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The impact of fishing on benthic habitats has previously been investigated however; a conclusive classification of potentially sensitive habitats per gear type does not exist. Currently only qualitative estimates of fishery impact using Broad-scale habitat maps are possible. Here a sensitivity matrix using both fishing pressure (fishing Intensity) and habitat sensitivity is employed to define habitat disturbance categories. The predominant fishing activities associated with physical abrasion of the seafloor area are from bottom contacting towed fishing gear. The swept area of the aforementioned gear in contact with the seabed is generally considered a function of gear width, vessel speed and fishing effort (ICES. 2015). The varying characteristics of fishing gear, their interaction with the sea floor and species being targeted; provide scope for differing interactions with subsurface (infaunal) and surface (epifaunal) dwelling communities. An evaluation of the abrasion pressure and habitat sensitivity split into surface and subsurface pressure allows greater insight to the ecological effects. Fishing intensity was calculated annually and based on the area of sea floor being swept (or swept area ratio SAR) by gear type. Calculations are based on SAR’s of gear types per area, per year. Fishing pressure ranks and habitat sensitivity ranks obtained from WGSFD working group (01 WGSFD - Report of the Working Group on Spatial Fisheries Data 2015) can be incorporated within a GIS environment to existing ICES fisheries data to provide habitat disturbance maps (fishing pressure maps+ habitat sensitivity maps) ICES. 2015. Report of the Working Group on Spatial Fisheries Data (WGSFD), 8–12 June 2015, ICES Headquarters, Copenhagen, Denmark. ICES CM 2015/SSGEPI:18. 150 pp.

This product attempt to follow up on the sea level rise per stretch of coast of the North Atlantic, over past 100 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 100 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.

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 absolute sea level trend for 10 years we extract the information from grided satellite altimetry data and extrapolate it to the nearest strecth of coast. The product is Provided in tabular form and as a map layer.

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.

This product attempt to follow up on the sea level rise per stretch of coast of the North Atlantic, over past 100 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 100 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.

Annual time series of eel recruitement, (2005-2014) • Time series of glass and yellow eel for those rivers used in the annual ICES advice to the EU • Location, data availability and long term annual (LTA) eel recruiment per river mouth

'''This product has been archived''' For operationnal and online products, please visit https://marine.copernicus.eu '''DEFINITION''' The trend map is derived from version 5 of the global climate-quality chlorophyll time series produced by the ESA Ocean Colour Climate Change Initiative (ESA OC-CCI, Sathyendranath et al. 2019; Jackson 2020) and distributed by CMEMS. The trend detection method is based on the Census-I algorithm as described by Vantrepotte et al. (2009), where the time series is decomposed as a fixed seasonal cycle plus a linear trend component plus a residual component. The linear trend is expressed in % year -1, and its level of significance (p) calculated using a t-test. Only significant trends (p < 0.05) are included. '''CONTEXT''' Phytoplankton are key actors in the carbon cycle and, as such, recognised as an Essential Climate Variable (ECV). Chlorophyll concentration is the most widely used measure of the concentration of phytoplankton present in the ocean. Drivers for chlorophyll variability range from small-scale seasonal cycles to long-term climate oscillations and, most importantly, anthropogenic climate change. Due to such diverse factors, the detection of climate signals requires a long-term time series of consistent, well-calibrated, climate-quality data record. Furthermore, chlorophyll analysis also demands the use of robust statistical temporal decomposition techniques, in order to separate the long-term signal from the seasonal component of the time series. '''CMEMS KEY FINDINGS''' The average global trend for the 1997-2020 period was 0.59% per year, with a maximum value of 25% per year and a minimum value of -6.1% per year. Positive trends are pronounced in the high latitudes of both northern and southern hemisphehres. The significant increases in chlorophyll reported in 2016-2017 (Sathyendranath et al., 2018b) for the Atlantic and Pacific oceans at high latitudes continued to be observed after the 2020 extension, as well as the negative trends over the equatorial Pacific and the Indian Ocean Gyre. Note: The key findings will be updated annually in November, in line with OMI evolutions. '''DOI (product):''' https://doi.org/10.48670/moi-00230

'''This product has been archived''' '''DEFINITION''' The temporal evolution of thermosteric sea level in an ocean layer is obtained from an integration of temperature driven ocean density variations, which are subtracted from a reference climatology to obtain the fluctuations from an average field. The regional thermosteric sea level values are then averaged from 60°S-60°N aiming to monitor interannual to long term global sea level variations caused by temperature driven ocean volume changes through thermal expansion as expressed in meters (m). '''CONTEXT''' The global mean sea level is reflecting changes in the Earth’s climate system in response to natural and anthropogenic forcing factors such as ocean warming, land ice mass loss and changes in water storage in continental river basins. Thermosteric sea-level variations result from temperature related density changes in sea water associated with volume expansion and contraction. Global thermosteric sea level rise caused by ocean warming is known as one of the major drivers of contemporary global mean sea level rise (Cazenave et al., 2018; Oppenheimer et al., 2019). '''CMEMS KEY FINDINGS''' Since the year 2005 the upper (0-700m) near-global (60°S-60°N) thermosteric sea level rises at a rate of 0.9±0.1 mm/year. Note: The key findings will be updated annually in November, in line with OMI evolutions. '''DOI (product):''' https://doi.org/10.48670/moi-00239

This product attempt to follow up on the sea level rise per stretch of coast of the North Atlantic, over 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 relative sea level trend for 50 years we extract the information from coastal tide gauges data available at each stretch of coast, if there is not a tide gauge there is a data gap. The product is Provided in tabular form and as a map layer.