This metadata refers to a dataset that shows the percentage of cities' administrative area (core city based on the Urban Morphological Zones dataset) inundated by the sea level rise of 2 metres, without any coastal flooding defences present for a series of individual coastal European cities (included in Urban Audit). The dataset has been computed using the CReSIS (Centre for Remote Sensing of Ice Sheets) dataset for 2018.

This dataset is the coastal zone land surface region from Europe, derived from the coastline towards inland, as a series of 10 consecutive buffers of 1km width each. The coastline is defined by the extent of the Corine Land Cover 2018 (raster 100m) version 20 accounting layer. In this version all Corine Land Cover pixels with a value of 523, corresponding to sea and oceans, were considered as non-land surface and thus were excluded from the buffer zone.

In integrated multi-trophic aquaculture (IMTA), multiple aquatic species from different trophic levels are farmed together. Thus, waste from one species can be used as input (fertiliser and food) for another species. The EU-funded ASTRAL project will develop IMTA production chains for the Atlantic markets. Focusing on a regional challenge-based perspective, it will bring together labs in Ireland and Scotland (open offshore labs), South Africa (flow-through inshore) and Brazil (recirculation inshore) as well as Argentina (prospective IMTA lab). The aim is to increase circularity by as much as 60 % compared to monoculture baseline aquaculture and to boost revenue diversification for aquaculture producers. ASTRAL will share, integrate, and co-generate knowledge, technology and best practices fostering a collaborative ecosystem along the Atlantic.

Le partenariat entre l’ensapBx et le GIP ATGeRi a permis la réalisation d’un atlas numérique via le catalogue et le visualiseur PIGMA. Cet atlas numérique donne accès à : - une carte sur laquelle sont situés des travaux d’étudiants et enseignants de l’ensapBx, - un lien vers le portail ArchiRès dans lequel sont décrits ces travaux de l’ensapBx avec téléchargement du document (lorsqu’il a été numérisé). De nombreux documents ont été référencés par l'ensapBx dans le catalogue PIGMA. Ils portent essentiellement sur les TPFE (travail personnel de fin d'études) et les PFE (projet de fin d'études). Ce référencement est alimenté progressivement par de nouveaux travaux.

'''DEFINITION''' The CMEMS MEDSEA_OMI_tempsal_extreme_var_temp_mean_and_anomaly OMI indicator is based on the computation of the annual 99th percentile of Sea Surface Temperature (SST) from model data. Two different CMEMS products are used to compute the indicator: The Iberia-Biscay-Ireland Multi Year Product (MEDSEA_MULTIYEAR_PHY_006_004) and the Analysis product (MEDSEA_ANALYSISFORECAST_PHY_006_013). Two parameters have been considered for this OMI: * Map of the 99th mean percentile: It is obtained from the Multi Year Product, the annual 99th percentile is computed for each year of the product. The percentiles are temporally averaged over the whole period (1987-2019). * Anomaly of the 99th percentile in 2020: The 99th percentile of the year 2020 is computed from the Near Real Time product. The anomaly is obtained by subtracting the mean percentile from the 2020 percentile. This indicator is aimed at monitoring the extremes of sea surface temperature every year and at checking their variations in space. The use of percentiles instead of annual maxima, makes this extremes study less affected by individual data. This study of extreme variability was first applied to the sea level variable (Pérez Gómez et al 2016) and then extended to other essential variables, such as sea surface temperature and significant wave height (Pérez Gómez et al 2018 and Alvarez Fanjul et al., 2019). More details and a full scientific evaluation can be found in the CMEMS Ocean State report (Alvarez Fanjul et al., 2019). '''CONTEXT''' The Sea Surface Temperature is one of the Essential Ocean Variables, hence the monitoring of this variable is of key importance, since its variations can affect the ocean circulation, marine ecosystems, and ocean-atmosphere exchange processes. As the oceans continuously interact with the atmosphere, trends of sea surface temperature can also have an effect on the global climate. In recent decades (from mid ‘80s) the Mediterranean Sea showed a trend of increasing temperatures (Ducrocq et al., 2016), which has been observed also by means of the CMEMS SST_MED_SST_L4_REP_OBSERVATIONS_010_021 satellite product and reported in the following CMEMS OMI: MEDSEA_OMI_TEMPSAL_sst_area_averaged_anomalies and MEDSEA_OMI_TEMPSAL_sst_trend. The Mediterranean Sea is a semi-enclosed sea characterized by an annual average surface temperature which varies horizontally from ~14°C in the Northwestern part of the basin to ~23°C in the Southeastern areas. Large-scale temperature variations in the upper layers are mainly related to the heat exchange with the atmosphere and surrounding oceanic regions. The Mediterranean Sea annual 99th percentile presents a significant interannual and multidecadal variability with a significant increase starting from the 80’s as shown in Marbà et al. (2015) which is also in good agreement with the multidecadal change of the mean SST reported in Mariotti et al. (2012). Moreover the spatial variability of the SST 99th percentile shows large differences at regional scale (Darmariaki et al., 2019; Pastor et al. 2018). '''CMEMS KEY FINDINGS''' The Mediterranean mean Sea Surface Temperature 99th percentile evaluated in the period 1987-2019 (upper panel) presents highest values (~ 28-30 °C) in the eastern Mediterranean-Levantine basin and along the Tunisian coasts especially in the area of the Gulf of Gabes, while the lowest (~ 23–25 °C) are found in the Gulf of Lyon (a deep water formation area), in the Alboran Sea (affected by incoming Atlantic waters) and the eastern part of the Aegean Sea (an upwelling region). These results are in agreement with previous findings in Darmariaki et al. (2019) and Pastor et al. (2018) and are consistent with the ones presented in CMEMS OSR3 (Alvarez Fanjul et al., 2019) for the period 1993-2016. The 2020 Sea Surface Temperature 99th percentile anomaly map (bottom panel) shows a general positive pattern up to +3°C in the North-West Mediterranean area while colder anomalies are visible in the Gulf of Lion and North Aegean Sea . This Ocean Monitoring Indicator confirms the continuous warming of the SST and in particular it shows that the year 2020 is characterized by an overall increase of the extreme Sea Surface Temperature values in almost the whole domain with respect to the reference period. This finding can be probably affected by the different dataset used to evaluate this anomaly map: the 2020 Sea Surface Temperature 99th percentile derived from the Near Real Time Analysis product compared to the mean (1987-2019) Sea Surface Temperature 99th percentile evaluated from the Reanalysis product which, among the others, is characterized by different atmospheric forcing). Note: The key findings will be updated annually in November, in line with OMI evolutions. '''DOI (product):''' https://doi.org/10.48670/moi-00266

The SDC_GLO_CLIM_N2 product contains seasonally averaged Brunt-Vaisala squared frequency profiles using the density profiles computed in SeadataCloud Global Ocean Climatology - Density Climatology. The Density Climatology product uses the Profiling Floats (PFL) data from World Ocean database 18 for the time period 2003 to 2017 with a Nonlinear Quality procedure applied on it. Computed BVF profiles are averaged seasonally into 5x5 degree boxes for Atlantic and Pacific Oceans. For data access, please register at http://www.marine-id.org/.

The GEBCO_2020 Grid was released in May 2020 and is the second global bathymetric product released by the General Bathymetric Chart of the Oceans (GEBCO) and has been developed through the Nippon Foundation-GEBCO Seabed 2030 Project. The GEBCO_2020 Grid provides global coverage of elevation data in meters on a 15 arc-second grid of 43200 rows x 86400 columns, giving 3,732,480,000 data points. Grid Development The GEBCO_2020 Grid is a continuous, global terrain model for ocean and land with a spatial resolution of 15 arc seconds. The grid uses as a ‘base’ Version 2 of the SRTM15+ data set (Tozer et al, 2019). This data set is a fusion of land topography with measured and estimated seafloor topography. It is augmented with the gridded bathymetric data sets developed by the four Seabed 2030 Regional Centers. The Regional Centers have compiled gridded bathymetric data sets, largely based on multibeam data, for their areas of responsibility. These regional grids were then provided to the Global Center. For areas outside of the polar regions (primarily south of 60°N and north of 50°S), these data sets are in the form of 'sparse grids', i.e. only grid cells that contain data were populated. For the polar regions, complete grids were provided due to the complexities of incorporating data held in polar coordinates. The compilation of the GEBCO_2020 Grid from these regional data grids was carried out at the Global Center, with the aim of producing a seamless global terrain model. In contrast to the development of the previous GEBCO grid, GEBCO_2019, the data sets provided as sparse grids by the Regional Centers were included on to the base grid without any blending, i.e. grid cells in the base grid were replaced with data from the sparse grids. This was with aim of avoiding creating edge effects, 'ridges and ripples', at the boundaries between the sparse grids and base grid during the blending process used previously. In addition, this allows a clear identification of the data source within the grid, with no cells being 'blended' values. Routines from Generic Mapping Tools (GMT) system were used to do the merging of the data sets. For the polar data sets, and the adjoining North Sea area, supplied in the form of complete grids these data sets were included using feather blending techniques from GlobalMapper software version 11.0, made available by Blue Marble Geographic. The GEBCO_2020 Grid includes data sets from a number of international and national data repositories and regional mapping initiatives. For information on the data sets included in the GEBCO_2020 Grid, please see the list of contributions included in this release of the grid (https://www.gebco.net/data_and_products/gridded_bathymetry_data/gebco_2020/#compilations).

'''DEFINITION''' The CMEMS NORTHWESTSHELF_OMI_tempsal_extreme_var_temp_mean_and_anomaly OMI indicator is based on the computation of the annual 99th percentile of Sea Surface Temperature (SST) from model data. Two different CMEMS products are used to compute the indicator: The North-West Shelf Multi Year Product (NWSHELF_MULTIYEAR_PHY_004_009) and the Analysis product (NORTHWESTSHELF_ANALYSIS_FORECAST_PHY_004_013). Two parameters are included on this OMI: * Map of the 99th mean percentile: It is obtained from the Multi Year Product, the annual 99th percentile is computed for each year of the product. The percentiles are temporally averaged over the whole period (1993-2019). * Anomaly of the 99th percentile in 2020: The 99th percentile of the year 2020 is computed from the Analysis product. The anomaly is obtained by subtracting the mean percentile from the 2020 percentile. This indicator is aimed at monitoring the extremes of sea surface temperature every year and at checking their variations in space. The use of percentiles instead of annual maxima, makes this extremes study less affected by individual data. This study of extreme variability was first applied to the sea level variable (Pérez Gómez et al 2016) and then extended to other essential variables, such as sea surface temperature and significant wave height (Pérez Gómez et al 2018 and Alvarez Fanjul et al., 2019). More details and a full scientific evaluation can be found in the CMEMS Ocean State report (Alvarez Fanjul et al., 2019). '''CONTEXT''' This domain comprises the North West European continental shelf where depths do not exceed 200m and deeper Atlantic waters to the North and West. For these deeper waters, the North-South temperature gradient dominates (Liu and Tanhua, 2021). Temperature over the continental shelf is affected also by the various local currents in this region and by the shallow depth of the water (Elliott et al., 1990). Atmospheric heat waves can warm the whole water column, especially in the southern North Sea, much of which is no more than 30m deep (Holt et al., 2012). Warm summertime water observed in the Norwegian trench is outflow heading North from the Baltic Sea and from the North Sea itself. '''CMEMS KEY FINDINGS''' The 99th percentile SST product can be considered to represent approximately the warmest 4 days for the sea surface in Summer. Maximum anomalies for 2020 are up to 4oC warmer than the 1993-2019 average in the western approaches, Celtic and Irish Seas, English Channel and the southern North Sea. For the atmosphere, Summer 2020 was exceptionally warm and sunny in southern UK (Kendon et al., 2021), with heatwaves in June and August. Further north in the UK, the atmosphere was closer to long-term average temperatures. Overall, the 99th percentile SST anomalies show a similar pattern, with the exceptional warm anomalies in the south of the domain. Note: The key findings will be updated annually in November, in line with OMI evolutions. '''DOI (product)''' https://doi.org/10.48670/moi-00273

The SDC_MED_DP2 product contains 55 sliding decadal temperature fields (1955-1964, 1956-1965, 1957-1966, …, 2009-2018) at 1/8° horizontal resolution obtained in the 0-2000m layer and two derived OHC annual anomaly estimates for the 0-700m and the 0-2000m layers. Sliding decades of annual Temperature fields were obtained from an integrated Mediterranean Sea dataset covering the time period 1955-2018, which combines data extracted from SeaDataNet infrastructure at the end of July 2019 (SDC_MED_DATA_TS_V2, https://doi.org/10.12770/3f8eaace-9f9b-4b1b-a7a4-9c55270e205a) and the Coriolis Ocean Dataset for Reanalysis (CORA 5.2, accessed in July 2020, https://archimer.ifremer.fr/doc/00595/70726/). The resulting annual OHC anomaly time series span the 1960-2014 period. The analysis was performed with the DIVAnd (Data-Interpolating Variational Analysis in n dimensions), version 2.6.1.

Sediment average grain size in the Mediterranean was generated from sediment categories. This rough granulometry estimate may be used for habitat models at meso- and large scale.