'''This product has been archived''' '''DEFINITION''' The temporal evolution of thermosteric sea level in an ocean layer is obtained from an integration of temperature driven ocean density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''' Most of the interannual variability and trends in regional sea level is caused by changes in steric sea level. At mid and low latitudes, the steric sea level signal is essentially due to temperature changes, i.e. the thermosteric effect (Stammer et al., 2013, Meyssignac et al., 2016). Salinity changes play only a local role. Regional trends of thermosteric sea level can be significantly larger compared to their globally averaged versions (Storto et al., 2018). Except for shallow shelf sea and high latitudes (> 60° latitude), regional thermosteric sea level variations are mostly related to ocean circulation changes, in particular in the tropics where the sea level variations and trends are the most intense over the last two decades. '''CMEMS KEY FINDINGS''' Significant (i.e. when the signal exceeds the noise) regional trends for the period 2005-2019 from the Copernicus Marine Service multi-ensemble approach show a thermosteric sea level rise at rates ranging from the global mean average up to more than 8 mm/year. There are specific regions where a negative trend is observed above noise at rates up to about -8 mm/year such as in the subpolar North Atlantic, or the western tropical Pacific. These areas are characterized by strong year-to-year variability (Dubois et al., 2018; Capotondi et al., 2020). Note: The key findings will be updated annually in November, in line with OMI evolutions. '''DOI (product):''' https://doi.org/10.48670/moi-00241

'''DEFINITION''' The CMEMS IBI_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 Iberia-Biscay-Ireland Multi Year Product (IBI_MULTIYEAR_PHY_005_002) and the Analysis product (IBI_ANALYSISFORECAST_PHY_005_001). Two parameters have been considered for 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 (1993-2021). • Anomaly of the 99th percentile in 2022: The 99th percentile of the year 2022 is computed from the Analysis product. The anomaly is obtained by subtracting the mean percentile from the 2022 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''' The Sea Surface Temperature is one of the essential ocean variables, hence the monitoring of this variable is of key importance, since its variations can affect the ocean circulation, marine ecosystems, and ocean-atmosphere exchange processes. As the oceans continuously interact with the atmosphere, trends of sea surface temperature can also have an effect on the global climate. While the global-averaged sea surface temperatures have increased since the beginning of the 20th century (Hartmann et al., 2013) in the North Atlantic, anomalous cold conditions have also been reported since 2014 (Mulet et al., 2018; Dubois et al., 2018). The IBI area is a complex dynamic region with a remarkable variety of ocean physical processes and scales involved. The Sea Surface Temperature field in the region is strongly dependent on latitude, with higher values towards the South (Locarnini et al. 2013). This latitudinal gradient is supported by the presence of the eastern part of the North Atlantic subtropical gyre that transports cool water from the northern latitudes towards the equator. Additionally, the Iberia-Biscay-Ireland region is under the influence of the Sea Level Pressure dipole established between the Icelandic low and the Bermuda high. Therefore, the interannual and interdecadal variability of the surface temperature field may be influenced by the North Atlantic Oscillation pattern (Czaja and Frankignoul, 2002; Flatau et al., 2003). Also relevant in the region are the upwelling processes taking place in the coastal margins. The most referenced one is the eastern boundary coastal upwelling system off the African and western Iberian coast (Sotillo et al., 2016), although other smaller upwelling systems have also been described in the northern coast of the Iberian Peninsula (Alvarez et al., 2011), the south-western Irish coast (Edwars et al., 1996) and the European Continental Slope (Dickson, 1980). '''CMEMS KEY FINDINGS''' In the IBI region, the 99th mean percentile for 1993-2021 shows a north-south pattern driven by the climatological distribution of temperatures in the North Atlantic. In the coastal regions of Africa and the Iberian Peninsula, the mean values are influenced by the upwelling processes (Sotillo et al., 2016). These results are consistent with the ones presented in Álvarez Fanjul (2019) for the period 1993-2016. The analysis of the 99th percentile anomaly in the year 2023 shows that this period has been affected by a severe impact of maximum SST values. Anomalies exceeding the standard deviation affect almost the entire IBI domain, and regions impacted by thermal anomalies surpassing twice the standard deviation are also widespread below the 43ºN parallel. Extreme SST values exceeding twice the standard deviation affect not only the open ocean waters but also the easter boundary upwelling areas such as the northern half of Portugal, the Spanish Atlantic coast up to Cape Ortegal, and the African coast south of Cape Aguer. It is worth noting the impact of anomalies that exceed twice the standard deviation is widespread throughout the entire Mediterranean region included in this analysis. '''Figure caption''' Iberia-Biscay-Ireland Surface Temperature extreme variability: Map of the 99th mean percentile computed from the Multi Year Product (left panel) and anomaly of the 99th percentile in 2022 computed from the Analysis product (right panel). Transparent grey areas (if any) represent regions where anomaly exceeds the climatic standard deviation (light grey) and twice the climatic standard deviation (dark grey). '''DOI (product):''' https://doi.org/10.48670/moi-00254

Moving 6-year analysis of Silicate at Atlantic Sea for each season. - winter: January-March, - spring: April-June, - summer: July-September, - autumn: October-December Every year of the time dimension corresponds to the 6-year centred average of each season. 6-year periods span - from 1972-1977 until 2015-2020 (winter), - from 1984-1989 until 2015-2020 (spring), - from 1972-1977 until 2015-2020 (summer), - from 1971-1976 until 2015-2020 (autumn). Observational data span from 1971 to 2020. Depth range (IODE standard depths): -2000.0, -1750, -1500.0, -1400.0, -1300.0, -1200.0, -1100.0, -1000.0, -900.0, -800.0, -700.0, -600.0, -500.0, -400.0, -300.0, -250.0, -200.0, -150.0, -125.0, -100.0, -75.0, -50.0,-40.0, -30.0, -20.0, -10.0, -5.0, -0.0 Data Sources: observational data from SeaDataNet/EMODNet Chemistry Data Network. Description of DIVA analysis: Geostatistical data analysis by DIVA (Data-Interpolating Variational Analysis) tool. GEBCO 1min topography is used for the contouring preparation. Analyzed filed masked using relative error threshold 0.3 and 0.5 DIVA settings. Correlation length was optimized and filtered vertically and a seasonally-averaged profile was used. Signal to noise ratio was fixed to 1. Logarithmic transformation applied to the data prior to the analysis. Background field: the data mean value is subtracted from the data. Detrending of data: no, Advection constraint applied: no. Units: umol/l

This product displays for Cadmium, positions with values counts that have been measured per matrix for each year and are present in EMODnet regional contaminants aggregated datasets, v2024. The product displays positions for every available year.

'''Short description:''' Near-Real-Time multi-mission global satellite-based spectral integral parameters. Only valid data are used, based on the L3 corresponding products. Included wave parameters are partition significant wave height, partition peak period and partition peak or principal direction. Those parameters are propagated in space and time at a 3-hour timestep and on a regular space grid, providing information of the swell propagation characteristics, from source to land. The ouput products corresponds to one file per month gathering all the swell systems at a global scale. This product is processed by the WAVE-TAC multi-mission SAR and CFOSAT/SWIM data processing system to serve in near-real time the main operational oceanography and climate forecasting centers in Europe and worldwide. It processes data from the following missions: SAR (Sentinel-1A and Sentinel-1B) and CFOSAT/SWIM. All the spectral parameter measurements are optimally interpolated using swell observations belonging to the same swell field. The spectral data processing system produces wave integral parameters by partition (partition significant wave height, partition peak period and partition peak or principal direction) and the associated standard deviation and density of propagated observations. '''DOI (product) :''' https://doi.org/10.48670/moi-00175

'''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 displays for Nickel, positions with percentages of all available data values per group of animals that are present in EMODnet regional contaminants aggregated datasets, v2022. The product displays positions for all available years.