'''DEFINITION''' The OMI_EXTREME_SST_NORTHWESTSHELF_sst_mean_and_anomaly_obs indicator is based on the computation of the 99th and the 1st percentiles from in situ data (observations). It is computed for the variable sea surface temperature measured by in situ buoys at depths between 0 and 5 meters. The use of percentiles instead of annual maximum and minimum values, makes this extremes study less affected by individual data measurement errors. The percentiles are temporally averaged, and the spatial evolution is displayed, jointly with the anomaly in the target year. This study of extreme variability was first applied to sea level variable (Pérez Gómez et al 2016) and then extended to other essential variables, sea surface temperature and significant wave height (Pérez Gómez et al 2018). '''CONTEXT''' Sea surface temperature (SST) is one of the essential ocean variables affected by climate change (mean SST trends, SST spatial and interannual variability, and extreme events). In Europe, several studies show warming trends in mean SST for the last years (von Schuckmann, 2016; IPCC, 2021, 2022). An exception seems to be the North Atlantic, where, in contrast, anomalous cold conditions have been observed since 2014 (Mulet et al., 2018; Dubois et al. 2018; IPCC 2021, 2022). Extremes may have a stronger direct influence in population dynamics and biodiversity. According to Alexander et al. 2018 the observed warming trend will continue during the 21st Century and this can result in exceptionally large warm extremes. Monitoring the evolution of sea surface temperature extremes is, therefore, crucial. The North-West Self area comprises part of the North Atlantic, where this refreshing trend has been observed, and the North Sea, where a warming trend has been taking place in the last three decades (e.g. Høyer and Karagali, 2016). '''COPERNICUS MARINE SERVICE KEY FINDINGS''' The mean 99th percentiles showed in the area present a range from 14-15ºC in the North of the British Isles, 16-19ºC in the West of the North Sea to 19-20ºC in the Helgoland Bight. The standard deviation ranges from 0.7-0.8ºC in the North of the British Isles, 0.6-2ºC in the West of the North Sea to 0.8-3ºC in in the Helgoland Bight. Results for this year show positive moderate anomalies (+0.3/+1.0ºC) in all the positions except in one station in the West of the Noth Sea where the anomaly is negative (-0.3ºC), all of them inside the standard deviation margin. '''DOI (product):''' https://doi.org/10.48670/moi-00274

'''DEFINITION''' Important note to users: These data are not to be used for navigation. The data is 100 m resolution and as high quality as possible. It has been produced with state-of-the-art technology and validated to the best of the producer’s ability and where sufficient high-quality data were available. These data could be useful for planning and modelling purposes. The user should independently assess the adequacy of any material, data and/or information of the product before relying upon it. Neither Mercator Ocean International/Copernicus Marine Service nor the data originators are liable for any negative consequences following direct or indirect use of the product information, services, data products and/or data. Product overview: This is a satellite derived bathymetry product covering the global coastal area (where data retrieval is possible), with 100 m resolution, based on Sentinel-2. This global coastal product has been developed based on 3 methodologies: Intertidal Satellite-Derived Bathymetry; Physics-based optical Satellite-Derived Bathymetry from RTE inversion; and Wave Kinematics Satellite-Derived Bathymetry from wave dispersion. There is one dataset for each of the methods (including a quality index based on uncertainty) and an additional one where the three datasets were merged (also includes a quality index). Using their expertise and special techniques the consortium tried to achieve an optimal balance between coverage and data quality. '''DOI (product):''' https://doi.org/10.48670/mds-00364

'''Short description:''' For The Global Ocean - The GHRSST Multi-Product Ensemble (GMPE) system has been implemented at the Met Office which takes inputs from various analysis production centres on a routine basis and produces ensemble products at 0.25deg.x0.25deg. horizontal resolution. A large number of sea surface temperature (SST) analyses are produced by various institutes around the world, making use of the SST observations provided by the Global High Resolution SST (GHRSST) project. These are used by a number of groups including: numerical weather prediction centres; ocean forecasting groups; climate monitoring and research groups. There is a requirement to develop international collaboration in this field in order to assess and inter-compare the different analyses, and to provide uncertainty estimates on both the analyses and observational products. The GMPE system has been developed for these purposes and is run on a daily basis at the Met Office, producing global ensemble median and standard deviations for SST on a regular 0.25 degree resolution global grid. '''DOI (product) :''' https://doi.org/10.48670/mds-00378

This daily High-Resolution (HR) Level 3 gridded wind product is derived from Copernicus Sentinel-1 SAR (Synthetic Aperture Radar) observations, over the North Western Atlantic ("ATL" area). It is based on the European Space Agency (ESA) Level-2 OCN products at the highest available resolution. Although L2-OCN products already contain wind vectors, those are calculated using the CMOD5.n Geophysical Model Function (GMF) applied to the co-polarized (co-pol) VV channel (emitting in Vertical polarization and receiving in Vertical polarization). This VV GMF was mapped from scatterometer sensors (Hersbach et al., 2007) which are only able to use co-pol measurements. However, these co-pol GMF are known to lose sensitivity for wind above 20 m/s. Therefore, wind based on such GMF alone, are known to under-estimate wind speed (Polverari et al., 2022). For the L3 products winds based on SAR, we take advantage of the available cross-polarized (cross-pol) VH channel (emitting in Vertical polarization and receiving in Horizontal polarization) for which GMF were specifically derived based on C-Band SAR (Mouche et al., 2017, Mouche et al., 2019). Winds estimated from the combination of both the co-pol and cross-pol channels are referred to as dual-polarization (or dual-pol) winds. As shown in Mouche et al. (2019), taking advantage of the dual polarization strongly improves the wind estimation for high wind conditions thanks to the much greater VH channel sensitivity compared to VV. These new wind estimations are then gridded with a 0.012 degree resolution (between 0.5 and 1.2 km in zonal direction depending on the latitude and 1.3 km in meridional direction) using a cylindrical equidistant projection, independently for ascending and descending satellite passes and for each satellite (so 4 wind fields are available per day for two satellites). This dataset is generated over all Sentinel-1 mission time series starting from March 2018 and updated in delayed mode with a 4-months delay. It is also produced for 4 other different European areas. This dataset is produced and disseminated in the frame of Copernicus Marine Service.

'''This product has been archived'''                For operationnal and online products, please visit https://marine.copernicus.eu '''DEFINITION''' The ibi_omi_tempsal_sst_trend product includes the Sea Surface Temperature (SST) trend for the Iberia-Biscay-Irish Seas over the period 1993-2019, i.e. the rate of change (°C/year). This OMI is derived from the CMEMS REP ATL L4 SST product (SST_ATL_SST_L4_REP_OBSERVATIONS_010_026), see e.g. the OMI QUID, http://marine.copernicus.eu/documents/QUID/CMEMS-OMI-QUID-ATL-SST.pdf), which provided the SSTs used to compute the SST trend over the Iberia-Biscay-Irish Seas. This reprocessed product consists of daily (nighttime) interpolated 0.05° grid resolution SST maps built from the ESA Climate Change Initiative (CCI) (Merchant et al., 2019) and Copernicus Climate Change Service (C3S) initiatives. Trend analysis has been performed by using the X-11 seasonal adjustment procedure (see e.g. Pezzulli et al., 2005), which has the effect of filtering the input SST time series acting as a low bandpass filter for interannual variations. Mann-Kendall test and Sens’s method (Sen 1968) were applied to assess whether there was a monotonic upward or downward trend and to estimate the slope of the trend and its 95% confidence interval. '''CONTEXT''' Sea surface temperature (SST) is a key climate variable since it deeply contributes in regulating climate and its variability (Deser et al., 2010). SST is then essential to monitor and characterise the state of the global climate system (GCOS 2010). Long-term SST variability, from interannual to (multi-)decadal timescales, provides insight into the slow variations/changes in SST, i.e. the temperature trend (e.g., Pezzulli et al., 2005). In addition, on shorter timescales, SST anomalies become an essential indicator for extreme events, as e.g. marine heatwaves (Hobday et al., 2018). '''CMEMS KEY FINDINGS''' Over the period 1993-2021, the Iberia-Biscay-Irish Seas mean Sea Surface Temperature (SST) increased at a rate of 0.011 ± 0.001 °C/Year. '''DOI (product):''' https://doi.org/10.48670/moi-00257

'''This product has been archived''' For operationnal and online products, please visit https://marine.copernicus.eu '''Short description :''' For the '''Global''' Ocean '''Satellite Observations''', ACRI-ST company (Sophia Antipolis, France) is providing '''Chlorophyll-a''' and '''Optics''' products [1997 - present] based on the '''Copernicus-GlobColour''' processor. * '''Chlorophyll and Bio''' products refer to Chlorophyll-a, Primary Production (PP) and Phytoplankton Functional types (PFT). Products are based on a multi sensors/algorithms approach to provide to end-users the best estimate. Two dailies Chlorophyll-a products are distributed: ** one limited to the daily observations (called L3), ** the other based on a space-time interpolation: the '''Cloud Free'''' (called L4). * '''Optics''' products refer to Reflectance (RRS), Suspended Matter (SPM), Particulate Backscattering (BBP), Secchi Transparency Depth (ZSD), Diffuse Attenuation (KD490) and Absorption Coef. (ADG/CDM). * The spatial resolution is 4 km. For Chlorophyll, a 1 km over the Atlantic (46°W-13°E , 20°N-66°N) is also available for the '''Cloud Free''' product, plus a 300m Global coastal product (OLCI S3A & S3B merged). *Products (Daily, Monthly and Climatology) are based on the merging of the sensors SeaWiFS, MODIS, MERIS, VIIRS-SNPP&JPSS1, OLCI-S3A&S3B. Additional products using only OLCI upstreams are also delivered. * Recent products are organized in datasets called NRT (Near Real Time) and long time-series in datasets called REP/MY (Multi-Years). The NRT products are provided one day after satellite acquisition and updated a few days after in Delayed Time (DT) to provide a better quality. An uncertainty is given at pixel level for all products. To find the '''Copernicus-GlobColour''' products in the catalogue, use the search keyword '''GlobColour''''. See [http://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-OC-QUID-009-030-032-033-037-081-082-083-085-086-098.pdf QUID document] for a detailed description and assessment. '''DOI (product) :''' https://doi.org/10.48670/moi-00072

'''DEFINITION''' The OMI_EXTREME_SL_IBI_slev_mean_and_anomaly_obs indicator is based on the computation of the 99th and the 1st percentiles from in situ data (observations). It is computed for the variable sea level measured by tide gauges along the coast. The use of percentiles instead of annual maximum and minimum values, makes this extremes study less affected by individual data measurement errors. The annual percentiles referred to annual mean sea level are temporally averaged and their spatial evolution is displayed in the dataset omi_extreme_sl_ibi_slev_mean_and_anomaly_obs, jointly with the anomaly in the target year. This study of extreme variability was first applied to sea level variable (Pérez Gómez et al 2016) and then extended to other essential variables, sea surface temperature and significant wave height (Pérez Gómez et al 2018). '''CONTEXT''' Sea level (SLEV) is one of the Essential Ocean Variables most affected by climate change. Global mean sea level rise has accelerated since the 1990’s (Abram et al., 2019, Legeais et al., 2020), due to the increase of ocean temperature and mass volume caused by land ice melting (WCRP, 2018). Basin scale oceanographic and meteorological features lead to regional variations of this trend that combined with changes in the frequency and intensity of storms could also rise extreme sea levels up to one meter by the end of the century (Vousdoukas et al., 2020, Tebaldi et al., 2021). This will significantly increase coastal vulnerability to storms, with important consequences on the extent of flooding events, coastal erosion and damage to infrastructures caused by waves (Boumis et al., 2023). The increase in extreme sea levels over recent decades is, therefore, primarily due to the rise in mean sea level. Note, however, that the methodology used to compute this OMI removes the annual 50th percentile, thereby discarding the mean sea level trend to isolate changes in storminess. The Iberian Biscay Ireland region shows positive sea level trend modulated by decadal-to-multidecadal variations driven by ocean dynamics and superposed to the long-term trend (Chafik et al., 2019). '''COPERNICUS MARINE SERVICE KEY FINDINGS''' The completeness index criteria is fulfilled by 62 stations in 2023, five more than those available in 2022 (57), recently added to the multi-year product INSITU_GLO_PHY_SSH_DISCRETE_MY_013_053. The mean 99th percentiles reflect the great tide spatial variability around the UK and the north of France. Minimum values are observed in the Irish eastern coast (e.g.: 0.66 m above mean sea level in Arklow Harbour) and the Canary Islands (e.g.: 0.93 and 0.96 m above mean sea level in Gomera and Hierro, respectively). Maximum values are observed in the Bristol Channel (e.g.: 6.25 and 5.78 m above mean sea level in Newport and Hinkley, respectively), and in the English Channel (e.g.: 5.16 m above mean sea level in St. Helier). The annual 99th percentiles standard deviation reflects the south-north increase of storminess, ranging between 1-3 cm in the Canary Islands to 15 cm in Hinkley (Bristol Channel). Negative or close to zero anomalies of 2023 99th percentile prevail throughout the region this year, reaching < -20 cm in several stations of the UK western coast and the English Channel (e.g.: -22 cm in Newport; -21 cm in St.Helier). Significantly positive anomaly of 2023 99th percentile is only found in Arcklow Harbour, in the eastern Irish coast. '''DOI (product):''' https://doi.org/10.48670/moi-00253

'''Short description:''' The Mean Dynamic Topography MDT-CMEMS_2020_MED is an estimate of the mean over the 1993-2012 period of the sea surface height above geoid for the Mediterranean Sea. This is consistent with the reference time period also used in the SSALTO DUACS products '''DOI (product) :''' https://doi.org/10.48670/moi-00151

'''This product has been archived''' For operationnal and online products, please visit https://marine.copernicus.eu '''Short description:''' Global Ocean - near real-time (NRT) in situ quality controlled observations, hourly updated and distributed by INSTAC within 24-48 hours from acquisition in average. Data are collected mainly through global networks (Argo, OceanSites, GOSUD, EGO) and through the GTS '''DOI (product) :''' https://doi.org/10.48670/moi-00036

This daily High-Resolution (HR) Level 3 gridded wind product is derived from Copernicus Sentinel-1 SAR (Synthetic Aperture Radar) observations, over the Mediterranean Sea ("MED" area). It is based on the European Space Agency (ESA) Level-2 OCN products at the highest available resolution. Although L2-OCN products already contain wind vectors, those are calculated using the CMOD5.n Geophysical Model Function (GMF) applied to the co-polarized (co-pol) VV channel (emitting in Vertical polarization and receiving in Vertical polarization). This VV GMF was mapped from scatterometer sensors (Hersbach et al., 2007) which are only able to use co-pol measurements. However, these co-pol GMF are known to lose sensitivity for wind above 20 m/s. Therefore, wind based on such GMF alone, are known to under-estimate wind speed (Polverari et al., 2022). For the L3 products winds based on SAR, we take advantage of the available cross-polarized (cross-pol) VH channel (emitting in Vertical polarization and receiving in Horizontal polarization) for which GMF were specifically derived based on C-Band SAR (Mouche et al., 2017, Mouche et al., 2019). Winds estimated from the combination of both the co-pol and cross-pol channels are referred to as dual-polarization (or dual-pol) winds. As shown in Mouche et al. (2019), taking advantage of the dual polarization strongly improves the wind estimation for high wind conditions thanks to the much greater VH channel sensitivity compared to VV. These new wind estimations are then gridded with a 0.012 degree resolution (between 0.5 and 1.2 km in zonal direction depending on the latitude and 1.3 km in meridional direction) using a cylindrical equidistant projection, independently for ascending and descending satellite passes and for each satellite (so 4 wind fields are available per day for two satellites). This dataset is generated over all Sentinel-1 mission time series starting from March 2018 and updated in delayed mode with a 4-months delay. It is also produced for 4 other different European areas. This dataset is produced and disseminated in the frame of Copernicus Marine Service.