'''This product has been archived''' '''DEFINITION''' Estimates of Ocean Heat Content (OHC) are obtained from integrated differences of the measured temperature and a climatology along a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 near-surface (0-300m) near-global (60°S-60°N) ocean warms at a rate of 0.4 ± 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-00233

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 data product selects sample areas of digital bathymetry, chosen for their relevance to marine activities and data sources alternative to GEBCO. The approach for building the digital map of water depth is to use GEBCO as a baseline and look at a set of sample areas where GEBCO could be improved upon. Sample areas have also been selected to be representative of each continent bordering the Atlantic and expected future requirements. Data sources include GEBCO, EMODNET, USGS and CHS.

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 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.

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 if the distance from the stretch of Coast to the nearest grid point is smaller than the horizontal grid resolution, if the distance is larger it´s considered to have a gap. The product is Provided in tabular form and as a map layer.

'''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 1997-2021 period was 0.51% 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 hemispheres. The significant increases in chlorophyll reported in 2016-2017 (Sathyendranath et al., 2018b) for the Atlantic and Pacific oceans at high latitudes appear to be plateauing after the 2021 extension. The negative trends shown in equatorial waters in 2020 appear to be remain consistent in 2021. '''DOI (product):''' https://doi.org/10.48670/moi-00230

'''DEFINITION''' Heat transport across lines are obtained by integrating the heat fluxes along some selected sections and from top to bottom of the ocean. The values are computed from models’ daily output. The mean value over a reference period (1993-2014) and over the last full year are provided for the ensemble product and the individual reanalysis, as well as the standard deviation for the ensemble product over the reference period (1993-2014). The values are given in PetaWatt (PW). '''CONTEXT''' The ocean transports heat and mass by vertical overturning and horizontal circulation, and is one of the fundamental dynamic components of the Earth’s energy budget (IPCC, 2013). There are spatial asymmetries in the energy budget resulting from the Earth’s orientation to the sun and the meridional variation in absorbed radiation which support a transfer of energy from the tropics towards the poles. However, there are spatial variations in the loss of heat by the ocean through sensible and latent heat fluxes, as well as differences in ocean basin geometry and current systems. These complexities support a pattern of oceanic heat transport that is not strictly from lower to high latitudes. Moreover, it is not stationary and we are only beginning to unravel its variability. '''CMEMS KEY FINDINGS''' The mean transports estimated by the ensemble global reanalysis are comparable to estimates based on observations; the uncertainties on these integrated quantities are still large in all the available products. Note: The key findings will be updated annually in November, in line with OMI evolutions. '''DOI (product):''' https://doi.org/10.48670/moi-00245

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.