'''DEFINITION''' The temporal evolution of thermosteric sea level in an ocean layer (here: 0-700m) is obtained from an integration of temperature driven ocean density variations, which are subtracted from a reference climatology (here 1993-2014) to obtain the fluctuations from an average field. The regional thermosteric sea level values from 1993 to close to real time 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''' The global mean sea level is reflecting changes in the Earth’s climate system in response to natural and anthropogenic forcing factors such as ocean warming, land ice mass loss and changes in water storage in continental river basins (IPCC, 2019). Thermosteric sea-level variations result from temperature related density changes in sea water associated with volume expansion and contraction (Storto et al., 2018). Global thermosteric sea level rise caused by ocean warming is known as one of the major drivers of contemporary global mean sea level rise (WCRP, 2018). '''CMEMS KEY FINDINGS''' Since the year 1993 the upper (0-700m) near-global (60°S-60°N) thermosteric sea level rises at a rate of 1.5±0.1 mm/year.

'''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

'''This product has been archived''' For operational and online products, please visit https://marine.copernicus.eu '''Short description:''' This product consists of vertical profiles of the concentration of nitrates, phosphates and silicates, computed for each Argo float equipped with an oxygen sensor. The method called CANYON (Carbonate system and Nutrients concentration from hYdrological properties and Oxygen using a Neural-network) is based on a neural-network trained using high quality nutrient data collected over the last 30 years (GLODAPv2 data base, https://www.glodap.info/). The method is applied to each Argo float equipped with an oxygen sensor using as input the properties measured by the float (pressure, temperature, salinity, oxygen), and its date and position. '''DOI (product) :''' https://doi.org/10.48670/moi-00048 '''Product Citation:''' Please refer to our Technical FAQ for citing products: http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169.

'''Short description:''' For the Global Ocean- In-situ observation yearly delivery in delayed mode. The In Situ delayed mode product designed for reanalysis purposes integrates the best available version of in situ data for temperature and salinity measurements. These data are collected from main global networks (Argo, GOSUD, OceanSITES, World Ocean Database) completed by European data provided by EUROGOOS regional systems and national system by the regional INS TAC components. It is updated on a yearly basis. The time coverage has been extended in the past by integration of EN4 data for the period 1950-1990. Acces through CMEMS Catalogue after registration: http://marine.copernicus.eu/ '''Detailed description: ''' Ocean circulation models need information on the interior of the ocean to be able to generate accurate forecast. This information is only available from in-situ measurement. However this information is acquired all around the world and not easily available to the operational users. Therefore, INS TAC , by connecting to a lot of international networks, collects, controls and disseminates the relevant in-situ data to operational users . For reanalysis purposes, operational centres needs to access to the best available datasets with the best possible coverage and where additional quality control procedures have been performed. This dataset suits research community needs Each year, a new release of this product is issued containing all the observations gathered by the INS TAC global component operated by Coriolis. '''Processing information:''' From the near real time INS TAC product validated on a daily and weekly basis for forecasting purposes, a scientifically validated product is created . It s a ""reference product"" updated on a yearly basis. This product has been controlled using an objective analysis (statistical tests) method and a visual quality control (QC). This QC procedure has been developed with the main objective to improve the quality of the dataset to the level required by the climate application and the physical ocean re-analysis activities. It provides T and S weekly gridded fields and individual profiles both on their original level and interpolated level. The measured parameters, depending on the data source, are : temperature, salinity. The reference level of measurements is immersion (in meters) or pressure (in decibars). The EN4 data were converted to the CORA NetCDF format without any additional validation. '''Quality/accuracy/calibration information:''' The process is done in two steps using two different time windows, corresponding to two runs of objective analysis, with an additional visual QC inserted between. The first run was done on a window of three weeks, to capture the most doubtful profiles which were then checked visually by an operator to decide whether or not it was bad data or real oceanic phenomena. The second run was done on a weekly basis to fit the modelling needs. '''Suitability, Expected type of users / uses: ''' The product is designed for assimilation into operational models operated by ocean forecasting centres for reanalysis purposes or for research community. These users need data aggregated and quality controlled in a reliable and documented manner.

'''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

'''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

Bonne Condititions Agricoles Environnementales (BCAE): - Périmètres des ADAR (Association pour le Développement Agricole Rural) - Réseau hydrographique de référence PAC (Politique Agricole Commune) (métadonnée en cours)