he Global ARMOR3D L4 Reprocessed dataset is obtained by combining satellite (Sea Level Anomalies, Geostrophic Surface Currents, Sea Surface Temperature) and in-situ (Temperature and Salinity profiles) observations through statistical methods. References : - ARMOR3D: Guinehut S., A.-L. Dhomps, G. Larnicol and P.-Y. Le Traon, 2012: High resolution 3D temperature and salinity fields derived from in situ and satellite observations. Ocean Sci., 8(5):845–857. - ARMOR3D: Guinehut S., P.-Y. Le Traon, G. Larnicol and S. Philipps, 2004: Combining Argo and remote-sensing data to estimate the ocean three-dimensional temperature fields - A first approach based on simulated observations. J. Mar. Sys., 46 (1-4), 85-98. - ARMOR3D: Mulet, S., M.-H. Rio, A. Mignot, S. Guinehut and R. Morrow, 2012: A new estimate of the global 3D geostrophic ocean circulation based on satellite data and in-situ measurements. Deep Sea Research Part II : Topical Studies in Oceanography, 77–80(0):70–81.

'''DEFINITION''' Heat transport across lines are obtained by integrating the heat 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 PetaWatt (PW). '''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. Note: The key findings will be updated annually in November, in line with OMI evolutions. '''DOI (product):''' https://doi.org/10.48670/moi-00245

'''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 - Arctic and Antarctic - Ocean. The OSI SAF delivers five global sea ice products in operational mode: sea ice concentration, sea ice edge, sea ice type (OSI-401, OSI-402, OSI-403, OSI-405 and OSI-408). The sea ice concentration, edge and type products are delivered daily at 10km resolution and the sea ice drift in 62.5km resolution, all in polar stereographic projections covering the Northern Hemisphere and the Southern Hemisphere. The sea ice drift motion vectors have a time-span of 2 days. These are the Sea Ice operational nominal products for the Global Ocean. '''DOI (product) :''' https://doi.org/10.48670/moi-00134

'''This product has been archived''' For operational and online products, please visit https://marine.copernicus.eu '''Short description:''' For the Global Ocean - the OSTIA diurnal skin Sea Surface Temperature product provides daily gap-free maps of: *Hourly mean skin Sea Surface Temperature at 0.25° x 0.25° horizontal resolution, using in-situ and satellite data from infra-red radiometers. The Operational Sea Surface Temperature and Ice Analysis (OSTIA) system is run by the Met Office. A 1/4° (approx. 28 km) hourly analysis of skin Sea Surface temperature (SST) is produced daily for the global ocean. The skin temperature of the ocean is the temperature measured by satellite infra-red radiometers and can experience a large diurnal cycle. The skin SST L4 product is created by combining: 1. the OSTIA foundation SST analysis which uses in-situ and satellite observations; 2. the OSTIA diurnal warm layer analysis which uses satellite observations; and 3. a cool skin model. OSTIA uses satellite data provided by the GHRSST project. '''DOI (product) :''' https://doi.org/10.48670/moi-00167

'''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 products used include three global reanalyses: GLORYS, C-GLORS, ORAS5 (GLOBAL_MULTIYEAR_PHY_ENS_001_031) and two in situ based reprocessed products: CORA5.2 (INSITU_GLO_PHY_TS_OA_MY_013_052) , ARMOR-3D (MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012). 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 2005-2023 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 -5 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

'''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''' 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. Thermosteric sea-level variations result from temperature related density changes in sea water associated with volume expansion and contraction. Global thermosteric sea level rise caused by ocean warming is known as one of the major drivers of contemporary global mean sea level rise (Cazenave et al., 2018; Oppenheimer et al., 2019). '''CMEMS KEY FINDINGS''' Since the year 2005 the upper (0-700m) near-global (60°S-60°N) thermosteric sea level rises at a rate of 0.9±0.1 mm/year. Note: The key findings will be updated annually in November, in line with OMI evolutions. '''DOI (product):''' https://doi.org/10.48670/moi-00239

'''Short description:''' For the Global Ocean- Sea Surface Temperature L3 Observations . This product provides daily foundation sea surface temperature from multiple satellite sources. The data are intercalibrated. This product consists in a fusion of sea surface temperature observations from multiple satellite sensors, daily, over a 0.05° resolution grid. It includes observations by polar orbiting from the ESA CCI / C3S archive . The L3S SST data are produced selecting only the highest quality input data from input L2P/L3P images within a strict temporal window (local nightime), to avoid diurnal cycle and cloud contamination. The observations of each sensor are intercalibrated prior to merging using a bias correction based on a multi-sensor median reference correcting the large-scale cross-sensor biases. '''DOI (product) :''' https://doi.org/10.48670/mds-00329

'''This product has been archived''' For operationnal and online products, please visit https://marine.copernicus.eu '''Short description:''' For the Global ocean, the ESA Ocean Colour CCI surface Chlorophyll (mg m-3, 4 km resolution) using the OC-CCI recommended chlorophyll algorithm is made available in CMEMS format. L3 products are daily files, while the L4 are monthly composites. Processing of these data was mainly carried out in the OC-CCI framework producing a climate-quality consistent dataset using the latest and most complete knowledge of satellite sensor calibration, characterization and attitude, complete (as far as possible) ancillary data sets, latest versions of models and algorithms etc. The data were then repackaged, using custom software, to suit the requirements of CMEMS. The remote sensing of Ocean Colour represents a measure of the spectral variations in the light leaving the water surface, subsequently interpreted in terms of concentrations of optically-significant constituents in the water. The electromagnetic signal collected by the sensor on-board the satellite is largely determined by photons that have never reached the water surface, but have been backscattered within the atmosphere through multiple interactions between gas molecules and aerosols. After removing the atmospheric contribution, the water leaving radiance recorded at a given time by the satellite reflects the optical properties of the water which, in turn, mirrors a specific structure and biogeochemical composition of the marine waters. A detailed description of calibration & validation is given in the relevant QUID, associated validation reports and quality documentation. '''How to reference product:''' The User will ensure that the original product OCEANCOLOUR_GLO_OPTICS_L3_REP_OBSERVATIONS_009_064 -or value added products or derivative works developed from it including pictures- shall credit CMEMS and ESA/CCI by explicitly making mention of the originator in the following manner: ""Generated using Copernicus Marine and ESA/CCI Product"". For publication purposes, the User shall ensure that the credits mention CMEMS and ESA/CCI in the following manner: ""This study has been conducted using Copernicus Marine and ESA/CCI Product"". For all detailed information concerning the use of this product, see the Service Commitments and Licence on the Copernicus Marine website. '''Processing information:''' Processing of these data was mainly carried out in the OC-CCI framework producing a climate-quality consistent dataset using the latest and most complete knowledge of satellite sensor calibration, characterization and attitude, complete (as far as possible) ancillary data sets, latest versions of models and algorithms etc. The data were then repackaged, using custom software, to suit the requirements of CMEMS. '''Description of observation methods/instruments:''' The remote sensing of Ocean Colour represents a measure of the spectral variations in the light leaving the water surface, subsequently interpreted in terms of concentrations of optically-significant constituents in the water. The electromagnetic signal collected by the sensor on-board the satellite is largely determined by photons that have never reached the water surface, but have been backscattered within the atmosphere through multiple interactions between gas molecules and aerosols. After removing the atmospheric contribution, the water leaving radiance recorded at a given time by the satellite reflects the optical properties of the water which, in turn, mirrors a specific structure and biogeochemical composition of the marine waters. '''Quality / Accuracy / Calibration information:''' The user is referred to the QUID documentation '''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. '''DOI (product) :''' https://doi.org/10.48670/moi-00103

'''DEFINITION''' The sea level ocean monitoring indicator is derived from the DUACS delayed-time (DT-2018 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 are also available in the CMEMS catalogue (SEALEVEL_GLO_PHY_CLIMATE_L4_REP_OBSERVATIONS_008_057). To compute the regional mean sea level during the last year, the daily sea level maps of this year are first processed to obtain anomalies referenced to the 1993-2014 period. Then, the obtained individual maps are averaged during the last year. The altimeter data have not been corrected for the effect of the Glacial Isostatic Adjustment (GIA). '''CONTEXT''' Mean sea level evolution has a direct impact on coastal areas and is a crucial index of climate change since it reflects both the amount of heat added in the ocean and the mass loss due to land ice melt (e.g. IPCC, 2013; Dieng et al., 2017). Long-term and inter-annual variations of the sea level are observed at global and regional scales. They are related to the internal variability observed at basin scale and these variations can strongly affect population living in coastal areas. '''CMEMS KEY FINDINGS''' The sea level anomaly field for 2018 compared to the 1993-2014 climatology shows a large negative anomaly in the western subtropical Pacific Ocean and a positive anomaly along the equator, likely associated with ENSO (Schiermeier 2015). Note that an opposite pattern was observed with the 2017 anomaly. In 2019, a rather negative/positive dipole is observed in the West/East subtropical Pacific (the positive equatorial anomaly observed in 2018 is no more observed westward of 160°E. While in 2016, the northward extension of the positive anomaly reached the western US coast (Legeais et al. 2018), it is reduced during 2017 and a negative anomaly is observed in this area. In 2018, this anomaly has almost disappeared and in 2019, a positive anomaly is observed along all the western coast of North and South America. The slightly negative anomaly observed north of the Gulf Stream close to Greenland in 2017 is still observed in 2018 but has a reduced signature in 2019. And the negative anomaly found in 2017 in the North Indian ocean has disappeared in 2018 and a strong East/West dipole is observed in 2019. No major evolution has been observed in the South Atlantic Ocean between 2017, 2018 and 2019. In the Mediterranean Sea, a slightly higher sea level has been observed in 2018 compared to its climatological mean over the entire basin. Such a basin-wide pattern can be related to a response to changes in mass flux through the Strait of Gibraltar forced by the wind (Fukumori et al. 2007) but also to the interannual variability observed in this region (Pinardi & Masetti 2000). Reduced anomalies are observed in 2019 in the Mediterranean Sea. In the Baltic Sea, the positive anomaly observed in 2017 has been linked to a major inflow event (Mohrholz et al. 2015) that took place in 2015-2016 and the amplitude of the Baltic sea level anomaly has strongly reduced in 2018 and 2019.