The SeaDataCloud TS historical data collection v1 for the North Atlantic Ocean, includes open access in situ data on temperature and salinity of water column in the North Atlantic Ocean from 10°N to 62°N, including the Labrador Sea, The data were retrieved from the SeaDataNet infrastructure at the end of November 2017. The dataset format is Ocean Data View (ODV - http://odv.awi.de/) binary collection. The quality control of the data has been performed with the help of ODV software. Data Quality Flags have been revised and set up using the elaborated by SeaDataNet2 project QC procedures in conjunction with the visual expert check. The final number of the Temperature and Salinity profiles (stations) in the collection is 9091773. For data access please register at http://www.marine-id.org/.

Calculation of the average annual sediment balance per stretch of coast for the past 10 years.

Annual time series of eel escapement, (2008-2011): • Time series of silver eel escapement biomass for rivers monitored by EU member state every 3 years since 2008, 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

'''DEFINITION''' Estimates of Arctic sea ice extent are obtained from the surface of oceans grid cells that have at least 15% sea ice concentration. These values are cumulated in the entire Northern Hemisphere (excluding ice lakes) and from 1993 up to the year 2019 aiming to: i) obtain the Arctic sea ice extent as expressed in millions of km square (106 km2) to monitor both the large-scale variability and mean state and change. ii) to monitor the change in sea ice extent as expressed in millions of km squared per decade (106 km2/decade), or in sea ice extent loss since the beginning of the time series as expressed in percent per decade (%/decade; reference period being the first date of the key figure b) dot-dashed trend line, Vaughan et al., 2013). These trends are calculated in three ways, i.e. (i) from the annual mean values; (ii) from the March values (winter ice loss); (iii) from September values (summer ice loss). The Arctic sea ice extent used here is based on the “multi-product” (GLOBAL_MULTIYEAR_PHY_ENS_001_031) approach as introduced in the second issue of the Ocean State Report (CMEMS OSR, 2017). Five global products have been used to build the ensemble mean, and its associated ensemble spread. '''CONTEXT''' Sea ice is frozen seawater that floats on the ocean surface. This large blanket of millions of square kilometers insulates the relatively warm ocean waters from the cold polar atmosphere. The seasonal cycle of the sea ice, forming and melting with the polar seasons, impacts both human activities and biological habitat. Knowing how and how much the sea ice cover is changing is essential for monitoring the health of the Earth as sea ice is one of the highest sensitive natural environments. Variations in sea ice cover can induce changes in ocean stratification, in global and regional sea level rates and modify the key rule played by the cold poles in the Earth engine (IPCC, 2019). The sea ice cover is monitored here in terms of sea ice extent quantity. More details and full scientific evaluations can be found in the CMEMS Ocean State Report (Samuelsen et al., 2016; Samuelsen et al., 2018). '''CMEMS KEY FINDINGS''' Since the year 1993 to 2023 the Arctic sea ice extent has decreased significantly at an annual rate of -0.57*106 km2 per decade. This represents an amount of -4.8 % per decade of Arctic sea ice extent loss over the period 1993 to 2023. Over the period 1993 to 2018, summer (September) sea ice extent loss amounts to -1.18*106 km2/decade (September values), which corresponds to -14.85% per decade. Winter (March) sea ice extent loss amounts to -0.57*106 km2/decade, which corresponds to -3.42% per decade. These values slightly exceed the estimates given in the AR5 IPCC assessment report (estimate up to the year 2012) as a consequence of continuing Northern Hemisphere sea ice extent loss. Main change in the mean seasonal cycle is characterized by less and less presence of sea ice during summertime with time. Note: The key findings will be updated annually in November, in line with OMI evolutions. '''DOI (product):''' https://doi.org/10.48670/moi-00190

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.

'''This product has been archived''' '''DEFINITION''' Estimates of Ocean Heat Content (OHC) are obtained from integrated differences of the measured temperature and a climatology along a vertical profile in the ocean (von Schuckmann et al., 2018). The regional OHC values are then averaged from 60°S-60°N aiming i) to obtain the mean OHC as expressed in Joules per meter square (J/m2) to monitor the large-scale variability and change. ii) to monitor the amount of energy in the form of heat stored in the ocean (i.e. the change of OHC in time), expressed in Watt per square meter (W/m2). Ocean heat content is one of the six Global Climate Indicators recommended by the World Meterological Organisation for Sustainable Development Goal 13 implementation (WMO, 2017). '''CONTEXT''' Knowing how much and where heat energy is stored and released in the ocean is essential for understanding the contemporary Earth system state, variability and change, as the ocean shapes our perspectives for the future (von Schuckmann et al., 2020). Variations in OHC can induce changes in ocean stratification, currents, sea ice and ice shelfs (IPCC, 2019; 2021); they set time scales and dominate Earth system adjustments to climate variability and change (Hansen et al., 2011); they are a key player in ocean-atmosphere interactions and sea level change (WCRP, 2018) and they can impact marine ecosystems and human livelihoods (IPCC, 2019). '''CMEMS KEY FINDINGS''' Since the year 2005, the upper (0-2000m) near-global (60°S-60°N) ocean warms at a rate of 1.0 ± 0.1 W/m2. Note: The key findings will be updated annually in November, in line with OMI evolutions. '''DOI (product):''' https://doi.org/10.48670/moi-00235

'''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

SDC_MED_DATA_TS_V1 SeaDataCloud Temperature and Salinity data collection for the Mediterranean Sea contains all open access temperature and salinity in situ data retrieved from SeaDataNet infrastructure at the end of October 2017. The data span between -9.25 and 37 degrees of longitude, thus including an Atlantic box and the Marmara Sea. It covers the time period 1900-2017. Data have been quality checked using ODV 5.0 software. Quality Flags of anomalous data have been revised using basic QC procedures. The dataset format is ODV binary collections. You can read, analyse and export from the ODV application provided by Alfred Wegener institute at http://odv.awi.de/. For data access please register at http://www.marine-id.org/.

The Oil Platform Leaks challenge attempts to determine the likely trajectory of the slick and to release rapid information on the oil movement and environmental and coastal impacts in the form of two impacts bulletins at 24 and 72 hours. Each bulletin indicates what information can be provided, evidencing the fitness for use of the current available marine datasets, as well as pointing out gaps in the current Emodnet data collection framework. This first product relies on an oil spill modelling tool operated by CLS and provide the status of datasets for the purpose of the oil Spill simulation exercice. The OSCAR model (Oil Spill Contingency and Response, operated at CLS under license) made available by SINTEF and used to simulate the oil spill fate and weathering at water surface, in the water column and along shorelines. The declarative data given for the OSCAR simulation are: Date and time of oil spill, Location and depth of oil spill, Oil API number or oil type name, Oil spill amount or oil spill rate

The Oil Platform Leaks challenge attempts to determine the likely trajectory of the slick and to release rapid information on the oil movement and environmental and coastal impacts in the form of a bulletin at 24 hours from the event. This bulletin indicates what information can be provided, evidencing the fitness for use of the current available marine datasets, as well as pointing out gaps in the current Emodnet data collection framework. This first product relies on an oil spill modelling tool operated by CLS and provide the status of datasets for the purpose of the oil Spill simulation exercice. The OSCAR model (Oil Spill Contingency and Response, operated at CLS under license) made available by SINTEF and used to simulate the oil spill fate and weathering at water surface, in the water column and along shorelines. The declarative data given for the OSCAR simulation are: Date and time of oil spill, Location and depth of oil spill, Oil API number or oil type name, Oil spill amount or oil spill rate