Annual time series of eel escapement, (2009-2014): • Time series of silver eel escapement biomass for rivers monitored by EU member state every 3 years since 2009, and as defined in their Eel Management Plans (EMPs) • Maps of silver eel escapement biomass per Eel Management Unit (EMU could be a river, basin district, a region or a whole

Map at 1 degree resolution of 10 year linear trend in sea water temperature at 3 levels: surface, 500m , bottom

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.

Rapport final ONF

Map at 1 degree resolution of 50-year linear trend in sea water temperature at 3 levels: surface, 500m, bottom.

This product attempt to follow up on the sea level rise per stretch of coast of the North Atlantic, over past 10 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 relative sea level trend for 10 years we extract the information from coastal tide gauge data available for each stretch of coast, if no tide gauge available there is a dat gap. The product is Provided in tabular form and as a map layer.

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.

'''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

'''DEFINITION''' The linear change of zonal mean subsurface temperature over the period 1993-2019 at each grid point (in depth and latitude) is evaluated to obtain a global mean depth-latitude plot of subsurface temperature trend, expressed in °C. The linear change is computed using the slope of the linear regression at each grid point scaled by the number of time steps (27 years, 1993-2019). A multi-product approach is used, meaning that the linear change is first computed for 5 different zonal mean temperature estimates. The average linear change is then computed, as well as the standard deviation between the five linear change computations. The evaluation method relies in the study of the consistency in between the 5 different estimates, which provides a qualitative estimate of the robustness of the indicator. See Mulet et al. (2018) for more details. '''CONTEXT''' Large-scale temperature variations in the upper layers are mainly related to the heat exchange with the atmosphere and surrounding oceanic regions, while the deeper ocean temperature in the main thermocline and below varies due to many dynamical forcing mechanisms (Bindoff et al., 2019). Together with ocean acidification and deoxygenation (IPCC, 2019), ocean warming can lead to dramatic changes in ecosystem assemblages, biodiversity, population extinctions, coral bleaching and infectious disease, change in behavior (including reproduction), as well as redistribution of habitat (e.g. Gattuso et al., 2015, Molinos et al., 2016, Ramirez et al., 2017). Ocean warming also intensifies tropical cyclones (Hoegh-Guldberg et al., 2018; Trenberth et al., 2018; Sun et al., 2017). '''CMEMS KEY FINDINGS''' The results show an overall ocean warming of the upper global ocean over the period 1993-2019, particularly in the upper 300m depth. In some areas, this warming signal reaches down to about 800m depth such as for example in the Southern Ocean south of 40°S. In other areas, the signal-to-noise ratio in the deeper ocean layers is less than two, i.e. the different products used for the ensemble mean show weak agreement. However, interannual-to-decadal fluctuations are superposed on the warming signal, and can interfere with the warming trend. For example, in the subpolar North Atlantic decadal variations such as the so called ‘cold event’ prevail (Dubois et al., 2018; Gourrion et al., 2018), and the cumulative trend over a quarter of a decade does not exceed twice the noise level below about 100m depth. Note: The key findings will be updated annually in November, in line with OMI evolutions. '''DOI (product):''' https://doi.org/10.48670/moi-00244

The average annual sediment balance per stretch of coast bordering the North Atlantic for the past 50 years.