Maisons éclusières sur les départements de la Gironde et du Lot-et-Garonne.

'''This product has been archived''' For operationnal and online products, please visit https://marine.copernicus.eu '''DEFINITION''' The time series are derived from the regional chlorophyll reprocessed (REP) products as distributed by CMEMS which, in turn, result from the application of the regional chlorophyll algorithms over remote sensing reflectances (Rrs) provided by the ESA Ocean Colour Climate Change Initiative (ESA OC-CCI, Sathyendranath et al. 2019; Jackson 2020). Daily regional mean values are calculated by performing the average (weighted by pixel area) over the region of interest. A fixed annual cycle is extracted from the original signal, using the Census-I method as described in Vantrepotte et al. (2009). The deasonalised time series is derived by subtracting the mean seasonal cycle from the original time series, and then fitted to a linear regression to, finally, obtain the linear trend. '''CONTEXT''' Phytoplankton – and chlorophyll concentration as a proxy for phytoplankton – respond rapidly to changes in environmental conditions, such as temperature, light and nutrients availability, and mixing. The response in the North Atlantic ranges from cyclical to decadal oscillations (Henson et al., 2009); it is therefore 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 North Atlantic are known to respond to climate variability associated with the North Atlantic Oscillation (NAO), with the initiation of the spring bloom showing a nominal correlation with sea surface temperature and the NAO index (Zhai et al., 2013). '''CMEMS KEY FINDINGS''' While the overall trend average for the 1997-2020 period in the North Atlantic Ocean is slightly positive (0.92 ± 0.13 % per year), an underlying low frequency harmonic signal can be seen in the deseasonalised data. The annual average for the region in 2020 is 0.31 mg m-3. Though no appreciable changes in the timing of the spring and autumn blooms have been observed during 2020, these reached higher chlorophyll values than the average for the time series. In particular, the spring bloom maximum in 2020, circa 0.80 mg m-3, showed an increase in chlorophyll concentration from the observations during the 2016-2019 spring blooms. Note: The key findings will be updated annually in November, in line with OMI evolutions. '''DOI (product):''' https://doi.org/10.48670/moi-00194

EMODnet (European Marine Observation and Data Network) is the long term marine data initiative supported by the European Commission since 2009 to ensure that European marine data will become easily accessible, interoperable, and free on restrictions on use. EMODnet Chemistry provides access to standardized, harmonized and validated chemical data collections for water quality evaluation at a regional scale, as defined by the Marine Strategy Framework Directive (MSFD). The data portal has adopted and adapted SeaDataNet standards and services, establishing interoperability between the data sets from the many different providers (more than 60 in EMODnet Chemistry network). Concentration maps of nutrients, chlorophyll-a and dissolved oxygen are computed on a standard grid, providing information at a regular time interval, per season and over several vertical layers, including the deepest one. Dedicated OGC standard services for browsing, viewing and downloading chemistry observation, data and data products for the European waters have been developed, and are actively maintained and monitored.

Map the occurrence of ice at 1-degree resolution over different periods of the last century (1915-2014, 1965-2014, 2005-2014, 2009-2014). For each entire period (100, 50, 10, 5 years) find and map all cells of the 1 degree grid that experience ice conditions in at least 1 month.

The data set aims to contribute to a better biological characterization of European marine ecosystems. As such it represents probabilities of EUNIS (EUropean Nature Information System) habitat presence at Level 3 for marine habitats including information on sea ice coverage (this corresponds to EUNIS level 2 for terrestrial habitats). The map combines spatially explicit data on marine bathymetry and sea-bed with non-spatially referenced habitat information of the EUNIS classification. The objective of the data set produced by EEA and its Topic Centre ETC/ULS is to improve the biological description of marine based ecosystem types and their spatial distribution. The work supports Target 2 Action 5 of the implementation of the EU Biodiversity Strategy to 2020, established to achieve the Aichi targets of the Convention of Biological Diversity (CBD). It further addresses the MAES process (Mapping and Assessing of Ecosystems and their Services). The data set represents 2 classes of the MAES classification level 3, namely “Marine inlets and transitional waters” and “Marine”. The dataset comprises the following information: • Sea region (1 – Arctic, 2 – Atlantic, 3 – Baltic, 4 – Mediterranean, 5 – Black Sea) • Sea zone (1 – Littoral, 2 – Infralittoral, 3 – Circalittoral, 4 – Offshore circalittoral, 5 – Upper bathyal, 6 – Lower bathyal, 7 – Abyssal,8 - Coastal Lagoons, 9 - Coastal Lagoons) • Substrate (0 – undetermined substrate, 1 – rock and biogenic, 3 – coarse sediment, 4 – mixed sediment, 5 – sand, 6 – mud) • Sea ice coverage (0 – no sea ice presence, 1 – seasonal sea ice presence, 2 – perennial sea ice presence)

Phyto plankton Abundance: Identify the 3 most abundant phytoplankton species in the North Atlantic and calculate a timeseries of their abundance within the basin.

'''DEFINITION''' Volume transport across lines are obtained by integrating the volume 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 Sverdrup (Sv). '''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. At Drake Passage, the multi-product approach (product no. 2.4.1) is larger than the value (130 Sv) of Lumpkin and Speer (2007), but smaller than the new observational based results of Colin de Verdière and Ollitrault, (2016) (175 Sv) and Donohue (2017) (173.3 Sv). Note: The key findings will be updated annually in November, in line with OMI evolutions. '''DOI (product):''' https://doi.org/10.48670/moi-00247

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/.

'''DEFINITION''' The global annual chlorophyll anomaly is computed by subtracting a reference climatology (1997-2014) from the annual chlorophyll mean, on a pixel-by-pixel basis and in log10 space. Both the annual mean and the climatology are computed employing ESA Ocean Colour Climate Change Initiative (ESA OC-CCI, Sathyendranath et al., 2018a) global products (i.e. using the standard OC-CCI chlorophyll algorithms, OCI) as distributed by CMEMS. '''CONTEXT''' Phytoplankton – and chlorophyll concentration as a proxy for phytoplankton – respond rapidly to changes in their physical environment. Some of those changes are seasonal and are determined by light and nutrient availability (Racault et al., 2012). By comparing annual mean values to a climatology, we effectively remove the seasonal signal, while retaining information on potential events during the year. Chlorophyll anomalies can be correlated to climate indexes in particular regions, such as the ENSO index in the equatorial Pacific (Behrenfeld et al. 2006; Racault et al., 2012) and the IOD index in the Indian Ocean (Brewin et al., 2012). It is important to study chlorophyll anomalies in consonance with sea surface temperature and sea level anomalies, as increases in chlorophyll are generally consistent with decreases in SST and sea level anomalies, suggesting an increase in mixing and vertical nutrient transport (von Schuckmann et al., 2016). '''CMEMS KEY FINDINGS''' The average global chlorophyll anomaly 2019 is -0.02 log10(mg m-3), with a maximum value of 1.7 log10(mg m-3) and a minimum value of -3.2 log10(mg m-3). That is to say that, in average, the annual 2019 mean value is slightly lower (96%) than the 1997-2014 climatological value. The positive signals reported in 2016 and 2017 (Sathyendranath et al., 2018b) in the southern Pacific Ocean could still be observed in the 2019 map, while the significant negative anomalies in the tropical waters of the northern Pacific Ocean were also detected to a lesser extent. Areas showing a change of anomaly sign from 2019 include the southern coast of Japan (no anomaly to positive) and the tropical Atlantic (anomalies close to zero for 2019). A marked increase in chlorophyll concentration was observed during 2019 in the Great Australian Bight, while negative anomalies became stronger in the Guatemala Basin and the region south of the Gulf of Guinea and, with values of chlorophyll reaching as low as 30% of the climatological value (anomaly < -0.5 log10(mg m-3)). The persistent positive anomalies in the higher latitudes of the North Atlantic (> 40°) match the cooling observed in the 2018 and previous years SST anomaly maps.

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.