'''Short description:''' The NWSHELF_ANALYSISFORECAST_PHY_LR_004_001 is produced by a coupled hydrodynamic-biogeochemical model system with tides, implemented over the North East Atlantic and Shelf Seas at 7 km of horizontal resolution and 24 vertical levels. The product is updated daily, providing 7-day forecast for temperature, salinity, currents, sea level and mixed layer depth. Products are provided at quarter-hourly, hourly, daily de-tided (with Doodson filter), and monthly frequency. '''DOI (product) :''' https://doi.org/10.48670/mds-00367

'''DEFINITION''' The OMI_EXTREME_WAVE_IBI_swh_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 significant wave height (swh) measured by in situ buoys. 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''' Projections on Climate Change foresee a future with a greater frequency of extreme sea states (Stott, 2016; Mitchell, 2006). The damages caused by severe wave storms can be considerable not only in infrastructure and buildings but also in the natural habitat, crops and ecosystems affected by erosion and flooding aggravated by the extreme wave heights. In addition, wave storms strongly hamper the maritime activities, especially in harbours. These extreme phenomena drive complex hydrodynamic processes, whose understanding is paramount for proper infrastructure management, design and maintenance (Goda, 2010). In recent years, there have been several studies searching possible trends in wave conditions focusing on both mean and extreme values of significant wave height using a multi-source approach with model reanalysis information with high variability in the time coverage, satellite altimeter records covering the last 30 years and in situ buoy measured data since the 1980s decade but with sparse information and gaps in the time series (e.g. Dodet et al., 2020; Timmermans et al., 2020; Young & Ribal, 2019). These studies highlight a remarkable interannual, seasonal and spatial variability of wave conditions and suggest that the possible observed trends are not clearly associated with anthropogenic forcing (Hochet et al. 2021, 2023). In the North Atlantic, the mean wave height shows some weak trends not very statistically significant. Young & Ribal (2019) found a mostly positive weak trend in the European Coasts while Timmermans et al. (2020) showed a weak negative trend in high latitudes, including the North Sea and even more intense in the Norwegian Sea. For extreme values, some authors have found a clearer positive trend in high percentiles (90th-99th) (Young, 2011; Young & Ribal, 2019). '''COPERNICUS MARINE SERVICE KEY FINDINGS''' The mean 99th percentiles showed in the area present a wide range from 2-3.5m in the Canary Island with 0.1-0.3 m of standard deviation (std), 3.5m in the Gulf of Cadiz with 0.5m of std, 3-6m in the English Channel and the Irish Sea with 0.5-0.6m of std, 4-7m in the Bay of Biscay with 0.4-0.9m of std to 8-10m in the West of the British Isles with 0.7-1.4m of std. Results for this year show slight negative anomalies in the Canary Island (-0.4/0.0m) and in the Gulf of Cadiz (-0.8m) barely out of the standard deviation range in both areas, slight positive or negative anomalies in the West of the British Isles (-0.6/+0.4m) and in the English Channel and the Irish Sea (-0.6/+0.3m) but inside the range of the standard deviation and a general positive anomaly in the Bay of Biscay reaching +1.0m but close to the limit of the standard deviation. '''DOI (product):''' https://doi.org/10.48670/moi-00250

'''This product has been archived''' For operationnal and online products, please visit https://marine.copernicus.eu '''Short description:''' The Global Ocean Satellite monitoring and marine ecosystem study group (GOS) of the Italian National Research Council (CNR), in Rome, distributes Level-4 product including the daily interpolated chlorophyll field with no data voids starting from the multi-sensor (MODIS-Aqua, NOAA-20-VIIRS, NPP-VIIRS, Sentinel3A-OLCI at 300m of resolution) (at 1 km resolution) and the monthly averaged chlorophyll concentration for the multi-sensor (at 1 km resolution) and Sentinel-OLCI Level-3 (at 300m resolution). Chlorophyll field are obtained by means of the Mediterranean regional algorithms: an updated version of the MedOC4 (Case 1 waters, Volpe et al., 2019, with new coefficients) and AD4 (Case 2 waters, Berthon and Zibordi, 2004). Discrimination between the two water types is performed by comparing the satellite spectrum with the average water type spectral signature from in situ measurements for both water types. Reference insitu dataset is MedBiOp (Volpe et al., 2019) where pure Case II spectra are selected using a k-mean cluster analysis (Melin et al., 2015). Merging of Case I and Case II information is performed estimating the Mahalanobis distance between the observed and reference spectra and using it as weight for the final merged value. The interpolated gap-free Level-4 Chl concentration is estimated by means of a modified version of the DINEOF algorithm by GOS (Volpe et al., 2018). DINEOF is an iterative procedure in which EOF are used to reconstruct the entire field domain. As first guess, it uses the SeaWiFS-derived daily climatological values at missing pixels and satellite observations at valid pixels. The other Level-4 dataset is the time averages of the L3 fields and includes the standard deviation and the number of observations in the monthly period of integration. '''Processing information:''' Multi-sensor products are constituted by MODIS-AQUA, NOAA20-VIIRS, NPP-VIIRS and Sentinel3A-OLCI. For consistency with NASA L2 dataset, BRDF correction was applied to Sentinel3A-OLCI prior to band shifting and multi sensor merging. Hence, the single sensor OLCI data set is also distributed after BRDF correction. Single sensor NASA Level-2 data are destriped and then all Level-2 data are remapped at 1 km spatial resolution (300m for Sentinel3A-OLCI) using cylindrical equirectangular projection. Afterwards, single sensor Rrs fields are band-shifted, over the SeaWiFS native bands (using the QAAv6 model, Lee et al., 2002) and merged with a technique aimed at smoothing the differences among different sensors. This technique is developed by The Global Ocean Satellite monitoring and marine ecosystem study group (GOS) of the Italian National Research Council (CNR, Rome). Then geophysical fields (i.e. chlorophyll, kd490, bbp, aph and adg) are estimated via state-of-the-art algorithms for better product quality. Level-4 includes both monthly time averages and the daily-interpolated fields. Time averages are computed on the delayed-time data. The interpolated product starts from the L3 products at 1 km resolution. At the first iteration, DINEOF procedure uses, as first guess for each of the missing pixels the relative daily climatological pixel. A procedure to smooth out spurious spatial gradients is applied to the daily merged image (observation and climatology). From the second iteration, the procedure uses, as input for the next one, the field obtained by the EOF calculation, using only a number of modes: that is, at the second round, only the first two modes, at the third only the first three, and so on. At each iteration, the same smoothing procedure is applied between EOF output and initial observations. The procedure stops when the variance explained by the current EOF mode exceeds that of noise. '''Description of observation methods/instruments:''' Ocean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the pre+D2sence of phytoplankton. '''Quality / Accuracy / Calibration information:''' A detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal. '''Suitability, Expected type of users / uses:''' This product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies. '''Dataset names:''' *dataset-oc-med-chl-multi-l4-chl_1km_monthly-rt-v02 *dataset-oc-med-chl-multi-l4-interp_1km_daily-rt-v02 *dataset-oc-med-chl-olci-l4-chl_300m_monthly-rt-v02 '''Files format:''' *CF-1.4 *INSPIRE compliant '''DOI (product) :''' https://doi.org/10.48670/moi-00113

'''This product has been archived'''                For operationnal and online products, please visit https://marine.copernicus.eu '''DEFINITION''' The ocean monitoring indicator on regional mean sea level is derived from the DUACS delayed-time (DT-2021 version) altimeter gridded maps of sea level anomalies based on a stable number of altimeters (two) in the satellite constellation. These products are distributed by the Copernicus Climate Change Service and the Copernicus Marine Service (SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057). The mean sea level evolution estimated in the Irish-Biscay-Iberian (IBI) region is derived from the average of the gridded sea level maps weighted by the cosine of the latitude. The annual and semi-annual periodic signals are removed (least square fit of sinusoidal function) and the time series is low-pass filtered (175 days cut-off). The curve is corrected for the regional mean effect of the Glacial Isostatic Adjustment (GIA) using the ICE5G-VM2 GIA model (Peltier, 2004). During 1993-1998, the Global men sea level (hereafter GMSL) has been known to be affected by a TOPEX-A instrumental drift (WCRP Global Sea Level Budget Group, 2018; Legeais et al., 2020). This drift led to overestimate the trend of the GMSL during the first 6 years of the altimetry record (about 0.04 mm/y at global scale over the whole altimeter period). A correction of the drift is proposed for the Global mean sea level (Legeais et al., 2020). Whereas this TOPEX-A instrumental drift should also affect the regional mean sea level (hereafter RMSL) trend estimation, currently this empirical correction is currently not applied to the altimeter sea level dataset and resulting estimated for RMSL. Indeed, the pertinence of the global correction applied at regional scale has not been demonstrated yet and there is no clear consensus achieved on the way to proceed at regional scale. Additionally, the estimation of such a correction at regional scale is not obvious, especially in areas where few accurate independent measurements (e.g. in situ)- necessary for this estimation - are available. The trend uncertainty is provided in a 90% confidence interval (Prandi et al., 2021). This estimate only considers errors related to the altimeter observation system (i.e., orbit determination errors, geophysical correction errors and inter-mission bias correction errors). The presence of the interannual signal can strongly influence the trend estimation considering to the altimeter period considered (Wang et al., 2021; Cazenave et al., 2014). The uncertainty linked to this effect is not taken into account. '''CONTEXT''' The indicator on area averaged sea level is a crucial index of climate change, and individual components contribute to sea level rise, including expansion due to ocean warming and melting of glaciers and ice sheets (WCRP Global Sea Level Budget Group, 2018). According to the recent IPCC 6th assessment report, global mean sea level (GMSL) increased by 0.20 (0.15 to 0.25) m over the period 1901 to 2018 with a rate 25 of rise that has accelerated since the 1960s to 3.7 (3.2 to 4.2) mm yr-1 for the period 2006–2018. Human activity was very likely the main driver of observed GMSL rise since 1970 (IPCC WGII, 2021). The weight of the different contributions evolves with time and in the recent decades the mass change has increased, contributing to the on-going acceleration of the GMSL trend (IPCC, 2022a; Legeais et al., 2020; Horwath et al., 2022). At regional scale, sea level does not change homogenously, and RMSL rise can also be influenced by various other processes, with different spatial and temporal scales, such as local ocean dynamic, atmospheric forcing, Earth gravity and vertical land motion changes (IPCC WGI, 2021). Rising sea level can strongly affect population and infrastructures in coastal areas, increase their vulnerability and risks for food security, particularly in low lying areas and island states. Adverse impacts from floods, storms and tropical cyclones with related losses and damages have increased due to sea level rise, and increase their vulnerability and increase risks for food security, particularly in low lying areas and island states (IPCC, 2022b). Adaptation and mitigation measures such as the restoration of mangroves and coastal wetlands, reduce the risks from sea level rise (IPCC, 2022c). In IBI region, the RMSL trend is modulated by decadal variations. As observed over the global ocean, the main actors of the long-term RMSL trend are associated with anthropogenic global/regional warming. Decadal variability is mainly linked to the strengthening or weakening of the Atlantic Meridional Overturning Circulation (AMOC) (e.g. Chafik et al., 2019). The latest is driven by the North Atlantic Oscillation (NAO) (e.g. Delworth and Zeng, 2016). Along the European coast, the NAO also influences the along-slope winds dynamic which in return significantly contributes to the local sea level variability observed (Chafik et al., 2019). '''CMEMS KEY FINDINGS''' Over the [1993/01/01, 2021/08/02] period, the basin-wide RMSL in the IBI area rises at a rate of 3.8  0.82 mm/year. '''DOI (product):''' https://doi.org/10.48670/moi-00252

'''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

'''Short description:''' For the Global Ocean - The product contains hourly Level-4 sea surface wind and stress fields at 0.125 degrees horizontal spatial resolution. Scatterometer observations for Metop-B and Metop-C ASCAT and their collocated European Centre for Medium-Range Weather Forecasts (ECMWF) operational model variables are used to calculate temporally-averaged difference fields. These fields are used to correct for persistent biases in hourly ECMWF operational model fields. The product provides stress-equivalent wind and stress variables as well as their divergence and curl. The applied bias corrections, the standard deviation of the differences (for wind and stress fields) and difference of variances (for divergence and curl fields) are included in the product. '''DOI (product) :''' https://doi.org/10.48670/moi-00305

'''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

'''DEFINITION''' The indicator of the Kuroshio extension phase variations is based on the standardized high frequency altimeter Eddy Kinetic Energy (EKE) averaged in the area 142-149°E and 32-37°N and computed from the DUACS delayed-time (CMEMS SEALEVEL_GLO_PHY_L4_MY_008_047) and near real-time (CMEMS SEALEVEL_GLO_PHY_L4_NRT _008_046) altimeter sea level gridded products. ""CONTEXT"" The Kuroshio Extension is an eastward-flowing current in the subtropical western North Pacific after the Kuroshio separates from the coast of Japan at 35°N, 140°E. Being the extension of a wind-driven western boundary current, the Kuroshio Extension is characterized by a strong variability and is rich in large-amplitude meanders and energetic eddies (Niiler et al., 2003; Qiu, 2003, 2002). The Kuroshio Extension region has the largest sea surface height variability on sub-annual and decadal time scales in the extratropical North Pacific Ocean (Jayne et al., 2009; Qiu and Chen, 2010, 2005). Prediction and monitoring of the path of the Kuroshio are of huge importance for local economies as the position of the Kuroshio extension strongly determines the regions where phytoplankton and hence fish are located. Unstable (contracted) phase of the Kuroshio enhance the production of Chlorophyll (Lin et al., 2014). ""CMEMS KEY FINDINGS"" The different states of the Kuroshio extension phase have been presented and validated by (Bessières et al., 2013) and further reported by Drévillon et al. (2018) in the Copernicus Ocean State Report #2. Two rather different states of the Kuroshio extension are observed: an ‘elongated state’ (also called ‘strong state’) corresponding to a narrow strong steady jet, and a ‘contracted state’ (also called ‘weak state’) in which the jet is weaker and more unsteady, spreading on a wider latitudinal band. When the Kuroshio Extension jet is in a contracted (elongated) state, the upstream Kuroshio Extension path tends to become more (less) variable and regional eddy kinetic energy level tends to be higher (lower). In between these two opposite phases, the Kuroshio extension jet has many intermediate states of transition and presents either progressively weakening or strengthening trends. In 2018, the indicator reveals an elongated state followed by a weakening neutral phase since then. '''DOI (product):''' https://doi.org/10.48670/moi-00222

'''Short description:''' The product MULTIOBS_GLO_PHY_SSS_L4_MY_015_015 is a reformatting and a simplified version of the CATDS L4 product called “SMOS-OI”. This product is obtained using optimal interpolation (OI) algorithm, that combine, ISAS in situ SSS OI analyses to reduce large scale and temporal variable bias, SMOS satellite image, SMAP satellite image, and satellite SST information. Kolodziejczyk Nicolas, Hamon Michel, Boutin Jacqueline, Vergely Jean-Luc, Reverdin Gilles, Supply Alexandre, Reul Nicolas (2021). Objective analysis of SMOS and SMAP Sea Surface Salinity to reduce large scale and time dependent biases from low to high latitudes. Journal Of Atmospheric And Oceanic Technology, 38(3), 405-421. Publisher's official version : https://doi.org/10.1175/JTECH-D-20-0093.1, Open Access version : https://archimer.ifremer.fr/doc/00665/77702/ '''DOI (product) :''' https://doi.org/10.48670/mds-00369

'''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