therefore, it is of critical importance to monitor chlorophyll concentration at multiple temporal and spatial scales, in order to be able to separate potential long-term climate signals from natural variability in the short term. In particular, phytoplankton in the Mediterranean Sea is known to respond to climate variability associated with the North Atlantic Oscillation (NAO) and El Niño Southern Oscillation (ENSO) (Basterretxea et al. 2018, Colella et al. 2016). '''KEY FINDINGS''' In the Mediterranean Sea, the average chlorophyll trend for the 1997–2024 period is slightly negative, at -0.77 ± 0.59% per year, reinforcing the findings of the previous releases. This result contrasts with the analysis by Sathyendranath et al. (2018), which reported increasing chlorophyll concentrations across all European seas. From around 2010–2011 onward, excluding the 2018–2019 period, a noticeable decline in chlorophyll levels is evident in the deseasonalized time series (green line) and in the observed maxima (grey line), particularly from 2015. This sustained decline over the past decade contributes to the overall negative trend observed in the Mediterranean Sea. '''DOI (product):''' https://doi.org/10.48670/moi-00259

Annual time series of salmon recruitement biomass (2005-2014): • Time series of atlantic salmon recruitment • Location and Long Term Average (LTA) of atlantic salmon recruitment per Management Unit, that could be a river, basin district, a region or a whole country.

One product and 3 components were developed in order to fulfill the third objectif ATLANTIC_CH02_Product_5 / Distribution of ocean monitoring systems to assess climate change existing into the MPA network • Physical parameter monitoring • Chemical parameter monitoring • Biological parameter monitoring The aim of the product is the identification of ocean monitoring systems to assess climate change in MPAs.

Pentadal (5-year average) resolution time-series of bottom temperature for North Atlantic ocean area deeper than 1000m. Calculate the 5 year average bottom temperature at each point on the grid and then calculate the area weighted average.